Sequence Composition Research Articles

Multi-frequency composite pulse sequences for sensitivity enhancement in hexagonal boron nitride quantum sensor

A negatively charged boron vacancy () in hexagonal boron nitride (hBN) has recently started to be used as a magnetic sensor. However, has magnetic resonance spectra broadened by hyperfine interactions, which decreases the signal contrast and thus degrades the magnetic field sensitivity. Here we propose multi-frequency composite pulse sequences to enhance the contrast of the measurement signal in the Rabi oscillation. We demonstrate that the sequence using five microwave frequencies enhances the signal contrast, improving the sensitivity by a factor of 2.3 compared to the conventional sequence.

Design and Synthesis of CdHgSe/HgS/CdZnS Core/Multi-Shell Quantum Dots Exhibiting High-Quantum-Yield Tissue-Penetrating Shortwave Infrared Luminescence.

Cdx Hg1- x Se/HgS/Cdy Zn1- y S core/multi-shell quantum dots (QDs) exhibiting bright tissue-penetrating shortwave infrared (SWIR; 1000-1700nm) photoluminescence (PL) are engineered. The new structure consists of a quasi-type-II Cdx Hg1- x Se/HgS core/inner shell domain creating luminescent bandgap tunable across SWIR window and a wide-bandgap Cdy Zn1- y S outer shell boosting the PL quantum yield (QY). This compositional sequence also facilitates uniform and coherent shell growth by minimizing interfacial lattice mismatches, resulting in high QYs in both organic (40-80%) and aqueous (20-70%) solvents with maximum QYs of 87and 73%, respectively, which are comparable to those of brightest visible-to-near infrared QDs. Moreover, they maintain bright PL in a photocurable resin (QY 40%, peak wavelength≈1300nm), enabling the fabrication of SWIR-luminescent composites of diverse morphology and concentration. These composites are used to localize controlled amounts of SWIR QDs inside artificial (Intralipid) and porcine tissues and quantitatively evaluate the applicability as luminescent probes for deep-tissue imaging.

Stable bulged G-quadruplexes in the human genome: identification, experimental validation and functionalization.

DNA sequence composition determines the topology and stability of G-quadruplexes (G4s). Bulged G-quadruplex structures (G4-Bs) are a subset of G4s characterized by 3D conformations with bulges. Current search algorithms fail to capture stable G4-B, making their genome-wide study infeasible. Here, we introduced a large family of computationally defined and experimentally verified potential G4-B forming sequences (pG4-BS). We found 478263 pG4-BS regions that do not overlap 'canonical' G4-forming sequences in the human genome and are preferentially localized in transcription regulatory regions including R-loops and open chromatin. Over 90% of protein-coding genes contain pG4-BS in their promoter or gene body. We observed generally higher pG4-BS content inR-loops and their flanks,longer genes that are associated with brain tissue, immune and developmental processes. Also, the presence of pG4-BS on both template and non-template strands in promoters is associated with oncogenesis, cardiovascular disease and stemness. Our G4-BS models predicted G4-forming ability in vitro with 91.5% accuracy. Analysis of G4-seq and CUT&Tag data strongly supports the existence of G4-BS conformations genome-wide. We reconstructed a novel G4-B 3D structure located in the E2F8 promoter. This study defines a large family of G4-like sequences, offering new insights into the essential biological functions and potential future therapeutic uses of G4-B.

HOTSPOT: hierarchical host prediction for assembled plasmid contigs with transformer.

As prevalent extrachromosomal replicons in many bacteria, plasmids play an essential role in their hosts' evolution and adaptation. The host range of a plasmid refers to the taxonomic range of bacteria in which it can replicate and thrive. Understanding host ranges of plasmids sheds light on studying the roles of plasmids in bacterial evolution and adaptation. Metagenomic sequencing has become a major means to obtain new plasmids and derive their hosts. However, host prediction for assembled plasmid contigs still needs to tackle several challenges: different sequence compositions and copy numbers between plasmids and the hosts, high diversity in plasmids, and limited plasmid annotations. Existing tools have not yet achieved an ideal tradeoff between sensitivity and precision on metagenomic assembled contigs. In this work, we construct a hierarchical classification tool named HOTSPOT, whose backbone is a phylogenetic tree of the bacterial hosts from phylum to species. By incorporating the state-of-the-art language model, Transformer, in each node's taxon classifier, the top-down tree search achieves an accurate host taxonomy prediction for the input plasmid contigs. We rigorously tested HOTSPOT on multiple datasets, including RefSeq complete plasmids, artificial contigs, simulated metagenomic data, mock metagenomic data, the Hi-C dataset, and the CAMI2 marine dataset. All experiments show that HOTSPOT outperforms other popular methods. The source code of HOTSPOT is available via: https://github.com/Orin-beep/HOTSPOT.

Mucin-microbiome signatures shape the tumor microenvironment in gastric cancer

Background and aimsWe aimed to identify mucin-microbiome signatures shaping the tumor microenvironment in gastric adenocarcinomas and clinical outcomes.MethodsWe performed high-throughput profiling of the mucin phenotypes present in 108 gastric adenocarcinomas and 20 functional dyspepsia cases using validated mucin-based RT-qPCRs with subsequent immunohistochemistry validation and correlated the data with clinical outcome parameters. The gastric microbiota was assessed by 16S rRNA gene sequencing, taxonomy, and community composition determined, microbial networks analyzed, and the metagenome inferred in association with mucin phenotypes and expression.ResultsGastric adenocarcinomas with an intestinal mucin environment or high-level MUC13 expression are associated with poor survival. On the contrary, gastric MUC5AC or MUC6 abundance was associated with a more favorable outcome. The oral taxa Neisseria, Prevotella, and Veillonella had centralities in tumors with intestinal and mixed phenotypes and were associated with MUC13 overexpression, highlighting their role as potential drivers in MUC13 signaling in GC. Furthermore, dense bacterial networks were observed in intestinal and mixed mucin phenotype tumors whereas the lowest community complexity was shown in null mucin phenotype tumors due to higher Helicobacter abundance resulting in a more decreased diversity. Enrichment of oral or intestinal microbes was mucin phenotype dependent. More specifically, intestinal mucin phenotype tumors favored the establishment of pro-inflammatory oral taxa forming strong co-occurrence networks.ConclusionsOur results emphasize key roles for mucins in gastric cancer prognosis and shaping microbial networks in the tumor microenvironment. Specifically, the enriched oral taxa associated with aberrant MUC13 expression can be potential biomarkers in predicting disease outcomes.1rhtBaudS52tVmHFZhoS5HVideo

Research progress on the study of transcriptome-wide poly(A) tails in mammalian oocytes and early embryos.

During mammalian oocyte-to-embryo transition, before zygotic genome activation, the transcription in oocytes and embryos is silenced, so the post-transcriptional regulation of mRNA plays an essential role in this process. Poly(A) tail is an important post-transcriptional modification that affects mRNA metabolism and translation efficiency. With the development of sequencing technology and analysis tools, especially the methods based on third-generation sequencing technology, the length and composition of poly(A) tails can be accurately measured, greatly expanding our understanding of poly(A) tails in mammalian early embryonic development. This review focuses on the achievements of poly(A) tail sequencing methods and the research progress of poly(A) tail in regulating oocyte-to-embryo transition, discussing the future applications for the investigation of mammalian early embryonic development and infertility related diseases.

Role of Strong Localized vs Weak Distributed Interactions in Disordered Protein Phase Separation.

Interaction strength and localization are critical parameters controlling the single-chain and condensed-state properties of intrinsically disordered proteins (IDPs). Here, we decipher these relationships using coarse-grained heteropolymers comprised of hydrophobic (H) and polar (P) monomers as model IDPs. We systematically vary the fraction of P monomers XP and employ two distinct particle-based models that include either strong localized attractions between only H-H pairs (HP model) or weak distributed attractions between both H-H and H-P pairs (HP+ model). To compare different sequences and models, we first carefully tune the attraction strength for all sequences to match the single-chain radius of gyration. Interestingly, we find that this procedure produces similar conformational ensembles, nonbonded potential energies, and chain-level dynamics for single chains of almost all sequences in both models, with some deviations for the HP model at large XP. However, we observe a surprisingly rich phase behavior for the sequences in both models that deviates from the expectation that similarity at the single-chain level will translate to a similar phase-separation propensity. Coexistence between dilute and dense phases is only observed up to a model-dependent XP, despite the presence of favorable interchain interactions, which we quantify using the second virial coefficient. Instead, the limited number of attractive sites (H monomers) leads to the self-assembly of finite-sized clusters of different sizes depending on XP. Our findings strongly suggest that models with distributed interactions favor the formation of liquid-like condensates over a much larger range of sequence compositions compared to models with localized interactions.

Multi-factorial examination of amplicon sequencing workflows from sample preparation to bioinformatic analysis

BackgroundThe development of sequencing technologies to evaluate bacterial microbiota composition has allowed new insights into the importance of microbial ecology. However, the variety of methodologies used among amplicon sequencing workflows leads to uncertainty about best practices as well as reproducibility and replicability among microbiome studies. Using a bacterial mock community composed of 37 soil isolates, we performed a comprehensive methodological evaluation of workflows, each with a different combination of methodological factors spanning sample preparation to bioinformatic analysis to define sources of artifacts that affect coverage, accuracy, and biases in the resulting compositional profiles.ResultsOf the workflows examined, those using the V4-V4 primer set enabled the highest level of concordance between the original mock community and resulting microbiome sequence composition. Use of a high-fidelity polymerase, or a lower-fidelity polymerase with an increased PCR elongation time, limited chimera formation. Bioinformatic pipelines presented a trade-off between the fraction of distinct community members identified (coverage) and fraction of correct sequences (accuracy). DADA2 and QIIME2 assembled V4-V4 reads amplified by Taq polymerase resulted in the highest accuracy (100%) but had a coverage of only 52%. Using mothur to assemble and denoise V4-V4 reads resulted in a coverage of 75%, albeit with marginally lower accuracy (99.5%).ConclusionsOptimization of microbiome workflows is critical for accuracy and to support reproducibility and replicability among microbiome studies. These considerations will help reveal the guiding principles of microbial ecology and impact the translation of microbiome research to human and environmental health.

Taxonomic and functional biogeographies of soil bacterial communities across the Tibet plateau are better explained by abiotic conditions than distance and plant community composition.

The processes governing soil bacteria biogeography are still not fully understood. It remains unknown how the importance of environmental filtering and dispersal differs between bacterial taxonomic and functional biogeography, and whether their importance is scale-dependent. We sampled soils across the Tibet plateau, with distances among plots ranging from 20 m to 1550 km. Taxonomic composition of bacterial community was characterized by 16S amplicon sequencing and functional community composition by qPCR targeting 9 functional groups involved in N dynamics. Factors representing climate, soil and plant community were measured to assess different facets of environmental dissimilarity. Both bacterial taxonomic and functional dissimilarities were more related to abiotic dissimilarity than biotic (vegetation) dissimilarity or distance. Taxonomic dissimilarity was mostly explained by differences in soil pH and mean annual temperature (MAT), while functional dissimilarity was linked to differences in soil N and P availabilities and N:P ratio. Soil pH and MAT remained the main determinants of taxonomic dissimilarity across spatial scales. In contrast, the explanatory variables of N-related functional dissimilarity varied across the scales, with soil moisture and organic matter having the highest role across short distances (<~330 km), and available P, N:P ratio and distance being important over long distances (>~660 km). Our results demonstrate how biodiversity dimension (taxonomic versus functional aspects) and spatial scale influence the factors driving soil bacterial biogeography.

Multislice B1 Mapping Method Using Magnetic Resonance Composite Spin Echo Sequences and Simultaneous Echo Refocusing

Radiofrequency (RF) transmit field (B1) mapping is a promising method in mitigating the B1 inhomogeneity in various magnetic resonance imaging (MRI) applications. Although several phase- or magnitude-based B1 mapping methods have been proposed, these methods often require complex modeling, long acquisition time, or specialized MRI sequences. A recently introduced simultaneous echo refocusing (SER) technique can be applied in the B1 mapping method to extend the three-dimensional (3D) spatial coverage only without long data acquisition. Therefore, in this study, a multislice B1 mapping method using composite spin echo sequences and SER techniques is proposed to obtain more accurate B1 mapping with short data acquisition time. To evaluate the performance of the proposed B1 mapping method, computational simulations were performed and compared with Morrell’s method, double angle method, and Yarnykh’s method. These results showed that the angle-to-noise ratio of the proposed B1 mapping method has wider B1 range compared to that of other B1 mapping methods. In addition, the proposed B1 mapping methods were compared to the multislice iterative signal intensity mapping method in both phantom and in vivo human experiments, and there was no remarkable difference between the two methods regarding the flip angle distribution in these experiments. Based on these results, this study demonstrated that the proposed B1 mapping method is suitable for accurately measuring B1 propagation under the condition providing reduced scan time and wider 3D coverage of B1 mapping by applying composite RF pulse and SER techniques into the phase-sensitive method.

Effects of Heavy Metal Pollution on the Structure of Microbial Communities in Different Habitats

Heavy metal pollution seriously threatens the diversity and composition of microbial communities in various ecosystems. However, little is known about the effects of heavy metal pollution on the structure of microbial communities in the three habitats of "surface water-sediment-groundwater." Here, with help of 16S rRNA high-throughput sequencing technology, the diversity and composition of microbial communities, as well as the underlying controlling factors, were investigated and compared among the surface water, sediment, and groundwater of the Tanghe sewage reservoir. The results showed significant differences in the diversity of microbial communities among different habitats, with the highest α diversity observed in groundwater rather than surface water or sediment. Meanwhile, microbial communities also displayed distinct compositions among the three different habitats. Specifically, Pedobacter, Hydrogenophaga, Flavobacterium, and Algoriphagus were dominant in surface water; metal-tolerant bacteria such as Ornatilinea, Longilinea, Thermomarinilinea, and Bellilinea prevailedin sediment; and Arthrobacter, Gallionella, and Thiothrix were abundant in groundwater. Furthermore, physicochemical factors and metal variables jointly determined the microbial community structure in the three habitats. Among the influencing factors,pH, NO3--N, and Li were the main factors affecting the microbial structure in surface water; TP, NH4+-N, Cr, Fe, Cu, and Zn significantly impacted microorganisms in sediment; and only pH (not metal pollutants) was weakly associated with the microbial composition in groundwater. Overall, heavy metal pollution significantly shaped the microbial community structure in sediment, followed by that in surface water and groundwater. These results provide important scientific guidance for the ecological restoration and the sustainable development of heavy metal-polluted ecosystems.

CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides.

To combat infection by microorganisms host organisms possess a primary arsenal via the innate immune system. Among them are defense peptides with the ability to target a wide range of pathogenic organisms, including bacteria, viruses, parasites, and fungi. Here, we present the development of a novel machine learning model capable of predicting the activity of antimicrobial peptides (AMPs), CalcAMP. AMPs, in particular short ones (<35 amino acids), can become an effective solution to face the multi-drug resistance issue arising worldwide. Whereas finding potent AMPs through classical wet-lab techniques is still a long and expensive process, a machine learning model can be useful to help researchers to rapidly identify whether peptides present potential or not. Our prediction model is based on a new data set constructed from the available public data on AMPs and experimental antimicrobial activities. CalcAMP can predict activity against both Gram-positive and Gram-negative bacteria. Different features either concerning general physicochemical properties or sequence composition have been assessed to retrieve higher prediction accuracy. CalcAMP can be used as an promising prediction asset to identify short AMPs among given peptide sequences.

Folding-Mediated DNA Delivery by α-Helical Amphipathic Peptides.

The renaissance gene therapy experiences these days requires specialist biomaterials and a systemic understanding of major factors influencing their ability to deliver genetic material. Peptide transfection systems represent a major class of such biomaterials. Several peptidic reagents have been commercialized to date. However, a comparative assessment of peptide sequences alone without auxiliary support or excipients against a common determinant for their ability to complex and deliver DNA has been lacking. This study cross-compares commercial and experimental transfection reagents from the same family of helical amphiphiles. Factors defining the efficacy of DNA delivery including cell uptake and gene expression are assessed along with cytotoxicity and DNA complexation. The results show that despite differences in sequence composition, length, and origin, peptide reagents of the same structural family exhibit similar characteristics and limitations with common variability trends. The cross-comparison revealed that functional DNA delivery is independent of the peptide sequence used but is mediated by the ability of the reagents to co-fold with DNA. Peptide folding proved to be the common determinant for DNA complexation and delivery by peptidic transfection reagents.

Investigation of Sequence-Penetration Relationships of Antisense Oligonucleotides.

A major limitation for the development of more effective oligonucleotide therapeutics has been a lack of understanding of their penetration into the cytosol. While prior work has shown how backbone modifications affect cytosolic penetration, it is unclear how cytosolic penetration is affected by other features including base composition, base sequence, length, and degree of secondary structure. We have applied the chloroalkane penetration assay, which exclusively reports on material that reaches the cytosol, to investigate the effects of these characteristics on the cytosolic uptake of druglike oligonucleotides. We found that base composition and base sequence had moderate effects, while length did not correlate directly with the degree of cytosolic penetration. Investigating further, we found that the degree of secondary structure had the largest and most predictable correlations with cytosolic penetration. These methods and observations add a layer of design for maximizing the efficacy of new oligonucleotide therapeutics.

Neutral Models of De Novo Gene Emergence Suggest that Gene Evolution has a Preferred Trajectory.

New protein coding genes can emerge from genomic regions that previously did not contain any genes, via a process called de novo gene emergence. To synthesize a protein, DNA must be transcribed as well as translated. Both processes need certain DNA sequence features. Stable transcription requires promoters and a polyadenylation signal, while translation requires at least an open reading frame. We develop mathematical models based on mutation probabilities, and the assumption of neutral evolution, to find out how quickly genes emerge and are lost. We also investigate the effect of the order by which DNA features evolve, and if sequence composition is biased by mutation rate. We rationalize how genes are lost much more rapidly than they emerge, and how they preferentially arise in regions that are already transcribed. Our study not only answers some fundamental questions on the topic of de novo emergence but also provides a modeling framework for future studies.

The motif composition of variable number tandem repeats impacts gene expression.

Understanding the impact of DNA variation on human traits is a fundamental question in human genetics. Variable number tandem repeats (VNTRs) make up ∼3% of the human genome but are often excluded from association analysis owing to poor read mappability or divergent repeat content. Although methods exist to estimate VNTR length from short-read data, it is known that VNTRs vary in both length and repeat (motif) composition. Here, we use a repeat-pangenome graph (RPGG) constructed on 35 haplotype-resolved assemblies to detect variation in both VNTR length and repeat composition. We align population-scale data from the Genotype-Tissue Expression (GTEx) Consortium to examine how variations in sequence composition may be linked to expression, including cases independent of overall VNTR length. We find that 9422 out of 39,125 VNTRs are associated with nearby gene expression through motif variations, of which only 23.4% are accessible from length. Fine-mapping identifies 174 genes to be likely driven by variation in certain VNTR motifs and not overall length. We highlight two genes, CACNA1C and RNF213, that have expression associated with motif variation, showing the utility of RPGG analysis as a new approach for trait association in multiallelic and highly variable loci.

Transcriptional activators in the early Drosophila embryo perform different kinetic roles.

Combinatorial regulation of gene expression by transcription factors (TFs) may in part arise from kinetic synergy-wherein TFs regulate different steps in the transcription cycle. Kinetic synergy requires that TFs play distinguishable kinetic roles. Here, we used live imaging to determine the kinetic roles of three TFs that activate transcription in the Drosophila embryo-Zelda, Bicoid, and Stat92E-by introducing their binding sites into the even-skipped stripe 2 enhancer. These TFs influence different sets of kinetic parameters, and their influence can change over time. All three TFs increased the fraction of transcriptionally active nuclei; Zelda also shortened the first-passage time into transcription and regulated the interval between transcription events. Stat92E also increased the lifetimes of active transcription. Different TFs can therefore play distinct kinetic roles in activating the transcription. This has consequences for understanding the composition and flexibility of regulatory DNA sequences and the biochemical function of TFs. A record of this paper's transparent peer review process is included in the supplemental information.

The investigation on dynamic damage evolution and energy absorption of FML pin joints with different ply stacking sequences

With fiber reinforced composites widely applied in aerospace, the crashworthiness aspects for aircraft have been an important part of the design. Fiber metal laminate (FML) is a kind of hybrid composite fabricated by fiber reinforced plastic composites and metal layers, showing a potential application on energy absorption. In this work, the damage evolution and energy absorption of FML pin joints are investigated comprehensively. The configurations of FML pin joints with constant geometric parameters are designed to avoid net-tension damage failure in the experiments. The damage evolution processes of specimens are captured by a high-speed camera, and the surface strain is calculated by Digital Image Correlation (DIC) method. Abaqus/Explicit solver is adopted to simulate the mechanical responses of FML pin joint considering ductile damage of metal, damage evolution of composites, and delamination of the interface. The results indicate that the initial dynamic ultimate bearing strength of FML incorporate notable contribution from inertia effects. The average bearing strength associated with energy absorption efficiency is related to the ply stacking sequence of composites. The fiber direction of laminates should be perpendicular to the movement direction of pin as much as possible under the premise that the initial ultimate bearing strength satisfies the design requirement. The results obtained in this work provide comprehensive design guidance for the improvement of crashworthiness in the composite fuselage.

Vaginal Microbiome Metagenome Inference Accuracy: Differential Measurement Error according to Community Composition.

Several studies have compared metagenome inference performance in different human body sites; however, none specifically reported on the vaginal microbiome. Findings from other body sites cannot easily be generalized to the vaginal microbiome due to unique features of vaginal microbial ecology, and investigators seeking to use metagenome inference in vaginal microbiome research are "flying blind" with respect to potential bias these methods may introduce into analyses. We compared the performance of PICRUSt2 and Tax4Fun2 using paired 16S rRNA gene amplicon sequencing and whole-metagenome sequencing data from vaginal samples from 72 pregnant individuals enrolled in the Pregnancy, Infection, and Nutrition (PIN) cohort. Participants were selected from those with known birth outcomes and adequate 16S rRNA gene amplicon sequencing data in a case-control design. Cases experienced early preterm birth (<32 weeks of gestation), and controls experienced term birth (37 to 41 weeks of gestation). PICRUSt2 and Tax4Fun2 performed modestly overall (median Spearman correlation coefficients between observed and predicted KEGG ortholog [KO] relative abundances of 0.20 and 0.22, respectively). Both methods performed best among Lactobacillus crispatus-dominated vaginal microbiotas (median Spearman correlation coefficients of 0.24 and 0.25, respectively) and worst among Lactobacillus iners-dominated microbiotas (median Spearman correlation coefficients of 0.06 and 0.11, respectively). The same pattern was observed when evaluating correlations between univariable hypothesis test P values generated with observed and predicted metagenome data. Differential metagenome inference performance across vaginal microbiota community types can be considered differential measurement error, which often causes differential misclassification. As such, metagenome inference will introduce hard-to-predict bias (toward or away from the null) in vaginal microbiome research. IMPORTANCE Compared to taxonomic composition, the functional potential within a bacterial community is more relevant to establishing mechanistic understandings and causal relationships between the microbiome and health outcomes. Metagenome inference attempts to bridge the gap between 16S rRNA gene amplicon sequencing and whole-metagenome sequencing by predicting a microbiome's gene content based on its taxonomic composition and annotated genome sequences of its members. Metagenome inference methods have been evaluated primarily among gut samples, where they appear to perform fairly well. Here, we show that metagenome inference performance is markedly worse for the vaginal microbiome and that performance varies across common vaginal microbiome community types. Because these community types are associated with sexual and reproductive outcomes, differential metagenome inference performance will bias vaginal microbiome studies, obscuring relationships of interest. Results from such studies should be interpreted with substantial caution and the understanding that they may over- or underestimate associations with metagenome content.

Narrowband composite two-qubit gates for crosstalk suppression

We propose a method to construct composite two-qubit gates with narrowband profiles with respect to the spin-spin coupling. The composite sequences are selective to the variations in the amplitude and duration of the spin-spin coupling, and can be used for highly selective qubit addressing with greatly reduced crosstalk, quantum logic spectroscopy, and quantum sensing.