High-throughput Sequencing Data Research Articles

Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data.

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in various biological contexts. The capability to profile the landscape of antigen-binding affinity of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for profiling antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of the binding between antibodies and antigens that can be scaled to high-throughput sequencing data. Cmai achieved an AUROC of 0.91 in our validation cohort. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data. We found that, during immune-related adverse events (irAEs) caused by immune-checkpoint inhibitor (ICI) treatment, the humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. In contrast, extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of ICI treatment response. Overall, Cmai and our biomarker approach filled in a gap that is not addressed by current antibody optimization works nor works such as AlphaFold3 that predict the structures of complexes of proteins that are known to bind.

Alternative splicing responses to salt stress in Glycyrrhiza uralensis revealed by global profiling of transcriptome RNA-seq datasets

Excessive reactive oxygen species stress due to salinity poses a significant threat to the growth of Glycyrrhiza uralensis Fisch. To adapt to salt stress, G. uralensis engages in alternative splicing (AS) to generate a variety of proteins that help it withstand the effects of salt stress. While several studies have investigated the impact of alternative splicing on plants stress responses, the mechanisms by which AS interacts with transcriptional regulation to modulate the salt stress response in G. uralensis remain poorly understood. In this study, we utilized high-throughput RNA sequencing data to perform a comprehensive analysis of AS events at various time points in G. uralensis under salt stress, with exon skipping (SE) being the predominant AS type. KEGG enrichment analysis was performed on the different splicing genes (DSG), and pathways associated with AS were significantly enriched, including RNA transport, mRNA surveillance, and spliceosome. This indicated splicing regulation of genes, resulting in AS events under salt stress conditions. Moreover, plant response to salt stress pathways were also enriched, such as mitogen-activated protein kinase signaling pathway – plant, flavonoid biosynthesis, and oxidative phosphorylation. We focused on four differentially significant genes in the MAPK pathway by AS and qRT-PCR analysis. The alternative splicing type of MPK4 and SnRK2 was skipped exon (SE). ETR2 and RbohD were retained intron (RI) and alternative 5’splice site (A5SS), respectively. The expression levels of isoform1 of these four genes displayed different but significant increases in different tissue sites and salt stress treatment times. These findings suggest that MPK4, SnRK2, ETR2, and RbohD in G. uralensis activate the expression of isoform1, leading to the production of more isoform1 protein and thereby enhancing resistance to salt stress. These findings suggest that salt-responsive AS directly and indirectly governs G. uralensis salt response. Further investigations into AS function and mechanism during abiotic stresses may offer novel references for bolstering plant stress tolerance.

Light-responsive transcription factors VvHYH and VvGATA24 mediate wax terpenoid biosynthesis in Vitis vinifera.

The cuticular wax that covers the surfaces of plants is the first barrier against environmental stresses and increasingly accumulates with light exposure. However, the molecular basis of light-responsive wax biosynthesis remains elusive. In grape (Vitis vinifera), light exposure resulted in higher wax terpenoid content and lower decay and abscission rates than controls kept in darkness. Assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and RNA-seq data were integrated to draw the chromatin accessibility and cis-elements regulatory map to identify the potential action sites. Terpenoid synthase 12 (VvTPS12) and 3-Hydroxy-3-methylglutaryl-CoA reductase 2 (VvHMGR2) were identified as grape wax biosynthesis targets, while VvHYH and VvGATA24 were identified as terpenoid biosynthesis activators, as more abundant wax crystals and higher wax terpenoid content were observed in transiently overexpressed grape berries and Nicotiana benthamiana leaves. The interaction between VvHYH and the open chromatin of VvTPS12 was confirmed qualitatively using a dual luciferase assay and quantitatively using surface plasma resonance, with an equilibrium dissociation constant of 2.81 nM identified via the latter approach. Molecular docking simulation implied the structural nature of this interaction, indicating that 24 amino acid residues of VvHYH, including Arg106A, could bind to the VvTPS12 G-box cis-element. VvGATA24 directly bound to the open chromatin of VvHMGR2, with an equilibrium dissociation constant of 8.59 nM. 12 amino acid residues of VvGATA24, including Pro218B, interacted with the VvHMGR2 GATA-box cis-element. Our work characterizes the mechanism underlying light-mediated wax terpenoid biosynthesis and provides gene targets for future molecular breeding.

Simulation of nanopore sequencing signal data with tunable parameters.

In silico simulation of high-throughput sequencing data is a technique used widely in the genomics field. However, there is currently a lack of effective tools for creating simulated data from nanopore sequencing devices, which measure DNA or RNA molecules in the form of time-series current signal data. Here, we introduce Squigulator, a fast and simple tool for simulation of realistic nanopore signal data. Squigulator takes a reference genome, a transcriptome, or read sequences, and generates corresponding raw nanopore signal data. This is compatible with basecalling software from Oxford Nanopore Technologies (ONT) and other third-party tools, thereby providing a useful substrate for development, testing, debugging, validation, and optimization at every stage of a nanopore analysis workflow. The user may generate data with preset parameters emulating specific ONT protocols or noise-free "ideal" data, or they may deterministically modify a range of experimental variables and/or noise parameters to shape the data to their needs. We present a brief example of Squigulator's use, creating simulated data to model the degree to which different parameters impact the accuracy of ONT basecalling and downstream variant detection. This analysis reveals new insights into the nature of ONT data and basecalling algorithms. We provide Squigulator as an open-source tool for the nanopore community.

EUKARYOME: the rRNA gene reference database for identification of all eukaryotes.

Molecular identification of micro- and macroorganisms based on nuclear markers has revolutionized our understanding of their taxonomy, phylogeny and ecology. Today, research on the diversity of eukaryotes in global ecosystems heavily relies on nuclear ribosomal RNA (rRNA) markers. Here, we present the research community-curated reference database EUKARYOME for nuclear ribosomal 18S rRNA, internal transcribed spacer (ITS) and 28S rRNA markers for all eukaryotes, including metazoans (animals), protists, fungi and plants. It is particularly useful for the identification of arbuscular mycorrhizal fungi as it bridges the four commonly used molecular markers-ITS1, ITS2, 18S V4-V5 and 28S D1-D2 subregions. The key benefits of this database over other annotated reference sequence databases are that it is not restricted to certain taxonomic groups and it includes all rRNA markers. EUKARYOME also offers a number of reference long-read sequences that are derived from (meta)genomic and (meta)barcoding-a unique feature that can be used for taxonomic identification and chimera control of third-generation, long-read, high-throughput sequencing data. Taxonomic assignments of rRNA genes in the database are verified based on phylogenetic approaches. The reference datasets are available in multiple formats from the project homepage, http://www.eukaryome.org.

IAVCP (Influenza A Virus Consensus and Phylogeny): Automatic Identification of the Genomic Sequence of the Influenza A Virus from High-Throughput Sequencing Data.

Recently, high-throughput sequencing of influenza A viruses has become a routine test. It should be noted that the extremely high diversity of the influenza A virus complicates the task of determining the sequences of all eight genome segments. For a fast and accurate analysis, it is necessary to select the most suitable reference for each segment. At the same time, there is no standardized method in the field of decoding sequencing results that allows the user to update the sequence databases to which the reads obtained by virus sequencing are compared. The IAVCP (influenza A virus consensus and phylogeny) was developed with the goal of automatically analyzing high-throughput sequencing data of influenza A viruses. Its goals include the extraction of a consensus genome directly from paired raw reads. In addition, the pipeline enables the identification of potential reassortment events in the evolutionary history of the virus of interest by analyzing the topological structure of phylogenetic trees that are automatically reconstructed.

Transcriptome analysis and development of EST-SSR markers in the mushroom Auricularia heimuer

Auricularia heimuer, the third most frequently cultivated edible mushroom species worldwide, has high medicinal value. However, a shortage of molecular marker hinders the efficiency and accuracy of genetic breeding efforts for A. heimuer. High-throughput transcriptome sequencing data are essential for gene discovery and molecular markers development. This study aimed to clarify the distribution of SSR loci across the A. heimuer transcriptome and to develop highly informative EST-SSR markers. These tools can be used for phylogenetic analysis, functional gene mining, and molecular marker-assisted breeding of A. heimuer. This study used Illumina high-throughput sequencing technology to obtain A. heimuer transcriptome data. The results revealed 37,538 unigenes in the A. heimuer transcriptome. Of these unigenes, 24,777 (66.01%) were annotated via comparison with the COG, Pfam, and NR databases. Overall, 2510 SSRs were identified from the unigenes, including 6 types of SSRs. The most abundant type of repeats were trinucleotides (1425, 56.77%), followed by mononucleotides (391, 15.58%) and dinucleotides (456, 18.17%). Primer pairs for 102 SSR loci were randomly designed for validity confirmation and polymorphism identification; this process yielded 53 polymorphic EST-SSR markers. Finally, 13 pairs of highly polymorphic EST-SSR primers were used to analyze the genetic diversity and population structure of 52 wild A. heimuer germplasms, revealing that the 52 germplasms could be divided into three categories. These results indicated that SSR loci were abundant in types, numbers, and frequencies, providing a potential basis for germplasm resource identification, genetic diversity analysis, and molecular marker-assisted breeding of A. heimuer.

High-throughput sequencing for plant virology diagnostics and its potential in plant health certification

High-throughput sequencing (HTS) technologies have revolutionized plant virology through simultaneous detection of mixed viral infections. HTS advances have uncovered and improved understanding of virus biology, ecology, and evolution which is vital for viral disease management. Plant viruses continue to threaten global agricultural productivity and strict quarantine measures are essential to prevent the introduction and spread of virulent viruses around the world. The gradual decrease in HTS operational costs, including improved computational systems and automation through robotics, has facilitated the adoption of this tool for plant diagnostics, including its use in surveillance and quarantine programs. However, the speed of technology advancements and distinct HTS chemistries, laboratory procedures, data management, and bioinformatic analyses have proven challenging. In addition, the lack of viral species reference sequences, compared with the estimated number of distinct viral taxa, makes classification and identification of novel viruses difficult. There is a need for standardized HTS testing, especially within plant health programs. In this review, we consider the application of HTS in plant virology, explore the technical challenges faced and the opportunities for HTS in plant health certification. We propose standards for overcoming current barriers and for ensuring reliable and reproducible results. These efforts will impact global plant health by reducing the risk of introduction and the spread of damaging novel viruses.

Spatial proximity and gene function: a new dimension in prokaryotic gene association network analysis with 3D-GeneNet.

Understanding the biological functions and processes of genes, particularly those not yet characterized, is crucial for advancing molecular biology and identifying therapeutic targets. The hypothesis guiding this study is that the 3D proximity of genes correlates with their functional interactions and relevance in prokaryotes. We introduced 3D-GeneNet, an innovative software tool that utilizes high-throughput sequencing data from chromosome conformation capture techniques and integrates topological metrics to construct gene association networks. Through a series of comparative analyses focused on spatial versus linear distances, we explored various dimensions such as topological structure, functional enrichment levels, distribution patterns of linear distances among gene pairs, and the area under the receiver operating characteristic curve by utilizing model organism Escherichia coli K-12. Furthermore, 3D-GeneNet was shown to maintain good accuracy compared to multiple algorithms (neighbourhood, co-occurrence, coexpression, and fusion) across multiple bacteria, including E. coli, Brucella abortus, and Vibrio cholerae. In addition, the accuracy of 3D-GeneNet's prediction of long-distance gene interactions was identified by bacterial two-hybrid assays on E. coli K-12 MG1655, where 3D-GeneNet not only increased the accuracy of linear genomic distance tripled but also achieved 60% accuracy by running alone. Finally, it can be concluded that the applicability of 3D-GeneNet will extend to various bacterial forms, including Gram-negative, Gram-positive, single-, and multi-chromosomal bacteria through Hi-C sequencing and analysis. Such findings highlight the broad applicability and significant promise of this method in the realm of gene association network. 3D-GeneNet is freely accessible at https://github.com/gaoyuanccc/3D-GeneNet.

Spatial proximity and gene function: a new dimension in prokaryotic gene association network analysis with 3D-GeneNet.

Understanding the biological functions and processes of genes, particularly those not yet characterized, is crucial for advancing molecular biology and identifying therapeutic targets. The hypothesis guiding this study is that the 3D proximity of genes correlates with their functional interactions and relevance in prokaryotes. We introduced 3D-GeneNet, an innovative software tool that utilizes high-throughput sequencing data from chromosome conformation capture techniques and integrates topological metrics to construct gene association networks. Through a series of comparative analyses focused on spatial versus linear distances, we explored various dimensions such as topological structure, functional enrichment levels, distribution patterns of linear distances among gene pairs, and the area under the receiver operating characteristic curve by utilizing model organism Escherichia coli K-12. Furthermore, 3D-GeneNet was shown to maintain good accuracy compared to multiple algorithms (neighbourhood, co-occurrence, coexpression, and fusion) across multiple bacteria, including E. coli, Brucella abortus, and Vibrio cholerae. In addition, the accuracy of 3D-GeneNet's prediction of long-distance gene interactions was identified by bacterial two-hybrid assays on E. coli K-12 MG1655, where 3D-GeneNet not only increased the accuracy of linear genomic distance tripled but also achieved 60% accuracy by running alone. Finally, it can be concluded that the applicability of 3D-GeneNet will extend to various bacterial forms, including Gram-negative, Gram-positive, single-, and multi-chromosomal bacteria through Hi-C sequencing and analysis. Such findings highlight the broad applicability and significant promise of this method in the realm of gene association network. 3D-GeneNet is freely accessible at https://github.com/gaoyuanccc/3D-GeneNet.

Impacts of fertilization methods on Salvia miltiorrhiza quality and characteristics of the epiphytic microbial community.

Plant epiphytic microorganisms have established a unique symbiotic relationship with plants, which has a significant impact on their growth, immune defense, and environmental adaptation. However, the impact of fertilization methods on the epiphytic microbial community and their correlation with the yield and quality of medicinal plant was still unclear. In current study, we conducted a field fertilization experiment and analyzed the composition of epiphytic bacterial and fungal communities employing high throughput sequencing data in different organs (roots, stems, and leaves) of Salvia miltiorrhiza, as well as their correlation with plant growth. The results showed that fertilization significantly affected the active ingredients and hormone content, soil physicochemical properties, and the composition of epiphytic microbial communities. After fertilization, the plant surface was enriched with a core microbial community mainly composed of bacteria from Firmicutes, Proteobacteria, and Actinobacteria, as well as fungi from Zygomycota and Ascomycota. Additionally, plant growth hormones were the principal factors leading to alterations in the epiphytic microbial community of S. miltiorrhiza. Thus, the most effective method of fertilization involved the application of base fertilizer in combination with foliar fertilizer. This study provides a new perspective for studying the correlation between microbial community function and the quality of S. miltiorrhiza, and also provides a theoretical basis for the cultivation and sustainable development of high-quality medicinal plants.

Panoramic RNA expression of fibroblast growth factors in human glioblastoma tissues and the impact on the survival of patients.

Fibroblast growth factors (FGFs) have a key role in various critical steps of tumor growth and progression through effects on angiogenesis, inflammation and the growth and invasion of malignant cells. Nevertheless, the role of the FGF family in human glioblastoma (GBM) has been rarely studied. The objective of the present study was to assess the RNA expression of all FGF family members in tissues obtained from patients with GBM and to analyze the association between FGF expression and the survival of these patients. For this, the RNA expression of FGF family members in the malignant and proximal tissues of 12 patients with GBM was determined by analyzing high-throughput RNA transcriptome sequencing data uploaded to the National Center for Biotechnology Information database. The relationship between FGF genes and the survival of patients with GBM and glioma was also respectively studied by analyzing data from The Cancer Genome Atlas database using the Gene Expression Profiling Interactive Analysis tool. The results showed that the expression of FGF1, FGF17, FGF20 and FGF22 in GBM tissues was lower than that in adjacent tissues, with a difference of >2 times. Analysis of the overall survival of patients with GBM indicated there were no significant relationships between the expression of FGF1, FGF17, FGF20, FGF22 and overall survival. Analysis of the overall survival of patients with glioma showed that glioma patients with low FGF1 expression achieved a longer survival time than patients with high FGF1 expression; however, high expression of FGF17 and FGF22 indicated a longer survival time. In summary, the results of the present study demonstrated the panoramic expression of FGF family members in patients with GBM, and indicated that FGF1, FGF17 and FGF22 did not affect the survival of patients with GBM, but had a notable influence on the survival of patients with glioma.

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences

BackgroundEmergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identification of ARGs, particularly from high-throughput sequencing data of the specimens, is the state-of-the-art method for comprehensively monitoring their spread and evolution. Current computational methods to identify ARGs mainly rely on alignment-based sequence similarities with known ARGs. Such approaches are limited by choice of reference databases and may potentially miss novel ARGs. The similarity thresholds are usually simple and could not accommodate variations across different gene families and regions. It is also difficult to scale up when sequence data are increasing.ResultsIn this study, we developed ARGNet, a deep neural network that incorporates an unsupervised learning autoencoder model to identify ARGs and a multiclass classification convolutional neural network to classify ARGs that do not depend on sequence alignment. This approach enables a more efficient discovery of both known and novel ARGs. ARGNet accepts both amino acid and nucleotide sequences of variable lengths, from partial (30–50 aa; 100–150 nt) sequences to full-length protein or genes, allowing its application in both target sequencing and metagenomic sequencing. Our performance evaluation showed that ARGNet outperformed other deep learning models including DeepARG and HMD-ARG in most of the application scenarios especially quasi-negative test and the analysis of prediction consistency with phylogenetic tree. ARGNet has a reduced inference runtime by up to 57% relative to DeepARG.ConclusionsARGNet is flexible, efficient, and accurate at predicting a broad range of ARGs from the sequencing data. ARGNet is freely available at https://github.com/id-bioinfo/ARGNet, with an online service provided at https://ARGNet.hku.hk.7nR91jX7ySRMRPZrE5tsTyVideo

Biofiltration of toluene in the presence of ethyl acetate or n-hexane: Performance and microbial community.

This study describes the operation of two independent parallel laboratory-scale biotrickling filters (BTFs) to degrade different types of binary volatile organic compound (VOC) mixtures. Comparison experiments were conducted to evaluate the effects of two typical VOCs, i.e., ethyl acetate (a hydrophilic VOC) and n-hexane (a hydrophobic VOC) on the removal performance of toluene (a moderately hydrophobic VOC) in BTFs ''A" and ''B", respectively. Experiments were carried out by stabilizing the toluene concentration at 1.64 g m-3 and varying the concentrations of gas-phase ethyl acetate (0.85-2.8 g m-3) and n-hexane (0.85-2.8 g m-3) at an empty bed residence time (EBRT) of 30 s. In the presence of ethyl acetate (850 ± 55 mg m-3), toluene exhibited the highest removal efficiency (95.4 ± 2.2%) in BTF "A". However, the removal rate of toluene varied from 48.1 ± 6.9% to 70.1 ± 6.8% when 850 ± 123 mg m-3 to 2800 ± 136 mg m-3 of n-hexane was introduced into BTF "B". The high-throughput sequencing data revealed that the genera Pseudomonas and Comamonadaceae_unclassified are the core microorganisms responsible for the degradation of toluene. The intensity of the inhibitory or synergistic effects on toluene removal was influenced by the type and concentration of the introduced VOC, as well as the number and activity of the genera Pseudomonas and Comamonadaceae_unclassified. It provides insights into the interaction between binary VOCs during biofiltration from a microscopic perspective.

Auxiliary diagnosis and prognosis evaluation of KIF4A, RAD51AP1 and CDKN3 in esophageal cancer

To investigate the expression of mRNA in esophageal cancer (ESCA) tissues and its potential and diagnostic and prognostic value by high-throughput sequencing data. Using the Cancer Genome Atlas Program (TCGA) database in USA by integrative bioinformatics analysis methods, the gene expression profiles and clinical data of 173 patients with ECSA were collected. The mRNA expression levels in ESCA tissue and para-cancerous tissue samples were analyzed using DESeq2, edgeR and limma to screen the differentially expressed genes (DEGs). DEGs-related protein network diagrams were drawn. GO and KEGG function enrichment analysis were performed and the hub genes were screened and the survival analysis of hub genes was analyzed. Genes related to the prognosis of ESCA were selected and their prognostic value in ESCA was analyzed. Finally, the receiver operating characteristic curve was drawn to evaluate its diagnostic value. The results showed that using TCGA cancer data, a total of 620 up-regulated DEGs and 668 down-regulated DEGs with significant differential expression between ESCA and para-cancerous tissues were screened. DEGs were mainly involved in receptor complexes, ubiquitin ligase complexes, etc., playing GTPase activity, phospholipid binding, and other molecular functions, and participating in the regulation of intracellular substance transport, small molecule metabolism, and other biological processes. Protein functional enrichment analysis showed that these proteins were mainly enriched in the IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, Epstein-Barr virus infection, neutrophil extracellular trap formation, and other pathways involved in the formation and development process of ESCA. Survival analysis showed that the overall survival rate of ESCA patients with high expression of KIF4A, RAD51AP1, and CDKN3 was significantly shortened, and the difference was statistically significant (P<0.05). Furthermore, the areas under the curve (AUC) of KIF4A, RAD51AP1, and CDKN3 for diagnosing esophageal cancer were 0.956, 0.951 and 0.979, respectively, with sensitivities and specificities both exceeding 80%. Additionally, ROC results of the combined diagnostic model of these three genes showed an AUC of 0.979, with sensitivities and specificities of 0.914 and 1, respectively. This indicates that KIF4A, RAD51AP1 and CDKN3 have individual or combined auxiliary diagnostic value for ESCA. In conclusion, KIF4A, RAD51AP1 and CDKN3 have high diagnostic efficiency for ESCA, and their increased expression is closely related to the prognosis, suggesting that these three genes could be used as auxiliary diagnostic and prognostic factors for ESCA.

The complete chloroplast genome of Illicium simonsii Maxim. (Illiciaceae), a species with important medicinal properties

Illicium simonsii Maxim (1888) is a medicinal species of the genus Illicium in the Illiciaceae family. It is commonly used to cure gastro-frigid vomiting, cystic hernia, gas pains in the chest, and scabies as folk medicine. To utilize its resources efficiently, the complete chloroplast genome of I. simonsii was sequenced, assembled, and annotated by using high-throughput sequencing data. The complete chloroplast genome was 143,038 bp in length, with a large single-copy region (LSC) of 101,094 bp, a short single-copy region (SSC) of 20,070 bp, and a pair of inverted repeats (IRs) of 21,874 bp. A total of 113 genes were annotated, including 79 protein-coding genes, 30 tRNA genes, and four rRNA genes. The phylogenetic tree exhibited that I. simonsii and Illicium burmanicum form a sister group, and were nested in the monophyletic clade of the Illicium genus.

Impact of root canal disinfection on the bacteriome present in primary endodontic infection: A next generation sequencing study.

To investigate the bacteriome present in teeth with primary endodontic infection (PEI) and apical periodontitis (AP) and to determine quantitatively and qualitatively the impact of chemomechanical preparation (CMP) using 2.5% sodium hypochlorite NAOCl on the bacteriome found in PEI with AP using the Illumina MiSeq platform. Thirty-six paired samples from 18 patients were successfully sequenced and analysed. Samples were collected at two sampling times: before (s1) and after (s2) CMP using 2.5% NaOCl. The DNA was extracted from s1 and s2 samples and quantified using quantitative PCR (qPCR). All 36 samples were sequenced using the Illumina MiSeq platform. Raw V3-V4 amplicon sequencing data were processed with the DADA2 pipeline to generate amplicon sequence variants (ASVs). Alpha diversity metrics representing abundance (Chao1) and diversity and evenness (Shannon, Simpson) were computed. The paired-sample Wilcoxon's test was used to compare alpha diversity metrics and qPCR counts between s1 and s2. The PERMANOVA method (with 999 permutations) was applied to compare community composition between sample types (s1 versus s2) and between patient IDs. ALDEx2 (ANOVA-like differential expression tool for high-throughput sequencing data) to investigate differentially abundant taxa between s1 and s2. A paired-sample Wilcoxon's test was used to compare alpha diversity metrics and qPCR counts between s1 and s2. The qPCR counts were significantly higher in s1 compared to s2 (p = .0007). The Chao1 index indicated no difference in alpha diversity (p < .7019); whereas Shannon (p = .0056) and Simpson (p = .02685) indexes showed higher values in s2. The PERMANOVA test using Adonis2 showed a significant effect of sample time on community composition (R2 = .0630, p = .012). Patient ID also showed a significant effect on community composition (R2 = .6961, p = .001). At the genus level, Dialister, Mogibacterium, Prevotella, and Olsenella were differentially enriched at s1, while Actinomyces, Stenotrophomonas_unclassified, Enterococcus_unclassified, and Actinomyces_unclassified were differentially enriched in s2. The bacteriome present in teeth with PEI with AP is complex and diverse. CMP using 2.5% NaOCl showed a high quantitatively and qualitatively disinfectant impact on the bacteriome present in PEI with AP.

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data.

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analyzing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated data sets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared with database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, data sets, and various classification software can be found at http://lab.malab.cn/~tqz/project/taxonomic/.

Mechanisms Underlying Long-Term Facilitation in the Carotid Body

Glutamate and γ-aminobutyric acid (GABA) are major modulators of excitatory and inhibitory transmission in the mammalian brain. Glutamate transmission is critical for neural plasticity, learning and memory, whereas GABA plays a fundamental role in regulating neuronal excitability and facilitating the generation of neural oscillations. Our data indicates that both glutamate and GABA are released in the carotid body (CB) to modulates its response to hypoxia. Objectives: We set out to investigate whether repeated stimulation with glutamate and GABA induces lasting effects on CB afferent discharge. Hypothesis: We hypothesised that glutamate/GABA mediates long-term facilitation (LTF) of CB afferent activity. Methods: We mined high-throughput RNA sequence data and used immunohistochemistry to map components underlying neuroplasticity in the CB in Wistar and Spontaneously-hypertensive rats (SHR). CSN discharge was measured as a functional readout of CB function from an ex vivo arterially perfused carotid artery-CB preparation. Data: We catalogue the expression of glutamate receptors (R) and transporters in the rat and mouse CB. We found N-methyl-D-aspartic acid receptor (NMDAR), α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPAR) and transporters to be localised to the CB chemosensory cells. Targeted administration of 15 mM glutamate activated the carotid sinus nerve ( p=0.003). Repetitive [5 times, 2-minute duration, 5-minute intervals] application of 15 mM glutamate evoked LTF of CB afferent discharge as demonstrated by both a 12-fold increase in basal firing frequency (0.78 ± 0.37 vs 1232 ± 596 spikes 1s−1; p = 0.004) over 60 minutes ( p = 0.004) and amplification of the evoked response to chemical hypoxia (CN−; 1.23 μmol bolus) by 2-fold ( p = 0.009). The stimulation paradigm was specific as neither application of glutamate, 5 times for 1-minute at 5-minute intervals, nor a single glutamate exposure evoked LTF. In contrast, repeated application of 15mM GABA [3 times for 1-minute at 5-minute intervals] evoked long-term depression (LTD) of CB afferent activity as seen by a reduced basal discharge from 10.5 to 3.5 spikes 1s−1 ( p = 0.0004) and a 2-fold suppression of the response to chemical hypoxia ( p = 3.71 x 10−5). Summary of the data: Repetitive exposure to glutamate using a specific protocol increased basal discharge of CB afferent output akin to LTF and caused sensitisation of evoked responses to CN−. Repeated exposures of GABA depressed the CB basal discharge akin to LTD. A statement of the conclusions: It is conceivable that processes such as LTP and LTD operate in the CB to modulate the set point of peripheral chemoreceptor sensitivity and its state-dependent tonicity; the role of LTP in peripheral chemoreflex sensitisation in disease remains to be determined. Research was supported by the Health Research Council of New Zealand and the Sidney Taylor Trust. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Performance and microbial community composition of full-scale high-rate cascade sludge digestion system via pie-shaped reactor configuration

A full-scale high-rate cascade anaerobic digestion (CAD) system was evaluated for its ability to enhance enzymatic sludge hydrolysis. The system included a newly built digester, innovatively divided into three pie-shaped compartments (500 m3 each), followed by an existing, larger digester (1500 m3). The system treated a mixture of waste activated sludge and primary sludge, achieving a stable total chemical oxygen demand reduction efficiency (56.1 ± 6.8 %), and enhanced sludge hydrolytic enzyme activities at a 14.5-day total solids retention time (SRT). High-throughput sequencing data revealed a consistent microbial community across reactors, dominated by consortia that govern hydrolysis and acidogenesis. Despite relatively short SRTs in the initial reactors of the CAD system, acetoclastic methanogens belonging to Methanosaeta became the most abundant archaea. ‬‬‬‬‬‬‬‬‬‬‬‬‬ This study proves that the CAD system achieves stable sludge reduction, accelerates enzymatic hydrolysis at full-scale, and paves the way for its industrialization in municipal waste sewage sludge treatment.