Next-generation Sequencing Datasets Research Articles

Identification and interaction analysis of molecular markers in myocardial infarction by bioinformatics and next-generation sequencing data analysis

BackgroundCardiovascular diseases are prevalent worldwide with any age, and it is characterized by sudden blockage of blood flow to heart and permanent damage to the heart muscle, whose cause and underlying molecular mechanisms are not fully understood. This investigation aimed to explore and identify essential genes and signaling pathways that contribute to the progression of MI.MethodsThe aim of this investigation was to use bioinformatics and next-generation sequencing (NGS) data analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in MI. NGS dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database. DEGs between MI and normal control samples were identified using the DESeq2 R bioconductor tool. The gene ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed using g:Profiler. Next, four kinds of algorithms in the protein–protein interaction (PPI) were performed to identify potential novel biomarkers. Next, miRNA-hub gene regulatory network analysis and TF-hub gene regulatory network were constructed by miRNet and NetworkAnalyst database, and Cytoscape software. Finally, the diagnostic effectiveness of hub genes was predicted by receiver operator characteristic curve (ROC) analysis and AUC more than 0.800 was considered as having the capability to diagnose MI with excellent specificity and sensitivity.ResultsA total of 958 DEGs were identified, consisting of 480 up-regulated genes and 478 down-regulated genes. The enriched GO terms and pathways of the DEGs include immune system, neuronal system, response to stimulus and multicellular organismal process. Ten hub genes (namely cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1) were obtained via protein–protein interaction analysis results. MiRNA-hub gene regulatory network and TF-hub gene regulatory network showed that hsa-mir-409-3p, hsa-mir-3200-3p, creb1 and tp63 might play an important role in the MI.ConclusionsAnalysis of next-generation sequencing dataset combined with global network information and validation presents a successful approach to uncover the risk hub genes and prognostic markers of MI. Our investigation identified four risk- and prognostic-related gene signatures, including cftr, cdk1, rps13, rps15a, rps27, notch1, mrpl12, nos2, ccdc85b and atn1. This gene sets contribute a new perspective to improve the diagnostic, prognostic, and therapeutic outcomes of MI.

AptamerRunner: An accessible aptamer structure prediction and clustering algorithm for visualization of selected aptamers

Aptamers are short single-stranded DNA or RNA molecules with high affinity and specificity for targets and are generated using the iterative Systematic Evolution of Ligands by EXponential enrichment (SELEX) process. Next-generation sequencing (NGS) revolutionized aptamer selections by allowing a more comprehensive analysis of SELEX-enriched aptamers as compared to Sanger sequencing. The current challenge with aptamer NGS datasets is identifying a diverse cohort of candidate aptamers with the highest likelihood of successful experimental validation. Herein we present AptamerRunner, an aptamer sequence and/or structure clustering algorithm that synergistically integrates computational analysis with visualization and expertise-directed decision making. The visual integration of networked aptamers with ranking data, such as fold enrichment or scoring algorithm results, represents a significant advancement over existing clustering tools by providing a natural context to depict groups of aptamers from which ranked or scored candidates can be chosen for experimental validation. The inherent flexibility, user-friendly design, and prospects for future enhancements with AptamerRunner has broad-reaching implications for aptamer researchers across a wide range of disciplines.

Lung-MAP Next-Generation Sequencing Analysis of Advanced Squamous Cell Lung Cancers (SWOG S1400)

IntroductionSquamous cell cancer (SqCC) is a lung cancer subtype with few targeted therapy options. Molecular characterization, that is, by next-generation sequencing (NGS), is needed to identify potential targets. Lung Cancer Master Protocol Southwest Oncology Group S1400 enrolled patients with previously treated stage IV or recurrent SqCC to assess NGS biomarkers for therapeutic sub-studies. MethodsTumors underwent NGS using Foundation Medicine’s FoundationOne research platform, which sequenced the exons and/or introns of 313 cancer-related genes. Mutually exclusive gene set analysis and Selected Events Linked by Evolutionary Conditions across Human Tumors were performed to identify mutually exclusive and co-occurring gene alterations. Comparisons were performed with data on 495 lung SqCC downloaded from The Cancer Genome Atlas. Cox proportional hazards models were used to assess associations between genetic variants and survival. ResultsNGS data are reported for 1672 patients enrolled on S1400 between 2014 and 2019. Mutually exclusive gene set analysis identified two non-overlapping sets of mutually exclusive alterations with a false discovery rate of less than 15%: NFE2L2, KEAP1, and PARP4; and CDKN2A and RB1. PARP4, a relatively uncharacterized gene, showed three frequent mutations suggesting functional significance: 3116T>C (I1039T), 3176A>G (Q1059R), and 3509C>T (T1170I). When taken together, NFE2L2 and KEAP1 alterations were associated with poorer survival. ConclusionsAs the largest dataset to date of lung SqCC profiled on a clinical trial, the S1400 NGS dataset establishes a rich resource for biomarker discovery. Mutual exclusivity of PARP4 and NFE2L2 or KEAP1 alterations suggests that PARP4 may have an uncharacterized role in a key pathway known to impact oxidative stress response and treatment resistance.

Bacterial microbiome dynamics in commercial integrated aquaculture systems growing Ulva in abalone effluent water

In South Africa, the green seaweed Ulva lacinulata is grown in land-based integrated multi-trophic aquaculture (IMTA) farms with the abalone Haliotis midae. The Ulva serves as a biofilter and the co-produced Ulva is often used as feed for the abalone. To better understand the potential benefits and risks associated with this practice, this study characterised the bacterial microbiome associated with the seawater and Ulva raceways receiving abalone effluent (IMTA system) and compared this to Ulva tanks supplied with fertilised seawater (non-IMTA; control). Ulva samples were collected from each Ulva system, and water samples were collected at the inlet and outlet of each system. Bacterial communities were assessed using a culture-based approach and next-generation sequencing (NGS) of the V3-V4 16S rDNA region. It was observed that Ulva has the potential to reduce the bacterial load of abalone effluent, with the total number of potential culturable Vibrio species declining from 150×103 cells mL-1 in the inlet to 37×103 cells mL-1 in the outlet of the Ulva system. The NGS dataset supported these findings, with a reduction observed in Vibrio and Pseudoalteromonas from the inlet to outlet samples. A lower number of genera (p < 0.05) were observed on Ulva when compared with water samples, indicating that Ulva has a beneficial, modulatory effect on bacteria. These findings contribute towards the growing body of evidence for the benefits of seaweeds in IMTA and addresses the biosecurity concerns of abalone farmers wishing to improve the circularity of their farming activities by incorporating seaweeds.

Insights into comparative genomics, structural features, and phylogenetic relationship of species from Eurasian Aster and its related genera (Asteraceae: Astereae) based on complete chloroplast genome.

Aster L. is an economically and phylogenetically important genus in the tribe Astereae. Here, the complete plastomes of the eight Aster species were assembled and characterized using next-generation sequencing datasets. The results indicated the complete plastomes of Aster had a quadripartite structure. These genomes were 152,045-152,729 bp in length and contained 132-133 genes, including 87 protein-coding genes, 37-38 tRNA genes, and eight rRNA genes. Expansion or contraction of inverted repeat regions and forward, palindromic, complement, and reverse repeats were detected in the eight Aster species. Additionally, our analyses showed the richest type of simple sequence repeats was A/T mononucleotides, and 14 highly variable regions were discovered by analyzing the border regions, sequence divergence, and hotspots. Phylogenetic analyses indicated that 27 species in Astereae were clustered into six clades, i.e., A to D, North American, and outgroup clades, and supported that the genera Heteropappus, Kalimeris, and Heteroplexis are nested within Aster. The results indicated the clades B to D might be considered as genera. Divergence time estimate showed the clades A, B, C, and D diverged at 23.15 Mya, 15.13 Mya, 24.29 Mya, and 21.66 Mya, respectively. These results shed light on the phylogenetic relationships of Aster and provided new information on species identification of Aster and its related genera.

Gene amplification of chromatin remodeling factor SMARCC2 and low protein expression of ACTL6A are unfavorable factors in ovarian high‑grade serous carcinoma.

Ovarian high-grade serous carcinoma (OHGSC) is the most common type of ovarian cancer worldwide. Genome sequencing has identified mutations in chromatin remodeling factors (CRFs) in gynecological cancer, such as clear cell carcinoma, endometrioid carcinoma and endometrial serous carcinoma. However, to the best of our knowledge, the association between CRFs and OHGSC remains unexplored. The present study aimed to investigate the clinicopathological and molecular characteristics of CRF dysfunction in OHGSC. CRF alterations were analyzed through numerous methods, including the analysis of public next-generation sequencing (NGS) data from 585 ovarian serous carcinoma cases from The Cancer Genome Atlas (TCGA), immunohistochemistry (IHC), and DNA copy number assays, which were performed on 203 surgically resected OHGSC samples. In the public NGS dataset, the most frequent genetic alteration was actin-like protein 6A (ACTL6A) amplification at 19.5%. Switch/sucrose non-fermentable related, matrix associated, actin dependent regulator of chromatin subfamily c member 2 (SMARCC2) amplification (3.1%) was associated with significantly decreased overall survival (OS). In addition, chromodomain-helicase-DNA-binding protein 4 (CHD4) amplification (5.7%) exhibited unfavorable outcome trends, although not statistically significant. IHC revealed the protein expression loss of ARID1A (2.5%), SMARCA2 (2.5%) and SMARCA4 (3.9%). The protein expression levels of ACTL6A, SMARCC2 and CHD4 were evaluated using H-score. Patients with low protein expression levels of ACTL6A showed a significantly decreased OS. Copy number gain or gene amplification was demonstrated in ACTL6A (66.2%) and SMARCC2 (33.5%), while shallow deletion or deep deletion was demonstrated in CHD4 (70.7%). However, there was no statistically significant difference in protein levels of these CRFs, between the different copy number alterations (CNAs). Overall, OHGSC exhibited CNAs and protein loss, indicating possible gene alterations in CRFs. Moreover, there was a significant association between the protein expression levels of ACTL6A and poor prognosis. Based on these findings, it is suggested that CRFs could serve as prognostic markers for OHGSC.

Robust multi-read reconstruction from noisy clusters using deep neural network for DNA storage

DNA holds immense potential as an emerging data storage medium. However, the recovery of information in DNA storage systems faces challenges posed by various errors, including IDS errors, strand breaks, and rearrangements, inevitably introduced during synthesis, amplification, sequencing, and storage processes. Sequence reconstruction, crucial for decoding, involves inferring the DNA reference from a cluster of erroneous copies. While most methods assume equal contributions from all reads within a cluster as noisy copies of the same reference, they often overlook the existence of contaminated sequences caused by DNA breaks, rearrangements, or mis-clustering reads. To address this issue, we propose RobuSeqNet, a robust multi-read reconstruction neural network specifically designed to robustly reconstruct multiple reads, accommodating noisy clusters with strand breakage, rearrangements, and mis-clustered strands. Leveraging the attention mechanism and an elaborate network design, RobuSeqNet exhibits resilience to highly-noisy clusters and effectively deals with in-strand IDS errors. The effectiveness and robustness of the proposed method are validated on three representative next-generation sequencing datasets. Results demonstrate that RobuSeqNet maintains high sequence reconstruction success rates of 99.74%, 99.58%, and 96.44% across three datasets, even in the presence of noisy clusters containing up to 20% contaminated sequences, outperforming known sequence reconstruction models. Additionally, in scenarios without contaminated sequences, it exhibits comparable performance to existing models, achieving success rates of 99.88%, 99.82%, and 97.68% across the three datasets.

RNA helicase DEAD-box-5 is involved in R-loop dynamics of preimplantation embryos.

R-loops are DNA:RNA triplex hybrids, and their metabolism is tightly regulated by transcriptional regulation, DNA damage response, and chromatin structure dynamics. R-loop homeostasis is dynamically regulated and closely associated with gene transcription in mouse zygotes. However, the factors responsible for regulating these dynamic changes in the R-loops of fertilized mouse eggs have not yet been investigated. This study examined the functions of candidate factors that interact with R-loops during zygotic gene activation. In this study, we used publicly available next-generation sequencing datasets, including low-input ribosome profiling analysis and polymerase II chromatin immunoprecipitation-sequencing (ChIP-seq), to identify potential regulators of R-loop dynamics in zygotes. These datasets were downloaded, reanalyzed, and compared with mass spectrometry data to identify candidate factors involved in regulating R-loop dynamics. To validate the functions of these candidate factors, we treated mouse zygotes with chemical inhibitors using in vitro fertilization. Immunofluorescence with an anti-R-loop antibody was then performed to quantify changes in R-loop metabolism. We identified DEAD-box-5 (DDX5) and histone deacetylase-2 (HDAC2) as candidates that potentially regulate R-loop metabolism in oocytes, zygotes and two-cell embryos based on change of their gene translation. Our analysis revealed that the DDX5 inhibition of activity led to decreased R-loop accumulation in pronuclei, indicating its involvement in regulating R-loop dynamics. However, the inhibition of histone deacetylase-2 activity did not significantly affect R-loop levels in pronuclei. These findings suggest that dynamic changes in R-loops during mouse zygote development are likely regulated by RNA helicases, particularly DDX5, in conjunction with transcriptional processes. Our study provides compelling evidence for the involvement of these factors in regulating R-loop dynamics during early embryonic development.

Lineage-determining transcription factor-driven promoters regulate cell type-specific macrophage gene expression.

Mammalian promoters consist of multifarious elements, which make them unique and support the selection of the proper transcript variants required under diverse conditions in distinct cell types. However, their direct DNA-transcription factor (TF) interactions are mostly unidentified. Murine bone marrow-derived macrophages (BMDMs) are a widely used model for studying gene expression regulation. Thus, this model serves as a rich source of various next-generation sequencing data sets, including a large number of TF cistromes. By processing and integrating the available cistromic, epigenomicand transcriptomic data from BMDMs, we characterized the macrophage-specific direct DNA-TF interactions, with a particular emphasis on those specific for promoters. Whilst active promoters are enriched for certain types of typically methylatable elements, more than half of them contain non-methylatable and prototypically promoter-distal elements. In addition, circa 14% of promoters-including that of Csf1r-are composed exclusively of 'distal' elements that provide cell type-specific gene regulation by specialized TFs. Similar to CG-rich promoters, these also contain methylatable CG sites that are demethylated in a significant portion and show high polymerase activity. We conclude that this unusual class of promoters regulates cell type-specific gene expression in macrophages, and such a mechanism might exist in other cell types too.

BigDEC: A multi-algorithm Big Data tool based on the [formula omitted]-mer spectrum method for scalable short-read error correction

Despite the significant improvements in both throughput and cost provided by modern Next-Generation Sequencing (NGS) platforms, sequencing errors in NGS datasets can still degrade the quality of downstream analysis. Although state-of-the-art correction tools can provide high accuracy to improve such analysis, they are limited to apply a single correction algorithm while also requiring long runtimes when processing large NGS datasets. Furthermore, current parallel correctors generally only provide efficient support for shared-memory systems lacking the ability to scale out across a cluster of multicore nodes, or they require the availability of specific hardware devices or features. In this paper we present a Big Data Error Correction (BigDEC) tool that overcomes all those limitations by: (1) implementing three different error correction algorithms based on the widely extended k-mer spectrum method; (2) providing scalable performance for large datasets by efficiently exploiting the capabilities of Big Data technologies on multicore clusters based on commodity hardware; (3) supporting two different Big Data processing frameworks (Spark and Flink) to provide greater flexibility to end users; (4) including an efficient, stream-based merge operation to ease downstream processing of the corrected datasets; and (5) significantly outperforming existing parallel tools, being up to 79% faster on a 16-node multicore cluster when using the same underlying correction algorithm. BigDEC is publicly available to download at https://github.com/UDC-GAC/BigDEC.

Evaluation of HLA typing content of next-generation sequencing datasets from family trios and individuals of arab ethnicity.

Introduction: HLA typing is a critical tool in both clinical and research applications at the individual and population levels. Benchmarking studies have indicated HLA-HD as the preferred tool for accurate and comprehensive HLA allele calling. The advent of next-generation sequencing (NGS) has revolutionized genetic analysis by providing high-throughput sequencing data. This study aims to evaluate, using the HLA-HD tool, the HLA typing content of whole exome, whole genome, and HLA-targeted panel sequence data from the consanguineous population of Arab ethnicity, which has been underrepresented in prior benchmarking studies. Methods: We utilized sequence data from family trios and individuals, sequenced on one or more of the whole exome, whole genome, and HLA-targeted panel sequencing technologies. The performance and resolution across various HLA genes were evaluated. We incorporated a comparative quality control analysis, assessing the results obtained from HLA-HD by comparing them with those from the HLA-Twin tool to authenticate the accuracy of the findings. Results: Our analysis found that alleles across 29 HLA loci can be successfully and consistently typed from NGS datasets. Clinical-grade whole exome sequencing datasets achieved the highest consistency rate at three-field resolution, followed by targeted HLA panel, research-grade whole exome, and whole genome datasets. Discussion: The study catalogues HLA typing consistency across NGS datasets for a large array of HLA genes and highlights assessments regarding the feasibility of utilizing available NGS datasets in HLA allele studies. These findings underscore the reliability of HLA-HD for HLA typing in underrepresented populations and demonstrate the utility of various NGS technologies in achieving accurate HLA allele calling.

Carrier screening for present disease prevalence and recessive genetic disorder in Taiwanese population.

Carrier screening is important to people have a higher prevalence of severe recessive or X-linked genetic conditions. This study is aimed that the frequency and uncertain nature of genetic variants was identified in Taiwanese population, providing individuals with information at risk of inherited diseases and their heritability to newborns. A total of 480 subjects receiving genetic counseling with no family history of inherited disorders were recruited into a cohort from 2018 to 2022. Next-generation sequencing (NGS) panel for autosomal dominant (AD), autosomal recessive (AR) and X-linked diseases was sequenced to assess disease prevalence and carrier frequency for the targeted diseases. Publicly available NGS datasets were analyzed following a tier-based system and ACMG recommendation. 5.3% of subjects showed the presence of variants for genetic disorder, and 2.3% of them were determined with AD. 14 of subjects with pathogenic variants were carriers for AR. The inherited genes were LDLR for AD disorders and AR disorders included GAA and ATP7B. 21.6% of subjects had highest carrier frequency of GJB2 gene. 0.5% of subjects had highest frequency of GJB6 for AR condition. In conclusions, the variants in LDLR, GAA and ATP7B genes were identified in Taiwanese population, indicating individuals had higher risk of Pompe disease, Wilson's disease and familial hypercholesterolemia. Taiwanese individuals carrying GJB2 and GJB6 had the considerable risk of hearing loss passing to their offspring.

DNA from non-viable bacteria biases diversity estimates in the corals Acropora loripes and Pocillopora acuta

BackgroundNucleic acid-based analytical methods have greatly expanded our understanding of global prokaryotic diversity, yet standard metabarcoding methods provide no information on the most fundamental physiological state of bacteria, viability. Scleractinian corals harbour a complex microbiome in which bacterial symbionts play critical roles in maintaining health and functioning of the holobiont. However, the coral holobiont contains both dead and living bacteria. The former can be the result of corals feeding on bacteria, rapid swings from hyper- to hypoxic conditions in the coral tissue, the presence of antimicrobial compounds in coral mucus, and an abundance of lytic bacteriophages.ResultsBy combining propidium monoazide (PMA) treatment with high-throughput sequencing on six coral species (Acropora loripes, A. millepora, A. kenti, Platygyra daedalea, Pocillopora acuta, and Porites lutea) we were able to obtain information on bacterial communities with little noise from non-viable microbial DNA. Metabarcoding of the 16S rRNA gene showed significantly higher community evenness (85%) and species diversity (31%) in untreated compared with PMA-treated tissue for A. loripes only. While PMA-treated coral did not differ significantly from untreated samples in terms of observed number of ASVs, > 30% of ASVs were identified in untreated samples only, suggesting that they originated from cell-free/non-viable DNA. Further, the bacterial community structure was significantly different between PMA-treated and untreated samples for A. loripes and P. acuta indicating that DNA from non-viable microbes can bias community composition data in coral species with low bacterial diversity.ConclusionsOur study is highly relevant to microbiome studies on coral and other host organisms as it delivers a solution to excluding non-viable DNA in a complex community. These results provide novel insights into the dynamic nature of host-associated microbiomes and underline the importance of applying versatile tools in the analysis of metabarcoding or next-generation sequencing data sets.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis.

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

Human immunodeficiency virus 1 5'-leader mutations in plasma viruses before and after the development of reverse transcriptase inhibitor-resistance mutations.

Human immunodeficiency virus 1 (HIV-1) reverse transcriptase (RT) initiation depends on interaction between viral 5'-leader RNA, RT and host tRNA3Lys. Therefore, we sought to identify co-evolutionary changes between the 5'-leader and RT in viruses developing RT-inhibitor resistance mutations. We sequenced 5'-leader positions 37-356 of paired plasma virus samples from 29 individuals developing the nucleoside RT inhibitor (NRTI)-resistance mutation M184V, 19 developing a non-nucleoside RT inhibitor (NNRTI)-resistance mutation and 32 untreated controls. 5'-Leader variants were defined as positions where ≥20 % of next-generation sequencing (NGS) reads differed from the HXB2 sequence. Emergent mutations were defined as nucleotides undergoing a ≥4-fold change in proportion between baseline and follow-up. Mixtures were defined as positions containing ≥2 nucleotides each present in ≥20 % of NGS reads. Among 80 baseline sequences, 87 positions (27.2 %) contained a variant; 52 contained a mixture. Position 201 was the only position more likely to develop a mutation in the M184V (9/29 vs 0/32; P=0.0006) or NNRTI-resistance (4/19 vs 0/32; P=0.02; Fisher's exact test) groups than the control group. Mixtures at positions 200 and 201 occurred in 45.0 and 28.8 %, respectively, of baseline samples. Because of the high proportion of mixtures at these positions, we analysed 5'-leader mixture frequencies in two additional datasets: five publications reporting 294 dideoxyterminator clonal GenBank sequences from 42 individuals and six National Center for Biotechnology Information (NCBI) BioProjects reporting NGS datasets from 295 individuals. These analyses demonstrated position 200 and 201 mixtures at proportions similar to those in our samples and at frequencies several times higher than at all other 5'-leader positions. Although we did not convincingly document co-evolutionary changes between RT and 5'-leader sequences, we identified a novel phenomenon, wherein positions 200 and 201 immediately downstream of the HIV-1 primer binding site exhibited an extraordinarily high likelihood of containing a nucleotide mixture. Possible explanations for the high mixture rates are that these positions are particularly error-prone or provide a viral fitness advantage.

Transcriptomics Meta-Analysis Reveals Phagosome and Innate Immune System Dysfunction as Potential Mechanisms in the Cortex of Alzheimer's Disease Mouse Strains.

Immune-related pathways can affect the immune system directly, such as the chemokine signaling pathway, or indirectly, such as the phagosome pathway. Alzheimer's disease (AD) is reportedly associated with several immune-related pathways. However, exploring its underlying mechanism is challenging in animal studies because AD mouse strains differentially express immune-related pathway characteristics. To overcome this problem, we performed a meta-analysis to identify significant and consistent immune-related AD pathways that are expressed in different AD mouse strains. Next-generation RNA sequencing (RNA-seq) and microarray datasets for the cortex of AD mice from different strains such as APP/PSEN1, APP/PS2, 3xTg, TREM, and 5xFAD were collected from the NCBI GEO database. Each dataset's quality control and normalization were already processed from each original study source using various methods depending on the high-throughput analysis platform (FastQC, median of ratios, RMA, between array normalization). Datasets were analyzed using DESeq2 for RNA-seq and GEO2R for microarray to identify differentially expressed (DE) genes. Significantly DE genes were meta-analyzed using Stouffer's method, with significant genes further analyzed for functional enrichment. Ten datasets representing 20 conditions were obtained from the NCBI GEO database, comprising 116 control and 120 AD samples. The DE analysis identified 284 significant DE genes. The meta-analysis identified three significantly enriched immune-related AD pathways: phagosome, the complement and coagulation cascade, and chemokine signaling. Phagosomes-related genes correlated with complement and immune system. Meanwhile, phagosomes and chemokine signaling genes overlapped with B cells receptors pathway genes indicating potential correlation between phagosome, chemokines, and adaptive immune system as well. The transcriptomic meta-analysis showed that AD is associated with immune-related pathways in the brain's cortex through the phagosome, complement and coagulation cascade, and chemokine signaling pathways. Interestingly, phagosome and chemokine signaling pathways had potential correlation with B cells receptors pathway.

Finding motifs using DNA images derived from sparse representations.

Motifs play a crucial role in computational biology, as they provide valuable information about the binding specificity of proteins. However, conventional motif discovery methods typically rely on simple combinatoric or probabilistic approaches, which can be biased by heuristics such as substring-masking for multiple motif discovery. In recent years, deep neural networks have become increasingly popular for motif discovery, as they are capable of capturing complex patterns in data. Nonetheless, inferring motifs from neural networks remains a challenging problem, both from a modeling and computational standpoint, despite the success of these networks in supervised learning tasks. We present a principled representation learning approach based on a hierarchical sparse representation for motif discovery. Our method effectively discovers gapped, long, or overlapping motifs that we show to commonly exist in next-generation sequencing datasets, in addition to the short and enriched primary binding sites. Our model is fully interpretable, fast, and capable of capturing motifs in a large number of DNA strings. A key concept emerged from our approach-enumerating at the image level-effectively overcomes the k-mers paradigm, enabling modest computational resources for capturing the long and varied but conserved patterns, in addition to capturing the primary binding sites. Our method is available as a Julia package under the MIT license at https://github.com/kchu25/MOTIFs.jl, and the results on experimental data can be found at https://zenodo.org/record/7783033.

Identification of Torquetenovirus Species in Patients with Kawasaki Disease Using a Newly Developed Species-Specific PCR Method.

A next-generation sequencing (NGS) study identified a very high viral load of Torquetenovirus (TTV) in KD patients. We aimed to evaluate the feasibility of a newly developed quantitative species-specific TTV-PCR (ssTTV-PCR) method to identify the etiology of KD. We applied ssTTV-PCR to samples collected from 11 KD patients and 22 matched control subjects who participated in our previous prospective study. We used the NGS dataset from the previous study to validate ssTTV-PCR. The TTV loads in whole blood and nasopharyngeal aspirates correlated highly (Spearman's R = 0.8931, p < 0.0001, n = 33), supporting the validity of ssTTV-PCR. The ssTTV-PCR and NGS results were largely consistent. However, inconsistencies occurred when ssTTV-PCR was more sensitive than NGS, when the PCR primer sequences mismatched the viral sequences in the participants, and when the NGS quality score was low. Interpretation of NGS requires complex procedures. ssTTV-PCR is more sensitive than NGS but may fail to detect a fast-evolving TTV species. It would be prudent to update primer sets using NGS data. With this precaution, ssTTV-PCR can be used reliably in a future large-scale etiological study for KD.

Tumour purity as an underlying key factor in tumour mutation detection in colorectal cancer

Tumour purity as an underlying key factor in tumour mutation detection in colorectal cancer

Exploring the interaction between T-cell antigen receptor-related genes and MAPT or ACHE using integrated bioinformatics analysis.

Alzheimer's disease (AD) is a neurodegenerative disease that primarily occurs in elderly individuals with cognitive impairment. Although extracellular β-amyloid (Aβ) accumulation and tau protein hyperphosphorylation are considered to be leading causes of AD, the molecular mechanism of AD remains unknown. Therefore, in this study, we aimed to explore potential biomarkers of AD. Next-generation sequencing (NGS) datasets, GSE173955 and GSE203206, were collected from the Gene Expression Omnibus (GEO) database. Analysis of differentially expressed genes (DEGs), gene ontology (GO) functional enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment, and protein-protein networks were performed to identify genes that are potentially associated with AD. Analysis of the DEG based protein-protein interaction (PPI) network using Cytoscape indicated that neuroinflammation and T-cell antigen receptor (TCR)-associated genes (LCK, ZAP70, and CD44) were the top three hub genes. Next, we validated these three hub genes in the AD database and utilized two machine learning models from different AD datasets (GSE15222) to observe their general relationship with AD. Analysis using the random forest classifier indicated that accuracy (78%) observed using the top three genes as inputs differed only slightly from that (84%) observed using all genes as inputs. Furthermore, another data set, GSE97760, which was analyzed using our novel eigenvalue decomposition method, indicated that the top three hub genes may be involved in tauopathies associated with AD, rather than Aβ pathology. In addition, protein-protein docking simulation revealed that the top hub genes could form stable binding sites with acetylcholinesterase (ACHE). This suggests a potential interaction between hub genes and ACHE, which plays an essential role in the development of anti-AD drug design. Overall, the findings of this study, which systematically analyzed several AD datasets, illustrated that LCK, ZAP70, and CD44 may be used as AD biomarkers. We also established a robust prediction model for classifying patients with AD.