Gene Sequence Information Research Articles

CO-19 PDB 2.0: A Comprehensive COVID-19 Database with Global Auto-Alerts, Statistical Analysis, and Cancer Correlations.

Biological databases serve as critical basics for modern research, and amid the dynamic landscape of biology, the COVID-19 database has emerged as an indispensable resource. The global outbreak of Covid-19, commencing in December 2019, necessitates comprehensive databases to unravel the intricate connections between this novel virus and cancer. Despite existing databases, a crucial need persists for a centralized and accessible method to acquire precise information within the research community. The main aim of the work is to develop a database which has all the COVID-19-related data available in just one click with auto global notifications. This gap is addressed by the meticulously designed COVID-19 Pandemic Database (CO-19 PDB 2.0), positioned as a comprehensive resource for researchers navigating the complexities of COVID-19 and cancer. Between December 2019 and June 2024, the CO-19 PDB 2.0 systematically collected and organized 120 datasets into six distinct categories, each catering to specific functionalities. These categories encompass a chemical structure database, a digital image database, a visualization tool database, a genomic database, a social science database, and a literature database. Functionalities range from image analysis and gene sequence information to data visualization and updates on environmental events. CO-19 PDB 2.0 has the option to choose either the search page for the database or the autonotification page, providing a seamless retrieval of information. The dedicated page introduces six predefined charts, providing insights into crucial criteria such as the number of cases and deaths', country-wise distribution, 'new cases and recovery', and rates of death and recovery. The global impact of COVID-19 on cancer patients has led to extensive collaboration among research institutions, producing numerous articles and computational studies published in international journals. A key feature of this initiative is auto daily notifications for standardized information updates. Users can easily navigate based on different categories or use a direct search option. The study offers up-to-date COVID-19 datasets and global statistics on COVID-19 and cancer, highlighting the top 10 cancers diagnosed in the USA in 2022. Breast and prostate cancers are the most common, representing 30% and 26% of new cases, respectively. The initiative also ensures the removal or replacement of dead links, providing a valuable resource for researchers, healthcare professionals, and individuals. The database has been implemented in PHP, HTML, CSS and MySQL and is available freely at https://www.co-19pdb.habdsk.org/. Database URL: https://www.co-19pdb.habdsk.org/.

Development of a metabolic engineering technology to simultaneously suppress the expression of multiple genes in yeast and application in carotenoid production.

In yeast metabolic engineering, there is a need for technologies that simultaneously suppress and regulate the expression of multiple genes and improve the production of target chemicals. In this study, we aimed to develop a novel technology that simultaneously suppresses the expression of multiple genes by combining RNA interference with global metabolic engineering strategy. Furthermore, using β-carotene as the target chemical, we attempted to improve its production by using the technology. First, we developed a technology to suppress the expression of the target genes with various strengths using RNA interference. Using this technology, total carotenoid production was successfully improved by suppressing the expression of a single gene out of 10 candidate genes. Then, using this technology, RNA interference strain targeting 10 candidate genes for simultaneous suppression was constructed. The total carotenoid production of the constructed RNA interference strain was 1.7 times compared with the parental strain. In the constructed strain, the expression of eight out of the 10 candidate genes was suppressed. We developed a novel technology that can simultaneously suppress the expression of multiple genes at various intensities and succeeded in improving carotenoid production in yeast. Because this technology can suppress the expression of any gene, even essential genes, using only gene sequence information, it is considered a useful technology that can suppress the formation of by-products during the production of various target chemicals by yeast.

In Silico Intensive Analysis for the E4 Gene Evolution of Human Adenovirus Species D.

Adenovirus (Ad) is a ubiquitous pathogen capable of infecting a wide range of animals and humans. Human Adenovirus (HAdV) can cause severe infection, particularly in individuals with compromised immune systems. To date, over 110 types of HAdV have been classified into seven species from A to G, with the majority belonging to the human adenovirus species D (HAdV-D). In the HAdV-D, the most significant factor for the creation of new adenovirus types is homologous recombination between viral genes involved in determining the virus tropism or evading immune system of host cells. The E4 gene, consisting of seven Open Reading Frames (ORFs), plays a role in both the regulation of host cell metabolism and the replication of viral genes. Despite long-term studies, the function of each ORF remains unclear. Based on our updated information, ORF2, ORF3, and ORF4 have been identified as regions with relatively high mutations compared to other ORFs in the E4 gene, through the use of in silico comparative analysis. Additionally, we managed to visualize high mutation sections, previously undetectable at the DNA level, through a powerful amino acid sequence analysis tool known as proteotyping. Our research has revealed the involvement of the E4 gene in the evolution of human adenovirus, and has established accurate sequence information of the E4 gene, laying the groundwork for further research.

Effect of target gene sequence evenness and dominance on real-time PCR quantification of artificial sulfate-reducing microbial communities.

Quantitative real-time PCR of phylogenetic and functional marker genes is among the most commonly used techniques to quantify the abundance of microbial taxa in environmental samples. However, in most environmental applications, the approach is a rough assessment of population abundance rather than an exact absolute quantification method because of PCR-based estimation biases caused by multiple factors. Previous studies on these technical issues have focused on primer or template sequence features or PCR reaction conditions. However, how target gene sequence characteristics (e.g., evenness and dominance) in environmental samples affect qPCR quantifications has not been well studied. Here, we compared three primer sets targeting the beta subunit of the dissimilatory sulfite reductase (dsrB) to investigate qPCR quantification performance under different target gene sequence evenness and dominance conditions using artificial gBlock template mixtures designed accordingly. Our results suggested that the qPCR quantification performance of all tested primer sets was determined by the comprehensive effect of the target gene sequence evenness and dominance in environmental samples. Generally, highly degenerate primer sets have equivalent or better qPCR quantification results than a more target-specific primer set. Low template concentration in this study (~105 copies/L) will exaggerate the qPCR quantification results difference among tested primer sets. Improvements to the accuracy and reproducibility of qPCR assays for gene copy number quantification in environmental microbiology and microbial ecology studies should be based on prior knowledge of target gene sequence information acquired by metagenomic analysis or other approaches, careful selection of primer sets, and proper reaction conditions optimization.

Clinical utility of the Oncomine Dx Target Test multi-CDx system and the possibility of utilizing those original sequence data.

Companion diagnostic tests play a crucial role in guiding treatment decisions for patients with non-small cell lung cancer (NSCLC). The Oncomine Dx Target Test (ODxTT) Multi-CDx System has emerged as a prominent companion diagnostic method. However, its efficacy in detecting driver gene mutations, particularly rare mutations, warrants investigation. This study aimed to assess the performance of the ODxTT in detecting driver gene mutations in NSCLC patients. Specifically, we aimed to evaluate its sensitivity in detecting epidermal growth factor receptor (EGFR) mutations, a key determinant of treatment selection in NSCLC. We conducted a retrospective analysis of NSCLC patients who underwent testing with the ODxTT at Keio University Hospital between May 2020 and March 2022. Patient samples were subjected to both DNA and RNA tests. Driver gene mutation status was assessed, and instances of missed mutations were meticulously examined. Of the 90 patients, five had nucleic acid quality problems, while 85 underwent both DNA and RNA tests. Driver gene mutations were detected in 56/90 (62.2%) patients. Of the 34 patient specimens, driver mutations were not detected using the ODxTT; however, epidermal growth factor receptor (EGFR) mutations were detected using polymerase chain reaction-based testing in two patients, and a KRAS mutation was detected by careful examination of the sequence data obtained using the ODxTT in one patient. For the above three cases, carefully examining the gene sequence information obtained using the ODxTT could identify driver mutations that were not mentioned in the returned test results. Additionally, we confirmed comparable instances of overlook results for EGFR mutations in the dataset from South Korea, implying that this type of oversight could occur in other countries using the ODxTT. EGFR mutation was missed in ODxTT in Japan (6.3%, 2/32), South Korea (2.0%, 1/49), and overall (3.7%, 3/81). Even if sufficient tumor samples are obtained, rare EGFR mutations (which are excluded from the ODxTT's genetic mutation list) might not be detected using the current ODxTT system due to the program used for sequence analysis. However, such rare EGFR mutations can still be accurately detected on ODxTT's sequence data using next-generation sequencing.

Evolving Together: Cassandra Retrotransposons Gradually Mirror Promoter Mutations of the 5S rRNA Genes.

The 5S rRNA genes are among the most conserved nucleotide sequences across all species. Similar to the 5S preservation we observe the occurrence of 5S-related nonautonomous retrotransposons, so-called Cassandras. Cassandras harbor highly conserved 5S rDNA-related sequences within their long terminal repeats, advantageously providing them with the 5S internal promoter. However, the dynamics of Cassandra retrotransposon evolution in the context of 5S rRNA gene sequence information and structural arrangement are still unclear, especially: (1) do we observe repeated or gradual domestication of the highly conserved 5S promoter by Cassandras and (2) do changes in 5S organization such as in the linked 35S-5S rDNA arrangements impact Cassandra evolution? Here, we show evidence for gradual co-evolution of Cassandra sequences with their corresponding 5S rDNAs. To follow the impact of 5S rDNA variability on Cassandra TEs, we investigate the Asteraceae family where highly variable 5S rDNAs, including 5S promoter shifts and both linked and separated 35S-5S rDNA arrangements have been reported. Cassandras within the Asteraceae mirror 5S rDNA promoter mutations of their host genome, likely as an adaptation to the host's specific 5S transcription factors and hence compensating for evolutionary changes in the 5S rDNA sequence. Changes in the 5S rDNA sequence and in Cassandras seem uncorrelated with linked/separated rDNA arrangements. We place all these observations into the context of angiosperm 5S rDNA-Cassandra evolution, discuss Cassandra's origin hypotheses (single or multiple) and Cassandra's possible impact on rDNA and plant genome organization, giving new insights into the interplay of ribosomal genes and transposable elements.

Constraining the oxygen requirements for modern microbial eukaryote diversity.

Eukaryotes originated prior to the establishment of modern marine oxygen (O2) levels. According to the body fossil and lipid biomarker records, modern (crown) microbial eukaryote lineages began diversifying in the ocean no later than ~800 Ma. While it has long been predicted that increasing atmospheric O2 levels facilitated the early diversification of microbial eukaryotes, the O2 levels needed to permit this diversification remain unconstrained. Using time-resolved geochemical parameter and gene sequence information from a model marine oxygen minimum zone spanning a range of dissolved O2 levels and redox states, we show that microbial eukaryote taxonomic richness and phylogenetic diversity remain the same until O2 declines to around 2 to 3% of present atmospheric levels, below which these diversity metrics become significantly reduced. Our observations suggest that increasing O2 would have only directly promoted early crown-eukaryote diversity if atmospheric O2 was below 2 to 3% of modern levels when crown-eukaryotes originated and then later met or surpassed this range as crown-eukaryotes diversified. If atmospheric O2 was already consistently at or above 2 to 3% of modern levels by the time that crown-eukaryotes originated, then the subsequent diversification of modern microbial eukaryotes was not directly driven by atmospheric oxygenation.

MNCLCDA: predicting circRNA-drug sensitivity associations by using mixed neighbourhood information and contrastive learning

BackgroundcircRNAs play an important role in drug resistance and cancer development. Recently, many studies have shown that the expressions of circRNAs in human cells can affect the sensitivity of cells to therapeutic drugs, thus significantly influencing the therapeutic effects of these drugs. Traditional biomedical experiments required to verify this sensitivity relationship are not only time-consuming but also expensive. Hence, the development of an efficient computational approach that can accurately predict the novel associations between drug sensitivities and circRNAs is a crucial and pressing need.MethodsIn this research, we present a novel computational framework called MNCLCDA, which aims to predict the potential associations between drug sensitivities and circRNAs to assist with medical research. First, MNCLCDA quantifies the similarity between the given drug and circRNA using drug structure information, circRNA gene sequence information, and GIP kernel information. Due to the existence of noise in similarity information, we employ a preprocessing approach based on random walk with restart for similarity networks to efficiently capture the useful features of circRNAs and drugs. Second, we use a mixed neighbourhood graph convolutional network to obtain the neighbourhood information of nodes. Then, a graph-based contrastive learning method is used to enhance the robustness of the model, and finally, a double Laplace-regularized least-squares method is used to predict potential circRNA-drug associations through the kernel matrices in the circRNA and drug spaces.ResultsNumerous experimental results show that MNCLCDA outperforms six other advanced methods. In addition, the excellent performance of our proposed model in case studies illustrates that MNCLCDA also has the ability to predict the associations between drug sensitivity and circRNA in practical situations.ConclusionsAfter a large number of experiments, it is illustrated that MNCLCDA is an efficient tool for predicting the potential associations between drug sensitivities and circRNAs, thereby can provide some guidance for clinical trials.

Molecular characterization of gonadotropin-releasing hormone receptor and its expression profiling in response to HCG stimulation in striped murrel, Channa striata (Bloch, 1793)

The gonadotropin releasing hormone receptors (GnRHR) play an important role in the activation of GnRH and elicit the release of gonadotropins that plays an important role in maturation and spawning. In the present work, molecular characterization of the GnRHR gene of snakehead murrel, Channa striata was carried out. The full-length cDNA of GnRH receptors of the 1581 bp sequence, which encodes 440 amino acids was identified by RACE-PCR. Signal peptide analysis confirmed that GnRHR is a non-secretory protein, and hydropathy profiles indicated its hydrophobic nature. The deduced amino acid sequences of the GnRHR showed the expected conserved domains, which include seven membrane-spanning domains, two potential N-linked glycosylation sites (631 Asp, 561 Asp), and 32 sites phosphorylation sites of serine and threonine residues. A homology search of GnRHR cDNA sequence revealed 88 % identity with Channa argus, followed by 85 % and 83 % sequence identities with Lates calcarifer, and Anabas testudineus at the protein level. Phylogenetic analysis depicted a close association with Channa argus and other fish species. Our findings showed differential GnRHR expression during different reproductive stages, with higher expression after HCG administration. Expression profiling revealed that GnRHR is highly expressed in the brain and testis at 16 h post-HCG injection, while it was neutrally regulated in female ovarian tissues. This study provides full-length coding sequence information of the GnRHR gene and will help to understand the reproductive biology of murrel using expression studies and devise strategies for induced breeding.

Nanoparticle-Shielded dsRNA Delivery for Enhancing RNAi Efficiency in Cotton Spotted Bollworm Earias vittella (Lepidoptera: Nolidae).

The spotted bollworm Earias vittella (Lepidoptera: Nolidae) is a polyphagous pest with enormous economic significance, primarily affecting cotton and okra. However, the lack of gene sequence information on this pest has a significant constraint on molecular investigations and the formulation of superior pest management strategies. An RNA-seq-based transcriptome study was conducted to alleviate such limitations, and de novo assembly was performed to obtain transcript sequences of this pest. Reference gene identification across E. vittella developmental stages and RNAi treatments were conducted using its sequence information, which resulted in identifying transcription elongation factor (TEF), V-type proton ATPase (V-ATPase), and Glyceraldehyde -3-phosphate dehydrogenase (GAPDH) as the most suitable reference genes for normalization in RT-qPCR-based gene expression studies. The present study also identified important developmental, RNAi pathway, and RNAi target genes and performed life-stage developmental expression analysis using RT-qPCR to select the optimal targets for RNAi. We found that naked dsRNA degradation in the E. vittella hemolymph is the primary reason for poor RNAi. A total of six genes including Juvenile hormone methyl transferase (JHAMT), Chitin synthase (CHS), Aminopeptidase (AMN), Cadherin (CAD), Alpha-amylase (AMY), and V-type proton ATPase (V-ATPase) were selected and knocked down significantly with three different nanoparticles encapsulated dsRNA conjugates, i.e., Chitosan-dsRNA, carbon quantum dots-dsRNA (CQD-dsRNA), and Lipofectamine-dsRNA conjugate. These results demonstrate that feeding nanoparticle-shielded dsRNA silences target genes and suggests that nanoparticle-based RNAi can efficiently manage this pest.

An ACE2, SARS-CoV-2 spike protein binding protein, -like enzyme isolated from food-related microorganisms.

Angiotensin-converting enzyme 2 (ACE2) is a binding target of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. An ACE2-like enzyme, such as bacterial M32-carboxypeptidase (M32-CAP), is assumed to be a potential therapeutic candidate for coronavirus disease 2019 (COVID-19). Here, we screened bacteria with an ACE2-like enzyme activity from Japanese fermented food and dietary products using the fluorogenic substrate for rapid screening. The strain showing the highest activity, Enterobacter sp. 200527-13, produced an enzyme with the same hydrolytic activity as ACE2 on Angiotensin II (Ang II). The enzymatic analysis using the heterologously-expressed enzyme in Escherichia coli revealed that the enzyme catalyzes the same reaction with that of ACE2, Ang II hydrolysis to Ang 1-7, and phenylalanine. The gene sequence information showed that the enzyme belongs to the M32-CAP family. These results suggested that the selected enzyme, M32-CAP (EntCP), from Enterobacter sp. 200527-13 was identified as an ACE2-like enzyme.

Rapid generation of high-quality recombinant antibodies using an Expi293F expression system for a 17 β-estradiol immunoassay

The rapid generation of high-quality target antibodies is essential for research employing immunoassays. The use of recombinant antibody technology that relies on genetic engineering is one such means to produce high-quality antibodies. Obtaining the gene sequence information of immunoglobulin is a prerequisite for the preparation of genetically engineered antibodies. At present, many researchers have shared their amino acid sequence data for various high-performance antibodies and their related properties. In this study, we obtained the protein sequence of a variable region of a 17 β-estradiol (E2) antibody from the Protein Data Bank (PDB) and subsequently constructed heavy (H) and light (L) chain expression vectors through codon optimization. The transient expression, purification, and performance identification of the immunoglobulin G (IgG), antigen-binding fragment (Fab), and single-chain variable fragment (scFv) antibodies were carried out, respectively. The effects of the different expression vectors on the expression yield of the IgG antibody were further compared. Among them, the expression yield based on the pTT5 vector was the highest, reaching 27 mg/L. Based on the expressed IgG and Fab antibodies, an indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) standard curve of E2 was constructed, and the half-maximal inhibitory concentrations (IC50) for these two antibodies were determined to be 0.129 ng/mL and 0.188 ng/mL, respectively. In addition, an immunochromatographic assay (ICA) based on the IgG antibody was constructed with an IC50 of 3.7 ng/mL. Therefore, in featuring the advantages of simplicity, high efficiency, rapid obtainment, and high titer yield, we propose the system for the rapid generation of high-quality recombinant antibodies by reusing the published antibody information and show that it has good implementation prospects in improving upon existing immunoassay techniques.

OrthoPhy: A Program to Construct Ortholog Data Sets Using Taxonomic Information.

Species phylogenetic trees represent the evolutionary processes of organisms, and they are fundamental in evolutionary research. Therefore, new methods have been developed to obtain more reliable species phylogenetic trees. A highly reliable method is the construction of an ortholog data set based on sequence information of genes, which is then used to infer the species phylogenetic tree. However, although methods for constructing an ortholog data set for species phylogenetic analysis have been developed, they cannot remove some paralogs, which is necessary for reliable species phylogenetic inference. To address the limitations of current methods, we developed OrthoPhy, a program that excludes paralogs and constructs highly accurate ortholog data sets using taxonomic information dividing analyzed species into monophyletic groups. OrthoPhy can remove paralogs, detecting inconsistencies between taxonomic information and phylogenetic trees of candidate ortholog groups clustered by sequence similarity. Performance tests using evolutionary simulated sequences and real sequences of 40 bacteria revealed that the precision of ortholog inference by OrthoPhy is higher than that of existing programs. Additionally, the phylogenetic analysis of species was more accurate when performed using ortholog data sets constructed by OrthoPhy than that performed using data sets constructed by existing programs. Furthermore, we performed a benchmark test of the Quest for Orthologs using real sequence data and found that the concordance rate between the phylogenetic trees of orthologs inferred by OrthoPhy and those of species was higher than the rates obtained by other ortholog inference programs. Therefore, ortholog data sets constructed using OrthoPhy enabled a more accurate phylogenetic analysis of species than those constructed using the existing programs, and OrthoPhy can be used for the phylogenetic analysis of species even for distantly related species that have experienced many evolutionary events.

HyperVR: a hybrid deep ensemble learning approach for simultaneously predicting virulence factors and antibiotic resistance genes.

Infectious diseases emerge unprecedentedly, posing serious challenges to public health and the global economy. Virulence factors (VFs) enable pathogens to adhere, reproduce and cause damage to host cells, and antibiotic resistance genes (ARGs) allow pathogens to evade otherwise curable treatments. Simultaneous identification of VFs and ARGs can save pathogen surveillance time, especially in situ epidemic pathogen detection. However, most tools can only predict either VFs or ARGs. Few tools that predict VFs and ARGs simultaneously usually have high false-negative rates, are sensitive to the cutoff thresholds and can only identify conserved genes. For better simultaneousprediction of VFs and ARGs, we propose a hybrid deep ensemble learning approach called HyperVR. By considering both best hit scores and statistical gene sequence patterns, HyperVR combines classical machine learning and deep learning to simultaneously and accurately predict VFs, ARGs and negative genes (neither VFs nor ARGs). For the prediction of individual VFs and ARGs, insilico spike-in experiment (the VFs and ARGs in real metagenomic data), and pseudo-VFs and -ARGs (gene fragments), HyperVR outperforms the current state-of-the-art prediction tools. HyperVR uses only gene sequence information without strict cutoff thresholds, hence making prediction straightforward and reliable.

A multifunctional enzyme portfolio for α-chaconine and α-solanine degradation in the Phthorimaea operculella gut bacterium Glutamicibacter halophytocola S2 encoded in a trisaccharide utilization locus.

Steroidal glycoalkaloids (SGAs) are secondary metabolites commonly found in members of the family Solanaceae, including potatoes, and are toxic to pests and humans. The predominant SGAs in potato are α-chaconine and α-solanine. We previously reported that Glutamicibacter halophytocola S2, a gut bacterium of the pest Phthorimaea operculella (potato tuber moth), can degrade α-chaconine and α-solanine in potatoes, which can improve the fitness of P. operculella to feed on potatoes with a high content of toxic SGAs. Glutamicibacter halophytocola S2 harbored a gene cluster containing three deglycosylase genes—GE000599, GE000600, and GE000601—that were predicted encode α-rhamnosidase (RhaA), β-glucosidase (GluA), and β-galactosidase (GalA). However, there is limited information is available on the enzyme activities of the three enzymes expressed by this gene cluster and how they degrade the major toxic α-chaconine and α-solanine. In the current study, each enzyme of this gene cluster was produced by a prokaryotic expression approach and the activity of the recombinant enzymes for their target substrate and α-chaconine and α-solanine were evaluated by EPOCH microplate spectrophotometer and liquid chromatography mass spectrometry (LC-MS). The three enzymes had multifunctional activities, with RhaA and GluA could hydrolyze α-rhamnose, β-glucose, and β-galactose, while GalA can hydrolyze β-glucose and β-galactose. The degradation of α-chaconine and α-solanine was consistent with the results of the enzyme activity assays. The final product solanidine could be generated by adding RhaA or GluA alone. In conclusion, this study characterized the multifunctional activity and specific degradation pathway of these three enzymes in G. halophytocola S2. The three multifunctional enzymes have high glycosidic hydrolysis activity and clear gene sequence information, which help facilitates understanding the detoxification mechanism of insect gut microbes. The enzymes have a broad application potential and may be valuable in the removal of toxic SGAs from for potato food consumption.

Understanding complex genetic architecture of rice grain weight through QTL-meta analysis and candidate gene identification

Quantitative trait loci (QTL) for rice grain weight identified using bi-parental populations in various environments were found inconsistent and have a modest role in marker assisted breeding and map-based cloning programs. Thus, the identification of a consistent consensus QTL region across populations is critical to deploy in marker aided breeding programs. Using the QTL meta-analysis technique, we collated rice grain weight QTL information from numerous studies done across populations and in diverse environments to find constitutive QTL for grain weight. Using information from 114 original QTL in meta-analysis, we discovered three significant Meta-QTL (MQTL) for grain weight on chromosome 3. According to gene ontology, these three MQTL have 179 genes, 25 of which have roles in developmental functions. Amino acid sequence BLAST of these genes indicated their orthologue conservation among core cereals with similar functions. MQTL3.1 includes the OsAPX1, PDIL, SAUR, and OsASN1 genes, which are involved in grain development and have been discovered to play a key role in asparagine biosynthesis and metabolism, which is crucial for source-sink regulation. Five potential candidate genes were identified and their expression analysis indicated a significant role in early grain development. The gene sequence information retrieved from the 3 K rice genome project revealed the deletion of six bases coding for serine and alanine in the last exon of OsASN1 led to an interruption in the synthesis of α-helix of the protein, which negatively affected the asparagine biosynthesis pathway in the low grain weight genotypes. Further, the MQTL3.1 was validated using linked marker RM7197 on a set of genotypes with extreme phenotypes. MQTL that have been identified and validated in our study have significant scope in MAS breeding and map-based cloning programs for improving rice grain weight.

Multi-Relation Graph Embedding for Predicting miRNA-Target Gene Interactions by Integrating Gene Sequence Information.

Accumulated studies have found that miRNAs are in charge of many complex diseases such as cancers by modulating gene expression. Predicting miRNA-target interactions is beneficial for uncovering the crucial roles of miRNAs in regulating target genes and the progression of diseases. The emergence of large-scale genomic and biological data as well as the recent development in heterogeneous networks provides new opportunities for miRNA target identification. Compared with conventional methods, computational methods become a decent solution for high efficiency. Thus, designing a method that could excavate valid information from the heterogeneous network and gene sequences is in great demand for improving the prediction accuracy. In this study, we proposed a graph-based model named MRMTI for the prediction of miRNA-target interactions. MRMTI utilized the multi-relation graph convolution module and the Bi-LSTM module to incorporate both network topology and sequential information. The learned embeddings of miRNAs and genes were then used to calculate the prediction scores of miRNA-target pairs. Comparisons with other state-of-the-art graph embedding methods and existing bioinformatic tools illustrated the superiority of MRMTI under multiple criteria metrics. Three variants of MRMTI implied the positive effect of multi-relation. The experimental results of case studies further demonstrated the prominent ability of MRMTI in predicting novel associations.

Transcriptome analysis of the reproduction of silkworm (Bombyx mori) under dimethoate stress

Dimethoate (DMT) is an organophosphorus pesticide which is widely used to prevent and control agricultural diseases and pests. But it also remains in crops and the environment, affecting other non-target organisms. Existing research mainly focuses on aquatic invertebrates, and research on terrestrial invertebrates is still relatively weak. This study selected the lepidopteran model insect silkworm (Bombyx mori) as the research object and revealed the influence of DMT on the reproduction of silkworms. This study used digital gene expression (DGE) and RT-qPCR analysis to compare gene expression changes in eggs laid by silkworms under the exposure of DMT (200 mg/L). A total of 320 differential genes were detected, of which 211 genes were up-regulated and 109 genes were down-regulated. The GO enrichment analysis bar graph shows those differential genes enriched in the BP's metabolic process, cellular process, CC's membrane part, cell, MF's catalytic activity, binding. KEGG enrichment analysis showed more differential genes enriched in signal transduction, endocrine system, cancers: Overview pathway. The results showed that the differential genes were mainly concentrated on promoting trehalase transporter genes, stress response-related genes, zinc finger protein genes, epidermal protein genes, and 5-HT pathway-related genes. The results of this study will provide important gene sequence information for insect toxicology studies, and also clarify the mechanism of influence of DMT on silkworm reproduction at the transcription level.

Development of a real-time RT-PCR assay for the detection of pan-human parechoviruses

BackgroundParechoviruses (PeV-As), which constitute a new genus within the family Picornaviridae, have been associated with numerous localized outbreaks of serious diseases, such as coryza, pneumonia, maculopapular exanthem, and conjunctivitis. However, to the best of our knowledge, only a few laboratories worldwide conduct tests for the identification of this group of viruses. Therefore, in this study, we aimed to develop and validate a real-time RT-PCR assay for the identification of PeV-As.MethodsTo design and validate a real-time PCR primer–probe targeting the 5′-UTR region of PeV-As, the 5′-UTR sequences of PeV-As available in GenBank were aligned using the MUSCLE algorithm in MEGA v7.0. Thereafter, the highly conserved 5′-UTR region was selected, and its primer–probe sequence was designed using Primer Premier v5.0. This primer–probe sequence was then evaluated for specificity, sensitivity, and repeatability, and for its validation, it was tested using fecal samples from 728 healthy children living in Beijing (China).ResultsThe PeV-A real-time RT-PCR assay detected only the RNA-positive standards of PeV-A genotypes (1–8, 14, 17, and 18), whereas 72 serotypes of non-PeV-A EV viruses were undetected. In addition, the VP1 region of these 11 PeV-A genotypes that tested positive were amplified using the primers designed in this study. Typing results indicated that eight, one, and two strains of the 11 were PeV-A1, PeV-A4, and PeV-A6, respectively. We also determined and presented the genetic characterization and phylogenetic analyses results corresponding to these 11 VP1 region sequences. Furthermore, real-time RT-PCR assay showed good sensitivity with LOD of 102 copies/μL. Positive results in eight parallel experiments at each concentration gradient from 107 copies/μL to 102 copies/μL, indicating good repeatability.ConclusionOur findings suggested that the real-time RT-PCR assay developed in this study can be applied for routine PeV-A identification. We detected PeV-A1, 4 and 6 genotypes in the 728 faecal samples using this method. Additionally, we believe that our results will serve as a foundation for further studies on PeV-As and facilitate the expansion of the gene sequence information available in GenBank.

Proteome-wide profiling of transcriptional machinery on accessible chromatin with biotinylated transposons.

To directly and quantitatively identify the transcriptional protein complexes assembled on accessible chromatin, we develop an assay for transposase-accessible chromatin using mass spectrum (ATAC-MS) based on direct transposition of biotinylated adaptors into open chromatin. Coupling with activated gene sequence information by ATAC-seq, ATAC-MS can profile the accessible chromatin-protein machinery. ATAC-MS, combined with fractionation strategies (fATAC-MS), can provide a high-resolution chromatin-transcriptional machinery atlas. ATAC-MS with a novel Tn5-dCas9 fusion protein [dCas9-targeted ATAC-MS (ctATAC-MS)] further facilitates systematic pinpointing of the transcriptional machinery at specific open chromatin regions. We used ATAC-MS and ATAC-seq to investigate transcriptional regulation during C2C12 cell differentiation and demonstrated the role of RFX1 in regulating the proliferation and differentiation of C2C12 cells. Our strategy provides a universal toolbox including ATAC-MS, fATAC-MS, and ctATAC-MS, which enables us to portray the transcriptional regulation machinery atlas in genome scale and investigate the protein-DNA complex at a specific genomic locus.