Genomics, gene expression and DNA arrays

Transcriptome analysis of avian livers reveals different molecular changes to three urban pollutants: Soot, artificial light at night and noise

Identifying key molecular pathways and genes involved in the response to urban pollutants is an important step in furthering our understanding of the impact of urbanisation on wildlife. The expansion of urban habitats and the associated human-introduced environmental changes are considered a global threat to the health and persistence of humans and wildlife. The present study experimentally investigates how short-term exposure to three urban-related pollutants -soot, artificial light at night (ALAN) and traffic noise-affects transcriptome-wide gene expression in livers from captive female zebra finches (Taeniopygia guttata). Compared to unexposed controls, 17, 52, and 28 genes were differentially expressed in soot, ALAN and noise-exposed birds, respectively. In soot-exposed birds, the enriched gene ontology (GO) terms were associated with a suppressed immune system such as interferon regulating genes (IRGs) and responses to external stimuli. For ALAN-exposed birds, enriched GO terms were instead based on downregulated genes associated with detoxification, redox, hormonal-, and metabolic processes. Noise exposure resulted in downregulation of genes associated with the GO terms: cellular responses to substances, catabolic and cytokine responses. Among the individually differentially expressed genes (DEGs), soot led to an increased expression of genes related to tumour progression. Likewise, ALAN revealed an upregulation of multiple genes linked to different cancer types. Both sensory pollutants (ALAN and noise) led to increased expression of genes linked to neuronal function. Interestingly, noise caused upregulation of genes associated with serotonin regulation and function (SLC6A4 and HTR7), which previous studies have shown to be under selection in urban birds. These outcomes indicate that short-term exposure to the three urban pollutants perturbate the liver transcriptome, but most often in different ways, which highlights future studies of multiple-stress exposure and their interactive effects, along with their long-term impacts for urban-dwelling wildlife.

Screening of sugarcane germplasm against Sporisorium scitamineum and its effects on setts germination and tillering

Sugarcane smut is the most damaging disease that is present almost across the globe, causing mild to severe yield losses depending upon the cultivar types, pathogen races and climatic conditions. Cultivation of smut-resistant cultivars is the most feasible and economical option to mitigate its damages. Previous investigations revealed that there is a scarcity of information on early detection and effective strategies to suppress etiological agents of smut disease due to the characteristics overlapping within species complexes. In this study, 104 sugarcane cultivars were screened by artificial inoculation with homogenate of all possible pathogen races of Sporisorium scitamineum during two consecutive growing seasons. The logistic smut growth pattern and the disease intrinsic rate were recorded by disease growth curve. Variable levels of disease incidence i.e., ranging from 0 to 54.10% were observed among these sugarcane cultivars. Besides, pathogen DNA in plant shoots of all the cultivars was successfully amplified by PCR method using smut-specific primers except 26 cultivars which showed an immune reaction in the field trial. Furthermore, the plant germination and tillering of susceptible sugarcane cultivars were greatly influenced by pathogen inoculation. In susceptible cultivars, S. scitamineum caused a significant reduction in setts germination, coupled with profuse tillering, resulting in fewer millable canes. Correlation analysis demonstrated that there was a positive relationship between reduction in setts germination and increase in the number of tillers. The present study would be helpful for the evaluation of smut resistance in a wide range of sugarcane germplasm, especially from the aspects of setts germination and tillers formation, and it also screened out several excellent germplasm for potential application in sugarcane breeding.

Inhibition of δ-catenin palmitoylation slows the progression of prostate cancer

Prostate cancer (PCa) is the second leading cause of death in males. It has been reported that δ-catenin expression is upregulated during the late stage of prostate cancer. Palmitoylation promotes protein transport to the cytomembrane and regulates protein localization and function. However, the effect of δ-catenin palmitoylation on the regulation of cancer remains unknown. In this study, we utilized prostate cancer cells overexpressing mutant δ-catenin (J6A cells) to induce a depalmitoylation phenotype and investigate its effect on prostate cancer. Our results indicated that depalmitoylation of δ-catenin not only reduced its membrane expression but also promoted its degradation in the cytoplasm, resulting in a decrease in the effect of EGFR and E-cadherin signaling. Consequently, depalmitoylation of δ-catenin reduced the proliferation and metastasis of prostate cancer cells. Our findings provide novel insights into potential therapeutic strategies for controlling the progression of prostate cancer through palmitoylation-based targeting of δ-catenin.

Post-stroke cognitive impairment: exploring molecular mechanisms and omics biomarkers for early identification and intervention

Post-stroke cognitive impairment (PSCI) is a major stroke consequence that has a severe impact on patients' quality of life and survival rate. For this reason, it is especially crucial to identify and intervene early in high-risk groups during the acute phase of stroke. Currently, there are no reliable and efficient techniques for the early diagnosis, appropriate evaluation, or prognostication of PSCI. Instead, plenty of biomarkers in stroke patients have progressively been linked to cognitive impairment in recent years. High-throughput omics techniques that generate large amounts of data and process it to a high quality have been used to screen and identify biomarkers of PSCI in order to investigate the molecular mechanisms of the disease. These techniques include metabolomics, which explores dynamic changes in the organism, gut microbiomics, which studies host-microbe interactions, genomics, which elucidates deeper disease mechanisms, transcriptomics and proteomics, which describe gene expression and regulation. We looked through electronic databases like PubMed, the Cochrane Library, Embase, Web of Science, and common databases for each omics to find biomarkers that might be connected to the pathophysiology of PSCI. As all, we found 34 studies: 14 in the field of metabolomics, 5 in the field of gut microbiomics, 5 in the field of genomics, 4 in the field of transcriptomics, and 7 in the field of proteomics. We discovered that neuroinflammation, oxidative stress, and atherosclerosis may be the primary causes of PSCI development, and that metabolomics may play a role in the molecular mechanisms of PSCI. In this study, we summarized the existing issues across omics technologies and discuss the latest discoveries of PSCI biomarkers in the context of omics, with the goal of investigating the molecular causes of post-stroke cognitive impairment. We also discuss the potential therapeutic utility of omics platforms for PSCI mechanisms, diagnosis, and intervention in order to promote the area's advancement towards precision PSCI treatment.

Elucidating the Role of Transcriptomic Networks and DNA Methylation in Collagen Deposition of Dezhou Donkey Skin

DNA methylation represents a predominant epigenetic modification with broad implications in various biological functions. Its role is particularly significant in the process of collagen deposition, a fundamental aspect of dermal development in donkeys. Despite its critical involvement, the mechanistic insights into how DNA methylation influences collagen deposition in donkey skin remain limited. In this study, we employed whole genome bisulfite sequencing (WGBS) and RNA sequencing (RNA-seq) to investigate the epigenetic landscape and gene expression profiles in the dorsal skin tissues of Dezhou donkeys across three developmental stages: embryonic (YD), juvenile (2-year-old, MD), and mature (8-year-old, OD). Our analysis identified numerous differentially methylated genes that play pivotal roles in skin collagen deposition and overall skin maturation, including but not limited to COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, GLUL, SFRP2, FOSL1, SERPINE1, MMP1, MMP2, MMP9, and MMP13. Notably, we observed an inverse relationship between gene expression and DNA methylation proximal to transcription start sites (TSSs), whereas a direct correlation was detected in regions close to transcription termination sites (TTSs). Detailed bisulfite sequencing analyses of the COL1A1 promoter region revealed a low methylation status during the embryonic stage, correlating with elevated transcriptional activity and gene expression levels. Collectively, our findings elucidate key genetic markers associated with collagen deposition in the skin of Dezhou donkeys, underscoring the significant regulatory role of DNA methylation. This research work contributes to the foundational knowledge necessary for the genetic improvement and selective breeding of Dezhou donkeys, aiming to enhance skin quality attributes.

DEFM: Delay-embedding-based forecast machine for time series forecasting by spatiotemporal information transformation

Making accurate forecasts for a complex system is a challenge in various practical applications. The major difficulty in solving such a problem concerns nonlinear spatiotemporal dynamics with time-varying characteristics. Takens' delay embedding theory provides a way to transform high-dimensional spatial information into temporal information. In this work, by combining delay embedding theory and deep learning techniques, we propose a novel framework, delay-embedding-based forecast Machine (DEFM), to predict the future values of a target variable in a self-supervised and multistep-ahead manner based on high-dimensional observations. With a three-module spatiotemporal architecture, the DEFM leverages deep neural networks to effectively extract both the spatially and temporally associated information from the observed time series even with time-varying parameters or additive noise. The DEFM can accurately predict future information by transforming spatiotemporal information to the delay embeddings of a target variable. The efficacy and precision of the DEFM are substantiated through applications in three spatiotemporally chaotic systems: a 90-dimensional (90D) coupled Lorenz system, the Lorenz 96 system, and the Kuramoto-Sivashinsky equation with inhomogeneity. Additionally, the performance of the DEFM is evaluated on six real-world datasets spanning various fields. Comparative experiments with five prediction methods illustrate the superiority and robustness of the DEFM and show the great potential of the DEFM in temporal information mining and forecasting.

Beyond genome: Advanced omics progress of Panax ginseng

Ginseng is a perennial herb of the genus Panax in the family Araliaceae as one of the most important traditional medicine. Genomic studies of ginseng assist in the systematic discovery of genes related to bioactive ginsenosides biosynthesis and resistance to stress, which are of great significance in the conservation of genetic resources and variety improvement. The transcriptome reflects the difference and consistency of gene expression, and transcriptomics studies of ginseng assist in screening ginseng differentially expressed genes to further explore the powerful gene source of ginseng. Protein is the ultimate bearer of ginseng life activities, and proteomic studies of ginseng assist in exploring the biosynthesis and regulation of secondary metabolites like ginsenosides and the molecular mechanism of ginseng adversity adaptation at the overall level. In this review, we summarize the current status of ginseng research in genomics, transcriptomics and proteomics, respectively. We also discuss and look forward to the development of ginseng genome allele mapping, ginseng spatiotemporal, single-cell transcriptome, as well as ginseng post-translational modification proteome. We hope that this review will contribute to the in-depth study of ginseng and provide a reference for future analysis of ginseng from a systems biology perspective.

Genome-Wide Screens Identify Genes Responsible for Intrinsic Boric Acid Resistance in Escherichia coli

Boric acid (BA) has antimicrobial properties and is used to combat bacterial infections, including Enterobacteria. However, the molecular mechanisms and cellular responses to BA are still unknown. This genomics study aims to provide new information on the genes and molecular mechanisms related to the antimicrobial effect of BA in Escherichia coli. The Keio collection of E. coli was used to screen 3985 single-gene knockout strains in order to identify mutant strains that were sensitive or hypersensitive to BA at certain concentrations. The mutant strains were exposed to different concentrations of BA ranging from 0 to 120 mM in LB media. Through genome-wide screens, 92 mutants were identified that were relatively sensitive to BA at least at one concentration tested. The related biological processes in the particular cellular system were listed. This study demonstrates that intrinsic BA resistance is the result of various mechanisms acting together. Additionally, we identified eighteen out of ninety-two mutant strains (Delta_aceF, aroK, cheZ, dinJ, galS, garP, glxK, nohA, talB, torR, trmU, trpR, yddE, yfeS, ygaV, ylaC, yoaC, yohN) that exhibited sensitivity using other methods. To increase sensitivity to BA, we constructed double and triple knockout mutants of the selected sensitive mutants. In certain instances, engineered double and triple mutants exhibited significantly amplified effects. Overall, our analysis of these findings offers further understanding of the mechanisms behind BA toxicity and intrinsic resistance in E. coli.

Development of Noninvasive Activity-Based Protein Profiling-Bioluminescence Resonance Energy Transfer Platform Technology Enables Target Engagement Studies with Absolute Specificity in Living Systems

Noninvasive, real-time, longitudinal imaging of protein functions in living systems with unprecedented specificity is one of the critical challenges of modern biomedical research. Toward that goal, here, we report a platform fusion technology called activity-based protein profiling-bioluminescence resonance energy transfer (ABPP-BRET). This method provides an opportunity to study the post-translational modification of a target protein in real time in living systems in a longitudinal manner. This semisynthetic BRET biosensor method is used for target engagement studies and further for inhibitor profiling in live cells. The simplicity of this method coupled with the critical physical distance-dependent BRET readout turned out to be a powerful method, thus pushing the activity-based protein profiling technology to the next level.

Interpretable predictions of chaotic dynamical systems using dynamical system deep learning

Making accurate predictions of chaotic dynamical systems is an essential but challenging task with many practical applications in various disciplines. However, the current dynamical methods can only provide short-term precise predictions, while prevailing deep learning techniques with better performances always suffer from model complexity and interpretability. Here, we propose a new dynamic-based deep learning method, namely the dynamical system deep learning (DSDL), to achieve interpretable long-term precise predictions by the combination of nonlinear dynamics theory and deep learning methods. As validated by four chaotic dynamical systems with different complexities, the DSDL framework significantly outperforms other dynamical and deep learning methods. Furthermore, the DSDL also reduces the model complexity and realizes the model transparency to make it more interpretable. We firmly believe that the DSDL framework is a promising and effective method for comprehending and predicting chaotic dynamical systems.

Integrated Genome Sequencing and Transcriptome Analysis Identifies Candidate Pathogenicity Genes from Ustilago crameri

Ustilago crameri is a pathogenic basidiomycete fungus that causes foxtail millet kernel smut (FMKS), a devastating grain disease in most foxtail-millet-growing regions of the world. Here, we report an assembled high-quality genome sequence of U. crameri strain SCZ-6 isolated from the diseased grains of foxtail millet in Changzhi, Shanxi Province, China. The genome size is 19.55 Mb, consisting of 73 contigs (N50 = 840,209 bp) with a G + C content of 54.09%, and encoding 6576 predicted genes and 6486 genes supported by RNA-seq. Evolutionarily, U. crameri lies close to the barley smut U. hordei, and an obvious co-linearity was observed between these two smut fungi. We annotated the genome of U. crameri strain SCZ-6 using databases, identifying 1827 pathogen-host interaction (PHI)-associated genes, 1324 genes encoding fungal virulence factors, 259 CAZy-related genes, 80 genes encoding transporters, and 206 putative cytochrome P450 genes; their expression profiles at different inoculation time points were also detected. Additionally, 70 candidate pathogen effectors were identified according to their expression patterns and predicted functions. In summary, our results provide important insights into the pathogenic mechanisms of the pathogenesis-related genes of U. crameri and a robust foundation for further investigation.

Pan-Omics in Sheep: Unveiling Genetic Landscapes

Multi-omics-integrated analysis, known as panomics, represents an advanced methodology that harnesses various high-throughput technologies encompassing genomics, epigenomics, transcriptomics, proteomics, and metabolomics. Sheep, playing a pivotal role in agricultural sectors due to their substantial economic importance, have witnessed remarkable advancements in genetic breeding through the amalgamation of multiomics analyses, particularly with the evolution of high-throughput technologies. This integrative approach has established a robust theoretical foundation, enabling a deeper understanding of sheep genetics and fostering improvements in breeding strategies. The comprehensive insights obtained through this approach shed light on diverse facets of sheep development, including growth, reproduction, disease resistance, and the quality of livestock products. This review primarily focuses on the application of principal omics analysis technologies in sheep, emphasizing correlation studies between multiomics data and specific traits such as meat quality, wool characteristics, and reproductive features. Additionally, this paper anticipates forthcoming trends and potential developments in this field.

Nanomaterials for detection of biomolecules and delivering therapeutic agents in theragnosis: A review

Nanomaterials are emerging facts used to deliver therapeutic agents in living systems. Nanotechnology is used as a compliment by implementing different kinds of nanotechnological applications such as nano-porous structures, functionalized nanomaterials, quantum dots, carbon nanomaterials, and polymeric nanostructures. The applications are in the initial stage, which led to achieving several diagnoses and therapy in clinical practice. This review conveys the importance of nanomaterials in post-genomic employment, which includes the design of immunosensors, immune assays, and drug delivery. In this view, genomics is a molecular tool containing large databases that are useful in choosing an apt molecular inhibitor such as drug, ligand and antibody target in the drug delivery process. This study identifies the expression of genes and proteins in analysis and classification of diseases. Experimentally, the study analyses the design of a disease model. In particular, drug delivery is a boon area to treat cancer. The identified drugs enter different phase trails (Trails I, II, and III). The genomic information conveys more essential entities to the phase I trials and helps to move further for other trails such as trails-II and III. In such cases, the biomarkers play a crucial role by monitoring the unique pathological process. Genetic engineering with recombinant DNA techniques can be employed to develop genetically engineered disease models. Delivering drugs in a specific area is one of the challenging issues achieved using nanoparticles. Therefore, genomics is considered as a vast molecular tool to identify drugs in personalized medicine for cancer therapy.

Technical assessment of different extraction methods and transcriptome profiling of RNA isolated from small volumes of blood

Transcriptome profiling of human whole blood is used to discover biomarkers of diseases and to assess phenotypic traits. Recently, finger-stick blood collection systems have allowed a less invasive and quicker collection of peripheral blood. Such non-invasive sampling of small volumes of blood offers practical advantages. The quality of gene expression data is strictly dependent on the steps used for the sample collection, extraction, preparation and sequencing. Here we have: (i) compared the manual and automated RNA extraction of small volumes of blood using the Tempus Spin RNA isolation kit and the MagMAX for Stabilized Blood RNA Isolation kit , respectively; and (ii) assessed the effect of TURBO DNA Free treatment on the transcriptomic data of RNA isolated from small volumes of blood. We have used the QuantSeq 3' FWD mRNA-Seq Library Prep kit to prepare RNA-seq libraries, which were sequenced on the Illumina NextSeq 500 system. The samples isolated manually displayed a higher variability in the transcriptomic data as compared to the other samples. The TURBO DNA Free treatment affected the RNA samples negatively, decreasing the RNA yield and reducing the quality and reproducibility of the transcriptomic data. We conclude that automated extraction systems should be preferred over manual extraction systems for data consistency, and that the TURBO DNA Free treatment should be avoided when working on RNA samples isolated manually from small volumes of blood.

Reveal key genes and factors affecting athletes performance in endurance sports using bioinformatic technologies

Medium-intensity activities comprise the major proportion of many sorts of sports. The energy consumption of athletes has been a research emphasis for the purpose of improving both training efficiency and competition performance. However, the evidence based on large-scale gene screen has been rarely performed. This is a bioinformatic study revealing the key factors contributed to the metabolic difference between subjects with different endurance activity capacities. A dataset comprised of high- (HCR) and low-capacity running (LCR) rats was used. Differentially expressed genes (DEGs) were identified and analysed. The Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment was obtained. The DEGs' protein-protein interaction (PPI) network was built, and the enriched terms of the PPI network were also analysed. Our findings showed that the GO terms were enriched in lipid metabolism-related terms. The KEGG signalling pathway analysis enriched in the ether lipid metabolism. Plb1, Acad1, Cd2bp2, and Pla2g7 were identified as the hub genes. This study provides a theoretical foundation showing lipid metabolism plays an important role in the performance of endurance activities. Plb1, Acad1, and Pla2g7 may be the key genes involved. The training plan and diet for athletes can be designed based on above results and expecting a better competitive performance.

Miniature Hierarchical DNA Hybridization Circuit for Amplified Multiplexed MicroRNA Imaging

Accurate diagnosis requires the development of multiple-guaranteed DNA circuits. Nevertheless, for reliable multiplexed molecular imaging, existing DNA circuits are limited by poor cell-delivering homogeneity due to their cumbersome and dispersive reactants. Herein, we developed a compact-yet-efficient hierarchical DNA hybridization (HDH) circuit for in situ simultaneous analysis of multiple miRNAs, which could be further exploited for specifically discriminating cancer cells from normal ones. By integrating the traditional hybridization chain reaction and catalytic hairpin assembly reactants into two highly organized hairpins, the HDH circuit is fitted with condensed components and multiple response domains, thus permitting the programmable multiple microRNA-guaranteed sequential activations and the localized cascaded signal amplification. The synergistic multi-recognition and amplification features of the HDH circuit facilitate the magnified detection of multiplex endogenous miRNAs in living cells. The in vitro and cellular imaging experimental results revealed that the HDH circuit displayed a reliable sensing performance with high selective cell-identification capacity. We anticipate that this compact design can provide a powerful toolkit for accurate diagnostics and pathological evolution.

Differentially expression analyses in fruit of cultivated and wild species of grape and peach

Through agronomic traits and sequencing data, the cultivated and wild varieties of grapes and peaches were analyzed and compared in terms of fruit size, fruit flavor, fruit resistance, and fruit color. Cultivated grapes and peaches have advantages in fruit size, soluble sugar content, sugar and acid ratio, etc. Wild grapes and peaches have utility value in resistance. The results showed that there were 878 and 301 differentially expressed genes in cultivated and wild grapes and peaches in the three growth stages, respectively based on the next-generation sequencing study. Ten and twelve genes related to the differences between cultivated and wild grapes and peaches were found respectively. Among them, three genes, namely chalcone synthase (CHS), glutathione S-transferase (GST) and malate dehydrogenase (MDH1) were present in both cultivated and wild grapes and peaches.

Molecular Characterization and Phylogenetic Analysis of Casein Gene Family in Camelus ferus

Camel milk is known for its exceptional medical uses. It has been used since ancient times to treat infant diarrhea, hepatitis, insulin-dependent diabetes (IDDM), lactose intolerance, alcohol-induced liver damage, allergies, and autism. It has the power to treat several diseases, with cancer being the most significant. This study investigated the evolutionary relationship, physiochemical characteristics, and comparative genomic analysis of the casein gene family (CSN1S1, CSN2, CSN1S2, and CSN3) in Camelus ferus. Molecular phylogenetics showing the camelid species clustered casein nucleotide sequences into four groups: CSN1S1, CSN2, CSN1S2, and CSN3. The casein proteins from camels were evaluated and found to be unstable, thermostable, and hydrophilic. CSN1S2, CSN2, and CSN3 were acidic, but CSN1S1 was basic. CSN1S1 showed positive selection for one amino acid (Q), CSN1S2 and CSN2 for three (T, K, Q), and CSN3 showed no positive selection. We also compared high-milk-output species such as cattle (Bos Tarus) and low-milk-yield species such as sheep (Ovies Aries) with camels (Camel ferus) and discovered that YY1 sites are more frequent in sheep than in camels and very low in cattle. We concluded that the ratio of YY1 sites in these species may affect milk production.

Integrated Transcriptomics Profiling in Chahua and Digao Chickens' Breast for Assessment Molecular Mechanism of Meat Quality Traits

Meat quality traits are an important economic trait and remain a major argument, from the producer to the consumer. However, there are a few candidate genes and pathways of chicken meat quality traits that were reported for chicken molecular breeding. The purpose of the present study is to identify the candidate genes and pathways associated with meat quality underlying variations in meat quality. Hence, transcriptome profiles of breast tissue in commercial Digao (DG, 5 male) and Chahua (CH, 5 male) native chicken breeds were analyzed at the age of 100 days. The results found 3525 differentially expressed genes (DEGs) in CH compared to DG with adjusted p-values of ≤0.05 and log2FC ≥ 0.1 FDR ≤ 0.05. Functional analysis of GO showed that the DEGs are mainly involved in the two types of processes of meat quality, such as positive regulation of the metabolic process, extracellular structure organization, collagen trimer, cellular amino acid metabolic process, cellular amino acid catabolic process, and heme binding. Functional analysis of KEGG showed that the DEGs are mainly involved in the two types of processes of meat quality, such as oxidative phosphorylation, carbon metabolism, valine, leucine, and isoleucine degradation, and fatty acid degradation. Many of the DEGs are well known to be related to meat quality, such as COL28A1, COL1A2, MB, HBAD, HBA1, ACACA, ACADL, ACSL1, ATP8A1, CAV1, FADS2, FASN, DCN, CHCHD10, AGXT2, ALDH3A2, and MORN4. Therefore, the current study detected multiple pathways and genes that could be involved in the control of the meat quality traits of chickens. These findings should be used as an essential resource to improve the accuracy of selection for meat traits in chickens using marker-assisted selection based on differentially expressed genes.

Upcoming progress of transcriptomics studies on plants: An overview

Transcriptome sequencing or RNA-Sequencing is a high-resolution, sensitive and high-throughput next-generation sequencing (NGS) approach used to study non-model plants and other organisms. In other words, it is an assembly of RNA transcripts from individual or whole samples of functional and developmental stages. RNA-Seq is a significant technique for identifying gene predictions and mining functional analysis that improves gene ontology understanding mechanisms of biological processes, molecular functions, and cellular components, but there is limited information available on this topic. Transcriptomics research on different types of plants can assist researchers to understand functional genes in better ways and regulatory processes to improve breeding selection and cultivation practices. In recent years, several advancements in RNA-Seq technology have been made for the characterization of the transcriptomes of distinct cell types in biological tissues in an efficient manner. RNA-Seq technologies are briefly introduced and examined in terms of their scientific applications. In a nutshell, it introduces all transcriptome sequencing and analysis techniques, as well as their applications in plant biology research. This review will focus on numerous existing and forthcoming strategies for improving transcriptome sequencing technologies for functional gene mining in various plants using RNA- Seq technology, based on the principles, development, and applications.

Regulation of mRNA and miRNA in the response to Salmonella enterica serovar Enteritidis infection in chicken cecum

BACKGROUND

Salmonella enterica, serovar Enteritidis (SE) is a food-borne pathogen, which can cause great threat to human health through consumption of the contaminated poultry products. Chicken is the main host of SE. The mRNA and microRNA (miRNA) expression profiles were analyzed on cecum of Shouguang chicken via next-generation sequencing and bioinformatics approaches. The treated group was inoculated SE, and the control group was inoculated with phosphate buffer saline (PBS).

RESULTS

There were 1760 differentially expressed mRNAs in the SE-infected group, of which 1046 were up-regulated mRNA, and 714 were down-regulated mRNA. In addition, a total of 821 miRNAs were identified, and 174 miRNAs were differentially expressed, of which 100 were up-regulated and 74 were down-regulated. Functional enrichment of differentially expressed mRNAs was similar to miRNA target genes. The functional analysis results of differentially expressed mRNAs and miRNAs were performed. Immune-related processes and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways were enriched by up-regulated mRNA. The down-regulated mRNAs were enriched in tissue development and metabolic-related KEGG pathways. The functional analysis of up-regulated miRNA target genes was similar to the down-regulated mRNAs. The down-regulated miRNA target genes were enriched in metabolic-related GO (Gene Ontology) -BP (Biological process) terms and KEGG pathways. The overlap of the up-regulated mRNA and the up-regulated miRNA target genes (class I) was 325, and the overlap of the down-regulated miRNA target genes (class II) was 169. The class I enriched in the immune-related GO-BP terms and KEGG pathways. The class II mainly enriched in metabolic-related GO-BP terms and KEGG pathways. Then we detected the expression of mRNA and miRNA through qRT-PCR. The results shown that the expression of HHIP, PGM1, HTR2B, ITGB5, RELN, SFRP1, TCF7L2, SCNN1A, NEK7, miR-20b-5p, miR-1662, miR-15a, miR-16-1-3p was significantly different between two groups. Dual-luciferase reporter assay was used to detect the relationship between miR-20b-5p and SCNN1A. The result indicated that miR-20b-5p regulate immune or metabolic responses after SE infection in Shouguang chickens by directly targeting SCNN1A.

CONCLUSIONS

The findings here contribute to the further analysis of the mechanism of mRNA and miRNA defense against SE infection, and provide a theoretical foundation for the molecular disease-resistant breeding of chickens.

Forgetful, sad and old: Do vascular cognitive impairment and depression share a common pre-disease network and how is it impacted by ageing?

Vascular cognitive impairment (VCI) and depression frequently coexist in geriatric populations and reciprocally increase disease risks. We assert that a shared pre-disease state of the psycho-immune-neuroendocrine (PINE) network model mechanistically explains bidirectional associations between VCI and depression. Five pathophysiological sub-networks are identified that are shared by VCI and depression: neuroinflammation, kynurenine pathway imbalance, hypothalamic-pituitary-adrenal (HPA) axis overactivity, impaired neurotrophic support and cerebrovascular dysfunction. These do not act independently, and their complex interactions necessitate a systems biology approach to better define disease pathogenesis. The PINE network is already established in the context of non-communicable diseases (NCDs) such as depression, hypertension, atherosclerosis, coronary heart disease and type 2 diabetes mellitus. We build on previous literature to specifically explore mechanistic links between MDD and VCI in the context of PINE pathways and discuss key mechanistic commonalities linking these comorbid conditions and identify a common pre-disease state which precedes transition to VCI and MDD. We expand the model to incorporate bidirectional interactions with biological ageing. Diathesis factors for both VCI and depression feed into this network and the culmination of shared mechanisms (on an ageing substrate) lead to a critical network transition to one or both disease states. A common pre-disease state underlying VCI and depression can provide clinicians a unique opportunity for early risk assessment and intervention in disease development. Establishing the mechanistic elements and systems biology of this network can reveal early warning or predictive biomarkers together with novel therapeutic targets. Integrative studies are recommended to elucidate the dynamic networked biology of VCI and depression over time.

Cancer Segmentation by Entropic Analysis of Ordered Gene Expression Profiles

The availability of massive gene expression data has been challenging in terms of how to cure, process, and extract useful information. Here, we describe the use of entropic measures as discriminating criteria in cancer using the whole data set of gene expression levels. These methods were applied in classifying samples between tumor and normal type for 13 types of tumors with a high success ratio. Using gene expression, ordered by pathways, results in complexity-entropy diagrams. The map allows the clustering of the tumor and normal types samples, with a high success rate for nine of the thirteen, studied cancer types. Further analysis using information distance also shows good discriminating behavior, but, more importantly, allows for discriminating between cancer types. Together, our results allow the classification of tissues without the need to identify relevant genes or impose a particular cancer model. The used procedure can be extended to classification problems beyond the reported results.

Analytical Studies of Antimicrobial Peptides as Diagnostic Biomarkers for the Detection of Bacterial and Viral Pneumonia

Pneumonia remains one of the leading causes of infectious mortality and significant economic losses among our growing population. The lack of specific biomarkers for correct and timely diagnosis to detect patients’ status is a bane towards initiating a proper treatment plan for the disease; thus, current biomarkers cannot distinguish between pneumonia and other associated conditions such as atherosclerotic plaques and human immunodeficiency virus (HIV). Antimicrobial peptides (AMPs) are potential candidates for detecting numerous illnesses due to their compensatory roles as theranostic molecules. This research sought to generate specific data for parental AMPs to identify viral and bacterial pneumonia pathogens using in silico technology. The parental antimicrobial peptides (AMPs) used in this work were AMPs discovered in our previous in silico analyses using the HMMER algorithm, which were used to generate derivative (mutated) AMPs that would bind with greater affinity, in order to detect the bacterial and viral receptors using an in silico site-directed mutagenesis approach. These AMPs’ 3D structures were subsequently predicted and docked against receptor proteins. The result shows putative AMPs with the potential capacity to detect pneumonia caused by these pathogens through their binding precision with high sensitivity, accuracy, and specificity for possible use in point-of-care diagnosis. These peptides’ tendency to detect receptor proteins of viral and bacterial pneumonia with precision justifies their use for differential diagnostics, in an attempt to reduce the problems of indiscriminate overuse, toxicity due to the wrong prescription, bacterial resistance, and the scarcity and high cost of existing pneumonia antibiotics.

A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients

Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Noninvasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography–tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753–0.9825), 0.7 and 1, respectively.

Precision medicine journey through omics approach

It has been well established that understanding the underlying heterogeneity of numerous complex disease process needs new strategies that present in precision medicine for prediction, prevention and personalized treatment strategies. This approach must be tailored for each individual's unique omics that lead to personalized management of disease. The correlation between different omics data should be considered in precision medicine approach. The interaction provides a hypothesis which is called domino effect in the present minireview. Here we review the various potentials of omics data including genomics, transcriptomics, proteomics, metabolomics, pharmacogenomics. We comprehensively summarize the impact of omics data and its major role in precision medicine and provide a description about the domino effect on the pathophysiology of diseases. Each constituent of the omics data typically provides different information in associated with disease. Current research, although inadequate, clearly indicate that the information of omics data can be applicable in the concept of precision medicine. Integration of different omics data type in domino effect hypothesis can explain the causative changes of disease as it is discussed in the system biology too. While most existing studies investigate the omics data separately, data integration is needed on the horizon of precision medicine by using machine learning.

DNA Privacy: Analyzing Malicious DNA Sequences Using Deep Neural Networks

Recent advances in next-generation sequencing technologies have led to the successful insertion of video information into DNA using synthesized oligonucleotides. Several attempts have been made to embed larger data into living organisms. This process of embedding messages is called steganography and it is used for hiding and watermarking data to protect intellectual property. In contrast, steganalysis is a group of algorithms that serves to detect hidden information from covert media. Various methods have been developed to detect messages embedded in conventional covert channels. However, conventional steganalysis algorithms are mostly limited to common covert media. Most common detection approaches, such as frequency analysis-based methods, often overlook important signals when directly applied to DNA steganography and are easily bypassed by recently developed steganography techniques. To address the limitations of conventional approaches, a sequence-learning-based malicious DNA sequence analysis method based on neural networks has been proposed. The proposed method learns intrinsic distributions and identifies distribution variations using a classification score to predict whether a sequence is to be a coding or non-coding sequence. Based on our experiments and results, we have developed a framework to safeguard security against DNA steganography.

Integrated co-expression network analysis uncovers novel tissue-specific genes in major depressive disorder and bipolar disorder

Tissue-specific gene expression has been found to be associated with multiple complex diseases including cancer, metabolic disease, aging, etc. However, few studies of brain-tissue-specific gene expression patterns have been reported, especially in psychiatric disorders. In this study, we performed joint analysis on large-scale transcriptome multi-tissue data to investigate tissue-specific expression patterns in major depressive disorder (MDD) and bipolar disorder (BP). We established the strategies of identifying tissues-specific modules, annotated pathways for elucidating biological functions of tissues, and tissue-specific genes based on weighted gene co-expression network analysis (WGCNA) and robust rank aggregation (RRA) with transcriptional profiling data from different human tissues and genome wide association study (GWAS) data, which have been expanded into overlapping tissue-specific modules and genes sharing with MDD and BP. Nine tissue-specific modules were identified and distributed across the four tissues in the MDD and six modules in the BP. In general, the annotated biological functions of differentially expressed genes (DEGs) in blood were mainly involved in MDD and BP progression through immune response, while those in the brain were in neuron and neuroendocrine response. Tissue-specific genes of the prefrontal cortex (PFC) in MDD-, such as IGFBP2 and HTR1A, were involved in disease-related functions, such as response to glucocorticoid, taste transduction, and tissue-specific genes of PFC in BP-, such as CHRM5 and LTB4R2, were involved in neuroactive ligand-receptor interaction. We also found PFC tissue-specific genes including SST and CRHBP were shared in MDD-BP, SST was enriched in neuroactive ligand-receptor interaction, and CRHBP shown was related to the regulation of hormone secretion and hormone transport.

Target DNA- and pH-responsive DNA hydrogel-based capillary assay for the optical detection of short SARS-CoV-2 cDNA

DNA is recognized as a powerful biomarker for clinical diagnostics because its specific sequences are closely related to the cause and development of diseases. However, achieving rapid, low-cost, and sensitive detection of short-length target DNA still remains a considerable challenge. Herein, we successfully combine the catalytic hairpin assembly (CHA) technique with capillary action to develop a new and cost-effective method, a target DNA- and pH-responsive DNA hydrogel–based capillary assay, for the naked eye detection of 24 nt short single-stranded target DNA. Upon contact of target DNA, three individual hairpin DNAs hybridize with each other to sufficiently amplify Y-shaped DNA nanostructures (Y-DNA) until they are completely consumed via CHA cycling reactions. Each arm of the resultant Y-DNA contains sticky ends with i-motif DNA structure-forming sequences that can be self-assembled in an acidic environment (pH 5.0) to form target DNA- and pH-responsive DNA hydrogels by means of i-motif DNA-driven crosslinking. When inserting a capillary tube in the resultant solution, the liquid level inside clearly reduces due to the decrease in capillary force induced by the gels. In this way, the developed assay demonstrates sensitive and quantitative detection, with a detection limit of approximately 10 pM of 24 nt short complementary DNA (cDNA) targeting SARS-CoV-2 RNA genes at room temperature within 1 h. The assay is further shown to successfully detect target cDNA in serum, and it is also applied to detect several types of target sequences. Requiring no analytic equipment, precise temperature control, or enzymatic reactions, the developed DNA hydrogel–based capillary assay has potential as a promising naked eye detection platform for target DNA in resource-limited clinical settings.

Efficient technique of microarray missing data imputation using clustering and weighted nearest neighbour

For most bioinformatics statistical methods, particularly for gene expression data classification, prognosis, and prediction, a complete dataset is required. The gene sample value can be missing due to hardware failure, software failure, or manual mistakes. The missing data in gene expression research dramatically affects the analysis of the collected data. Consequently, this has become a critical problem that requires an efficient imputation algorithm to resolve the issue. This paper proposed a technique considering the local similarity structure that predicts the missing data using clustering and top K nearest neighbor approaches for imputing the missing value. A similarity-based spectral clustering approach is used that is combined with the K-means. The spectral clustering parameters, cluster size, and weighting factors are optimized, and after that, missing values are predicted. For imputing each cluster’s missing value, the top K nearest neighbor approach utilizes the concept of weighted distance. The evaluation is carried out on numerous datasets from a variety of biological areas, with experimentally inserted missing values varying from 5 to 25%. Experimental results prove that the proposed imputation technique makes accurate predictions as compared to other imputation procedures. In this paper, for performing the imputation experiments, microarray gene expression datasets consisting of information of different cancers and tumors are considered. The main contribution of this research states that local similarity-based techniques can be used for imputation even when the dataset has varying dimensionality and characteristics.

CLARITY: comparing heterogeneous data using dissimilarity

Integrating datasets from different disciplines is hard because the data are often qualitatively different in meaning, scale and reliability. When two datasets describe the same entities, many scientific questions can be phrased around whether the (dis)similarities between entities are conserved across such different data. Our method, CLARITY, quantifies consistency across datasets, identifies where inconsistencies arise and aids in their interpretation. We illustrate this using three diverse comparisons: gene methylation versus expression, evolution of language sounds versus word use, and country-level economic metrics versus cultural beliefs. The non-parametric approach is robust to noise and differences in scaling, and makes only weak assumptions about how the data were generated. It operates by decomposing similarities into two components: a 'structural' component analogous to a clustering, and an underlying 'relationship' between those structures. This allows a 'structural comparison' between two similarity matrices using their predictability from 'structure'. Significance is assessed with the help of re-sampling appropriate for each dataset. The software, CLARITY, is available as an R package from github.com/danjlawson/CLARITY.

DNA sequencing: an overview of solid-state and biological nanopore-based methods

The field of sequencing is a topic of significant interest since its emergence and has become increasingly important over time. Impressive achievements have been obtained in this field, especially in relations to DNA and RNA sequencing. Since the first achievements by Sanger and colleagues in the 1950s, many sequencing techniques have been developed, while others have disappeared. DNA sequencing has undergone three generations of major evolution. Each generation has its own specifications that are mentioned briefly. Among these generations, nanopore sequencing has its own exciting characteristics that have been given more attention here. Among pioneer technologies being used by the third-generation techniques, nanopores, either biological or solid-state, have been experimentally or theoretically extensively studied. All sequencing technologies have their own advantages and disadvantages, so nanopores are not free from this general rule. It is also generally pointed out what research has been done to overcome the obstacles. In this review, biological and solid-state nanopores are elaborated on, and applications of them are also discussed briefly.

RNA Polymerase-Mediated Stepwise RNA-Primed RNA Polymerization for Site-Specific Multiple Labeling into RNA: A Fluorescence Resonance Energy Transfer Probe Detects the Structural Change of an RNA G-Quadruplex

In this paper, we report a stepwise RNA-primed RNA polymerization method for the site-specific incorporation of multiple fluorescent moieties into RNA, mediated by an RNA polymerase. A screen of several RNA polymerases revealed that T7 RNA polymerase was the only one that functioned in the RNA-primed RNA polymerization. In the first fluorescence labeling step, a fluorescent rU_thioTP residue was incorporated directly into the RNA using T7 RNA polymerase; the second fluorescence labeling step was performed using a post-labeling strategy: directly introducing an rU_amiTP residue into RNA, using T7 RNA polymerase, and then reacting with ylidenemalononitrile enamine (P3). The whole process for the site-specific introduction of the multiple labeled moieties was performed through stepwise RNA-primed RNA polymerization. Interestingly, the resulting multiple-labeled RNA exhibited fluorescence resonance energy transfer (FRET) between the two fluorescent labels in the RNA. We optimized the FRET-breaking point in the RNA by changing of distance between the two fluorescent labels and then used this property for the detection of the structural change of the RNA. The FRET signal increased in intensity upon the transformation of the RNA from a single-strand structure to the G-quadruplex. This approach for site-specific FRET labeling into RNA using RNA polymerase suggests the possibility of performing other diverse site-specific modifications at predefined positions in RNA.

Glutathione, carbohydrate and other metabolites of Larix olgensis A. Henry reponse to polyethylene glycol-simulated drought stress

Drought stress in trees limits their growth, survival, and productivity and it negatively affects the afforestation survival rate. Our study focused on the molecular responses to drought stress in a coniferous species Larix olgensis A. Henry. Drought stress was simulated in one-year-old seedlings using 25% polyethylene glycol 6000. The drought stress response in these seedlings was assessed by analyzing select biochemical parameters, along with gene expression and metabolite profiles. The soluble protein content, peroxidase activity, and malondialdehyde content of L. olgensis were significantly changed during drought stress. Quantitative gene expression analysis identified a total of 8172 differentially expressed genes in seedlings processed after 24 h, 48 h, and 96 h of drought stress treatment. Compared with the gene expression profile of the untreated control, the number of up-regulated genes was higher than that of down-regulated genes, indicating that L. olgensis mainly responded to drought stress through positive regulation. Metabolite analysis of the control and stress-treated samples showed that under drought stress, the increased abundance of linoleic acid was the highest among up-regulated metabolites, which also included some saccharides. A combined analysis of the transcriptome and metabolome revealed that genes dominating the differential expression profile were involved in glutathione metabolism, galactose metabolism, and starch and sucrose metabolism. Moreover, the relative abundance of specific metabolites of these pathways was also altered. Thus, our results indicated that L. olgensis prevented free radical-induced damage through glutathione metabolism and responded to drought through sugar accumulation.

Developing an Embedding, Koopman and Autoencoder Technologies-Based Multi-Omics Time Series Predictive Model (EKATP) for Systems Biology research

It is very important for systems biologists to predict the state of the multi-omics time series for disease occurrence and health detection. However, it is difficult to make the prediction due to the high-dimensional, nonlinear and noisy characteristics of the multi-omics time series data. For this reason, this study innovatively proposes an Embedding, Koopman and Autoencoder technologies-based multi-omics time series predictive model (EKATP) to predict the future state of a high-dimensional nonlinear multi-omics time series. We evaluate this EKATP by using a genomics time series with chaotic behavior, a proteomics time series with oscillating behavior and a metabolomics time series with flow behavior. The computational experiments demonstrate that our proposed EKATP can substantially improve the accuracy, robustness and generalizability to predict the future state of a time series for multi-omics data.

A review on omics-based biomarkers discovery for Alzheimer's disease from the bioinformatics perspectives: Statistical approach vs machine learning approach

Alzheimer's Disease (AD) is a neurodegenerative disease that affects cognition and is the most common cause of dementia in the elderly. As the number of elderly individuals increases globally, the incidence and prevalence of AD are expected to increase. At present, AD is diagnosed clinically, according to accepted criteria. The essential elements in the diagnosis of AD include a patients history, a physical examination and neuropsychological testing, in addition to appropriate investigations such as neuroimaging. The omics-based approach is an emerging field of study that may not only aid in the diagnosis of AD but also facilitate the exploration of factors that influence the development of the disease. Omics techniques, including genomics, transcriptomics, proteomics and metabolomics, may reveal the pathways that lead to neuronal death and identify biomolecular markers associated with AD. This will further facilitate an understanding of AD neuropathology. In this review, omics-based approaches that were implemented in studies on AD were assessed from a bioinformatics perspective. Current state-of-the-art statistical and machine learning approaches used in the single omics analysis of AD were compared based on correlations of variants, differential expression, functional analysis and network analysis. This was followed by a review of the approaches used in the integration and analysis of multi-omics of AD. The strengths and limitations of multi-omics analysis methods were explored and the issues and challenges associated with omics studies of AD were highlighted. Lastly, future studies in this area of research were justified.

rMisbeta: A robust missing value imputation approach in transcriptomics and metabolomics data

Transcriptomics and metabolomics data often contain missing values or outliers due to limitations of the data acquisition techniques. Most of the statistical methods require complete datasets for downstream analysis. A number of methods have been developed for missing value imputation using the classical mean and variance based on maximum likelihood estimators, which are not robust against outliers. Consequently, the performance of these methods deteriorates in the presence of outliers. Hence precise imputation of missing values and outliers handling are both concurrently important. Therefore, in this paper, we developed a robust iterative approach using robust estimators based on the minimum beta divergence method, which simultaneously impute missing values and outliers. We investigate the performance of the proposed method in a comparison with six frequently used missing value imputation methods such as Zero, KNN, robust SVD, EM, random forest (RF) and weighted least square approach (WLSA) through feature selection using both simulated and real datasets. Ten performance indices were used to explore the optimal method such as Frobenius norm (FOBN), accuracy (ACC), sensitivity (SN), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), detection rate (DR), misclassification error rate (MER), the area under the ROC curve (AUC) and computational runtime. Evaluation based on both simulated and real data suggests the superiority of the proposed method over the other traditional methods in terms of various rates of outliers and missing values. The suggested approach also keeps almost equal performance in absence of outliers with the other methods. The proposed method is accurate, simple, and consumes lower computational time compared to the other methods. Therefore, our recommendation is to apply the proposed procedure for large-scale transcriptomics and metabolomics data analysis. The computational tool has been implemented in an R package, which is publicly available from https://CRAN.R-project.org/package=rMisbeta.

Stimuli-Responsive Chiroptical Switching

"Chirality" governs many fundamental properties in chemistry and biochemistry. While early investigations on stereochemistry are primarily dedicated to static chirality, there is an increasing interest in the field of dynamic chirality (chiral switches). These chiral switches are essential in controlling the directionality in molecular motors. Dynamic chiralities are equally crucial in switchable stereoselectivity, switchable asymmetric catalysis and enantioselective separation. Herein, we limit our discussion to recent advances on stimuli-induced chiroptical switching of axial, helical, and planar chirality in response to external stimuli. We also discuss a few examples of applications of the switchable chirality.

Pillararenes: fascinating planar chiral macrocyclic arenes

Chiral macrocycles possess significant value in chiral science and supramolecular chemistry. Pillararenes, as a class of relatively young supramolecular macrocyclic hosts, have been widely used for host-guest recognition and self-assembly. Since the position of substituents on the benzene rings breaks the molecular symmetry (symmetric plane and symmetric center), pillararenes possess planar chirality. However, it is a great challenge to synthesize stable and resolvable enantiomers because of the easy rotation of the phenylene group. In this review, we summarize the construction methods of resolvable chiral pillararenes. We also focus on their applications in enantioselective recognition, chiral switches, chirality sensing, asymmetric catalysis, circularly polarized luminescence, metal-organic frameworks, and highly permeable membranes. Finally, we discuss the future research perspectives in this field of pillararene-based planar chiral materials. We hope that this review will encourage more researchers to work in this exciting field.

Advances in the toxicology research of microcystins based on Omics approaches

Microcystins (MCs) are the most widely distributed cyanotoxins, which can be ingested by animals and human body in multiple ways, resulting in a threat to human health and the biodiversity of wildlife. Therefore, the study on toxic effects and mechanisms of MCs is one of the focuses of attention. Recently, the Omics techniques, i.e. genomics, transcriptomics, proteomics and metabolomics, have significantly contributed to the comprehensive understanding and revealing of the molecular mechanisms about the toxicity of MCs. This paper mainly reviews current literature using the Omics approaches to explore the toxicity mechanism of MCs in liver, gonad, spleen, brain, intestine and lung of multiple species. It was found that MCs can exert strong toxic effects on various metabolic activities and cell signal transduction in cell cycle, apoptosis, destruction of cell cytoskeleton and redox disorder, at protein, transcription and metabolism level. Meanwhile, it was also revealed that the alteration of non-coding RNAs (miRNA, circRNA and lncRNA, etc.) and gut microbiota plays an essential regulatory role in the toxic effects of MCs, especially in hepatotoxicity and reproductive toxicity. In addition, we summarized current research gaps and pointed out the future directions for research. The detailed information in this paper shows that the application and development of Omics techniques have significantly promoted the research on MCs toxicity, and it is also a valuable resource for exploring the toxic mechanism of MCs.

Transcriptome sequencing reveals high-salt diet-induced abnormal liver metabolic pathways in mice

BACKGROUND

Although salt plays an important role in maintaining the normal physiological metabolism of the human body, many abnormalities in the liver caused by a high-salt diet, especially with normal pathological results, are not well characterized.

METHODS

Eight-week-old female C57BL/6 mice were randomly divided into a normal group and a high salt group. These groups were then fed with normal or sodium-rich chow (containing 6% NaCl) for 6 weeks. Liver injury was evaluated, and the influences of a high-salt diet on the liver were analyzed by transcriptome sequencing at the end of week 6.

RESULTS

We found that although no liver parenchymal injury could be found after high-salt feeding, many metabolic abnormalities had formed based on transcriptome sequencing results. GO and KEGG enrichment analyses of differentially expressed genes revealed that at least 15 enzymatic activities and the metabolism of multiple substances were affected by a high-salt diet. Moreover, a variety of signaling and metabolic pathways, as well as numerous biological functions, were involved in liver dysfunction due to a high-salt diet. This included some known pathways and many novel ones, such as retinol metabolism, linoleic acid metabolism, steroid hormone biosynthesis, and signaling pathways.

CONCLUSIONS

A high-salt diet can induce serious abnormal liver metabolic activities in mice at the transcriptional level, although substantial physical damage may not yet be visible. This study, to our knowledge, was the first to reveal the impact of a high-salt diet on the liver at the omics level, and provides theoretical support for potential clinical risk evaluation, pathogenic mechanisms, and drug design for combating liver dysfunction. This study also provides a serious candidate direction for further research on the physiological impacts of high-salt diets.

Transcriptome sequencing revealed the inhibitory mechanism of ketoconazole on clinical Microsporum canis

Background

Microsporum canis is a zoonotic disease that can cause dermatophytosis in animals and humans.

Objectives

In clinical practice, ketoconazole (KTZ) and other imidazole drugs are commonly used to treat M. canis infection, but its molecular mechanism is not completely understood. The antifungal mechanism of KTZ needs to be studied in detail.

Methods

In this study, one strain of fungi was isolated from a canine suffering with clinical dermatosis and confirmed as M. canis by morphological observation and sequencing analysis. The clinically isolated M. canis was treated with KTZ and transcriptome sequencing was performed to identify differentially expressed genes in M. canis exposed to KTZ compared with those unexposed thereto.

Results

At half-inhibitory concentration (½MIC), compared with the control group, 453 genes were significantly up-regulated and 326 genes were significantly down-regulated (p < 0.05). Quantitative reverse transcription polymerase chain reaction analysis verified the transcriptome results of RNA sequencing. Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis revealed that the 3 pathways of RNA polymerase, steroid biosynthesis, and ribosome biogenesis in eukaryotes are closely related to the antifungal mechanism of KTZ.

Conclusions

The results indicated that KTZ may change cell membrane permeability, destroy the cell wall, and inhibit mitosis and transcriptional regulation through CYP51, SQL, ERG6, ATM, ABCB1, SC, KER33, RPA1, and RNP genes in the 3 pathways. This study provides a new theoretical basis for the effective control of M. canis infection and the effect of KTZ on fungi.

Omics study of harmful algal blooms in China: Current status, challenges, and future perspectives

In the past two decades, the frequency, scale, and scope of harmful algal blooms (HABs) have increased significantly in the coastal waters of China. HABs have become a major ecological and environmental problem in China that seriously threatens the structure and function of marine ecosystems, the sustainable development of mariculture, and the health of human beings. Much effort has been devoted to studying HABs in China, and great achievements have been made in understanding the oceanographic and ecological mechanisms of HABs as well as the biology and physiological ecology of HAB-causing species. Furthermore, state-of-the-art omics technologies, such as transcriptomics and proteomics, have been used to elucidate the physiological responses of HAB-causing species to environmental changes, the biosynthesis of paralytic shellfish toxin, and the mechanisms underlying the formation of HABs. This review summarizes omics studies of HABs in China over the past few years and discusses challenges and future perspectives of HAB research.

Real-time monitoring of bead-based DNA hybridization in a microfluidic system: study of amplicon hybridization behavior on solid supports

DNA hybridization phenomena occurring on solid supports are not understood as clearly as aqueous phase hybridizations and mathematical models cannot predict some empirically obtained results. Ongoing research has identified important parameters but remains incomplete to accurately account for all interactions. It has previously been shown that the length of the overhanging (dangling) end of the target DNA strand following hybridization to the capture probe is correlated to interactions with the complementary strand in solution which can result in unbinding of the target and its release from the surface. We have developed an instrument for real-time monitoring of DNA hybridization on spherical particles functionalized with oligonucleotide capture probes and arranged in the form of a tightly packed monolayer bead bed inside a microfluidic cartridge. The instrument is equipped with a pneumatic module to mediate displacement of fluid on the cartridge. We compared this system to both conventional (passive) and centrifugally-driven (active) microfluidic microarray hybridization on glass slides to establish performance levels for the detection of single nucleotide polymorphisms. The system was also used to study the effect of the dangling end's length in real-time when the immobilized target DNA is exposed to the complementary strand in solution. Our findings indicate that increasing the length of the dangling end leads to desorption of target amplicons from bead-bound capture probes at a rate approaching that of the initial hybridization process. Finally, bead bed hybridization was performed with Streptococcus agalactiae cfb gene amplicons obtained from randomized clinical samples, which allowed for identification of group B streptococci within 5-15 min. The methodology presented here is useful for investigating competitive hybridization mechanisms on solid supports and to rapidly validate the suitability of microarray capture probes.

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis

Background

Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists.

Results

In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients’ transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer’s disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases.

Conclusions

We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00336-1.

Identification of genes related to stress affecting thymus immune function in a chicken stress model using transcriptome analysis

With the rapid development of the poultry breeding industry and highly intensive production management, the losses caused by stress responses are becoming increasingly serious. To screen candidate genes related to chicken stress and provide a basis for future research on the molecular mechanisms governing the effects of stress on chicken immune function, we successfully constructed a chicken stress model by exogenously introducing corticosterone (CORT). RNA-seq technology was used to identify and analyze the mRNA and enrichment pathways of the thymus in the stress model group and the control group. The results showed that there were 101 significantly differentially expressed genes (SDEGs) (Padj < 0.05, |log2fold changes| ≥ 1 and FPKM >1), of which 44 were upregulated genes, while 57 were downregulated genes. Gene Ontology (GO) enrichment analysis found that the terms related to immunity or stress mainly included antigen processing and presentation, positive regulation of T cell-mediated immunity, and immune effector process. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis showed that the main pathways related to immunity or stress were the PPAR signaling pathway, NOD-like receptor signaling pathway, and intestinal immune network for IgA production. Among the SDEGs, XCL1, HSPA8, DMB1 and BAG3 are strongly related to immunity or stress and may be important genes involved in regulating stress affecting the immune function of chickens. The above results provide a theoretical reference for subsequent research on the molecular regulatory mechanisms by which stress affects the immune function of poultry.

Tensor envelope mixture model for simultaneous clustering and multiway dimension reduction

In the form of multidimensional arrays, tensor data have become increasingly prevalent in modern scientific studies and biomedical applications such as computational biology, brain imaging analysis, and process monitoring system. These data are intrinsically heterogeneous with complex dependencies and structure. Therefore, ad-hoc dimension reduction methods on tensor data may lack statistical efficiency and can obscure essential findings. Model-based clustering is a cornerstone of multivariate statistics and unsupervised learning; however, existing methods and algorithms are not designed for tensor-variate samples. In this article, we propose a tensor envelope mixture model (TEMM) for simultaneous clustering and multiway dimension reduction of tensor data. TEMM incorporates tensor-structure-preserving dimension reduction into mixture modeling and drastically reduces the number of free parameters and estimative variability. An expectation-maximization-type algorithm is developed to obtain likelihood-based estimators of the cluster means and covariances, which are jointly parameterized and constrained onto a series of lower dimensional subspaces known as the tensor envelopes. We demonstrate the encouraging empirical performance of the proposed method in extensive simulation studies and a real data application in comparison with existing vector and tensor clustering methods.

Simple and sensitive detection of deoxyribonucleic acid using a RecA-GFP fusion protein-DNA filament as probe

A simple, rapid and highly sensitive method for detection of double-stranded DNA (dsDNA) was developed using a novel fluorescence probe composed of a RecA-GFP fusion protein that had specific recognition of ssDNA complexes (RecA-GFP-DNA filament). The RecA-GFP fusion protein not only had strong fluorescence, but could also occur by homologous recombination. In the presence of the target dsDNA, the complementary ssDNA of the RecA-GFP-DNA filaments invaded one end of the dsDNA chain. In addition, the other end of the ssDNA dissociated the RecA-GFP filaments. An assay of the probe showed a linear relationship with dsDNA concentration in the range 1-11 nM, with a correlation coefficient of 0.9923. The limit of detection for dsDNA was determined experimentally to be 0.3 nM (3δ). Compared with conventional methods, this method has the advantages of simple operation, high specificity, and high sensitivity.