High-throughput sequencing for biology and medicine

Uncovering the potential of APOD as a biomarker in gastric cancer: A retrospective and multi-center study

Gastric cancer (GC) poses a significant health challenge worldwide, necessitating the identification of predictive biomarkers to improve prognosis. Dysregulated lipid metabolism is a well-recognized hallmark of tumorigenesis, prompting investigation into apolipoproteins (APOs). In this study, we focused on apolipoprotein D (APOD) following comprehensive analyses of APOs in pan-cancer. Utilizing data from the TCGA-STAD and GSE62254 cohorts, we elucidated associations between APOD expression and multiple facets of GC, including prognosis, tumor microenvironment (TME), cancer biomarkers, mutations, and immunotherapy response, and identified potential anti-GC drugs. Single-cell analyses and immunohistochemical staining confirmed APOD expression in fibroblasts within the GC microenvironment. Additionally, we independently validated the prognostic significance of APOD in the ZN-GC cohort. Our comprehensive analyses revealed that high APOD expression in GC patients was notably associated with unfavorable clinical outcomes, reduced microsatellite instability and tumor mutation burden, alterations in the TME, and diminished response to immunotherapy. These findings provide valuable insights into the potential prognostic and therapeutic implications of APOD in GC.

HiPerMAb: a tool for judging the potential of small sample size biomarker pilot studies

Common statistical approaches are not designed to deal with so-called "short fat data" in biomarker pilot studies, where the number of biomarker candidates exceeds the sample size by magnitudes. High-throughput technologies for omics data enable the measurement of ten thousands and more biomarker candidates for specific diseases or states of a disease. Due to the limited availability of study participants, ethical reasons and high costs for sample processing and analysis researchers often prefer to start with a small sample size pilot study in order to judge the potential of finding biomarkers that enable - usually in combination - a sufficiently reliable classification of the disease state under consideration. We developed a user-friendly tool, called HiPerMAb that allows to evaluate pilot studies based on performance measures like multiclass AUC, entropy, area above the cost curve, hypervolume under manifold, and misclassification rate using Monte-Carlo simulations to compute the p-values and confidence intervals. The number of "good" biomarker candidates is compared to the expected number of "good" biomarker candidates in a data set with no association to the considered disease states. This allows judging the potential in the pilot study even if statistical tests with correction for multiple testing fail to provide any hint of significance.

Artificial intelligence and database for NGS-based diagnosis in rare disease

Rare diseases (RDs) are rare complex genetic diseases affecting a conservative estimate of 300 million people worldwide. Recent Next-Generation Sequencing (NGS) studies are unraveling the underlying genetic heterogeneity of this group of diseases. NGS-based methods used in RDs studies have improved the diagnosis and management of RDs. Concomitantly, a suite of bioinformatics tools has been developed to sort through big data generated by NGS to understand RDs better. However, there are concerns regarding the lack of consistency among different methods, primarily linked to factors such as the lack of uniformity in input and output formats, the absence of a standardized measure for predictive accuracy, and the regularity of updates to the annotation database. Today, artificial intelligence (AI), particularly deep learning, is widely used in a variety of biological contexts, changing the healthcare system. AI has demonstrated promising capabilities in boosting variant calling precision, refining variant prediction, and enhancing the user-friendliness of electronic health record (EHR) systems in NGS-based diagnostics. This paper reviews the state of the art of AI in NGS-based genetics, and its future directions and challenges. It also compare several rare disease databases.

Comparative transcriptome analysis of E. coli & Staphylococcus aureus infected goat mammary epithelial cells reveals genes associated with infection

Mastitis, an inflammatory disease of the mammary gland, imposes a significant financial burden on the dairy sector. However, the specific molecular mechanisms underlying their interactions with goat mammary epithelial cells (GMECs) remain poorly understood. This study aimed to investigate the transcriptomic response of GMECs during infection with E. coli and S. aureus, providing insights into the host-pathogen interactions. Differential expression of gene (DEGs) analysis was done to find genes and pathways dysregulated in the wake of infection. E. coli infection triggered a robust upregulation of immune response genes, including pro-inflammatory chemokines and cytokines as well as genes involved in tissue repair and remodeling. Conversely, S. aureus infection showed a more complex pattern, involving the activation of immune-related gene as well as those involved in autophagy, apoptosis and tissue remodeling. Furthermore, several key pathways, such as Toll-like receptor signaling and cytokine-cytokine receptor interaction, were differentially modulated in response to each pathogen. Understanding the specific responses of GMECs to these pathogens will provide a foundation for understanding the complex dynamics of infection and host response, offering potential avenues for the development of novel strategies to prevent and treat bacterial infections in both animals and humans.

rCRUX: A Rapid and Versatile Tool for Generating Metabarcoding Reference libraries in R

The sequencing revolution requires accurate taxonomic classification of DNA sequences. Key to making accurate taxonomic assignments are curated, comprehensive reference barcode databases. However, the generation and curation of such databases has remained challenging given the large and continuously growing volumes of both DNA sequence data and novel reference barcode targets. Monitoring and research applications require a greater diversity of specialized gene regions and targeted taxa then are currently curated by professional staff. Thus there is a growing need for an easy to implement computational tool that can generate comprehensive metabarcoding reference libraries for any bespoke locus. We address this need by reimagining CRUX from the Anacapa Toolkit and present the rCRUX package in R which, like it's predecessor, relies on sequence homology and PCR primer compatibility instead of keyword-searches to avoid limitations of user-defined metadata. The typical workflow involves searching for plausible seed amplicons (get_seeds_local() or get_seeds_remote()) by simulating in silico PCR to acquire a set of sequences analogous to PCR products containing a user-defined set of primer sequences. Next, these seeds are used to iteratively blast search seed sequences against a local copy of the National Center for Biotechnology Information (NCBI) formatted nt database using a taxonomic-rank based stratified random sampling approach ( blast_seeds() ). This results in a comprehensive set of sequence matches. This database is dereplicated and cleaned (derep_and_clean_db()) by identifying identical reference sequences and collapsing the taxonomic path to the lowest taxonomic agreement across all matching reads. This results in a curated, comprehensive database of primer-specific reference barcode sequences from NCBI. Databases can then be compared (compare_db()) to determine read and taxonomic overlap. We demonstrate that rCRUX provides more comprehensive reference databases for the MiFish Universal Teleost 12S, Taberlet trnl, fungal ITS, and Leray CO1 loci than CRABS, MetaCurator, RESCRIPt, and ecoPCR reference databases. We then further demonstrate the utility of rCRUX by generating 24 reference databases for 20 metabarcoding loci, many of which lack dedicated reference database curation efforts. The rCRUX package provides a simple to use tool for the generation of curated, comprehensive reference databases for user-defined loci, facilitating accurate and effective taxonomic classification of metabarcoding and DNA sequence efforts broadly.

Effect of Fibroblast Growth Factor 10 and an Interacting Non-Coding RNA on Secondary Hair Follicle Dermal Papilla Cells in Cashmere Goats' Follicle Development Assessed by Whole-Transcriptome Sequencing Technology

Cashmere, a keratinised product of secondary hair follicles (SHFs) in cashmere goats, holds an important place in international high-end textiles. However, research on the complex molecular and signal regulation during the development and growth of hair follicles (HFs), which is essential for the development of the cashmere industry, is limited. Moreover, increasing evidence indicates that non-coding RNAs (ncRNAs) participate in HF development. Herein, we systematically investigated a competing endogenous RNA (ceRNA) regulatory network mediated by circular RNAs (circRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) in skin samples of cashmere goat embryos, using whole-transcriptome sequencing technology. We obtained 6468, 394, and 239 significantly differentially expressed mRNAs, circRNAs, and miRNAs, respectively. These identified RNAs were further used to construct a ceRNA regulatory network, mediated by circRNAs, for cashmere goats at a late stage of HF development. Among the molecular species identified, miR-184 and fibroblast growth factor (FGF) 10 exhibited competitive targeted interactions. In secondary HF dermal papilla cells (SHF-DPCs), miR-184 promotes proliferation, inhibits apoptosis, and alters the cell cycle via the competitive release of FGF10. This study reports that FGF10 and its interaction with ncRNAs significantly affect SHF-DPCs, providing a reference for research on the biology of HFs in cashmere goats and other mammals.

Development and validation of a ligand-receptor pairs signature to predict outcome and provide a therapeutic strategy in gastric cancer

BACKGROUND

An important factor in tumor development and progression is the tumor microenvironment (TME), which is heterogeneous. Previous studies have mainly investigated the expression profile and prognostic values of genes in gastric cancer (GC) at the cell population level but neglected the interactions and heterogeneity between cells.

METHODS

The pattern of ligand-receptor (LR) interactions was delineated on a scRNA-seq dataset containing 44,953 cells from nine GC patients and a fourth bulk RNA-seq dataset including data from 1159 GC patients. We then constructed an LR.Score scoring model to comprehensively evaluate the influence of LR-pairs on the TME, overall survival, and immunotherapy response in GC patients from several cohorts.

RESULTS

Cell communication network among 13 cell types was constructed based on the LR-pairs. We proposed a new molecular subtyping model for GC based on the LR-pairs and revealed the differences in prognosis, pathophysiologic features, mutation characteristics, function enrichment, and immunological characteristics among the three subtypes. Finally, an LR.Score model based on LR-pairs was developed and validated on several datasets.

CONCLUSIONS

Based on the selected LR-pairs, we successfully constructed a novel prediction model and observed its well performance on molecular subtyping, target and pathway screening, prognosis judging, and immunotherapy response predicting.

rCRUX: A Rapid and Versatile Tool for Generating Metabarcoding Reference libraries in R

Key to making accurate taxonomic assignments are curated, comprehensive reference barcode databases. However, the generation and curation of such databases has remained challenging given the large and continuously growing volumes of DNA sequence data and novel reference barcode targets. Monitoring and research applications require a greater diversity of specialized gene regions and targeted taxa to meet taxonomic classification goals then are currently curated by professional staff. Thus, there is a growing need for an easy to implement tool that can generate comprehensive metabarcoding reference libraries for any bespoke locus. We address this need by reimagining CRUX from the Anacapa Toolkit and present the rCRUX package in R. The typical workflow involves searching for plausible seed amplicons (get_seeds_local() or get_seeds_remote()) by simulating in silico PCR to acquire seed sequences containing a user-defined primer set. Next these seeds are used to iteratively blast search seed sequences against a local NCBI formatted database using a taxonomic rank based stratified random sampling approach (blast_seeds()) that results in a comprehensive set of sequence matches. This database is dereplicated and cleaned (derep_and_clean_db()) by identifying identical reference sequences and collapsing the taxonomic path to the lowest taxonomic agreement across all matching reads. This results in a curated, comprehensive database of primer specific reference barcode sequences from NCBI. We demonstrate that rCRUX provides more comprehensive reference databases for the MiFish Universal Teleost 12S, Taberlet trnl, and fungal ITS locus than CRABS, METACURATOR, RESCRIPt, and ECOPCR reference databases. We then further demonstrate the utility of rCRUX by generating 16 reference databases for metabarcoding loci that lack dedicated reference database curation efforts. The rCRUX package provides a simple to use tool for the generation of curated, comprehensive reference databases for user-defined loci, facilitating accurate and effective taxonomic classification of metabarcoding and DNA sequence efforts broadly.

Use of modified ichip for the cultivation of thermo-tolerant microorganisms from the hot spring

BACKGROUND

Thermostable microorganisms are extremophiles. They have a special genetic background and metabolic pathway and can produce a variety of enzymes and other active substances with special functions. Most thermo-tolerant microorganisms from environmental samples have resisted cultivation on artificial growth media. Therefore, it is of great significance to isolate more thermo-tolerant microorganisms and study their characteristics to explore the origin of life and exploit more thermo-tolerant enzymes. Tengchong hot spring in Yunnan contains a lot of thermo-tolerant microbial resources because of its perennial high temperature. The ichip method was developed by D. Nichols in 2010 and can be used to isolate so-called "uncultivable" microorganisms from different environments. Here, we describe the first application of modified ichip to isolate thermo-tolerant bacteria from hot springs.

RESULTS

In this study, 133 strains of bacteria belonging to 19 genera were obtained. 107 strains of bacteria in 17 genera were isolated by modified ichip, and 26 strains of bacteria in 6 genera were isolated by direct plating methods. 25 strains are previously uncultured, 20 of which can only be cultivated after being domesticated by ichip. Two strains of previously unculturable Lysobacter sp., which can withstand 85 °C, were isolated for the first time. Alkalihalobacillus, Lysobacter and Agromyces genera were first found to have 85 °C tolerance.

CONCLUSION

Our results indicate that the modified ichip approach can be successfully applied in a hot spring environment.

Clear Cell Renal Cell Carcinoma: A Comprehensive in silico Study in Searching for Therapeutic Targets

INTRODUCTION

Clear cell renal cell carcinoma (ccRCC) is recognized as one of the leading causes of illness and death worldwide. Understanding the molecular mechanisms in ccRCC pathogenesis is crucial for discovering novel therapeutic targets and developing efficient drugs. With the application of a comprehensive in silico analysis of the ccRCC-related array sets, the main objective of this study was to discover the top molecules and pathways in the pathogenesis of this cancer.

METHODS

ccRCC microarray datasets were downloaded from the Gene Expression Omnibus database, and after quality checking, normalization, and analysis using the Limma algorithm, differentially expressed genes (DEGs) were identified, considering the adjusted p value <0.049. The intensity values of the identified DEGs were introduced to the Weighted Gene Co-Expression Network Analysis (WGCNA) algorithm to construct co-expression modules. Functional enrichment analyses were performed using the DEGs in the disease-correlated module, and hub genes were identified among the top genes in a protein-protein interaction network and the disease most correlated module. The expression analysis of hub genes was done by utilizing GEPIA, and the GSCA server was used to compare the expression patterns of hub genes in ccRCC and other cancers. DGIdb database was utilized to identify the hub gene-related drugs.

RESULTS

Three datasets, including GSE11151, GSE12606, and GSE36897, were retrieved, merged, normalized, and analyzed. Using WGCNA, the DEGs were clustered into eight different modules. Translocation of ZAP-70 to immunological synapse, endosomal/vacuolar pathway, cell surface interactions at the vascular wall, and immune-related pathways were the topmost enriched terms for the ccRCC-correlated DEGs. Twelve genes including PTPRC, ITGAM, TLR2, CD86, PLEK, TYROBP, ITGB2, RAC2, CSF1R, CCR5, CCL5, and LCP2 were introduced as hub genes. All the 12 hub genes were upregulated in ccRCC samples and showed a positive correlation with the infiltration of different immune cells. According to the DGIdb database, 127 drugs, including tyrosine kinase inhibitors, glucocorticoids, and chemotaxis targeting molecules, were identified to interact with the hub genes.

CONCLUSION

By utilizing an integrative bioinformatics approach, this experiment shed light on the underlying pathways in the pathogenesis of ccRCC and introduced several potential therapeutic targets for repurposing or developing novel drugs for an efficient treatment of this cancer. Our next step would be to assess the gene expression profiles of the identified hubs in different cell populations in the tumor microenvironment.

Transcriptome analysis creates a new era of precision medicine for managing recurrent hepatocellular carcinoma

The high incidence of hepatocellular carcinoma (HCC) recurrence negatively impacts outcomes of patients treated with curative intent despite advances in surgical techniques and other locoregional liver-targeting therapies. Over the past few decades, the emergence of transcriptome analysis tools, including real-time quantitative reverse transcription PCR, microarrays, and RNA sequencing, has not only largely contributed to our knowledge about the pathogenesis of recurrent HCC but also led to the development of outcome prediction models based on differentially expressed gene signatures. In recent years, the single-cell RNA sequencing technique has revolutionized our ability to study the complicated crosstalk between cancer cells and the immune environment, which may benefit further investigations on the role of different immune cells in HCC recurrence and the identification of potential therapeutic targets. In the present article, we summarized the major findings yielded with these transcriptome methods within the framework of a causal model consisting of three domains: primary cancer cells; carcinogenic stimuli; and tumor microenvironment. We provided a comprehensive review of the insights that transcriptome analyses have provided into diagnostics, surveillance, and treatment of HCC recurrence.

Focus on the molecular mechanisms of cisplatin resistance based on multi-omics approaches

Cisplatin is commonly used in combination with other cytotoxic agents as a standard treatment regimen for a variety of solid tumors, such as lung, ovarian, testicular, and head and neck cancers. However, the effectiveness of cisplatin is accompanied by toxic side effects, for instance, nephrotoxicity and neurotoxicity. The response of tumors to cisplatin treatment involves multiple physiological processes, and the efficacy of chemotherapy is limited by the intrinsic and acquired resistance of tumor cells. Although enormous efforts have been made toward molecular mechanisms of cisplatin resistance, the development of omics provides new insights into the understanding of cisplatin resistance at genome, transcriptome, proteome, metabolome and epigenome levels. Mechanism studies using different omics approaches revealed the necessity of multi-omics applications, which provide information at different cellular function levels and expand our recognition of the peculiar genetic and phenotypic heterogeneity of cancer. The present work systematically describes the underlying mechanisms of cisplatin resistance in different tumor types using multi-omics approaches. In addition to the classical mechanisms such as enhanced drug efflux, increased DNA damage repair and changes in the cell cycle and apoptotic pathways, other changes like increased protein damage clearance, increased protein glycosylation, enhanced glycolytic process, dysregulation of the oxidative phosphorylation pathway, ferroptosis suppression and mRNA m6A methylation modification can also induce cisplatin resistance. Therefore, utilizing the integrated omics to identify key signaling pathways, target genes and biomarkers that regulate chemoresistance are essential for the development of new drugs or strategies to restore tumor sensitivity to cisplatin.

Signal and noise in metabarcoding data

Metabarcoding is a powerful molecular tool for simultaneously surveying hundreds to thousands of species from a single sample, underpinning microbiome and environmental DNA (eDNA) methods. Deriving quantitative estimates of underlying biological communities from metabarcoding is critical for enhancing the utility of such approaches for health and conservation. Recent work has demonstrated that correcting for amplification biases in genetic metabarcoding data can yield quantitative estimates of template DNA concentrations. However, a major source of uncertainty in metabarcoding data stems from non-detections across technical PCR replicates where one replicate fails to detect a species observed in other replicates. Such non-detections are a special case of variability among technical replicates in metabarcoding data. While many sampling and amplification processes underlie observed variation in metabarcoding data, understanding the causes of non-detections is an important step in distinguishing signal from noise in metabarcoding studies. Here, we use both simulated and empirical data to 1) suggest how non-detections may arise in metabarcoding data, 2) outline steps to recognize uninformative data in practice, and 3) identify the conditions under which amplicon sequence data can reliably detect underlying biological signals. We show with both simulations and empirical data that, for a given species, the rate of non-detections among technical replicates is a function of both the template DNA concentration and species-specific amplification efficiency. Consequently, we conclude metabarcoding datasets are strongly affected by (1) deterministic amplification biases during PCR and (2) stochastic sampling of amplicons during sequencing-both of which we can model-but also by (3) stochastic sampling of rare molecules prior to PCR, which remains a frontier for quantitative metabarcoding. Our results highlight the importance of estimating species-specific amplification efficiencies and critically evaluating patterns of non-detection in metabarcoding datasets to better distinguish environmental signal from the noise inherent in molecular detections of rare targets.

Computational Integration of HSV-1 Multi-omics Data

Functional genomics techniques based on next-generation sequencing provide new avenues for studying host responses to viral infections at multiple levels, including transcriptional and translational processes and chromatin organization. This chapter provides an overview on the computational integration of multiple types of "omics" data on lytic herpes simplex virus 1 (HSV-1) infection. It summarizes methods developed and applied in two publications that combined 4sU-seq for studying de novo transcription, ribosome profiling for investigating active translation, RNA-seq of subcellular RNA fractions for determining subcellular location of transcripts, and ATAC-seq for profiling chromatin accessibility genome-wide. These studies revealed an unprecedented disruption of transcription termination in HSV-1 infection resulting in widespread read-through transcription beyond poly(A) sites for most but not all host genes. This impacts chromatin architecture by increasing chromatin accessibility selectively in downstream regions of affected genes. In this way, computational integration of multi-omics data identified novel and unsuspected mechanisms at play in lytic HSV-1 infection.

Applying multi-omics techniques to the discovery of biomarkers for acute aortic dissection

Acute aortic dissection (AAD) is a cardiovascular disease that manifests suddenly and fatally. Due to the lack of specific early symptoms, many patients with AAD are often overlooked or misdiagnosed, which is undoubtedly catastrophic for patients. The particular pathogenic mechanism of AAD is yet unknown, which makes clinical pharmacological therapy extremely difficult. Therefore, it is necessary and crucial to find and employ unique biomarkers for Acute aortic dissection (AAD) as soon as possible in clinical practice and research. This will aid in the early detection of AAD and give clear guidelines for the creation of focused treatment agents. This goal has been made attainable over the past 20 years by the quick advancement of omics technologies and the development of high-throughput tissue specimen biomarker screening. The primary histology data support and add to one another to create a more thorough and three-dimensional picture of the disease. Based on the introduction of the main histology technologies, in this review, we summarize the current situation and most recent developments in the application of multi-omics technologies to AAD biomarker discovery and emphasize the significance of concentrating on integration concepts for integrating multi-omics data. In this context, we seek to offer fresh concepts and recommendations for fundamental investigation, perspective innovation, and therapeutic development in AAD.

Network Analysis of Inflammatory Bowel Disease Research: Towards the Interactome

BACKGROUND AND AIMS

Modern views accept that inflammatory bowel diseases [IBD] emerge from complex interactions among the multiple components of a biological network known as the 'IBD interactome'. These diverse components belong to different functional levels including cells, molecules, genes and biological processes. This diversity can make it difficult to integrate available empirical information from human patients into a collective view of aetiopathogenesis, a necessary step to understand the interactome. Herein, we quantitatively analyse how the representativeness of components involved in human IBD and their relationships ha ve changed over time.

METHODS

A bibliographic search in PubMed retrieved 25 971 abstracts of experimental studies on IBD in humans, published between 1990 and 2020. Abstracts were scanned automatically for 1218 IBD interactome components proposed in recent reviews. The resulting databases are freely available and were visualized as networks indicating the frequency at which different components are referenced together within each abstract.

RESULTS

As expected, over time there was an increase in components added to the IBD network and heightened connectivity within and across functional levels. However, certain components were consistently studied together, forming preserved motifs in the networks. These overrepresented and highly linked components reflect main 'hypotheses' in IBD research in humans. Interestingly, 82% of the components cited in reviews were absent or showed low frequency, suggesting that many aspects of the proposed IBD interactome still have weak experimental support in humans.

CONCLUSIONS

A reductionist and fragmented approach to the study of IBD has prevailed in previous decades, highlighting the importance of transitioning towards a more integrated interactome framework.

Molecular mapping of quantitative trait loci for 3 husk traits using genotyping by sequencing in maize (Zea mays L.)

The maize (Zea mays L.) husk consists of multiple leaf layers and plays an important role in grain growth and development. Despite significant achievements in physiological and morphological research, few studies have focused on the detection of genetic loci underlying husk-related traits due to the lack of efficient tools. In this study, we constructed an ultra-high-density linkage map using genotyping by sequencing based on a recombinant inbred line population to estimate the genetic variance and heritability of 3 husk traits, i.e. husk length, husk width, and husk layer number in 3 field environments and the combined environment. The 3 husk traits showed broad phenotypic variation and high heritability; the broad-sense heritability (H²) was 0.92, 0.84, and 0.86. Twenty quantitative trait loci were consistently detected more than 1 environment, including 9 for husk length, 6 for husk width, and 5 for husk layer number. These loci were considered as stable quantitative trait loci. Based on the quantitative trait loci mapping in the recombinant inbred line population, qHL6 and qHN4 were detected across all environments and inferred to be reliable and major-effect quantitative trait loci for husk length and husk layer number, respectively. In addition, several predicted candidate genes were identified in the region of qHL6 and qHN4, of which 17 candidate genes potentially play a role in biological processes related to development process and energy metabolism. These results will be as a useful resource for performing functional studies aimed at understanding the molecular pathways involved in husk growth and development.

Deep learning and machine intelligence: New computational modeling techniques for discovery of the combination rules and pharmacodynamic characteristics of Traditional Chinese Medicine

It has been increasingly accepted that Multi-Ingredient-Based interventions provide advantages over single-target therapy for complex diseases. With the growing development of Traditional Chinese Medicine (TCM) and continually being refined of a holistic view, "multi-target" and "multi-pathway" integration characteristics of which are being accepted. However, its effector substances, efficacy targets, especially the combination rules and mechanisms remain unclear, and more powerful strategies to interpret the synergy are urgently needed. Artificial intelligence (AI) and computer vision lead to a rapidly expanding in many fields, including diagnosis and treatment of TCM. AI technology significantly improves the reliability and accuracy of diagnostics, target screening, and new drug research. While all AI techniques are capable of matching models to biological big data, the specific methods are complex and varied. Retrieves literature by the keywords such as "artificial intelligence", "machine learning", "deep learning", "traditional Chinese medicine" and "Chinese medicine". Search the application of computer algorithms of TCM between 2000 and 2021 in PubMed, Web of Science, China National Knowledge Infrastructure (CNKI), Elsevier and Springer. This review concentrates on the application of computational in herb quality evaluation, drug target discovery, optimized compatibility and medical diagnoses of TCM. We describe the characteristics of biological data for which different AI techniques are applicable, and discuss some of the best data mining methods and the problems faced by deep learning and machine learning methods applied to Chinese medicine.

mRNA microarray analysis for the identification of potential biomarkers for vital reaction in burned skin: a preliminary pilot study

The identification of ante- and post-mortem burns is challenging in forensic pathology. In this study, microarray analysis was used to detect the mRNA expression profiles in the skin of an experimental burn mouse model; the results were validated using RT-qPCR. Differentially expressed mRNAs (DE-mRNAs) were assessed using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Our results revealed that mRNA expression of 501 genes was significantly different, of which 273 were upregulated and 228 were downregulated in ante-mortem burned mice skin. The expression levels of eight random mRNAs were consistent when measured using the microarray assay-based method and RT-qPCR. Genes from different functional categories and signalling pathways were enriched, including interleukin-20 binding, type IV hypersensitivity, negative regulation of acute inflammatory response, sensory organ development, endocytosis, neuroactive ligand-receptor interaction, and Jak-STAT signalling pathway. Only five of the eight mRNAs exhibited consistent changes in expression between burned skin samples of mice and human autopsy specimens. Our findings showed that DE-mRNAs revealed using microarray are potential biomarkers of ante-mortem burns. However, DE-mRNAs identified from experimental animal models cannot be directly extended to autopsy specimens without careful validation.

Fermented shellfish condiments: A comprehensive review

Fermented shellfish condiments are globally consumed especially among Asian countries. Condiments, commonly used as flavor enhancers, have unique sensory characteristics and are associated with umami and meaty aroma. The main reactions that occur during fermentation of shellfish include proteolysis by endogenous enzymes and microbial activities to produce peptides and amino acids. The actions of proteolytic enzymes and microorganisms (predominantly bacteria) are found to be largely responsible for the formation of taste and aroma compounds. This review elaborates different aspects of shellfish fermentation including classification, process, substrates, microbiota, changes in both physicochemical and biochemical components, alterations in nutritional composition, flavor characteristics and sensory profiles, and biological activities and their undesirable impacts on health. The characteristics of traditional shellfish production such as long duration and high salt concentration not only limit nutritional value but also inhibit the formation of toxic biogenic amines. In addition, this review article also covers novel bioprocesses such as low salt fermentation and use of novel starter cultures and/or novel enzymes to accelerate fermentation and produce shellfish condiments that are of better quality and safer for consumption. Practical Application: The review paper summarized the comprehensive information on shellfish fermentation to provide alternative strategies to produce shellfish comdiments that are of better quality and safer for consumption.

Biodiversity and Oenological Property Analysis of Non-Saccharomyces Yeasts Isolated from Korla Fragrant Pears (Pyrus sinkiangensis Yu)

Oenological yeasts play a critical role in the winemaking process. In this study, the biodiversity of the non-Saccharomyces yeast was analyzed and monitored using high-throughput sequencing and culture-dependent approaches. Oenological and fermentation characteristics of these native yeasts were further investigated. A total of 241 fungus species and 5 species of culturable non-Saccharomyces yeasts were detected using high-throughput sequencing and culture-dependent approaches, respectively. Five strains of aroma-producing yeasts (K4, K14, K19, K21, and K26) were isolated, and their growth characteristics, carbon source utilization, hydrogen sulfide production performance, and β-glucosidase activity were different. The oenological condition tolerances of most strains were lower than that of commercial S. cerevisiae X16. The co-inoculum of these strains and S. cerevisiae X16 regulated the volatile aroma characteristics of the fermented Korla fragrant pear (KFP) fruit wine, enriching and complicating the aroma flavor. Thus, the combined inoculation of these indigenous wine yeasts and S. cerevisiae has some application potential in the production of KFP wine.

Bioinformatics Analysis of Exercise-Related Biomarkers in Diabetes

Background

Exercise is a regular behavioral activity that not only helps to lose weight but also reduces the risk of cardiovascular and cerebrovascular diseases. Diabetes is a common disease that plagues human health. It is shown that regular exercise can improve the insulin sensitivity of diabetic patients and have an important function in adjuvant therapy.

Methods

We downloaded the GSE101931 dataset from the Gene Expression Omnibus (GEO) database, 10 samples were obtained from the GSE101931 dataset, including 5 before exercise and 5 postexercise samples, and GEO2R was used to screen the differentially expressed genes (DEGs) exhibited by a heat map. Then, the enrichment analysis of DEGs in Gene Ontology (GO) function was analyzed by Metascape, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of DEGs was also analyzed by gene set enrichment analysis (GSEA). Next, the protein-protein interaction (PPI) network maps were drawn, and the hub genes were identified through Metascape. Finally, the expressions of the hub genes in the dataset were analyzed.

Results

Totally, 116 upregulated DEGs and 1017 downregulated DEGs were identified from these data. These DEGs were mainly enriched in the platelet-derived growth factor receptor signaling pathway and mRNA processing. Then, the GSEA analysis showed that 6 KEGG pathways were associated with postexercise prediabetic samples, namely, ABC transporters, focal adhesion, MAPK signaling pathway, prion diseases, melanogenesis, and gap junction. Afterward, three hub genes (HSPA8, STIP1, and HSPH1) were highly expressed after exercise through the box plot analysis.

Conclusion

A myriad of research results confirms that there is a certain connection between exercise and diabetes, which provides a favorable basis for emerging exercise into the treatment of diabetic patients.

Uncovering the genetic links of SARS-CoV-2 infections on heart failure co-morbidity by a systems biology approach

Aims

The co‐morbidities contribute to the inferior prognosis of COVID‐19 patients. Recent reports suggested that the higher co‐morbidity rate between COVID‐19 and heart failure (HF) leads to increased mortality. However, the common pathogenic mechanism between them remained elusive. Here, we aimed to reveal underlying molecule mechanisms and genetic correlation between COVID‐19 and HF, providing a new perspective on current clinical management for patients with co‐morbidity.

Methods

The gene expression profiles of HF (GSE26887) and COVID‐19 (GSE147507) were retrieved from the GEO database. After identifying the common differentially expressed genes (|log2FC| > 1 and adjusted P < 0.05), integrated analyses were performed, namely, enrichment analyses, protein–protein interaction network, module construction, critical gene identification, and functional co‐expression analysis. The performance of critical genes was validation combining hierarchical clustering, correlation, and principal component analysis in external datasets (GSE164805 and GSE9128). Potential transcription factors and miRNAs were obtained from the JASPER and RegNetwork repository used to construct co‐regulatory networks. The candidate drug compounds in potential genetic link targets were further identified using the DSigDB database.

Results

The alteration of 12 genes was identified as a shared transcriptional signature, with the role of immune inflammatory pathway, especially Toll‐like receptor, NF‐kappa B, chemokine, and interleukin‐related pathways that primarily emphasized in response to SARS‐CoV‐2 complicated with HF. Top 10 critical genes (TLR4, TLR2, CXCL8, IL10, STAT3, IL1B, TLR1, TP53, CCL20, and CXCL10) were identified from protein–protein interaction with topological algorithms. The unhealthy microbiota status and gut–heart axis in co‐morbidity were identified as potential disease roads in bridging pathogenic mechanism, and lipopolysaccharide acts as a potential marker for monitoring HF during COVID‐19. For transcriptional and post‐transcriptional levels, regulation networks tightly coupling with both disorders were constructed, and significant regulator signatures with high interaction degree, especially FOXC1, STAT3, NF‐κB1, miR‐181, and miR‐520, were detected to regulate common differentially expressed genes. According to genetic links targets, glutathione‐based antioxidant strategy combined with muramyl dipeptide‐based microbe‐derived immunostimulatory therapies was identified as promising anti‐COVID‐19 and anti‐HF therapeutics.

Conclusions

This study identified shared transcriptomic and corresponding regulatory signatures as emerging therapeutic targets and detected a set of pharmacologic agents targeting genetic links. Our findings provided new insights for underlying pathogenic mechanisms between COVID‐19 and HF.

The global scientific publications on gut microbiota in type 2 diabetes; a bibliometric, Scientometric, and descriptive analysis

Background

Modifying gut dysbiosis has achieved great success in managing type 2 diabetes mellitus (T2DM) and also T2DM affected the gut microbial composition.

Objectives

To determine the research trend of scientific publications on the relationship between gut microbiota and T2DM through a bibliometric and descriptive approach.

Method

We included originals and reviews related to both topics of gut microbiota and T2DM through searching in Scopus up to 31 December 2019 and then characterized their bibliometric profiles including the number of publications, citations, institutions, journals, countries, and the collaboration network of authors, countries, terms and keywords. Moreover, we performed a descriptive evaluation of the clinical trials based on their intervention type and its influence on gut dysbiosis.

Results

We achieved 877 articles (436 originals and 441 reviews) according to our inclusion criteria. The annual publications were constantly increased over time and reached 220 publications in 2019. Out of 436 original articles, 231 animal studies and 174 human studies were found. The majority of human studies were clinical trials (n = 77) investigating the influence of drugs (n = 21), regimens (n = 21), pre/pro/symbiotic (n = 19), surgeries (n = 15), or both drug and regimen (n = 1) on gut dysbiosis. Roux-en-Y gastric bypass and metformin were assessed the most in these trials. Obesity side by side T2DM has been assessed in this area of literature based on term and keyword analyses showing their possible similar pathways mediated by gut microbiota.

Conclusion

The exponentially growing documents on gut microbiota and T2DM had been published during the last decade and revealed gut microbiota alteration mediated antidiabetic effect of many interventions. Thus, we suggest other researchers to consider this pathway in efficacy assessment of therapeutic modalities and to find the optimal composition of gut microbiota that guarantees healthy insulin sensitivity.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-021-00920-1.

Comprehensive Review on Development of Early Diagnostics on Oral Cancer with a Special Focus on Biomarkers

One of the most frequent head and neck cancers is oral cancer, with less than half of those diagnosed surviving five years. Despite breakthroughs in the treatment of many other cancers, the prognosis for people with OSCC remains dismal. The conventional methods of detection include a thorough clinical examination, biochemical investigations, and invasive biopsies. Early identification and treatment are important for a better chance of extending a patient’s life. Early diagnosis may be possible by identifying biomarkers in biological fluids. Currently, the primary method for diagnosing oral lesions is a visual oral examination; however, such a technique has certain drawbacks, as individuals are recognized after their cancer has advanced to a severe degree. The first section of this review discusses several diagnostic techniques for cancer detection, while the second section discusses the present state of knowledge about known existing predictive markers for the timely identification of malignant lesions, as well as disease activity tracking. The aim of the paper is to conduct a critical review of existing oral cancer diagnostic processes and to consider the possible application of innovative technology for early detection. This might broaden our diagnostic choices and enhance our capacity to identify and treat oral malignant tumors more effectively.

Systems Biology and Bioinformatics approach to Identify blood based signatures molecules and drug targets of patient with COVID-19

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection results in the development of a highly contagious respiratory ailment known as new coronavirus disease (COVID-19). Despite the fact that the prevalence of COVID-19 continues to rise, it is still unclear how people become infected with SARS-CoV-2 and how patients with COVID-19 become so unwell. Detecting biomarkers for COVID-19 using peripheral blood mononuclear cells (PBMCs) may aid in drug development and treatment. This research aimed to find blood cell transcripts that represent levels of gene expression associated with COVID-19 progression. Through the development of a bioinformatics pipeline, two RNA-Seq transcriptomic datasets and one microarray dataset were studied and discovered 102 significant differentially expressed genes (DEGs) that were shared by three datasets derived from PBMCs. To identify the roles of these DEGs, we discovered disease-gene association networks and signaling pathways, as well as we performed gene ontology (GO) studies and identified hub protein. Identified significant gene ontology and molecular pathways improved our understanding of the pathophysiology of COVID-19, and our identified blood-based hub proteins TPX2, DLGAP5, NCAPG, CCNB1, KIF11, HJURP, AURKB, BUB1B, TTK, and TOP2A could be used for the development of therapeutic intervention. In COVID-19 subjects, we discovered effective putative connections between pathological processes in the transcripts blood cells, suggesting that blood cells could be used to diagnose and monitor the disease’s initiation and progression as well as developing drug therapeutics.

pwrBRIDGE: a user-friendly web application for power and sample size estimation in batch-confounded microarray studies with dependent samples

Batch effect Reduction of mIcroarray data with Dependent samples usinG Empirical Bayes (BRIDGE) is a recently developed statistical method to address the issue of batch effect correction in batch-confounded microarray studies with dependent samples. The key component of the BRIDGE methodology is the use of samples run as technical replicates in two or more batches, "bridging samples", to inform batch effect correction/attenuation. While previously published results indicate a relationship between the number of bridging samples, M, and the statistical power of downstream statistical testing on the batch-corrected data, there is of yet no formal statistical framework or user-friendly software, for estimating M to achieve a specific statistical power for hypothesis tests conducted on the batch-corrected data. To fill this gap, we developed pwrBRIDGE, a simulation-based approach to estimate the bridging sample size, M, in batch-confounded longitudinal microarray studies. To illustrate the use of pwrBRIDGE, we consider a hypothetical, longitudinal batch-confounded study whose goal is to identify Alzheimer's disease (AD) progression-associated genes from amnestic mild cognitive impairment (aMCI) to AD in human blood after a 5-year follow-up. pwrBRIDGE helps researchers design and plan batch-confounded microarray studies with dependent samples to avoid over- or under-powered studies.

A novel approach to co-expression network analysis identifies modules and genes relevant for moulting and development in the Atlantic salmon louse (Lepeophtheirus salmonis)

Background

The salmon louse (Lepeophtheirus salmonis) is an obligate ectoparasitic copepod living on Atlantic salmon and other salmonids in the marine environment. Salmon lice cause a number of environmental problems and lead to large economical losses in aquaculture every year. In order to develop novel parasite control strategies, a better understanding of the mechanisms of moulting and development of the salmon louse at the transcriptional level is required.

Methods

Three weighted gene co-expression networks were constructed based on the pairwise correlations of salmon louse gene expression profiles at different life stages. Network-based approaches and gene annotation information were applied to identify genes that might be important for the moulting and development of the salmon louse. RNA interference was performed for validation. Regulatory impact factors were calculated for all the transcription factor genes by examining the changes in co-expression patterns between transcription factor genes and deferentially expressed genes in middle stages and moulting stages.

Results

Eight gene modules were predicted as important, and 10 genes from six of the eight modules have been found to show observable phenotypes in RNA interference experiments. We knocked down five hub genes from three modules and observed phenotypic consequences in all experiments. In the infection trial, no copepodids with a RAB1A-like gene knocked down were found on fish, while control samples developed to chalimus-1 larvae. Also, a FOXO-like transcription factor obtained highest scores in the regulatory impact factor calculation.

Conclusions

We propose a gene co-expression network-based approach to identify genes playing an important role in the moulting and development of salmon louse. The RNA interference experiments confirm the effectiveness of our approach and demonstrated the indispensable role of a RAB1A-like gene in the development of the salmon louse. We propose that our approach could be generalized to identify important genes associated with a phenotype of interest in other organisms.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12864-021-08054-7).

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.

Single-cell RNA sequencing in Drosophila: Technologies and applications

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cell states and functions at the single-cell level. It has greatly revolutionized transcriptomic studies in many life science research fields, such as neurobiology, immunology, and developmental biology. With the fast development of both experimental platforms and bioinformatics approaches over the past decade, scRNA-seq is becoming economically feasible and experimentally practical for many biomedical laboratories. Drosophila has served as an excellent model organism for dissecting cellular and molecular mechanisms that underlie tissue development, adult cell function, disease, and aging. The recent application of scRNA-seq methods to Drosophila tissues has led to a number of exciting discoveries. In this review, I will provide a summary of recent scRNA-seq studies in Drosophila, focusing on technical approaches and biological applications. I will also discuss current challenges and future opportunities of making new discoveries using scRNA-seq in Drosophila. This article is categorized under:   Technologies > Analysis of the Transcriptome.

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.

Genomic Approaches for Conservation Management in Australia under Climate Change

Conservation genetics has informed threatened species management for several decades. With the advent of advanced DNA sequencing technologies in recent years, it is now possible to monitor and manage threatened populations with even greater precision. Climate change presents a number of threats and challenges, but new genomics data and analytical approaches provide opportunities to identify critical evolutionary processes of relevance to genetic management under climate change. Here, we discuss the applications of such approaches for threatened species management in Australia in the context of climate change, identifying methods of facilitating viability and resilience in the face of extreme environmental stress. Using genomic approaches, conservation management practices such as translocation, targeted gene flow, and gene-editing can now be performed with the express intention of facilitating adaptation to current and projected climate change scenarios in vulnerable species, thus reducing extinction risk and ensuring the protection of our unique biodiversity for future generations. We discuss the current barriers to implementing conservation genomic projects and the efforts being made to overcome them, including communication between researchers and managers to improve the relevance and applicability of genomic studies. We present novel approaches for facilitating adaptive capacity and accelerating natural selection in species to encourage resilience in the face of climate change.

Sapling: accelerating suffix array queries with learned data models

MOTIVATION

As genomic data becomes more abundant, efficient algorithms and data structures for sequence alignment become increasingly important. The suffix array is a widely used data structure to accelerate alignment, but the binary search algorithm used to query, it requires widespread memory accesses, causing a large number of cache misses on large datasets.

RESULTS

Here, we present Sapling, an algorithm for sequence alignment, which uses a learned data model to augment the suffix array and enable faster queries. We investigate different types of data models, providing an analysis of different neural network models as well as providing an open-source aligner with a compact, practical piecewise linear model. We show that Sapling outperforms both an optimized binary search approach and multiple widely used read aligners on a diverse collection of genomes, including human, bacteria and plants, speeding up the algorithm by more than a factor of two while adding <1% to the suffix array's memory footprint.

AVAILABILITY AND IMPLEMENTATION

The source code and tutorial are available open-source at https://github.com/mkirsche/sapling.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Green chemistry and coronavirus

The novel coronavirus pandemic has rapidly spread around the world since December 2019. Various techniques have been applied in identification of SARS-CoV-2 or COVID-19 infection including computed tomography imaging, whole genome sequencing, and molecular methods such as reverse transcription polymerase chain reaction (RT-PCR). This review article discusses the diagnostic methods currently being deployed for the SARS-CoV-2 identification including optical biosensors and point-of-care diagnostics that are on the horizon. These innovative technologies may provide a more accurate, sensitive and rapid diagnosis of SARS-CoV-2 to manage the present novel coronavirus outbreak, and could be beneficial in preventing any future epidemics. Furthermore, the use of green synthesized nanomaterials in the optical biosensor devices could leads to sustainable and environmentally-friendly approaches for addressing this crisis.

IBD Systems Biology Is Here to Stay

BACKGROUND

Systems biology is a rapidly advancing field of science that allows us to look into disease mechanisms, patient diagnosis and stratification, and drug development in a completely new light. It is based on the utilization of unbiased computational systems free of the traditional experimental approaches based on personal choices of what is important and what select experiments should be performed to obtain the expected results.

METHODS

Systems biology can be applied to inflammatory bowel disease (IBD) by learning basic concepts of omes and omics and how omics-derived "big data" can be integrated to discover the biological networks underlying highly complex diseases like IBD. Once these biological networks (interactomes) are identified, then the molecules controlling the disease network can be singled out and specific blockers developed.

RESULTS

The field of systems biology in IBD is just emerging, and there is still limited information on how to best utilize its power to advance our understanding of Crohn disease and ulcerative colitis to develop novel therapeutic strategies. Few centers have embraced systems biology in IBD, but the creation of international consortia and large biobanks will make biosamples available to basic and clinical IBD investigators for further research studies.

CONCLUSIONS

The implementation of systems biology is indispensable and unavoidable, and the patient and medical communities will both benefit immensely from what it will offer in the near future.

DNA Sequencing Flow Cells and the Security of the Molecular-Digital Interface

DNA sequencing is the molecular-to-digital conversion of DNA molecules, which are made up of a linear sequence of bases (A,C,G,T), into digital information. Central to this conversion are specialized fluidic devices, called sequencing flow cells, that distribute DNA onto a surface where the molecules can be read. As more computing becomes integrated with physical systems, we set out to explore how sequencing flow cell architecture can affect the security and privacy of the sequencing process and downstream data analysis. In the course of our investigation, we found that the unusual nature of molecular processing and flow cell design contributes to two security and privacy issues. First, DNA molecules are ‘sticky’ and stable for long periods of time. In a manner analogous to data recovery from discarded hard drives, we hypothesized that residual DNA attached to used flow cells could be collected and re-sequenced to recover a significant portion of the previously sequenced data. In experiments we were able to recover over 23.4% of a previously sequenced genome sample and perfectly decode image files encoded in DNA, suggesting that flow cells may be at risk of data recovery attacks. Second, we hypothesized that methods used to simultaneously sequence separate DNA samples together to increase sequencing throughput (multiplex sequencing), which incidentally leaks small amounts of data between samples, could cause data corruption and allow samples to adversarially manipulate sequencing data. We find that a maliciously crafted synthetic DNA sample can be used to alter targeted genetic variants in other samples using this vulnerability. Such a sample could be used to corrupt sequencing data or even be spiked into tissue samples, whenever untrusted samples are sequenced together. Taken together, these results suggest that, like many computing boundaries, the molecular-to-digital interface raises potential issues that should be considered in future sequencing and molecular sensing systems, especially as they become more ubiquitous.

Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients

This study aimed to identify significant gene expression profiles of the human lung epithelial cells caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. We performed a comparative genomic analysis to show genomic observations between SARS-CoV and SARS-CoV-2. A phylogenetic tree has been carried for genomic analysis that confirmed the genomic variance between SARS-CoV and SARS-CoV-2. Transcriptomic analyses have been performed for SARS-CoV-2 infection responses and pulmonary arterial hypertension (PAH) patients' lungs as a number of patients have been identified who faced PAH after being diagnosed with coronavirus disease 2019 (COVID-19). Gene expression profiling showed significant expression levels for SARS-CoV-2 infection responses to human lung epithelial cells and PAH lungs as well. Differentially expressed genes identification and integration showed concordant genes (SAA2, S100A9, S100A8, SAA1, S100A12 and EDN1) for both SARS-CoV-2 and PAH samples, including S100A9 and S100A8 genes that showed significant interaction in the protein-protein interactions network. Extensive analyses of gene ontology and signaling pathways identification provided evidence of inflammatory responses regarding SARS-CoV-2 infections. The altered signaling and ontology pathways that have emerged from this research may influence the development of effective drugs, especially for the people with preexisting conditions. Identification of regulatory biomolecules revealed the presence of active promoter gene of SARS-CoV-2 in Transferrin-micro Ribonucleic acid (TF-miRNA) co-regulatory network. Predictive drug analyses provided concordant drug compounds that are associated with SARS-CoV-2 infection responses and PAH lung samples, and these compounds showed significant immune response against the RNA viruses like SARS-CoV-2, which is beneficial in therapeutic development in the COVID-19 pandemic.

An integrated analysis of lncRNA and mRNA expression profiles in the kidneys of mice with lupus nephritis

Long noncoding RNAs (lncRNAs) are persistently expressed and have been described as potential biomarkers and therapeutic targets in various diseases. However, there is limited information regarding lncRNA expression in the tissue of kidney exhibiting lupus nephritis (LN)a serious complication of systemic lupus erythematosus (SLE). In this study, RNA sequencing (RNA-seq) was performed to characterize the lncRNA and mRNA expression in kidney tissues from LN (MRL/lpr) and control mice. We identified 12,979 novel lncRNAs in mouse. The expression profiles of both mRNAs and lncRNAs were differed significantly between LN and control mice. In particular, there were more upregulated lncRNAs and mRNAs than downregulated ones in the kidney tissues of LN mice. However, GO analysis showed that more downregulated genes were enriched in immune and inflammatory response-associated pathways. KEGG analysis showed that both downregulated and upregulated genes were enriched in a number of pathways, including the SLE pathway, and approximately half of these SLE-associated genes encoded inflammatory factors. Moreover, we observed that 2,181 DElncRNAs may have targeted and regulated the expression of 778 mRNAs in LN kidney tissues. The results of this study showed that 11 DElncRNAs targeted and were co-expressed with six immune and SLE-associated genes. qPCR analysis confirmed that lncRNA Gm20513 positively regulated the expression of the SLE-associated gene H2-Aa. In conclusion, the results of our study demonstrates that lncRNAs influence the progression of LN and provide some cues for further study of lncRNAs in LN. These results regarding the lncRNA-mRNAregulatory network may have important value in LN diagnosis and therapy.

Epigenetic modification and therapeutic targets of diabetes mellitus

The prevalence of diabetes and its related complications are increasing significantly globally. Collected evidence suggested that several genetic and environmental factors contribute to diabetes mellitus. Associated complications such as retinopathy, neuropathy, nephropathy and other cardiovascular complications are a direct result of diabetes. Epigenetic factors include deoxyribonucleic acid (DNA) methylation and histone post-translational modifications. These factors are directly related with pathological factors such as oxidative stress, generation of inflammatory mediators and hyperglycemia. These result in altered gene expression and targets cells in the pathology of diabetes mellitus without specific changes in a DNA sequence. Environmental factors and malnutrition are equally responsible for epigenetic states. Accumulated evidence suggested that environmental stimuli alter the gene expression that result in epigenetic changes in chromatin. Recent studies proposed that epigenetics may include the occurrence of ‘metabolic memory’ found in animal studies. Further study into epigenetic mechanism might give us new vision into the pathogenesis of diabetes mellitus and related complication thus leading to the discovery of new therapeutic targets. In this review, we discuss the possible epigenetic changes and mechanism that happen in diabetes mellitus type 1 and type 2 separately. We highlight the important epigenetic and non-epigenetic therapeutic targets involved in the management of diabetes and associated complications.

RNA-Seq Whole Transcriptome Analysis of Bovine Mammary Epithelial Cells in Response to Intracellular Staphylococcus aureus

Staphylococcus aureus (S. aureus), a common mastitis pathogen widespread in the natural environment of dairy farms, is capable of invading mammary epithelial cells making treatment difficult. However, the mechanism of the response of bovine mammary epithelial cell to S. aureus invasion remains elusive. In this study, transcriptomic analysis and bioinformatics tools were applied to explore the differentially expressed RNAs in bovine mammary epithelial cells (bMECs) between the control and S. aureus-treated group. A total of 259 differentially expressed mRNAs (DEmRNAs), 27 differentially expressed microRNAs (DEmiRNAs), and 21 differentially expressed long non-coding RNAs (DElncRNAs) were found. These RNAs mainly enrich the inflammatory response, immune response, endocytosis, and cytokine-cytokine receptor interaction. qRT-PCR was used to analyze the quality of the RNA-seq results. In particular, to the defense mechanism of bovine mammary epithelial cells against intracellular S. aureus, the PPAR signaling pathway and the genes (ACOX2, CROT, and NUDT12) were found to be up-regulated to promote the production of peroxisomes and ROS, DRAM1 expression was also up-regulated to facilitate the activation of autophagy, indicating that the above mechanisms were involved in the elimination of intracellular S. aureus in bovine mammary epithelial cells.

Differentially expressed transcripts and associated protein pathways in basilar artery smooth muscle cells of the high-salt intake-induced hypertensive rat

The pathology of cerebrovascular disorders, such as hypertension, is associated with genetic changes and dysfunction of basilar artery smooth muscle cells (BASMCs). Long-term high-salt diets have been associated with the development of hypertension. However, the molecular mechanisms underlying salt-sensitive hypertension-induced BASMC modifications have not been well defined, especially at the level of variations in gene transcription. Here, we utilized high-throughput sequencing and subsequent signaling pathway analyses to find a two–fold change or greater upregulated expression of 203 transcripts and downregulated expression of 165 transcripts in BASMCs derived from rats fed a high-salt diet compared with those from control rats. These differentially expressed transcripts were enriched in pathways involved in cellular, morphological, and structural plasticity, autophagy, and endocrine regulation. These transcripts changes in the BASMCs derived from high-salt intake–induced hypertensive rats may provide critical information about multiple cellular processes and biological functions that occur during the development of cerebrovascular disorders and provide potential new targets to help control or block the development of hypertension.

Accelerated knowledge discovery from omics data by optimal experimental design

How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. We present an optimal experimental design method (coined OPEX) to identify informative omics experiments using machine learning models for both experimental space exploration and model training. OPEX-guided exploration of Escherichia coli’s populations exposed to biocide and antibiotic combinations lead to more accurate predictive models of gene expression with 44% less data. Analysis of the proposed experiments shows that broad exploration of the experimental space followed by fine-tuning emerges as the optimal strategy. Additionally, analysis of the experimental data reveals 29 cases of cross-stress protection and 4 cases of cross-stress vulnerability. Further validation reveals the central role of chaperones, stress response proteins and transport pumps in cross-stress exposure. This work demonstrates how active learning can be used to guide omics data collection for training predictive models, making evidence-driven decisions and accelerating knowledge discovery in life sciences.

How to design experiments that accelerate knowledge discovery on complex biological landscapes remains a tantalizing question. Here, the authors present OPEX, an optimal experimental design method to identify informative omics experiments for both experimental space exploration and model training.

Nonstandard conditionally specified models for nonignorable missing data

We consider data-analysis settings where data are missing not at random. In these cases, the two basic modeling approaches are 1) pattern-mixture models, with separate distributions for missing data and observed data, and 2) selection models, with a distribution for the data preobservation and a missing-data mechanism that selects which data are observed. These two modeling approaches lead to distinct factorizations of the joint distribution of the observed-data and missing-data indicators. In this paper, we explore a third approach, apparently originally proposed by J. W. Tukey as a remark in a discussion between Rubin and Hartigan, and reported by Holland in a two-page note, which has been so far neglected.

Data analyses typically rely upon assumptions about the missingness mechanisms that lead to observed versus missing data, assumptions that are typically unassessable. We explore an approach where the joint distribution of observed data and missing data are specified in a nonstandard way. In this formulation, which traces back to a representation of the joint distribution of the data and missingness mechanism, apparently first proposed by J. W. Tukey, the modeling assumptions about the distributions are either assessable or are designed to allow relatively easy incorporation of substantive knowledge about the problem at hand, thereby offering a possibly realistic portrayal of the data, both observed and missing. We develop Tukey’s representation for exponential-family models, propose a computationally tractable approach to inference in this class of models, and offer some general theoretical comments. We then illustrate the utility of this approach with an example in systems biology.

Point-of-Use Rapid Detection of SARS-CoV-2: Nanotechnology-Enabled Solutions for the COVID-19 Pandemic

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused the COVID-19 pandemic that has been spreading around the world since December 2019. More than 10 million affected cases and more than half a million deaths have been reported so far, while no vaccine is yet available as a treatment. Considering the global healthcare urgency, several techniques, including whole genome sequencing and computed tomography imaging have been employed for diagnosing infected people. Considerable efforts are also directed at detecting and preventing different modes of community transmission. Among them is the rapid detection of virus presence on different surfaces with which people may come in contact. Detection based on non-contact optical techniques is very helpful in managing the spread of the virus, and to aid in the disinfection of surfaces. Nanomaterial-based methods are proven suitable for rapid detection. Given the immense need for science led innovative solutions, this manuscript critically reviews recent literature to specifically illustrate nano-engineered effective and rapid solutions. In addition, all the different techniques are critically analyzed, compared, and contrasted to identify the most promising methods. Moreover, promising research ideas for high accuracy of detection in trace concentrations, via color change and light-sensitive nanostructures, to assist fingerprint techniques (to identify the virus at the contact surface of the gas and solid phase) are also presented.

Epigallocatechin gallate reverses gastric cancer by regulating the long noncoding RNA LINC00511/miR-29b/KDM2A axis

Epigallocatechin gallate (EGCG), as one of the main ingredients of green tea, has been reported to have potential prevention on a variety of solid tumors. However, the system-wide molecular mechanisms targeted to EGCG's anti-tumor effect have not been illustrated. Here, AGS and SGC7901 GC cells were used to investigate the EGCG-mediated change of gene expression. Our data showed that EGCG retarded cell growth and promoted cell death of GC in dose-dependent manner. Analyses based on transcription, translation as well as function were performed to explore the elusive anticancer role of EGCG. Of them, cell cycle was probably implicated key pathway of EGCG. Besides, our data revealed numerous LncRNAs activated after EGCG treatment. In this study, LINC00511 was discovered to be suppressed by EGCG and highly expressed in GC cells and tissues. Knockdown of LINC00511 inhibited cell growth and promoted cell death ratio in GC. Additionally, our data suggested LINC00511 could decrease the expression of miR-29b, followed by inducing GC development. Knockdown of miR-29b recovered the effects of LINC00511 silencing. In addition, we found overexpression of KDM2A, a target of miR-29b, would rescue the level of LINC00511. All the data showed that the LINC00511/miR-29b/KDM2A axis can be used as a diagnostic and therapeutic target for GC.