DNA microarray technology: devices, systems, and applications

Applications of gene pair methods in clinical research: advancing precision medicine

The rapid evolution of high-throughput sequencing technologies has revolutionized biomedical research, producing vast amounts of gene expression data that hold immense potential for biological discovery and clinical applications. Effectively mining these large-scale, high-dimensional data is crucial for facilitating disease detection, subtype differentiation, and understanding the molecular mechanisms underlying disease progression. However, the conventional paradigm of single-gene profiling, measuring absolute expression levels of individual genes, faces critical limitations in clinical implementation. These include vulnerability to batch effects and platform-dependent normalization requirements. In contrast, emerging approaches analyzing relative expression relationships between gene pairs demonstrate unique advantages. By focusing on binary comparisons of two genes' expression magnitudes, these methods inherently normalize experimental variations while capturing biologically stable interaction patterns. In this review, we systematically evaluate gene pair-based analytical frameworks. We classify eleven computational approaches into two fundamental categories: expression value-based methods quantifying differential expression patterns, and rank-based methods exploiting transcriptional ordering relationships. To bridge methodological development with practical implementation, we establish a reproducible analytical pipeline incorporating feature selection, classifier construction, and model evaluation modules using real-world benchmark datasets from pulmonary tuberculosis studies. These findings position gene pair analysis as a transformative paradigm for mining high-dimensional omics data, with direct implications for precision biomarker discovery and mechanistic studies of disease progression.

An Electrochemical CMOS Biosensor Array Using Phase-Only Modulation With 0.035% Phase Error and In-Pixel Averaging

This paper presents a 16 × 20 CMOS biosensor array based on electrochemical impedance spectroscopy (EIS), a highly sensitive label-free technique for rapid disease detection at the point-of-care. This high-density system implements polar-mode detection with phase-only EIS measurement over a 5 kHz - 1 MHz frequency range. The design features predominantly digital readout circuitry, ensuring scalability with technology, along with a load-compensated transimpedance amplifier, all within a 140 × 140 µm2 pixel. The architecture enables in-pixel digitization and accumulation, which increases the SNR by 10 dB for each 10× increase in readout time. Implemented in a 180 nm CMOS process, the 3 × 4 mm2 chip achieves state-of-the-art performance with an rms phase error of 0.035% at 100 kHz through a duty-cycle insensitive phase detector and one of the smallest per pixel areas with in-pixel quantization.

Meta-analysis to Unravel Core Transcriptomic Responses in Penaeus vannamei Exposed to Biotic and Abiotic Stresses

Shrimp farming, a dominant economic activity in coastal areas, is affected by different abiotic and biotic stress factors. These stressors, under poor management conditions, could affect growth and health of farmed animals. Understanding the common gene expressions in response to stress, regardless of the specific stress factor, holds significant importance in the field of functional genomics. Scope of this study is to identify the core transcriptomic responses in the shrimp species Penaeus vannamei exposed to various abiotic and biotic stress conditions and to decipher their functional importance. To achieve our objective, we gathered and analyzed multiple RNA-seq datasets related to twelve abiotic and nine biotic stress conditions. Through the in silico meta-analysis, we predicted 961 differentially expressed genes (meta-DEGs) for abiotic stress conditions and 517 meta-DEGs for biotic stress conditions, respectively. These meta-DEGs represent genes that are commonly expressed across different stress factors and are indicative of the organism's general response to stress. The annotation of nineteen core up-regulated meta-DEGs revealed their diverse functions in detoxification, cell adhesion, metal ion binding, and oxidative phosphorylation. These genes play a crucial role in stress response and immune defense. For abiotic stress, significant pathways associated with the stress response include tryptophan metabolism, starch and sucrose metabolism, fatty acid degradation, carbohydrate digestion and absorption, phenylalanine metabolism, drug metabolism-other enzymes, arachidonic acid metabolism, and fatty acid elongation. Similarly, for biotic stress, metabolism of xenobiotics by cytochrome P450, pentose and glucuronate interconversions, steroid hormone biosynthesis, and drug metabolism-cytochrome P450 were found to be significant pathway associations. In addition, the study also predicted 17 stress regulatory motifs present in the identified meta-DEGs. These motifs have significance in identifying the stress responses of the organism. The metabolic pathways and regulatory motifs associated with abiotic and biotic stress factors identified through this study could be a valuable resource for developing stress management approaches in shrimp aquaculture.

A Sequencing Overview of Malignant Peripheral Nerve Sheath Tumors: Findings and Implications for Treatment

Malignant peripheral nerve sheath tumors (MPNSTs) are rare but aggressive malignancies with a low 5-year survival rate despite current treatments. MPNSTs frequently harbor mutations in key genes such as NF1, CDKN2A, TP53, and PRC2 components (EED or SUZ12) across different disease stages. With the rapid advancement of high-throughput sequencing technologies, the molecular characteristics driving MPNST development are becoming clearer. This review summarizes recent sequencing studies on peripheral nerve sheath tumors, including plexiform neurofibromas (PNs), atypical neurofibromatous neoplasm with uncertain biologic potential (ANNUBP), and MPNSTs, highlighting key mutation events in tumor progression from the perspectives of epigenetics, transcriptomics, genomics, proteomics, and metabolomics. We also discuss the therapeutic implications of these genomic findings, focusing on preclinical and clinical trials targeting these alterations. Finally, we conclude that overcoming tumor resistance through combined targeted therapies and personalized treatments based on the molecular characteristics of MPNSTs will be a key direction for future treatment strategies.

High-Throughput Microarray Approaches for Predicting the Stability of Drug-Polymer Solid Dispersions

Amorphous solid dispersions (ASDs) offer a well-recognized strategy to improve the effective solubility and, hence, bioavailability of poorly soluble drugs. In this study, we developed an extensive library of a significant number of solid dispersion formulations using a library of chemically diverse drugs combined with a water-soluble polymer (polyvinylpyrrolidone vinyl acetate, PVPVA) at different loadings. These formulations were printed as microarrays of solid dispersion formulations, utilizing minimal material amounts (nanograms). They were subjected to a six-month stability study under accelerated conditions (40 °C and 75% relative humidity). Physical stability outcomes varied significantly among the different drug-polymer combinations, with stability ranging from immediate drug crystallization to several days of stability. The comprehensive data set obtained from this high-throughput screening was used to construct multiple linear regression models to correlate the stability of ASDs with the physicochemical properties of the used Active Pharmaceutical Ingredients (APIs). Our findings reveal that increased stability of ASDs is associated with a lower number of hydrogen bond acceptors alongside a higher overall count of heteroatoms and oxygen atoms in the drug molecules. This suggests that, while heteroatoms and oxygen are abundant, their role as hydrogen bond acceptors is limited due to their specific chemical environments, contributing to overall stability. Additionally, drugs with lower melting points formed more stable ASDs within the polymer matrix. This study, hence, highlights the importance of minimizing repulsive drug-polymer interactions to yield a physically stable ASD. The developed models, validated through Leave-One-Out Cross-Validation, demonstrated good predictability of stability trends. Hence, the high-throughput 2D inkjet printing technique that was used to manufacture the microarrays proved valuable for assessing drug-polymer crystallization onset risks and predicting stability outcomes. In conclusion, this study demonstrates a novel approach to solid dispersion formulation physical stability screening, enhancing efficiency, minimizing material requirements, and expanding the range of samples evaluated. Our findings provide insights into the critical physicochemical properties influencing ASD stability, offering a significant advancement in developing stable ASDs.

DeepDR: a deep learning library for drug response prediction

SUMMARY

Accurate drug response prediction is critical to advancing precision medicine and drug discovery. Recent advances in deep learning (DL) have shown promise in predicting drug response; however, the lack of convenient tools to support such modeling limits their widespread application. To address this, we introduce DeepDR, the first DL library specifically developed for drug response prediction. DeepDR simplifies the process by automating drug and cell featurization, model construction, training, and inference, all achievable with brief programming. The library incorporates three types of drug features along with nine drug encoders, four types of cell features along with nine cell encoders, and two fusion modules, enabling the implementation of up to 135 DL models for drug response prediction. We also explored benchmarking performance with DeepDR, and the optimal models are available on a user-friendly visual interface.

AVAILABILITY AND IMPLEMENTATION

DeepDR can be installed from PyPI (https://pypi.org/project/deepdr). The source code and experimental data are available on GitHub (https://github.com/user15632/DeepDR).

The application of omics tools in food mycology

This chapter explores the application of omics technologies in food mycology, emphasizing the significant impact of filamentous fungi on agriculture, medicine, biotechnology and the food industry. The chapter delves into the importance of understanding fungal secondary metabolism due to its implications for human health and industrial use. Several omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, are reviewed for their role in studying the genetic potential and metabolic capabilities of food-related fungi. The potential of CRISPR/Cas9 in fungal research is highlighted, showing its ability to unlock the full genetic potential of these organisms. The chapter also addresses the challenges posed by Big Data research in Omics and the need for advanced data processing methods. Through these discussions, the chapter highlights the future benefits and challenges of omics-based research in food mycology and its potential to revolutionize our understanding and utilization of fungi in various domains.

Application of omics technologies in studies on antitumor effects of Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is considered to be one of the most comprehensive and influential form of traditional medicine. It plays an important role in clinical treatment and adjuvant therapy for cancer. However, the complex composition of TCM presents challenges to the comprehensive and systematic understanding of its antitumor mechanisms, which hinders further development of TCM with antitumor effects. Omics technologies can immensely help in elucidating the mechanism of action of drugs. They utilize high-throughput sequencing and detection techniques to provide deeper insights into biological systems, revealing the intricate mechanisms through which TCM combats tumors. Multi-omics approaches can be used to elucidate the interrelationships among different omics layers by integrating data from various omics disciplines. By analyzing a large amount of data, these approaches further unravel the complex network of mechanisms underlying the antitumor effects of TCM and explain the mutual regulations across different molecular levels. In this study, we presented a comprehensive overview of the recent progress in single-omics and multi-omics research focused on elucidating the mechanisms underlying the antitumor effects of TCM. We discussed the significance of omics technologies in advancing research on the antitumor properties of TCM and also provided novel research perspectives and methodologies for further advancing this research field.

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects

Recent advancements in high-throughput sequencing technologies have significantly enhanced our ability to unravel the intricacies of gene regulatory processes. A critical challenge in this endeavor is the identification of variant effects, a key factor in comprehending the mechanisms underlying gene regulation. Non-coding variants, constituting over 90% of all variants, have garnered increasing attention in recent years. The exploration of gene variant impacts and regulatory mechanisms has spurred the development of various deep learning approaches, providing new insights into the global regulatory landscape through the analysis of extensive genetic data. Here, we provide a comprehensive overview of the development of the non-coding variants models based on bulk and single-cell sequencing data and their model-based interpretation and downstream tasks. This review delineates the popular sequencing technologies for epigenetic profiling and deep learning approaches for discerning the effects of non-coding variants. Additionally, we summarize the limitations of current approaches in variant effect prediction research and outline opportunities for improvement. We anticipate that our study will offer a practical and useful guide for the bioinformatic community to further advance the unraveling of genetic variant effects.

Combinatorial transcriptomic and genetic dissection of insulin/IGF-1 signaling-regulated longevity in Caenorhabditis elegans

Classical genetic analysis is invaluable for understanding the genetic interactions underlying specific phenotypes, but requires laborious and subjective experiments to characterize polygenic and quantitative traits. Contrarily, transcriptomic analysis enables the simultaneous and objective identification of multiple genes whose expression changes are associated with specific phenotypes. Here, we conducted transcriptomic analysis of genes crucial for longevity using datasets with daf-2/insulin/IGF-1 receptor mutant Caenorhabditis elegans. Our analysis unraveled multiple epistatic relationships at the transcriptomic level, in addition to verifying genetically established interactions. Our combinatorial analysis also revealed transcriptomic changes associated with longevity conferred by daf-2 mutations. In particular, we demonstrated that the extent of lifespan changes caused by various mutant alleles of the longevity transcription factor daf-16/FOXO matched their effects on transcriptomic changes in daf-2 mutants. We identified specific aging-regulating signaling pathways and subsets of structural and functional RNA elements altered by different genes in daf-2 mutants. Lastly, we elucidated the functional cooperation between several longevity regulators, based on the combination of transcriptomic and molecular genetic analysis. These data suggest that different biological processes coordinately exert their effects on longevity in biological networks. Together our work demonstrates the utility of transcriptomic dissection analysis for identifying important genetic interactions for physiological processes, including aging and longevity.

The Present and Future Landscapes of Molecular Diagnostics

Nucleic acid testing is the cornerstone of modern molecular diagnostics. This review describes the current status and future directions of molecular diagnostics, focusing on four major techniques: polymerase chain reaction (PCR), next-generation sequencing (NGS), isothermal amplification methods such as recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP), and clustered regularly interspaced short palindromic repeats (CRISPR)-based detection methods. We explore the advantages and limitations of each technique, describe how each overlaps with or complements other techniques, and examine current clinical offerings. This review provides a broad perspective into the landscape of molecular diagnostics and highlights potential future directions in this rapidly evolving field.

An evaluation of nucleic acid-based molecular methods for the detection of plant viruses: a systematic review

Precise and timely diagnosis of plant viruses is a prerequisite for the implementation of efficient management strategies, considering factors like globalization of trade and climate change facilitating the spread of viruses that lead to agriculture yield losses of billions yearly worldwide. Symptomatic diagnosis alone may not be reliable due to the diverse symptoms and confusion with plant abiotic stresses. It is crucial to detect plant viruses accurately and reliably and do so with little time. A complete understanding of the various detection methods is necessary to achieve this. Enzyme-linked immunosorbent assay (ELISA), has become more popular as a method for detecting viruses but faces limitations such as antibody availability, cost, sample volume, and time. Advanced techniques like polymerase chain reaction (PCR) have surpassed ELISA with its various sensitive variants. Over the last decade, nucleic acid-based molecular methods have gained popularity and have quickly replaced other techniques, such as serological techniques for detecting plant viruses due to their specificity and accuracy. Hence, this review enables the reader to understand the strengths and weaknesses of each molecular technique starting with PCR and its variations, along with various isothermal amplification followed by DNA microarrays, and next-generation sequencing (NGS). As a result of the development of new technologies, NGS is becoming more and more accessible and cheaper, and it looks possible that this approach will replace others as a favoured approach for carrying out regular diagnosis. NGS is also becoming the method of choice for identifying novel viruses.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-024-00863-0.

Toward Greater DNA Stability by Leveraging the Proton-Donating Ability of Protic Ionic Liquids

Deoxyribonucleic acid (DNA) stability is a prerequisite in many applications, ranging from DNA-based vaccines and data storage to gene therapy. However, the strategies to enhance DNA stability are limited, and the underlying mechanisms are poorly understood. Ionic liquids (ILs), molten salts of organic cations and organic/inorganic anions, are showing tremendous prospects in myriads of applications. With a judicious choice of constituent ions, the protic nature of ILs can be tuned. In this work, we investigate the relative stability of full-length genomic DNA in aqueous IL solutions of increasing protic nature. Our experimental measurements show that the protic ionic liquids (PILs) enhance the DNA melting temperature significantly while unaltering its native B-conformation. Molecular dynamics simulations and quantum mechanical calculation results suggest that the intramolecular Watson-Crick H-bonding in DNA remains unaffected and, in addition, the PILs induce stronger H-bonding networks in solution through their ability to make multiple intermolecular H-bonds with the nucleobases and among its constituent ions, thus aiding greater DNA stability. The detailed understanding obtained from this study could bring about the much-awaited breakthrough in improved DNA stability for its sustained use in the aforesaid applications!

A developmental validation of the Quick TargSeq 1.0 integrated system for automated DNA genotyping in forensic science for reference samples

Analysis of short tandem repeats (STRs) is a global standard method for human identification. Insertion/Deletion polymorphisms (DIPs) can be used for biogeographical ancestry inference. Current DNA typing involves a trained forensic worker operating several specialized instruments in a controlled laboratory environment, which takes 6-8 h. We developed the Quick TargSeq 1.0 integrated system (hereinafter abbreviated to Quick TargSeq) for automated generation of STR and DIP profiles from buccal swab samples and blood stains. The system fully integrates the processes of DNA extraction, polymerase chain reaction (PCR) amplification, and electrophoresis separation using microfluidic biochip technology. Internal validation studies were performed using RTyper 21 or DIP 38 chip cartridges with single-source reference samples according to the Scientific Working Group for DNA Analysis Methods guidelines. These results indicated that the Quick TargSeq system can process reference samples and generate STR or DIP profiles in approximately 2 h, and the profiles were concordant with those determined using traditional STR or DIP analysis methods. Thus, reproducible and concordant DNA profiles were obtained from reference samples. Throughout the study, no lane-to-lane or run-to-run contamination was observed. The Quick TargSeq system produced full profiles from buccal swabs with at least eight swipes, dried blood spot cards with two 2-mm disks, or 10 ng of purified DNA. Potential PCR inhibitors (i.e., coffee, smoking tobacco, and chewing tobacco) did not appear to affect the amplification reactions of the instrument. The overall success rate and concordance rate of 153 samples were 94.12% and 93.44%, respectively, which is comparable to other commercially available rapid DNA instruments. A blind test initiated by a DNA expert group showed that the system can correctly produce DNA profiles with 97.29% genotype concordance with standard bench-processing methods, and the profiles can be uploaded into the national DNA database. These results demonstrated that the Quick TargSeq system can rapidly generate reliable DNA profiles in an automated manner and has the potential for use in the field and forensic laboratories.

Sweet but Challenging: Tackling the Complexity of GAGs with Engineered Tailor-Made Biomaterials

Glycosaminoglycans (GAGs) play a crucial role in tissue homeostasis by regulating the activity and diffusion of bioactive molecules. Incorporating GAGs into biomaterials has emerged as a widely adopted strategy in medical applications, owing to their biocompatibility and ability to control the release of bioactive molecules. Nevertheless, immobilized GAGs on biomaterials can elicit distinct cellular responses compared to their soluble forms, underscoring the need to understand the interactions between GAG and bioactive molecules within engineered functional biomaterials. By controlling critical parameters such as GAG type, density, and sulfation, it becomes possible to precisely delineate GAG functions within a biomaterial context and to better mimic specific tissue properties, enabling tailored design of GAG-based biomaterials for specific medical applications. However, this requires access to pure and well-characterized GAG compounds, which remains challenging. This review focuses on different strategies for producing well-defined GAGs and explores high-throughput approaches employed to investigate GAG-growth factor interactions and to quantify cellular responses on GAG-based biomaterials. These automated methods hold considerable promise for improving the understanding of the diverse functions of GAGs. In perspective, the scientific community is encouraged to adopt a rational approach in designing GAG-based biomaterials, taking into account the in vivo properties of the targeted tissue for medical applications.

An open-source, 3D printed inkjet DNA synthesizer

Synthetic oligonucleotides have become a fundamental tool in a wide range of biological fields, including synthetic biology, biosensing, and DNA storage. Reliable access to equipment for synthesizing high-density oligonucleotides in the laboratory ensures research security and the freedom of research expansion. In this study, we introduced the Open-Source Inkjet DNA Synthesizer (OpenIDS), an open-source inkjet-based microarray synthesizer that offers ease of construction, rapid deployment, and flexible scalability. Utilizing 3D printing, Arduino, and Raspberry Pi, this newly designed synthesizer achieved robust stability with an industrial inkjet printhead. OpenIDS maintains low production costs and is therefore suitable for self-fabrication and optimization in academic laboratories. Moreover, even non-experts can create and control the synthesizer with a high degree of freedom for structural modifications. Users can easily add printheads or alter the design of the microarray substrate according to their research needs. To validate its performance, we synthesized oligonucleotides on 144 spots on a 15 × 25-mm silicon wafer filled with controlled pore glass. The synthesized oligonucleotides were analyzed using urea polyacrylamide gel electrophoresis.

BIC-LP: A Hybrid Higher-Order Dynamic Bayesian Network Score Function for Gene Regulatory Network Reconstruction

Reconstructing gene regulatory networks(GRNs) is an increasingly hot topic in bioinformatics. Dynamic Bayesian network(DBN) is a stochastic graph model commonly used as a vital model for GRN reconstruction. But probabilistic characteristics of biological networks and the existence of data noise bring great challenges to GRN reconstruction and always lead to many false positive/negative edges. ScoreLasso is a hybrid DBN score function combining DBN and linear regression with good performance. Its performance is, however, limited by first-order assumption and ignorance of the initial network of DBN. In this article, an integrated model based on higher-order DBN model, higher-order Lasso linear regression model and Pearson correlation model is proposed. Based on this, a hybrid higher-order DBN score function for GRN reconstruction is proposed, namely BIC-LP. BIC-LP score function is constructed by adding terms based on Lasso linear regression coefficients and Pearson correlation coefficients on classical BIC score function. Therefore, it could capture more information from dataset and curb information loss, compared with both many existing Bayesian family score functions and many state-of-the-art methods for GRN reconstruction. Experimental results show that BIC-LP can reasonably eliminate some false positive edges while retaining most true positive edges, so as to achieve better GRN reconstruction performance.

Identification of driver genes in lupus nephritis based on comprehensive bioinformatics and machine learning

Background

Lupus nephritis (LN) is a common and severe glomerulonephritis that often occurs as an organ manifestation of systemic lupus erythematosus (SLE). However, the complex pathological mechanisms associated with LN have hindered the progress of targeted therapies.

Methods

We analyzed glomerular tissues from 133 patients with LN and 51 normal controls using data obtained from the GEO database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Weighted gene co-expression network analysis (WGCNA) was utilized to identify key gene modules. The least absolute shrinkage and selection operator (LASSO) and random forest were used to identify hub genes. We also analyzed immune cell infiltration using CIBERSORT. Additionally, we investigated the relationships between hub genes and clinicopathological features, as well as examined the distribution and expression of hub genes in the kidney.

Results

A total of 270 DEGs were identified in LN. Using weighted gene co-expression network analysis (WGCNA), we clustered these DEGs into 14 modules. Among them, the turquoise module displayed a significant correlation with LN (cor=0.88, p<0.0001). Machine learning techniques identified four hub genes, namely CD53 (AUC=0.995), TGFBI (AUC=0.997), MS4A6A (AUC=0.994), and HERC6 (AUC=0.999), which are involved in inflammation response and immune activation. CIBERSORT analysis suggested that these hub genes may contribute to immune cell infiltration. Furthermore, these hub genes exhibited strong correlations with the classification, renal function, and proteinuria of LN. Interestingly, the highest hub gene expression score was observed in macrophages.

Conclusion

CD53, TGFBI, MS4A6A, and HERC6 have emerged as promising candidate driver genes for LN. These hub genes hold the potential to offer valuable insights into the molecular diagnosis and treatment of LN.

Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.

Medical viruses: diagnostic techniques

The recent epidemics and pandemics caused by different viruses such as SARS-CoV-2, monkey pox, H1N1, ebola virus etc. have been a cause of mass destruction in the human race, the biggest decline slope in the global economy and mental trauma. A number of viruses have been discovered that may cause serious problems and to overcome this problem, early diagnosis of the viruses and understanding their infection pattern is a must. Early detection of viruses inside the host provides timely management in a strategic manner. Scientists have developed some effective and efficient methods to detect the viruses. In this review, we have explained a few types of diagnostic techniques: Biosensor based, immunological-based, and molecular-based diagnostic techniques that are prominent methodologies to identify and detect the course of infection related to the medical viruses. In biosensor-based diagnostic technique, an analytical device consisting of biological elements and physicochemical component gives a signal upon detection of viral antigen. In immunological-based diagnostic techniques, enzyme-linked antibodies are utilized to find the particular antiviral antibody or viral antigen in human specimens, and nucleic acid-based diagnostic techniques are based on the principle of amplification of the viral genome.

The effect of environmental pollution on gene expression of seabirds: A review

One of the biggest challenges for ecotoxicologists is to detect harmful effects of contaminants on individual organisms before they have caused significant harm to natural populations. One possible approach for discovering sub-lethal, negative health effects of pollutants is to study gene expression, to identify metabolic pathways and physiological processes affected by contaminants. Seabirds are essential components of ecosystems but highly threatened by environmental changes. Being at the top of the food chain and exhibiting a slow pace of life, they are highly exposed to contaminants and to their ultimate impacts on populations. Here we provide an overview of the currently available seabird-related gene expression studies in the context of environmental pollution. We show that studies conducted, so far, mainly focus on a small selection of xenobiotic metabolism genes, often using lethal sampling protocols, while the greater promise of gene expression studies for wild species may lie in non-invasive procedures focusing on a wider range of physiological processes. However, as whole genome approaches might still be too expensive for large-scale assessments, we also bring out the most promising candidate biomarker genes for future studies. Based on the biased geographical representativeness of the current literature, we suggest expanding studies to temperate and tropical latitudes and urban environments. Also, as links with fitness traits are very rare in the current literature, but would be highly relevant for regulatory purposes, we point to an urgent need for establishing long-term monitoring programs in seabirds that would link pollutant exposure and gene expression to fitness traits.

Recent Progress in Development and Application of DNA, Protein, Peptide, Glycan, Antibody, and Aptamer Microarrays

Microarrays are one of the trailblazing technologies of the last two decades and have displayed their importance in all the associated fields of biology. They are widely explored to screen, identify, and gain insights on the characteristics traits of biomolecules (individually or in complex solutions). A wide variety of biomolecule-based microarrays (DNA microarrays, protein microarrays, glycan microarrays, antibody microarrays, peptide microarrays, and aptamer microarrays) are either commercially available or fabricated in-house by researchers to explore diverse substrates, surface coating, immobilization techniques, and detection strategies. The aim of this review is to explore the development of biomolecule-based microarray applications since 2018 onwards. Here, we have covered a different array of printing strategies, substrate surface modification, biomolecule immobilization strategies, detection techniques, and biomolecule-based microarray applications. The period of 2018-2022 focused on using biomolecule-based microarrays for the identification of biomarkers, detection of viruses, differentiation of multiple pathogens, etc. A few potential future applications of microarrays could be for personalized medicine, vaccine candidate screening, toxin screening, pathogen identification, and posttranslational modifications.

Profiling disease-selective drug targets: From proteomics to ligandomics

Despite advancements in omics technologies, including proteomics and transcriptomics, identification of therapeutic targets remains challenging. Ligandomics recently emerged as a unique technology of functional proteomics for global profiling of cell-binding protein ligands. When applied to diseased versus healthy vasculatures, comparative ligandomics systematically maps novel disease-restricted ligands that allow selective targeting of pathological but not physiological pathways, providing high efficacy with intrinsic safety. In this review, we discuss the potential of cellular ligands as therapeutic targets and summarize the development of ligandomics. We further compare the advantages and limitations of different omics technologies for drug target discovery and discuss target selection criteria to improve drug R&D success rates.

Topological biclustering ARTMAP for identifying within bicluster relationships

Biclustering is a powerful tool for exploratory data analysis in domains such as social networking, data reduction, and differential gene expression studies. Topological learning identifies connected regions that are difficult to find using other traditional clustering methods and produces a graphical representation. Therefore, to improve the quality of biclustering and module extraction, this work combines the adaptive resonance theory (ART)-based methods of biclustering ARTMAP (BARTMAP) and topological ART (TopoART), to produce TopoBARTMAP. The latter inherits the ability to detect topological associations while performing data reduction. The capabilities of TopoBARTMAP were benchmarked using 35 real world cancer datasets and contrasted with other (bi)clustering methods, where it showed a statistically significant improvement over the other assessed methods on ordered and shuffled data experiments. In experiments with 12 synthetic datasets, the method was observed to perform better at identifying constant, scale, shift, and shift scale type biclusters. The produced graphical representation was refined to represent gene bicluster associations and was assessed on the NCBI GSE89116 dataset containing expression levels of 39,326 probes sampled over 38 observations.

Insight into molecular diagnosis for antimalarial drug resistance of Plasmodium falciparum parasites: A review

Malaria is an infectious disease transmitted by the female Anopheles mosquito and poses a severe threat to human health. At present, antimalarial drugs are the primary treatment for malaria. The widespread use of artemisinin-based combination therapies (ACTs) has dramatically reduced the number of malaria-related deaths; however, the emergence of resistance has the potential to reverse this progress. Accurate and timely diagnosis of drug-resistant strains of Plasmodium parasites via detecting molecular markers (such as Pfnhe1, Pfmrp, Pfcrt, Pfmdr1, Pfdhps, Pfdhfr, and Pfk13) is essential for malaria control and elimination. Here, we review the current techniques which commonly used for molecular diagnosis of antimalarial resistance in P. falciparum and discuss their sensitivities and specificities for different drug resistance-associated molecular markers, with the aim of providing insights into possible directions for future precise point-of-care testing (POCT) of antimalarial drug resistance of malaria parasites.

Development of a unified gradient theory for ion-pair chromatography using oligonucleotide separations as a model case

Ion-pair chromatography is the de facto standard for separating oligonucleotides and related impurities, particularly for analysis but also often for small-scale purification. Currently, there is limited understanding of the quantitative modeling of both analytical and overloaded elution profiles obtained during gradient elution in ion-pair chromatography. Here we will investigate a recently introduced gradient mode, the so-called ion-pairing reagent gradient mode, for both analytical and overloaded separations of oligonucleotides. The first part of the study demonstrates how the electrostatic theory of ion-pair chromatography can be applied for modeling gradient elution of oligonucleotides. When the ion-pair gradient mode is used in a region where the electrostatic surface potential can be linearized, a closed-form expression of retention time can be derived. A unified retention model was then derived, applicable for both ion-pair reagent gradient mode as well as co-solvent gradient mode. The model was verified for two different experimental systems and homo- and heteromeric oligonucleotides of different lengths. Quantitative modeling of overloaded chromatography using the ion-pairing reagent gradient mode was also investigated. Firstly, a unified adsorption isotherm model was developed for both gradient modes. Then, adsorption isotherms parameter of a model oligonucleotide and two major synthetic impurities were estimated using the inverse method. Secondly, the parameters of the adsorption isotherm were then used to investigate how the productivity of oligonucleotide varies with injection volume, gradient slope, and initial retention factor. Here, the productivity increased when using a shallow gradient slope combined with a low initial retention factor. Finally, experiments were conducted to confirming some of the model predictions. Comparison with the conventional co-solvent gradient mode showed that the ion-pairing reagent gradient leads to both higher yield and productivity while consuming less co-solvent.

PyGenePlexus: a Python package for gene discovery using network-based machine learning

SUMMARY

PyGenePlexus is a Python package that enables a user to gain insight into any gene set of interest through a molecular interaction network informed supervised machine learning model. PyGenePlexus provides predictions of how associated every gene in the network is to the input gene set, offers interpretability by comparing the model trained on the input gene set to models trained on thousands of known gene sets, and returns the network connectivity of the top predicted genes.

AVAILABILITY AND IMPLEMENTATION

https://pypi.org/project/geneplexus/ and https://github.com/krishnanlab/PyGenePlexus.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Encoding microcarriers for biomedicine

High throughput biological analysis has become an important topic in modern biomedical research and clinical diagnosis. The flow encoding scheme based on the encoding microcarriers provides a feasible strategy for the multiplexed biological analysis. Different encoding characteristics invest the microcarriers with different encoding mechanisms. Biosensor analysis, drug screening, cell culture, and the construction and evaluation of bionic organ chips can be realized by decoding the microcarriers and quantifying the detection signal intensity. In this review, the encoding strategy of microcarriers was divided into the optical and non-optical encoding approaches according to their encoding elements, and the research progress of the microcarrier encoding strategy was elaborated. Finally, we summarized the biomedical applications and predicted their future prospects.

Electrokinetics in antimicrobial resistance analysis: A review

Infections caused by antimicrobial resistance are a serious problem in the world. Currently, commercial devices for antimicrobial susceptibility testing and resistant bacteria identification are time-consuming. There is an urgent need to develop fast and accurate methods, especially in the process of sample pretreatment. Electrokinetic (EK) is a family of electric-field-based kinetic phenomena of fluid or embedded objects, and EK applications have been found in various fields. In this paper, EK bacteria manipulation, including enrichment and separation, is reviewed. Focus is given to the rapid electric-based minimum inhibitory concentration measurement. The future directions and major challenges in this field are also outlined.

Molecular Methods for Identification and Quantification of Foodborne Pathogens

Foodborne pathogens that enter the human food chain are a significant threat worldwide to human health. Timely and cost-effective detection of them became challenging for many countries that want to improve their detection and control of foodborne illness. We summarize simple, rapid, specific, and highly effective molecular technology that is used to detect and identify foodborne pathogens, including polymerase chain reaction, isothermal amplification, loop-mediated isothermal amplification, nucleic acid sequence-based amplification, as well as gene chip and gene probe technology. The principles of their operation, the research supporting their application, and the advantages and disadvantages of each technology are summarized.

A Python Clustering Analysis Protocol of Genes Expression Data Sets

Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.

TripletGO: Integrating Transcript Expression Profiles with Protein Homology Inferences for Gene Function Prediction

Gene Ontology (GO) has been widely used to annotate functions of genes and gene products. Here, we proposed a new method, TripletGO, to deduce GO terms of protein-coding and non-coding genes, through the integration of four complementary pipelines built on transcript expression profile, genetic sequence alignment, protein sequence alignment, and naïve probability. TripletGO was tested on a large set of 5754 genes from 8 species (human, mouse, Arabidopsis, rat, fly, budding yeast, fission yeast, and nematoda) and 2433 proteins with available expression data from the third Critical Assessment of Protein Function Annotation challenge (CAFA3). Experimental results show that TripletGO achieves function annotation accuracy significantly beyond the current state-of-the-art approaches. Detailed analyses show that the major advantage of TripletGO lies in the coupling of a new triplet network-based profiling method with the feature space mapping technique, which can accurately recognize function patterns from transcript expression profiles. Meanwhile, the combination of multiple complementary models, especially those from transcript expression and protein-level alignments, improves the coverage and accuracy of the final GO annotation results. The standalone package and an online server of TripletGO are freely available at https://zhanggroup.org/TripletGO/.

A statistical network pre-processing method to improve relevance and significance of gene lists in microarray gene expression studies

BACKGROUND

Microarrays can perform large scale studies of differential expressed gene (DEGs) and even single nucleotide polymorphisms (SNPs), thereby screening thousands of genes for single experiment simultaneously. However, DEGs and SNPs are still just as enigmatic as the first sequence of the genome. Because they are independent from the affected biological context. Pathway enrichment analysis (PEA) can overcome this obstacle by linking both DEGs and SNPs to the affected biological pathways and consequently to the underlying biological functions and processes.

RESULTS

To improve the enrichment analysis results, we present a new statistical network pre-processing method by mapping DEGs and SNPs on a biological network that can improve the relevance and significance of the DEGs or SNPs of interest to incorporate pathway topology information into the PEA. The proposed methodology improves the statistical significance of the PEA analysis in terms of computed p value for each enriched pathways and limit the number of enriched pathways. This helps reduce the number of relevant biological pathways with respect to a non-specific list of genes.

CONCLUSION

The proposed method provides two-fold enhancements. Network analysis reveals fewer DEGs, by selecting only relevant DEGs and the detected DEGs improve the enriched pathways' statistical significance, rather than simply using a general list of genes.

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.

A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Over the past decade, conventional lab work strategies have gradually shifted from being limited to a laboratory setting towards a bioinformatics era to help manage and process the vast amounts of data generated by omics technologies. The present work outlines the latest contributions of bioinformatics in analyzing microarray data and their application to cancer. We dissect different microarray platforms and their use in gene expression in cancer models. We highlight how computational advances empowered the microarray technology in gene expression analysis. The study on protein-protein interaction databases classified into primary, derived, meta-database, and prediction databases describes the strategies to curate and predict novel interaction networks in silico. In addition, we summarize the areas of bioinformatics where neural graph networks are currently being used, such as protein functions, protein interaction prediction, and in silico drug discovery and development. We also discuss the role of deep learning as a potential tool in the prognosis, diagnosis, and treatment of cancer. Integrating these resources efficiently, practically, and ethically is likely to be the most challenging task for the healthcare industry over the next decade; however, we believe that it is achievable in the long term.

Gap Effect on Electric Field Enhancement and Photothermal Conversion in Gold Nanostructures

Plasmonic optical tweezers and thermophoresis are promising tools for nanomaterial manipulation. When a gold nanostructure is irradiated with laser light, an electric field around the nanostructure is enhanced because of the localized surface plasmon resonance, which increases the optical radiation pressure applied to the nanomaterials. In addition, a temperature gradient is also generated by the photothermal conversion, and thermophoretic force is then generated. This study numerically evaluated the electric and temperature fields induced by the localized surface plasmon resonance between two gold nanostructures. Here, we focused on the effect of the gap width between nanostructures on the optical radiation pressure and thermophoretic force. The simulation results show that the electric field is locally enhanced according to the gap width, but the effect on the temperature rise due to the photothermal heating is small. This fact suggests that the gap effect between the nanostructures is particularly dominant in nanomanipulation using optical force, whereas it has little effect in nanomanipulation using thermophoresis.

GenePlexus: a web-server for gene discovery using network-based machine learning

Biomedical researchers take advantage of high-throughput, high-coverage technologies to routinely generate sets of genes of interest across a wide range of biological conditions. Although these technologies have directly shed light on the molecular underpinnings of various biological processes and diseases, the list of genes from any individual experiment is often noisy and incomplete. Additionally, interpreting these lists of genes can be challenging in terms of how they are related to each other and to other genes in the genome. In this work, we present GenePlexus (https://www.geneplexus.net/), a web-server that allows a researcher to utilize a powerful, network-based machine learning method to gain insights into their gene set of interest and additional functionally similar genes. Once a user uploads their own set of human genes and chooses between a number of different human network representations, GenePlexus provides predictions of how associated every gene in the network is to the input set. The web-server also provides interpretability through network visualization and comparison to other machine learning models trained on thousands of known process/pathway and disease gene sets. GenePlexus is free and open to all users without the need for registration.

Protein Sequencing, One Molecule at a Time

Despite tremendous gains over the past decade, methods for characterizing proteins have generally lagged behind those for nucleic acids, which are characterized by extremely high sensitivity, dynamic range, and throughput. However, the ability to directly characterize proteins at nucleic acid levels would address critical biological challenges such as more sensitive medical diagnostics, deeper protein quantification, large-scale measurement, and discovery of alternate protein isoforms and modifications and would open new paths to single-cell proteomics. In response to this need, there has been a push to radically improve protein sequencing technologies by taking inspiration from high-throughput nucleic acid sequencing, with a particular focus on developing practical methods for single-molecule protein sequencing (SMPS). SMPS technologies fall generally into three categories: sequencing by degradation (e.g., mass spectrometry or fluorosequencing), sequencing by transit (e.g., nanopores or quantum tunneling), and sequencing by affinity (as in DNA hybridization-based approaches). We describe these diverse approaches, which range from those that are already experimentally well-supported to the merely speculative, in this nascent field striving to reformulate proteomics.

Huaier Polysaccharide Interrupts PRV Infection via Reducing Virus Adsorption and Entry

A pseudorabies virus (PRV) novel virulent variant outbreak occurred in China in 2011. However, little is known about PRV prevention and treatment. Huaier polysaccharide has been used to treat some solid cancers, although its antiviral activity has not been reported. Our study confirmed that the polysaccharide can effectively inhibit infection of PRV XJ5 in PK15 cells. It acted in a dose-dependent manner when blocking virus adsorption and entry into PK15 cells. Moreover, it suppressed PRV replication in PK15 cells. In addition, the results suggest that Huaier polysaccharide plays a role in treating PRV XJ5 infection by directly inactivating PRV XJ5. In conclusion, Huaier polysaccharide might be a novel therapeutic agent for preventing and controlling PRV infection.

Multi-view Random-walk Graph regularization Low-Rank Representation for cancer clustering and Differentially Expressed Gene Selection

Cancer genome data generally consists of multiple views from different sources. These views provide different levels of information about gene activity, as well as more comprehensive cancer information. The low-rank representation (LRR) method, as a powerful subspace clustering method, has been extended and applied in cancer data research. However, most methods based on low-rank representation only study single-view data in cancer genome data, such as gene expression data. The methods based on single-view genome data usually ignore the complementary relationship between the views, which is not conducive to further study of cancer. Therefore, this paper proposes a new method named Multi-view Random-walk Graph regularization Low-Rank Representation (MRGLRR) to comprehensively analyze multi-view genomics data. This method uses multi-view model to find the common centroid of view. By constructing a joint affinity matrix to learn the low-rank subspace representation of multiple sets of data, the hidden information of each view is fully obtained. In addition, this method introduces random walk graph regularization constraint to obtain more accurate similarity between samples. Different from the traditional graph regularization constraint, after constructing the KNN graph, we use the random walk algorithm to obtain the weight matrix. The random walk algorithm can retain more local geometric information and better learn the topological structure of the data. What's more, a feature gene selection strategy suitable for multi-view model is proposed to find more differentially expressed genes with research value. Experimental results show that our method is better than other representative methods in terms of clustering and feature gene selection for cancer multi-omics data.

The expression of microRNAs and exposure to environmental contaminants related to human health: a review

Environmental contaminants exposure may lead to detrimental changes to the microRNAs (miRNAs) expression resulting in several health effects. miRNAs, small non-coding RNAs that regulate gene expression, have multiple transcript targets and thereby regulate several signalling molecules. Even a minor alteration in the abundance of one miRNA can have deep effects on global gene expression. Altered patterns of miRNAs can be responsible for changes linked to various health outcomes, suggesting that specific miRNAs are activated in pathophysiological processes. In this review, we provide an overview of studies investigating the impact of air pollution, organic chemicals, and heavy metals on miRNA expression and the potential biologic effects on humans.Abbreviations: AHRR, aryl-hydrocarbon receptor repressor; AHR, aryl-hydrocarbon receptor; As, arsenic; BCL2, B-cell lymphoma 2; BCL2L11, B-cell lymphoma 2 like 11; BCL6, B-cell lymphoma 6; BPA, bisphenol A; CVD, cardiovascular diseases; CD40, cluster of differentiation 40; CCND1, Cyclin D1; CDKN1A, cyclin-dependent kinase inhibitor 1A; Cr, chromium; CTBP1, C-terminal binding protein 1; CXCL12, C-X-C motif chemokine ligand 12; DAZAP1, deleted in azoospermia associated protein 1; DEP, diesel exhaust particles; EGFR, epidermal growth factor receptor; eNOS, endothelial nitric oxide synthase; EVs, extracellular vesicles; FAK, focal adhesion kinase; FAS, fas cell surface death receptor; FOXO, forkhead box O; HbA1c, glycated hemoglobin; Hg, mercury; HLA-A, human leukocyte antigen A; HMGB, high-mobility group protein B; IFNAR2, interferon alpha receptor subunit 2; IL-6, interleukin-6; IRAK1, interleukin 1 receptor associated kinase 1; JAK/STAT, janus kinase/signal transducers and activators of transcription; MAPK, mitogen-activated protein kinase; miRNAs, microRNAs; MVs, microvesicles; NCDs, noncommunicable diseases; NFAT, nuclear factor of activated T cells; NFkB, nuclear factor kappa B; NRF2, nuclear factor, erythroid-derived 2; NRG3, neuregulin 3; O₃, ozone; OP, organophosphorus pesticides; PAHs, polycyclic aromatic hydrocarbons; Pb, lead; PCBs, polychlorinated biphenyls; PDCD4, programmed cell death 4; PDGFB, platelet derived growth factor subunit beta; PDGFR, platelet-derived growth factor receptor; PI3K/Akt, phosphoinositide-3-kinase/protein kinase B; PKA, protein kinase A; PM, particulate matter; PRKCQ, protein kinase C theta; PTEN, phosphatase and tensin homolog; SORT1, sortilin 1; TGFβ, transforming growth factor-β; TLR, toll-like receptor; TNF, tumor necrosis factors; TRAF1, tumor necrosis factors-receptor associated factors 1; TRAP, traffic-related air pollution; TREM1, triggering receptor expressed on myeloid cells 1; TRIAP1, TP53 regulated inhibitor of apoptosis 1; VCAM-1, vascular cell adhesion molecule 1; VEGFA, vascular endothelial growth factor A; XRCC2, X-ray repair cross complementing 2; YBX2, Y-box-binding protein 2; ZEB1, zinc finger E-box-binding homeobox 1; ZEB2, zinc finger E-box-binding homeobox 2; 8-OH-dG, 8-hydroxy-guanine.

Microarray Data Analysis Protocol

Microarrays are broadly used in the omic investigation and have several areas of applications in biology and medicine, providing a significant amount of data for a single experiment. Different kinds of microarrays are available, identifiable by characteristics such as the type of probes, the surface used as support, and the method used for the target detection. To better deal with microarray datasets, the development of microarray data analysis protocols simple to use as well as able to produce accurate reports, and comprehensible results arise. The object of this paper is to provide a general protocol showing how to choose the best software tool to analyze microarray data, allowing to efficiently figure out genomic/pharmacogenomic biomarkers.

Identification of Key Genes and Pathways in Osteosarcoma by Bioinformatics Analysis

Purpose

Osteosarcoma (OS) is the most primary bone malignant tumor in adolescents. Although the treatment of OS has made great progress, patients' prognosis remains poor due to tumor invasion and metastasis.

Materials and Methods

We downloaded the expression profile GSE12865 from the Gene Expression Omnibus database. We screened differential expressed genes (DEGs) by making use of the R limma software package. Based on Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, we performed the function and pathway enrichment analyses. Then, we constructed a Protein-Protein Interaction network and screened hub genes through the Search Tool for the Retrieval of Interacting Genes.

Result

By analyzing the gene expression profile GSE12865, we obtained 703 OS-related DEGs, which contained 166 genes upregulated and 537 genes downregulated. The DEGs were primarily abundant in ribosome, cell adhesion molecules, ubiquitin-ubiquitin ligase activity, and p53 signaling pathway. The hub genes of OS were KDR, CDH5, CD34, CDC42, RBX1, POLR2C, PPP2CA, and RPS2 through PPI network analysis. Finally, GSEA analysis showed that cell adhesion molecules, chemokine signal pathway, transendothelial migration, and focal adhesion were associated with OS.

Conclusion

In this study, through analyzing microarray technology and bioinformatics analysis, the hub genes and pathways about OS are identified, and the new molecular mechanism of OS is clarified.

High-scale random access on DNA storage systems

Due to the rapid cost decline of synthesizing and sequencing deoxyribonucleic acid (DNA), high information density, and its durability of up to centuries, utilizing DNA as an information storage medium has received the attention of many scientists. State-of-the-art DNA storage systems exploit the high capacity of DNA and enable random access (predominantly random reads) by primers, which serve as unique identifiers for directly accessing data. However, primers come with a significant limitation regarding the maximum available number per DNA library. The number of different primers within a library is typically very small (e.g. ≈10). We propose a method to overcome this deficiency and present a general-purpose technique for addressing and directly accessing thousands to potentially millions of different data objects within the same DNA pool. Our approach utilizes a fountain code, sophisticated probe design, and microarray technologies. A key component is locality-sensitive hashing, making checks for dissimilarity among such a large number of probes and data objects feasible.

Identification of Differentially Expressed Genes in COVID-19 and Integrated Bioinformatics Analysis of Signaling Pathways

Coronavirus disease 2019 (COVID-19) is acutely infectious pneumonia. Currently, the specific causes and treatment targets of COVID-19 are still unclear. Herein, comprehensive bioinformatics methods were employed to analyze the hub genes in COVID-19 and tried to reveal its potential mechanisms. First of all, 34 groups of COVID-19 lung tissues and 17 other diseases' lung tissues were selected from the GSE151764 gene expression profile for research. According to the analysis of the DEGs (differentially expressed genes) in the samples using the limma software package, 84 upregulated DEGs and 46 downregulated DEGs were obtained. Later, by the Database for Annotation, Visualization, and Integrated Discovery (DAVID), they were enriched in the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. It was found that the upregulated DEGs were enriched in the type I interferon signaling pathway, AGE-RAGE signaling pathway in diabetic complications, coronavirus disease, etc. Downregulated DEGs were in cellular response to cytokine stimulus, IL-17 signaling pathway, FoxO signaling pathway, etc. Then, based on GSEA, the enrichment of the gene set in the sample was analyzed in the GO terms, and the gene set was enriched in the positive regulation of myeloid leukocyte cytokine production involved in immune response, programmed necrotic cell death, translesion synthesis, necroptotic process, and condensed nuclear chromosome. Finally, with the help of STRING tools, the PPI (protein-protein interaction) network diagrams of DEGs were constructed. With degree ≥13 as the cutoff degree, 3 upregulated hub genes (ISG15, FN1, and HLA-G) and 4 downregulated hub genes (FOXP3, CXCR4, MMP9, and CD69) were screened out for high degree. All these findings will help us to understand the potential molecular mechanisms of COVID-19, which is also of great significance for its diagnosis and prevention.