Microarray and its applications

Accelerating crop improvement via integration of transcriptome-based network biology and genome editing

MAIN CONCLUSION

Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.

Point-of-care testing for early-stage liver cancer diagnosis and personalized medicine: Biomarkers, current technologies and perspectives

Liver cancer is a highly prevalent and lethal form of cancer worldwide. In the absence of early diagnosis, treatment options for this disease are severely restricted. Recent advancements in genomics and bioinformatics have facilitated the discovery of a multitude of novel biomarkers that accurately depict an individual's disease diagnosis, progression, and treatment response. Leveraging these breakthroughs, personalized medicine employs an individual's biomarker profile to enable early detection of liver cancer and inform decisions regarding treatment selection, dosage determination, and prognosis assessment. The current lack of readily applicable, timely, and economically viable tools for biomarker analysis has hindered the incorporation of personalized medicine into regular clinical procedures. Over the past decade, significant advancements have been achieved in the field of molecular point-of-care testing (POCT) and amplification techniques, leading to substantial improvements in the diagnosis of liver cancer and the implementation of precision medicine. Instrument-free PCR technology or plasma PCR technology can shorten the complex procedure of in vitro detection of nucleic acid-based biomarkers. Also, compared to traditional ELISA, various nanomaterials modified with monoclonal antibodies to target proteins for recognition, capture, and detection have improved the efficiency of protein-based biomarker detection. These advances have reduced the time and cost of clinical detection of early-stage hepatocellular carcinoma and improved the efficiency of timely diagnosis and survival of suspected patients while reducing unnecessary testing costs and procedures. This review aims to provide a comprehensive overview of the current and emerging biomarkers employed in the early detection of liver cancer, as well as the advancements in point-of-care molecular testing technology and platforms. The primary objective is to assess their potential in facilitating the implementation of personalized medicine. This review ultimately revealed that the diagnosis of early-stage hepatocellular carcinoma not only requires sensitive biomarkers, but its various modifications and changes during the progression of cirrhosis to early-stage hepatocellular carcinoma will be a greater focus of our attention in the future. The rapid development of POCT has facilitated the opportunity to readily detect liver cancer in the general population in the future, and the integration of multi-pathway multiplexing and intelligent algorithms has improved the sensitivity and accuracy of early liver cancer biomarker detection. It is expected that the integration of point-of-care technology will be instrumental in the widespread adoption of personalized medicine in the foreseeable future.

Genotype imputation in human genomic studies

Imputation is a method that supplies missing information about genetic variants that could not be directly genotyped with DNA microarrays or low-coverage sequencing. Imputation plays a critical role in genome-wide association studies (GWAS). It leads to a significant increase in the number of studied variants, which improves the resolution of the method and enhances the comparability of data obtained in different cohorts and/or by using different technologies, which is important for conducting meta-analyses. When performing imputation, genotype information from the study sample, in which only part of the genetic variants are known, is complemented using the standard (reference) sample, which has more complete genotype data (most often the results of whole-genome sequencing). Imputation has become an integral part of human genomic research due to the benefits it provides and the increasing availability of imputation tools and reference sample data. This review focuses on imputation in human genomic research. The first section of the review provides a description of technologies for obtaining information about human genotypes and characteristics of these types of data. The second section describes the imputation methodology, lists the stages of its implementation and the corresponding programs, provides a description of the most popular reference panels and methods for assessing the quality of imputation. The review concludes with examples of the use of imputation in genomic studies of samples from Russia. This review shows the importance of imputation, provides information on how to carry it out, and systematizes the results of its application using Russian samples.

Recent advances and challenges in plant viral diagnostics

Accurate and timely diagnosis of plant viral infections plays a key role in effective disease control and maintaining agricultural productivity. Recent advances in the diagnosis of plant viruses have significantly expanded our ability to detect and monitor viral pathogens in agricultural crops. This review discusses the latest advances in diagnostic technologies, including both traditional methods and the latest innovations. Conventional methods such as enzyme-linked immunosorbent assay and DNA amplification-based assays remain widely used due to their reliability and accuracy. However, diagnostics such as next-generation sequencing and CRISPR-based detection offer faster, more sensitive and specific virus detection. The review highlights the main advantages and limitations of detection systems used in plant viral diagnostics including conventional methods, biosensor technologies and advanced sequence-based techniques. In addition, it also discusses the effectiveness of commercially available diagnostic tools and challenges facing modern diagnostic techniques as well as future directions for improving informed disease management strategies. Understanding the main features of available diagnostic methodologies would enable stakeholders to choose optimal management strategies against viral threats and ensure global food security.

Medical and molecular biophysical techniques as substantial tools in the era of mRNA-based vaccine technology

The COVID-19 pandemic prompted the advancement of vaccine technology using mRNA delivery into the host cells. Consequently, mRNA-based vaccines have emerged as a practical approach against SARS-CoV-2 owing to their inherent properties, such as cost-effectiveness, rapid manufacturing, and preservation. These features are vital, especially in resource-constrained regions. Nevertheless, the design of mRNA-based vaccines is intricately intertwined with the refinement of biophysical technologies, thereby establishing their high potential. The preparation of mRNA-based vaccines involves a sequence of phases combining medical and molecular biophysical technologies. Furthermore, their efficiency depends on the capability to optimize their positive attributes, thus paving the way for their subsequent preclinical and clinical evaluations. Using biophysical techniques, the characterization of nucleic acids extends from their initial formulation to their cellular internalization abilities and encapsulation in biomolecule complexes, such as lipid nanoparticles (LNPs), for designing mRNA-based LNPs. Furthermore, nanoparticles are subjected to a series of careful screening steps to assess their physical and chemical characteristics before achieving an optimum formulation suitable for preclinical and clinical studies. This review provides a comprehensive understanding of the fundamental role of biophysical techniques in the complex development of mRNA-based vaccines and their role in the recent success during the COVID-19 pandemic.

Transcriptomic Approaches to Cardiomyocyte-Biomaterial Interactions: A Review

Biomaterials, essential for supporting, enhancing, and repairing damaged tissues, play a critical role in various medical applications. This Review focuses on the interaction of biomaterials and cardiomyocytes, emphasizing the unique significance of transcriptomic approaches in understanding their interactions, which are pivotal in cardiac bioengineering and regenerative medicine. Transcriptomic approaches serve as powerful tools to investigate how cardiomyocytes respond to biomaterials, shedding light on the gene expression patterns, regulatory pathways, and cellular processes involved in these interactions. Emerging technologies such as bulk RNA-seq, single-cell RNA-seq, single-nucleus RNA-seq, and spatial transcriptomics offer promising avenues for more precise and in-depth investigations. Longitudinal studies, pathway analyses, and machine learning techniques further improve the ability to explore the complex regulatory mechanisms involved. This review also discusses the challenges and opportunities of utilizing transcriptomic techniques in cardiomyocyte-biomaterial research. Although there are ongoing challenges such as costs, cell size limitation, sample differences, and complex analytical process, there exist exciting prospects in comprehensive gene expression analyses, biomaterial design, cardiac disease treatment, and drug testing. These multimodal methodologies have the capacity to deepen our understanding of the intricate interaction network between cardiomyocytes and biomaterials, potentially revolutionizing cardiac research with the aim of promoting heart health, and they are also promising for studying interactions between biomaterials and other cell types.

Microbial allies: exploring fungal endophytes for biosynthesis of terpenoid indole alkaloids

Terpenoid indole alkaloids (TIAs) are natural compounds found in medicinal plants that exhibit various therapeutic activities, such as antimicrobial, anti-inflammatory, antioxidant, anti-diabetic, anti-helminthic, and anti-tumor properties. However, the production of these alkaloids in plants is limited, and there is a high demand for them due to the increasing incidence of cancer cases. To address this research gap, researchers have focused on optimizing culture media, eliciting metabolic pathways, overexpressing genes, and searching for potential sources of TIAs in organisms other than plants. The insufficient number of essential genes and enzymes in the biosynthesis pathway is the reason behind the limited production of TIAs. As the field of natural product discovery from biological species continues to grow, endophytes are being investigated more and more as potential sources of bioactive metabolites with a variety of chemical structures. Endophytes are microorganisms (fungi, bacteria, archaea, and actinomycetes), that exert a significant influence on the metabolic pathways of both the host plants and the endophytic cells. Bio-prospection of fungal endophytes has shown the discovery of novel, high-value bioactive compounds of commercial significance. The discovery of therapeutically significant secondary metabolites has been made easier by endophytic entities' abundant but understudied diversity. It has been observed that fungal endophytes have better intermediate processing ability due to cellular compartmentation. This paper focuses on fungal endophytes and their metabolic ability to produce complex TIAs, recent advancements in this area, and addressing the limitations and future perspectives related to TIA production.

Inflammatory Bowel Disease: A Comprehensive Analysis of Molecular Bases, Predictive Biomarkers, Diagnostic Methods, and Therapeutic Options

In inflammatory bowel diseases (IBDs), such as Crohn's disease (CD) and ulcerative colitis (UC), the immune system relentlessly attacks intestinal cells, causing recurrent tissue damage over the lifetime of patients. The etiology of IBD is complex and multifactorial, involving environmental, microbiota, genetic, and immunological factors that alter the molecular basis of the organism. Among these, the microbiota and immune cells play pivotal roles; the microbiota generates antigens recognized by immune cells and antibodies, while autoantibodies target and attack the intestinal membrane, exacerbating inflammation and tissue damage. Given the altered molecular framework, the analysis of multiple molecular biomarkers in patients proves exceedingly valuable for diagnosing and prognosing IBD, including markers like C reactive protein and fecal calprotectin. Upon detection and classification of patients, specific treatments are administered, ranging from conventional drugs to new biological therapies, such as antibodies to neutralize inflammatory molecules like tumor necrosis factor (TNF) and integrin. This review delves into the molecular basis and targets, biomarkers, treatment options, monitoring techniques, and, ultimately, current challenges in IBD management.

Lactic acid bacteria as an eco-friendly approach in plant production: Current state and prospects

Since the late nineteenth century, the agricultural sector has experienced a tremendous increase in chemical use in response to the growing population. Consequently, the intensive and indiscriminate use of these substances caused serious damage on several levels, including threatening human health, disrupting soil microbiota, affecting wildlife ecosystems, and causing groundwater pollution. As a solution, the application of microbial-based products presents an interesting and ecological restoration tool. The use of Plant Growth-Promoting Microbes (PGPM) affected positive production, by increasing its efficiency, reducing production costs, environmental pollution, and chemical use. Among these microbial communities, lactic acid bacteria (LAB) are considered an interesting candidate to be formulated and applied as effective microbes. Indeed, these bacteria are approved by the European Food Safety Authority (EFSA) and Food and Drug Administration (FDA) as Qualified Presumption of Safety statute and Generally Recognized as Safe for various applications. To do so, this review comes as a road map for future research, which addresses the different steps included in LAB formulation as biocontrol, bioremediation, or plant growth promoting agents from the isolation process to their field application passing by the different identification methods and their various uses. The plant application methods as well as challenges limiting their use in agriculture are also discussed.

Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions

OBJECTIVES

This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome.

METHODS

Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions.

RESULTS

Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players.

CONCLUSION

This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine

Despite advancements in treatment protocols, cancer is one of the leading cause of deaths worldwide. Therefore, there is a need to identify newer and personalized therapeutic targets along with screening technologies to combat cancer. With the advent of pan-omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and lipidomics, the scientific community has witnessed an improved molecular and metabolomic understanding of various diseases, including cancer. In addition, three-dimensional (3-D) disease models have been efficiently utilized for understanding disease pathophysiology and as screening tools in drug discovery. An integrated approach utilizing pan-omics technologies and 3-D in vitro tumor models has led to improved understanding of the intricate network encompassing various signalling pathways and molecular cross-talk in solid tumors. In the present review, we underscore the current trends in omics technologies and highlight their role in understanding genotypic-phenotypic co-relation in cancer with respect to 3-D in vitro tumor models. We further discuss the challenges associated with omics technologies and provide our outlook on the future applications of these technologies in drug discovery and precision medicine for improved management of cancer.

Protein prognostic biomarkers in stage II colorectal cancer: implications for post-operative management

Colorectal cancer (CRC) poses a significant threat to many human lives worldwide and survival following resection is predominantly stage dependent. For early-stage cancer, patients are not routinely advised to undergo additional post-operative adjuvant chemotherapy. Acceptable clinical management guidelines are well established for patients in pTNM stages I, III and IV. However, recommendations for managing CRC stage II patients remain controversial and many studies have been conducted to segregate stage II patients into low- and high-risk of recurrence using genomic, transcriptomic and proteomic molecular markers. As proteins provide valuable insights into cellular functions and disease state and have a relatively easy translation to the clinic, this review aims to discuss potential prognostic protein biomarkers proposed for predicting tumour relapse in early-stage II CRC. It is suggested that a panel of markers may be more effective than a single marker and further evaluation is required to translate these into clinical practice.

Meta-analysis of microarray data to determine gene indicators involved in cisplatin resistance in non-small cell lung cancer

BACKGROUND

Lung cancer is a major cause of cancer-related mortality worldwide, with a 5-year survival rate of approximately 22%. Cisplatin is one of the standard first-line chemotherapeutic agents for non-small cell lung cancer (NSCLC), but its efficacy is often limited by the development of resistance. Despite extensive research on the molecular mechanisms of chemoresistance, the underlying causes remain elusive and complex.

AIMS

We analyzed three microarray datasets to find the gene signature and key pathways related to cisplatin resistance in NSCLC.

METHODS AND RESULTS

We compared the gene expression of sensitive and resistant NSCLC cell lines treated with cisplatin. We found 274 DEGs, including 111 upregulated and 163 downregulated genes, in the resistant group. Gene set enrichment analysis showed the potential roles of several DEGs, such as TUBB2B, MAPK7, TUBAL3, MAP2K5, SMUG1, NTHL1, PARP3, NTRK1, G6PD, PDK1, HEY1, YTHDF2, CD274, and MAGEA1, in cisplatin resistance. Functional analysis revealed the involvement of pathways, such as gap junction, base excision repair, central carbon metabolism, and Notch signaling in the resistant cell lines.

CONCLUSION

We identified several molecular factors that contribute to cisplatin resistance in NSCLC cell lines, involving genes and pathways that regulate gap junction communication, DNA damage repair, ROS balance, EMT induction, and stemness maintenance. These genes and pathways could be targets for future studies to overcome cisplatin resistance in NSCLC.

Transcriptomics : Approaches to Quantifying Gene Expression and Their Application to Studying the Human Brain

To date, the field of transcriptomics has been characterized by rapid methods development and technological advancement, with new technologies continuously rendering older ones obsolete.This chapter traces the evolution of approaches to quantifying gene expression and provides an overall view of the current state of the field of transcriptomics, its applications to the study of the human brain, and its place in the broader emerging multiomics landscape.

Label-Free Sample Preparation for Single-Cell Proteomics

In recent years, single-cell proteomics (SCP) has become a valuable addition to other single-cell omics technologies for studying cellular heterogeneity. The amount of protein in a single cell is very limited, and in contrast to sequencing techniques, there are currently no means for protein amplification. Therefore, most single-cell proteomics approaches aim to maximize sample preparation efficiency while minimizing peptide loss. By reducing processing volumes to sub-microliters and avoiding manual transfer steps that could lead to peptide loss, peptide recovery, and the robustness of SCP workflows have been significantly improved. In this chapter, we describe a protocol for label-free SCP sample preparation using the cellenONE® platform and the proteoCHIP LF 48 substrate prior to analysis with high-performance liquid chromatography-mass spectrometry.

Incorporating omics-based tools into endophytic fungal research

Fungal endophytes are valuable sources of bioactive compounds with diverse applications. The exploration of these compounds not only contributes to our understanding of ecological interactions but also holds promise for the development of novel products with agricultural, medicinal, and industrial significance. Continued exploration of fungal endophyte diversity and understanding the ecological roles of bioactive compounds present opportunities for new discoveries and applications. Omics techniques, which include genomics, transcriptomics, proteomics, and metabolomics, contribute to the discovery of novel bioactive compounds produced by fungal endophytes with their potential applications. The omics techniques play a critical role in unraveling the complex interactions between fungal endophytes and their host plants, providing valuable insights into the molecular mechanisms and potential applications of these relationships. This review provides an overview of how omics techniques contribute to the study of fungal endophytes.

Navigating the Expansive Landscapes of Soft Materials: A User Guide for High-Throughput Workflows

Synthetic polymers are highly customizable with tailored structures and functionality, yet this versatility generates challenges in the design of advanced materials due to the size and complexity of the design space. Thus, exploration and optimization of polymer properties using combinatorial libraries has become increasingly common, which requires careful selection of synthetic strategies, characterization techniques, and rapid processing workflows to obtain fundamental principles from these large data sets. Herein, we provide guidelines for strategic design of macromolecule libraries and workflows to efficiently navigate these high-dimensional design spaces. We describe synthetic methods for multiple library sizes and structures as well as characterization methods to rapidly generate data sets, including tools that can be adapted from biological workflows. We further highlight relevant insights from statistics and machine learning to aid in data featurization, representation, and analysis. This Perspective acts as a "user guide" for researchers interested in leveraging high-throughput screening toward the design of multifunctional polymers and predictive modeling of structure-property relationships in soft materials.

Deep Learning-Based Prediction of Alzheimer's Disease Using Microarray Gene Expression Data

Alzheimer's disease is a genetically complex disorder, and microarray technology provides valuable insights into it. However, the high dimensionality of microarray datasets and small sample sizes pose challenges. Gene selection techniques have emerged as a promising solution to this challenge, potentially revolutionizing AD diagnosis. The study aims to investigate deep learning techniques, specifically neural networks, in predicting Alzheimer's disease using microarray gene expression data. The goal is to develop a reliable predictive model for early detection and diagnosis, potentially improving patient care and intervention strategies. This study employed gene selection techniques, including Singular Value Decomposition (SVD) and Principal Component Analysis (PCA), to pinpoint pertinent genes within microarray datasets. Leveraging deep learning principles, we harnessed a Convolutional Neural Network (CNN) as our classifier for Alzheimer's disease (AD) prediction. Our approach involved the utilization of a seven-layer CNN with diverse configurations to process the dataset. Empirical outcomes on the AD dataset underscored the effectiveness of the PCA-CNN model, yielding an accuracy of 96.60% and a loss of 0.3503. Likewise, the SVD-CNN model showcased remarkable accuracy, attaining 97.08% and a loss of 0.2466. These results accentuate the potential of our method for gene dimension reduction and classification accuracy enhancement by selecting a subset of pertinent genes. Integrating gene selection methodologies with deep learning architectures presents a promising framework for elevating AD prediction and promoting precision medicine in neurodegenerative disorders. Ongoing research endeavors aim to generalize this approach for diverse applications, explore alternative gene selection techniques, and investigate a variety of deep learning architectures.

Genetic Enhancement of Cereals Using Genomic Resources for Nutritional Food Security

Advances in genomics resources have facilitated the evolution of cereal crops with enhanced yield, improved nutritional values, and heightened resistance to various biotic and abiotic stresses. Genomic approaches present a promising avenue for the development of high-yielding varieties, thereby ensuring food and nutritional security. Significant improvements have been made within the omics domain, specifically in genomics, transcriptomics, and proteomics. The advent of Next-Generation Sequencing (NGS) techniques has yielded an immense volume of data, accompanied by substantial progress in bioinformatic tools for proficient analysis. The synergy between genomics and computational tools has been acknowledged as pivotal for unravelling the intricate mechanisms governing genome-wide gene regulation. Within this review, the essential genomic resources are delineated, and their harmonization in the enhancement of cereal crop varieties is expounded upon, with a paramount focus on fulfilling the nutritional requisites of humankind. Furthermore, an encompassing compendium of the available genomic resources for cereal crops is presented, accompanied by an elucidation of their judicious utilization in the advancement of crop attributes.

Identification of the shared gene signatures between pulmonary fibrosis and pulmonary hypertension using bioinformatics analysis

Pulmonary fibrosis (PF) and pulmonary hypertension (PH) have common pathophysiological features, such as the significant remodeling of pulmonary parenchyma and vascular wall. There is no effective specific drug in clinical treatment for these two diseases, resulting in a worse prognosis and higher mortality. This study aimed to screen the common key genes and immune characteristics of PF and PH by means of bioinformatics to find new common therapeutic targets. Expression profiles are downloaded from the Gene Expression Database. Weighted gene co-expression network analysis is used to identify the co-expression modules related to PF and PH. We used the ClueGO software to enrich and analyze the common genes in PF and PH and obtained the protein-protein interaction (PPI) network. Then, the differential genes were screened out in another cohort of PF and PH, and the shared genes were crossed. Finally, RT-PCR verification and immune infiltration analysis were performed on the intersection genes. In the result, the positive correlation module with the highest correlation between PF and PH was determined, and it was found that lymphocyte activation is a common feature of the pathophysiology of PF and PH. Eight common characteristic genes (ACTR2, COL5A2, COL6A3, CYSLTR1, IGF1, RSPO3, SCARNA17 and SEL1L) were gained. Immune infiltration showed that compared with the control group, resting CD4 memory T cells were upregulated in PF and PH. Combining the results of crossing characteristic genes in ImmPort database and RT-PCR, the important gene IGF1 was obtained. Knocking down IGF1 could significantly reduce the proliferation and apoptosis resistance in pulmonary microvascular endothelial cells, pulmonary smooth muscle cells, and fibroblasts induced by hypoxia, platelet-derived growth factor-BB (PDGF-BB), and transforming growth factor-β1 (TGF-β1), respectively. Our work identified the common biomarkers of PF and PH and provided a new candidate gene for the potential therapeutic targets of PF and PH in the future.

Current and Novel Biomarkers of Progression Risk in Children with Chronic Kidney Disease

BACKGROUND

Due to the complexity of chronic kidney disease (CKD) pathophysiology, biomarkers representing different mechanistic pathways have been targeted for the study and development of novel biomarkers. The discovery of clinically useful CKD biomarkers would allow for the identification of those children at the highest risk of kidney function decline for timely interventions and enrollment in clinical trials.

SUMMARY

Glomerular filtration rate and proteinuria are traditional biomarkers to classify and prognosticate CKD progression in clinical practice but have several limitations. Over the recent decades, novel biomarkers have been identified from blood or urine with metabolomic screening studies, proteomic screening studies, and an improved knowledge of CKD pathophysiology. This review highlights promising biomarkers associated with the progression of CKD that could potentially serve as future prognostic markers in children with CKD.

KEY MESSAGES

Further studies are needed in children with CKD to validate putative biomarkers, particularly candidate proteins and metabolites, for improving clinical management.

Differentially expressed genes in systemic sclerosis: Towards predictive medicine with new molecular tools for clinicians

Systemic sclerosis (SSc) is a rare and chronic autoimmune disease characterized by a pathogenic triad of immune dysregulation, vasculopathy, and progressive fibrosis. Clinical tools commonly used to assess patients, such as the modified Rodnan skin score, difference between limited or diffuse forms of skin involvement, presence of lung, heart or kidney involvement, or of various autoantibodies, are important prognostic factors, but still fail to reflect the large heterogeneity of the disease. SSc treatment options are diverse, ranging from conventional drugs to autologous hematopoietic stem cell transplantation, and predicting response is challenging. Genome-wide technologies, such as high throughput microarray analyses and RNA sequencing, allow accurate, unbiased, and broad assessment of alterations in expression levels of multiple genes. In recent years, many studies have shown robust changes in the gene expression profiles of SSc patients compared to healthy controls, mainly in skin tissues and peripheral blood cells. The objective analysis of molecular patterns in SSc is a powerful tool that can further classify SSc patients with similar clinical phenotypes and help predict response to therapy. In this review, we describe the journey from the first discovery of differentially expressed genes to the identification of enriched pathways and intrinsic subsets identified in SSc, using machine learning algorithms. Finally, we discuss the use of these new tools to predict the efficacy of various treatments, including stem cell transplantation. We suggest that the use of RNA gene expression-based classifications according to molecular subsets may bring us one step closer to precision medicine in Systemic Sclerosis.

Applications of Genomics and Transcriptomics in Precision Medicine for Myopia Control or Prevention

Myopia is a globally emerging concern accompanied by multiple medical and socio-economic burdens with no well-established causal treatment to control thus far. The study of the genomics and transcriptomics of myopia treatment is crucial to delineate disease pathways and provide valuable insights for the design of precise and effective therapeutics. A strong understanding of altered biochemical pathways and underlying pathogenesis leading to myopia may facilitate early diagnosis and treatment of myopia, ultimately leading to the development of more effective preventive and therapeutic measures. In this review, we summarize current data about the genomics and transcriptomics of myopia in human and animal models. We also discuss the potential applicability of these findings to precision medicine for myopia treatment.

An omics approach to delineating the molecular mechanisms that underlie the biological effects of physical plasma

The use of physical plasma to treat cancer is an emerging field, and interest in its applications in oncology is increasing rapidly. Physical plasma can be used directly by aiming the plasma jet onto cells or tissue, or indirectly, where a plasma-treated solution is applied. A key scientific question is the mechanism by which physical plasma achieves selective killing of cancer over normal cells. Many studies have focused on specific pathways and mechanisms, such as apoptosis and oxidative stress, and the role of redox biology. However, over the past two decades, there has been a rise in omics, the systematic analysis of entire collections of molecules in a biological entity, enabling the discovery of the so-called "unknown unknowns." For example, transcriptomics, epigenomics, proteomics, and metabolomics have helped to uncover molecular mechanisms behind the action of physical plasma, revealing critical pathways beyond those traditionally associated with cancer treatments. This review showcases a selection of omics and then summarizes the insights gained from these studies toward understanding the biological pathways and molecular mechanisms implicated in physical plasma treatment. Omics studies have revealed how reactive species generated by plasma treatment preferentially affect several critical cellular pathways in cancer cells, resulting in epigenetic, transcriptional, and post-translational changes that promote cell death. Finally, this review considers the outlook for omics in uncovering both synergies and antagonisms with other common cancer therapies, as well as in overcoming challenges in the clinical translation of physical plasma.

Integrating Cutting-Edge Methods to Oral Cancer Screening, Analysis, and Prognosis

Oral cancer (OC) has become a significant barrier to health worldwide due to its high morbidity and mortality rates. OC is among the most prevalent types of cancer that affect the head and neck region, and the overall survival rate at 5 years is still around 50%. Moreover, it is a multifactorial malignancy instigated by genetic and epigenetic variabilities, and molecular heterogeneity makes it a complex malignancy. Oral potentially malignant disorders (OPMDs) are often the first warning signs of OC, although it is challenging to predict which cases will develop into malignancies. Visual oral examination and histological examination are still the standard initial steps in diagnosing oral lesions; however, these approaches have limitations that might lead to late diagnosis of OC or missed diagnosis of OPMDs in high-risk individuals. The objective of this review is to present a comprehensive overview of the currently used novel techniques viz., liquid biopsy, next-generation sequencing (NGS), microarray, nanotechnology, lab-on-a-chip (LOC) or microfluidics, and artificial intelligence (AI) for the clinical diagnostics and management of this malignancy. The potential of these novel techniques in expanding OC diagnostics and clinical management is also reviewed.

Development of a clinical microarray system for genetic analysis screening

Objectives

Research on the relationship between diseases and genes and the advancement of genetic analysis technologies have made genetic testing in medical care possible. There are various methods for genetic testing, including PCR-based methods and next-generation sequencing; however, screening tests in clinical laboratories are becoming more diverse; therefore, novel measurement systems and equipment are required to meet the needs of each situation. In this study, we aimed to develop a novel microarray-based genetic analysis system that uses a Peltier element to overcome the issues of conventional microarrays, such as the long measurement time and high cost.

Methods

We constructed a microarray system to detect the UDP-glucuronosyltransferase gene polymorphisms UGT1A1*6 and UGT1A1*28 in patients eligible for irinotecan hydrochloride treatment for use in clinical laboratories. To evaluate the performance of the system, the hybridization temperature and reaction time were determined, and the results were compared with those obtained using a conventional hybridization oven.

Results

The hybridization temperature reached its target in 1/27th of the time required by the conventional system. We assessed 111 human clinical samples and found that our results agreed with those obtained using existing methods. The total time for the newly developed device was reduced by 85 min compared to that for existing methods, as the automated DNA microarray eliminates the time that existing methods spend on manual operation.

Conclusions

The surface treatment technology used in our system enables high-density and strong DNA fixation, allowing the construction of a measurement system suitable for clinical applications.

Multi-Omics Approaches and Resources for Systems-Level Gene Function Prediction in the Plant Kingdom

In higher plants, the complexity of a system and the components within and among species are rapidly dissected by omics technologies. Multi-omics datasets are integrated to infer and enable a comprehensive understanding of the life processes of organisms of interest. Further, growing open-source datasets coupled with the emergence of high-performance computing and development of computational tools for biological sciences have assisted in silico functional prediction of unknown genes, proteins and metabolites, otherwise known as uncharacterized. The systems biology approach includes data collection and filtration, system modelling, experimentation and the establishment of new hypotheses for experimental validation. Informatics technologies add meaningful sense to the output generated by complex bioinformatics algorithms, which are now freely available in a user-friendly graphical user interface. These resources accentuate gene function prediction at a relatively minimal cost and effort. Herein, we present a comprehensive view of relevant approaches available for system-level gene function prediction in the plant kingdom. Together, the most recent applications and sought-after principles for gene mining are discussed to benefit the plant research community. A realistic tabulation of plant genomic resources is included for a less laborious and accurate candidate gene discovery in basic plant research and improvement strategies.

Performance Analysis of Ovarian Cancer Detection and Classification for Microarray Gene Data

The most common gynecologic cancer, behind cervical and uterine, is ovarian cancer. Ovarian cancer is a severe concern for women. Abnormal cells form and spread throughout the body. Ovarian cancer microarray data can diagnose and prognosis. Typically, ovarian cancer microarray data contains tens of thousands of genes. In order to reduce computational complexity, selecting the most critical genes or attributes in the entire dataset is necessary. Because microarray datasets have limited samples and many characteristics, classifier detection lags. So, dimensionality reduction measures are essential to protect disease classification genes. In this research, initially the ANOVA method is used for gene selection and then two clustering-based and three transform-based feature extraction methods, namely, Fuzzy C Means, Softmax Discriminant Algorithm (SDA), Hilbert Transform, Fast Fourier Transform (FFT), and Discrete Cosine Transform (DCT), respectively, are used to select relevant genes further. Six classifiers further classify the features as normal and abnormal. The NLR classifier gives the highest accuracy for SDA features at 92%, and KNN gives the lowest accuracy of 55% for SDA, Hilbert, and DCT features. With correlation distance feature selection, the NLR classifier attains the lowest accuracy of 53%, and the highest accuracy of 88% is obtained by the GMM classifier.

Bioinformatics strategies for studying the molecular mechanisms of fungal extracellular vesicles with a focus on infection and immune responses

Fungal extracellular vesicles (EVs) are released during pathogenesis and are found to be an opportunistic infection in most cases. EVs are immunocompetent with their host and have paved the way for new biomedical approaches to drug delivery and the treatment of complex diseases including cancer. With computing and processing advancements, the rise of bioinformatics tools for the evaluation of various parameters involved in fungal EVs has blossomed. In this review, we have complied and explored the bioinformatics tools to analyze the host-pathogen interaction, toxicity, omics and pathogenesis with an array of specific tools that have depicted the ability of EVs as vector/carrier for therapeutic agents and as a potential theme for immunotherapy. We have also discussed the generation and pathways involved in the production, transport, pathogenic action and immunological interactions of EVs in the host system. The incorporation of network pharmacology approaches has been discussed regarding fungal pathogens and their significance in drug discovery. To represent the overview, we have presented and demonstrated an in silico study model to portray the human Cryptococcal interactions.

Simultaneous Visualization of MiRNA-221 and Caspase-3 in Cancer Cells for Investigating the Feasibility of MiRNA-Targeted Therapy with a Dual-Color Fluorescent Nanosensor

MiRNA-targeted therapy holds great promise for precision cancer therapy. It is important to investigate the effect of changes in miRNA expression on apoptosis in order to evaluate miRNA-targeted therapy and achieve personalized therapy. In this study, we designed a dual-color fluorescent nanosensor consisting of grapheme oxide modified with a molecular beacon and peptide. The nanosensor can simultaneously detect and image miRNA-221 and apoptotic protein caspase-3 in living cells. Intracellular experiments showed that the nanosensor could be successfully applied for in situ monitoring of the effect of miRNA-221 expression changes on apoptosis by dual-color imaging. The current strategy could provide new avenues for investigating the feasibility of miRNA-targeted therapy, screening new anti-cancer drugs targeting miRNA and developing personalized treatment plans.

RNA-Seq Based Toxicity Analysis of Mesoporous Polydopamine Nanoparticles in Mice Following Different Exposure Routes

Mesoporous polydopamine nanoparticles (MPDA NPs) are promising nanomaterials that have the prospect of clinical application for multi-strategy antitumor therapy, while the biosecurity of MPDA NPs remains indistinct. Here, transcriptome sequencing (RNA-Seq) was performed to systematically reveal the toxicity of MPDA NPs to five categories of organs after three different exposure routes, including intravenous injection, intramuscular injection, and intragastric administration. Our results uncovered that MPDA NPs could be deposited in various organs in small amounts after intravenous administration, not for the other two exposure routes. The number of differentially expressed genes (DEGs) identified in the heart, liver, spleen, lung, and kidney from the intragastric administration group was from 22 to 519. Similarly, the corresponding number was from 23 to 64 for the intramuscular injection group and was from 11 to 153 for the intravenous injection group. Functional enrichment analyses showed 6, 39, and 4 GO terms enriched for DEGs in intragastric administration, intramuscular injection, and intravenous injection groups, respectively. One enriched pathway was revealed in intragastric administration group, while no enriched pathway was found in other groups. Our results indicated that MPDA NPs produced only slight changes at the transcriptome level in mice, which provided new insights for further clinical application of MPDA NPs.

Genomics and Epigenomics of Gestational Diabetes Mellitus: Understanding the Molecular Pathways of the Disease Pathogenesis

One of the most common complications during pregnancy is gestational diabetes mellitus (GDM), hyperglycemia that occurs for the first time during pregnancy. The condition is multifactorial, caused by an interaction between genetic, epigenetic, and environmental factors. However, the underlying mechanisms responsible for its pathogenesis remain elusive. Moreover, in contrast to several common metabolic disorders, molecular research in GDM is lagging. It is important to recognize that GDM is still commonly diagnosed during the second trimester of pregnancy using the oral glucose tolerance test (OGGT), at a time when both a fetal and maternal pathophysiology is already present, demonstrating the increased blood glucose levels associated with exacerbated insulin resistance. Therefore, early detection of metabolic changes and associated epigenetic and genetic factors that can lead to an improved prediction of adverse pregnancy outcomes and future cardio-metabolic pathologies in GDM women and their children is imperative. Several genomic and epigenetic approaches have been used to identify the genes, genetic variants, metabolic pathways, and epigenetic modifications involved in GDM to determine its etiology. In this article, we explore these factors as well as how their functional effects may contribute to immediate and future pathologies in women with GDM and their offspring from birth to adulthood. We also discuss how these approaches contribute to the changes in different molecular pathways that contribute to the GDM pathogenesis, with a special focus on the development of insulin resistance.

Understanding Gene Expression and Transcriptome Profiling of COVID-19: An Initiative Towards the Mapping of Protective Immunity Genes Against SARS-CoV-2 Infection

The COVID-19 pandemic has created an urgent situation throughout the globe. Therefore, it is necessary to identify the differentially expressed genes (DEGs) in COVID-19 patients to understand disease pathogenesis and the genetic factor(s) responsible for inter-individual variability. The DEGs will help understand the disease's potential underlying molecular mechanisms and genetic characteristics, including the regulatory genes associated with immune response elements and protective immunity. This study aimed to determine the DEGs in mild and severe COVID-19 patients versus healthy controls. The Agilent-085982 Arraystar human lncRNA V5 microarray GEO dataset (GSE164805 dataset) was used for this study. We used statistical tools to identify the DEGs. Our 15 human samples dataset was divided into three groups: mild, severe COVID-19 patients and healthy control volunteers. We compared our result with three other published gene expression studies of COVID-19 patients. Along with significant DEGs, we developed an interactome map, a protein-protein interaction (PPI) pattern, a cluster analysis of the PPI network, and pathway enrichment analysis. We also performed the same analyses with the top-ranked genes from the three other COVID-19 gene expression studies. We also identified differentially expressed lncRNA genes and constructed protein-coding DEG-lncRNA co-expression networks. We attempted to identify the regulatory genes related to immune response elements and protective immunity. We prioritized the most significant 29 protein-coding DEGs. Our analyses showed that several DEGs were involved in forming interactome maps, PPI networks, and cluster formation, similar to the results obtained using data from the protein-coding genes from other investigations. Interestingly we found six lncRNAs (TALAM1, DLEU2, and UICLM CASC18, SNHG20, and GNAS) involved in the protein-coding DEG-lncRNA network; which might be served as potential biomarkers for COVID-19 patients. We also identified three regulatory genes from our study and 44 regulatory genes from the other investigations related to immune response elements and protective immunity. We were able to map the regulatory genes associated with immune elements and identify the virogenomic responses involved in protective immunity against SARS-CoV-2 infection during COVID-19 development.

Foodomic-Based Approach for the Control and Quality Improvement of Dairy Products

The food quality assurance before selling is a needed requirement intended for protecting consumer interests. In the same way, it is also indispensable to promote continuous improvement of sensory and nutritional properties. In this regard, food research has recently contributed with studies focused on the use of 'foodomics'. This review focuses on the use of this technology, represented by transcriptomics, proteomics, and metabolomics, for the control and quality improvement of dairy products. The complex matrix of these foods requires sophisticated technology able to extract large amounts of information with which to influence their aptitude for consumption. Thus, throughout the article, different applications of the aforementioned technologies are described and discussed in essential matters related to food quality, such as the detection of fraud and/or adulterations, microbiological safety, and the assessment and improvement of transformation industrial processes (e.g., fermentation and ripening). The magnitude of the reported results may open the door to an in-depth transformation of the most conventional analytical processes, with the introduction of new techniques that allow a greater understanding of the biochemical phenomena occurred in this type of food.

Identification of key genes and pathways in mild and severe nonalcoholic fatty liver disease by integrative analysis

Background

The global prevalence of nonalcoholic fatty liver disease (NAFLD) is increasing. The pathogenesis of NAFLD is multifaceted, and the underlying mechanisms are elusive. We conducted data mining analysis to gain a better insight into the disease and to identify the hub genes associated with the progression of NAFLD.

Methods

The dataset GSE49541, containing the profile of 40 samples representing mild stages of NAFLD and 32 samples representing advanced stages of NAFLD, was acquired from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified using the R programming language. The Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool and Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database were used to perform the enrichment analysis and construct protein–protein interaction (PPI) networks, respectively. Subsequently, transcription factor networks and key modules were identified. The hub genes were validated in a mice model of high fat diet (HFD)-induced NAFLD and in cultured HepG2 cells by real-time quantitative PCR.

Results

Based on the GSE49541 dataset, 57 DEGs were selected and enriched in chemokine activity and cellular component, including the extracellular region. Twelve transcription factors associated with DEGs were indicated from PPI analysis. Upregulated expression of five hub genes (SOX9, CCL20, CXCL1, CD24, and CHST4), which were identified from the dataset, was also observed in the livers of HFD-induced NAFLD mice and in HepG2 cells exposed to palmitic acid or advanced glycation end products.

Conclusion

The hub genes SOX9, CCL20, CXCL1, CD24, and CHST4 are involved in the aggravation of NAFLD. Our results offer new insights into the underlying mechanism of NAFLD progression.

Advanced Genomics-Based Approaches for Defining Allograft Rejection With Single Cell Resolution

Solid organ transplant recipients require long-term immunosuppression for prevention of rejection. Calcineurin inhibitor (CNI)-based immunosuppressive regimens have remained the primary means for immunosuppression for four decades now, yet little is known about their effects on graft resident and infiltrating immune cell populations. Similarly, the understanding of rejection biology under specific types of immunosuppression remains to be defined. Furthermore, development of innovative, rationally designed targeted therapeutics for mitigating or preventing rejection requires a fundamental understanding of the immunobiology that underlies the rejection process. The established use of microarray technologies in transplantation has provided great insight into gene transcripts associated with allograft rejection but does not characterize rejection on a single cell level. Therefore, the development of novel genomics tools, such as single cell sequencing techniques, combined with powerful bioinformatics approaches, has enabled characterization of immune processes at the single cell level. This can provide profound insights into the rejection process, including identification of resident and infiltrating cell transcriptomes, cell-cell interactions, and T cell receptor α/β repertoires. In this review, we discuss genomic analysis techniques, including microarray, bulk RNAseq (bulkSeq), single-cell RNAseq (scRNAseq), and spatial transcriptomic (ST) techniques, including considerations of their benefits and limitations. Further, other techniques, such as chromatin analysis via assay for transposase-accessible chromatin sequencing (ATACseq), bioinformatic regulatory network analyses, and protein-based approaches are also examined. Application of these tools will play a crucial role in redefining transplant rejection with single cell resolution and likely aid in the development of future immunomodulatory therapies in solid organ transplantation.

Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization

High-throughput data make it possible to study expression levels of thousands of genes simultaneously under a particular condition. However, only few of the genes are discriminatively expressed. How to identify these biomarkers precisely is significant for disease diagnosis, prognosis, and therapy. Many studies utilized pathway information to identify the biomarkers. However, most of these studies only incorporate the group information while the pathway structural information is ignored. In this paper, we proposed a Bayesian gene selection with a network-constrained regularization method, which can incorporate the pathway structural information as priors to perform gene selection. All the priors are conjugated; thus, the parameters can be estimated effectively through Gibbs sampling. We present the application of our method on 6 microarray datasets, comparing with Bayesian Lasso, Bayesian Elastic Net, and Bayesian Fused Lasso. The results show that our method performs better than other Bayesian methods and pathway structural information can improve the result.

Lithographic Processes for the Scalable Fabrication of Micro- and Nanostructures for Biochips and Biosensors

Since the early 2000s, extensive research has been performed to address numerous challenges in biochip and biosensor fabrication in order to use them for various biomedical applications. These biochips and biosensor devices either integrate biological elements (e.g., DNA, proteins or cells) in the fabrication processes or experience post fabrication of biofunctionalization for different downstream applications, including sensing, diagnostics, drug screening, and therapy. Scalable lithographic techniques that are well established in the semiconductor industry are now being harnessed for large-scale production of such devices, with additional development to meet the demand of precise deposition of various biological elements on device substrates with retained biological activities and precisely specified topography. In this review, the lithographic methods that are capable of large-scale and mass fabrication of biochips and biosensors will be discussed. In particular, those allowing patterning of large areas from 10 cm² to m², maintaining cost effectiveness, high throughput (>100 cm² h^-1), high resolution (from micrometer down to nanometer scale), accuracy, and reproducibility. This review will compare various fabrication technologies and comment on their resolution limit and throughput, and how they can be related to the device performance, including sensitivity, detection limit, reproducibility, and robustness.

Structure and Oligonucleotide Binding Efficiency of Differently Prepared Click Chemistry-Type DNA Microarray Slides Based on 3-Azidopropyltrimethoxysilane

The structural characterization of glass slides surface-modified with 3-azidopropyltrimethoxysilane and used for anchoring nucleic acids, resulting in the so-called DNA microarrays, is presented. Depending on the silanization conditions, the slides were found to show different oligonucleotide binding efficiency, thus, an attempt was made to correlate this efficiency with the structural characteristics of the silane layers. Atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and X-ray reflectometry (XRR) measurements provided information on the surface topography, chemical composition and thickness of the silane films, respectively. The surface for which the best oligonucleotides binding efficiency is observed, has been found to consist of a densely-packed silane layer, decorated with a high-number of additional clusters that are believed to host exposed azide groups.

The Role of Surface Chemistry in the Efficacy of Protein and DNA Microarrays for Label-Free Detection: An Overview

The importance of microarrays in diagnostics and medicine has drastically increased in the last few years. Nevertheless, the efficiency of a microarray-based assay intrinsically depends on the density and functionality of the biorecognition elements immobilized onto each sensor spot. Recently, researchers have put effort into developing new functionalization strategies and technologies which provide efficient immobilization and stability of any sort of molecule. Here, we present an overview of the most widely used methods of surface functionalization of microarray substrates, as well as the most recent advances in the field, and compare their performance in terms of optimal immobilization of the bioreceptor molecules. We focus on label-free microarrays and, in particular, we aim to describe the impact of surface chemistry on two types of microarray-based sensors: microarrays for single particle imaging and for label-free measurements of binding kinetics. Both protein and DNA microarrays are taken into consideration, and the effect of different polymeric coatings on the molecules' functionalities is critically analyzed.

Methods for exploring the faecal microbiome of premature infants: a review

The premature infant gut microbiome plays an important part in infant health and development, and recognition of the implications of microbial dysbiosis in premature infants has prompted significant research into these issues. The approaches to designing investigations into microbial populations are many and varied, each with its own benefits and limitations. The technique used can influence results, contributing to heterogeneity across studies. This review aimed to describe the most common techniques used in researching the preterm infant microbiome, detailing their various limitations. The objective was to provide those entering the field with a broad understanding of available methodologies, so that the likely effects of their use can be factored into literature interpretation and future study design. We found that although many techniques are used for characterising the premature infant microbiome, 16S rRNA short amplicon sequencing is the most common. 16S rRNA short amplicon sequencing has several benefits, including high accuracy, discoverability and high throughput capacity. However, this technique has limitations. Each stage of the protocol offers opportunities for the injection of bias. Bias can contribute to variability between studies using 16S rRNA high throughout sequencing. Thus, we recommend that the interpretation of previous results and future study design be given careful consideration.

Deciphering Biochemical and Molecular Signatures Associated with Obesity in Context of Metabolic Health

This study aims to identify the clinical and genetic markers related to the two uncommon nutritional statuses—metabolically unhealthy normal-weight (MUNW) and metabolically healthy overweight/obese (MHOW) individuals in the physically active individuals. Physically active male volunteers (n = 120) were recruited, and plasma samples were analyzed for the clinical parameters. Triglycerides, HDL-Cholesterol, LDL-cholesterol, total cholesterol, C-reactive protein, and insulin resistance were considered as markers of metabolic syndrome. The subjects were classified as ‘healthy’ (0 metabolic abnormalities) or ‘unhealthy’ (≥1 metabolic abnormalities) in their respective BMI group with a cut-off at 24.9 kg/m². Analysis of biochemical variables was done using enzyme linked immunosorbent assay (ELISA) kits with further confirmation using western blot analysis. The microarray was conducted, followed by quantitative real-time PCR to identify and analyze differentially expressed genes (DEGs). The MHOW group constituted 12.6%, while the MUNW group constituted 32.4% of the total study population. Pro-inflammatory markers like interleukin-6, tumor necrosis factor (TNF)-α, and ferritin were increased in metabolically unhealthy groups in comparison to metabolically healthy groups. Gene expression profiling of MUNW and MHOW individuals resulted in differential expression of 7470 and 5864 genes, respectively. The gene ontology (GO) biological pathway analysis showed significant enrichment of the ‘JAK/STAT signaling pathway’ in MUNW and ‘The information-processing pathway at the IFN-β enhancer′ pathway in MHOW. The G6PC3 gene has genetically emerged as a new distinct gene showing its involvement in insulin resistance. Biochemical, as well as genetic analysis, revealed that MUNW and MHOW are the transition state between healthy and obese individuals with simply having fewer metabolic abnormalities. Moreover, it is possible that the state of obesity is a biological adaptation to cope up with the unhealthy parameters.

Expression profiles of interferon-related genes in cells infected with influenza A viruses or transiently transfected with plasmids encoding viral RNA polymerase

Influenza A viruses frequently change their genetic characteristics, which leads to the emergence of new viruses. Consequently, elucidation of the relationship between influenza A virus and host cells has a great importance to cope with viral infections. In this study, it was aimed to determine expression profiles of interferon response genes in human embryonic kidney 293 (HEK293) cells infected with human (A/WSN-H1N1) and avian influenza A viruses (duck/Pennsylvania/10218/84/H5N2) or transfected with plasmids encoding viral RdRP subunits and, to obtain clues about the genes that may be important for the viral pathogenesis. The HEK293 cells cultured in a 12-well plate were infected with influenza A viruses or transfected with plasmids encoding viral polymerase. Total RNA extraction and cDNA preparation were carried out with commercial kits. Qiagen 96-well-RT² Profiler PCR Array plates designated for interferons response genes were used for quantitation of the transcripts. The relative quantities of transcripts were normalized with STAT3 gen, and the results were evaluated. Quantitative RT-PCR results showed that there are substantial differences of the interferon response gene transcription in cells infected with viruses or transfected with plasmids. A higher number of interferon-related genes were found to be downregulated in the cells infected with DkPen compared to WSN. On the other hand, significant differences in the expression profiles of interferon response genes were observed in the cells expressing viral PA protein. In particular, avian influenza PA protein was found to cause more aggressive changes on the transcript levels. Human and avian influenza A viruses cause a substantial change in interferon response gene expression in HEK293 cells. However, a higher number of genes were downregulated in the cells infected with avian influenza DkPen compared to WSN. It has been also concluded that the viral PA protein is one of the important viral factors affecting the transcript level of host genes.

GEOlimma: differential expression analysis and feature selection using pre-existing microarray data

Background

Differential expression and feature selection analyses are essential steps for the development of accurate diagnostic/prognostic classifiers of complicated human diseases using transcriptomics data. These steps are particularly challenging due to the curse of dimensionality and the presence of technical and biological noise. A promising strategy for overcoming these challenges is the incorporation of pre-existing transcriptomics data in the identification of differentially expressed (DE) genes. This approach has the potential to improve the quality of selected genes, increase classification performance, and enhance biological interpretability. While a number of methods have been developed that use pre-existing data for differential expression analysis, existing methods do not leverage the identities of experimental conditions to create a robust metric for identifying DE genes.

Results

In this study, we propose a novel differential expression and feature selection method—GEOlimma—which combines pre-existing microarray data from the Gene Expression Omnibus (GEO) with the widely-applied Limma method for differential expression analysis. We first quantify differential gene expression across 2481 pairwise comparisons from 602 curated GEO Datasets, and we convert differential expression frequencies to DE prior probabilities. Genes with high DE prior probabilities show enrichment in cell growth and death, signal transduction, and cancer-related biological pathways, while genes with low prior probabilities were enriched in sensory system pathways. We then applied GEOlimma to four differential expression comparisons within two human disease datasets and performed differential expression, feature selection, and supervised classification analyses. Our results suggest that use of GEOlimma provides greater experimental power to detect DE genes compared to Limma, due to its increased effective sample size. Furthermore, in a supervised classification analysis using GEOlimma as a feature selection method, we observed similar or better classification performance than Limma given small, noisy subsets of an asthma dataset.

Conclusions

Our results demonstrate that GEOlimma is a more effective method for differential gene expression and feature selection analyses compared to the standard Limma method. Due to its focus on gene-level differential expression, GEOlimma also has the potential to be applied to other high-throughput biological datasets.

HDG-select: A novel GUI based application for gene selection and classification in high dimensional datasets

The selection and classification of genes is essential for the identification of related genes to a specific disease. Developing a user-friendly application with combined statistical rigor and machine learning functionality to help the biomedical researchers and end users is of great importance. In this work, a novel stand-alone application, which is based on graphical user interface (GUI), is developed to perform the full functionality of gene selection and classification in high dimensional datasets. The so-called HDG-select application is validated on eleven high dimensional datasets of the format CSV and GEO soft. The proposed tool uses the efficient algorithm of combined filter-GBPSO-SVM and it was made freely available to users. It was found that the proposed HDG-select outperformed other tools reported in literature and presented a competitive performance, accessibility, and functionality.

MicroRNAs in Chronic Kidney Disease: Four Candidates for Clinical Application

There are still major challenges regarding the early diagnosis and treatment of chronic kidney disease (CKD), which is in part due to the fact that its pathophysiology is very complex and not clarified in detail. The diagnosis of CKD commonly is made after kidney damage has occurred. This highlights the need for better mechanistic insight into CKD as well as improved clinical tools for both diagnosis and treatment. In the last decade, many studies have focused on microRNAs (miRs) as novel diagnostic tools or clinical targets. MiRs are small non-coding RNA molecules that are involved in post-transcriptional gene regulation and many have been studied in CKD. A wide array of pre-clinical and clinical studies have highlighted the potential role for miRs in the pathogenesis of hypertensive nephropathy, diabetic nephropathy, glomerulonephritis, kidney tubulointerstitial fibrosis, and some of the associated cardiovascular complications. In this review, we will provide an overview of the miRs studied in CKD, especially highlighting miR-103a-3p, miR-192-5p, the miR-29 family and miR-21-5p as these have the greatest potential to result in novel therapeutic and diagnostic strategies.

Electrospray: More than just an ionization source

Electrospraying (ES) is a potential-driven process of liquid atomization, which is employed in the field of analytical chemistry, particularly as an ionization technique for mass spectrometric analyses of biomolecules. In this review, we demonstrate the extraordinary versatility of the electrospray by overviewing the specifics and advanced applications of ES-based processing of low molecular mass compounds, biomolecules, polymers, nanoparticles, and cells. Thus, under suitable experimental conditions, ES can be used as a powerful tool for highly controlled deposition of homogeneous films or various patterns, which may sometimes even be organized into 3D structures. We also emphasize its capacity to produce composite materials including encapsulation systems and polymeric fibers. Further, we present several other, less common ES-based applications. This review provides an insight into the remarkable potential of ES, which can be very useful in the designing of innovative and unique strategies.