Microarray technology: beyond transcript profiling and genotype analysis

Cardiopulmonary bypass associated acute kidney injury: better understanding and better prevention

Cardiopulmonary bypass (CPB) is a common technique in cardiac surgery but is associated with acute kidney injury (AKI), which carries considerable morbidity and mortality. In this review, we explore the range and definition of CPB-associated AKI and discuss the possible impact of different disease recognition methods on research outcomes. Furthermore, we introduce the specialized equipment and procedural intricacies associated with CPB surgeries. Based on recent research, we discuss the potential pathogenesis of AKI that may result from CPB, including compromised perfusion and oxygenation, inflammatory activation, oxidative stress, coagulopathy, hemolysis, and endothelial damage. Finally, we explore current interventions aimed at preventing and attenuating renal impairment related to CPB, and presenting these measures from three perspectives: (1) avoiding CPB to eliminate the fundamental impact on renal function; (2) optimizing CPB by adjusting equipment parameters, optimizing surgical procedures, or using improved materials to mitigate kidney damage; (3) employing pharmacological or interventional measures targeting pathogenic factors.

Applications and advances in molecular diagnostics: revolutionizing non-tuberculous mycobacteria species and subspecies identification

Non-tuberculous mycobacteria (NTM) infections pose a significant public health challenge worldwide, affecting individuals across a wide spectrum of immune statuses. Recent epidemiological studies indicate rising incidence rates in both immunocompromised and immunocompetent populations, underscoring the need for enhanced diagnostic and therapeutic approaches. NTM infections often present with symptoms similar to those of tuberculosis, yet with less specificity, increasing the risk of misdiagnosis and potentially adverse outcomes for patients. Consequently, rapid and accurate identification of the pathogen is crucial for precise diagnosis and treatment. Traditional detection methods, notably microbiological culture, are hampered by lengthy incubation periods and a limited capacity to differentiate closely related NTM subtypes, thereby delaying diagnosis and the initiation of targeted therapies. Emerging diagnostic technologies offer new possibilities for the swift detection and accurate identification of NTM infections, playing a critical role in early diagnosis and providing more accurate and comprehensive information. This review delineates the current molecular methodologies for NTM species and subspecies identification. We critically assess the limitations and challenges inherent in these technologies for diagnosing NTM and explore potential future directions for their advancement. It aims to provide valuable insights into advancing the application of molecular diagnostic techniques in NTM infection identification.

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.

Identification of potential biomarkers and therapeutic targets for antineutrophil cytoplasmic antibody-associated glomerulonephritis

Exploring key genes for antineutrophil cytoplasmic antibody (ANCA)-associated glomerulonephritis (ANCA-GN) is of great significance. Through bioinformatics analysis, 79 immune protein-differentially expressed genes (IP-DEGs) were obtained. Six hub genes (PTPRC, CD86, TLR2, IL1B, CSF-1R, and CCL2) were identified and verified to be increased in ANCA-GN patients. Random forest algorithm and ROC analysis showed that CSF-1R was a potential biomarker. Plasma CSF-1R levels increased significantly in ANCA-GN-active patients compared with remission stage and control. Correlation analysis revealed that CSF-1R levels had positive relationship with serum creatinine and Birmingham scoring, while inversely correlated with eGFR. Multivariate analysis revealed that plasma CSF-1R were an independent poor prognostic variable for end-stage renal disease or death, after adjusting for age and gender (HR = 3.05, 95% CI = 1.45-6.43, p = 0.003). Overall, we revealed that the CSF-1R is related to disease activity and might be a vital gene associated with the pathogenesis of ANCA-GN.

Identification of PDK4 as Hub Gene for Diabetic Nephropathy Using Co-Expression Network Analysis

INTRODUCTION

Diabetic nephropathy (DN) is related to type 1 and type 2 diabetes. They are the leading cause of end-stage renal disease, but the underling specific pathogenesis of DN is not yet clear. Our study was conducted to explore how DN changed the transcriptome profiles in the kidney.

METHODS

The gene expression profile of microdissected glomeruli of 41 type 2 DN patients and 20 healthy controls were included. The sample dataset GSE96804 was obtained from the GEO database. Differentially expressed genes (DEGs) were analyzed in R with the limma package and the important modules were found by weighted gene co-expression network analysis (WGCNA) clustering. The modules were then analyzed based on Gene Ontology (GO) gene set enrichment analysis, and the hub genes were found out. We next validated the hub gene, PDK4, in a cell model of DN. We also constructed the PDK4-related PPI network to investigate the correlation between PDK4 expression and other genes.

RESULTS

Heatmap and volcano map were drawn to illustrate the mRNA expression profile of 1,204 DEGs in both samples of DN patients and the control group. Using WGCNA, we selected the blue module in which genes showed the strongest correlation with the phenotype and the smallest p value. We also identified PDK4 as a hub gene. PDK4 expression was upregulated in human diabetic kidney tissue. Moreover, PDK4 was speculated to play a role in glomerular basement membrane development and kidney development according to the enrichment of functions and signaling pathways. Furthermore, PDK4 and two key genes GSTA2 and G6PC protein expression were verified highly expressed in the cell model of DN.

CONCLUSION

During the pathogenesis of DN, many genes may change expression in a coordinated manner. The discovery of PDK4 as key gene using WGCNA is of great significance for the development of new treatment strategies to block the development of DN.

PSMD8 can serve as potential biomarker and therapeutic target of the PSMD family in ovarian cancer: based on bioinformatics analysis and in vitro validation

BACKGROUND

The ubiquity-proteasome system is an indispensable mechanism for regulating intracellular protein degradation, thereby affecting human antigen processing, signal transduction, and cell cycle regulation. We used bioinformatics database to predict the expression and related roles of all members of the PSMD family in ovarian cancer. Our findings may provide a theoretical basis for early diagnosis, prognostic assessment, and targeted therapy of ovarian cancer.

METHODS

GEPIA, cBioPortal, and Kaplan-Meier Plotter databases were used to analyze the mRNA expression levels, gene variation, and prognostic value of PSMD family members in ovarian cancer. PSMD8 was identified as the member with the best prognostic value. The TISIDB database was used to analyze the correlation between PSMD8 and immunity, and the role of PSMD8 in ovarian cancer tissue was verified by immunohistochemical experiments. The relationship of PSMD8 expression with clinicopathological parameters and survival outcomes of ovarian cancer patients was analyzed. The effects of PSMD8 on malignant biological behaviors of invasion, migration, and proliferation of ovarian cancer cells were studied by in vitro experiments.

RESULTS

The expression levels of PSMD8/14 mRNA in ovarian cancer tissues were significantly higher than those in normal ovarian tissues, and the expression levels of PSMD2/3/4/5/8/11/12/14 mRNA were associated with prognosis. Up-regulation of PSMD4/8/14 mRNA expression was associated with poor OS, and the up-regulation of PSMD2/3/5/8 mRNA expression was associated with poor PFS in patients with ovarian serous carcinomas. Gene function and enrichment analysis showed that PSMD8 is mainly involved in biological processes such as energy metabolism, DNA replication, and protein synthesis. Immunohistochemical experiments showed that PSMD8 was mainly expressed in the cytoplasm and the expression level was correlated with FIGO stage. Patients with high PSMD8 expression had poor prognosis. Overexpression of PSMD8 significantly enhanced the proliferation, migration, and invasion abilities in ovarian cancer cells.

CONCLUSION

We observed different degrees of abnormal expression of members of PSMD family in ovarian cancer. Among these, PSMD8 was significantly overexpressed in ovarian malignant tissue, and was associated with poor prognosis. PSMDs, especially PSMD8, can serve as potential diagnostic and prognostic biomarkers and therapeutic targets in ovarian cancer.

The Emerging Burden of Genetic Instability and Mutation in Melanoma: Role of Molecular Mechanisms

Melanoma is a severe skin cancer affecting thousands of people and a growing public health concern worldwide. The potential hallmarks of melanoma are genetic instability and mutation (GIAM), which are driving mechanisms for phenotypic variation and adaptation in melanoma. In metastatic melanoma, DNA repair-associated genes are frequently expressed at higher levels than in primary cancers, suggesting melanoma cells rely on genetic stability to spread distantly. The tumour microenvironment is affected by genomic instability and melanoma mutation (GIMM), which plays significant roles in developing GIMM and their contributions to the overall disease burden. The GIAM is the crucial vulnerability of cancer cells, determining their sensitivity to harmful treatments, including radiation and many chemotherapeutics. The high incidence of melanoma is typically associated with genetic modifications, and several clinical and genetic interventions have been critical in easing the burden.

DNA-POINT: DNA Patterning of Optical Imprint for Nanomaterials Topography

Engineering well-defined scale-spanning structures through transfer of diverse biomolecules and materials to a surface is of tremendous interest in life sciences research yet remains profoundly challenging. Here, we report a novel method, termed as DNA patterning of optical imprint for nanomaterials topography (DNA-POINT), for rapid photopatterning of large area, geometrically complex surfaces via light-responsive DNA. Our method employs top-down multiphoton-driven patterning of azobenzene-modified DNA strands, offering precise position control of molecules and nanoparticles along the axial plane and a template for bottom-up self-assembly of multiple layers of different chemical composition along the vertical plane. We demonstrate the surface patterning of plasmonic gold nanoparticles, fluorophore-labeled oligonucleotides, and multiple layers consisting of molecule-nanoparticle hybrid patterns into preconceived shapes without compromising on the functionality of the biomolecules. Furthermore, we exhibit scanning mode operation of DNA-POINT, thereby paving the way for maskless and cleanroom-free fast fabrication of biochips for high-throughput diagnostics and biosensing applications.

Short and long-term effect of dexamethasone on the transcriptome profile of primary human trabecular meshwork cells in vitro

In the quest of identifying newer molecular targets for the management of glucocorticoid-induced ocular hypertension (GC-OHT) and glaucoma (GCG), several microarray studies have attempted to investigate the genome-wide transcriptome profiling of primary human trabecular meshwork (TM) cells in response to dexamethasone (DEX). However, no studies are reported so far to demonstrate the temporal changes in the expression of genes in the cultured human TM cells in response to DEX treatment. Therefore, in the present study, the time-dependent changes in the genome-wide expression of genes in primary human TM cells after short (16 hours: 16 h) and long exposure (7 days: 7 d) of DEX was investigated using RNA sequencing. There were 199 (118 up-regulated; 81 down-regulated) and 525 (119 up-regulated; 406 down-regulated) DEGs in 16 h and 7 d treatment groups respectively. The unique genes identified in 16 h and 7 d treatment groups were 152 and 478 respectively. This study found a distinct gene signature and pathways between two treatment regimes. Longer exposure of DEX treatment showed a dys-regulation of Wnt and Rap1 signaling and so highlighted potential therapeutic targets for pharmacological management of GC-OHT/glaucoma.

A Comparative Genome-Wide Transcriptome Analysis of Glucocorticoid Responder and Non-Responder Primary Human Trabecular Meshwork Cells

Aim: To investigate genes and pathways involved in differential glucocorticoid (GC) responsiveness in human trabecular meshwork (HTM) cells using RNA sequencing. Methods: Using paired human donor eyes, human organ-cultured anterior segment (HOCAS) was established in one eye to characterize GC responsiveness based on intra ocular pressure (IOP) change and, in the other eye, primary HTM cell culture was established. For RNA sequencing, total RNA extracted from GC-responder (GC-R) and non-responder (GC-NR) cells after dexamethasone (DEX) or ethanol (ETH) treatment for 7d was used. Differentially expressed genes (DEGs) were compared among five groups and validated. Results: In total, 616 and 216 genes were identified as significantly dysregulated in Group #1 and #2 (#1: ETH vs. DEX-treated GC-R; #2: ETH vs. DEX-treated GC-NR), respectively. Around 80 genes were commonly dysregulated in Group #3 (overlapping DEGs between #1 and #2), whereas 536 and 136 genes were uniquely expressed in GC-R (#4) and GC-NR HTM (#5) cells, respectively. Pathway analysis revealed that WNT signaling, drug metabolism cytochrome p450, cell adhesion, TGF-β signaling, and MAPK signaling were associated with GC responsiveness. Conclusion: This is the first study reporting distinct gene signatures and their associated pathways for GC-R and GC-NR HTM cells. WNT and MAPK signaling are potential therapeutic targets for the management of GC-induced glaucoma.

RNA Microarray-Based Comparison of Innate Immune Phenotypes between Human THP-1 Macrophages Stimulated with Two BCG Strains

Currently, the only available vaccine against tuberculosis is Mycobacterium bovis Bacille Calmette-Guérin (BCG). Pulmonary tuberculosis protection provided by the vaccine varies depending on the strain, the patient’s age and the evaluated population. Although the adaptive immune responses induced by different BCG strains have been widely studied, little conclusive data is available regarding innate immune responses, especially in macrophages. Here, we aimed to characterize the innate immune responses of human THP-1-derived macrophages at the transcriptional level following a challenge with either the BCG Mexico (M.BCG) or Phipps (P.BCG) strains. After a brief in vitro characterization of the bacterial strains and the innate immune responses, including nitric oxide production and cytokine profiles, we analyzed the mRNA expression patterns and performed pathway enrichment analysis using RNA microarrays. Our results showed that multiple biological processes were enriched, especially those associated with innate inflammatory and antimicrobial responses, including tumor necrosis factor (TNF)-α, type I interferon (IFN-I) and IFN-γ. However, four DEGs were identified in macrophages infected with M.BCG compared to P. BCG. These findings indicated the proinflammatory stimulation of macrophages induced by both BCG strains, at the cytokine level and in terms of gene expression, suggesting a differential expression pattern of innate immune transcripts depending on the mycobacterial strain.

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.

Lipid-related protein NECTIN2 is an important marker in the progression of carotid atherosclerosis: An intersection of clinical and basic studies

Background:

The nectin cell adhesion molecule 2 (NECTIN2) protein is a cell adhesion molecule involved in lipid metabolism. We aimed to explore the potential role of NECTIN2 in carotid atherosclerosis (CA).

Materials and Methods:

Patients who underwent carotid endarterectomy (CEA) at the First Affiliated Hospital of Zhengzhou University were enrolled in this study. APOE-/- rats fed western or normal diet were used to model early pathological changes in CA. The relationship between patients’ lipid indices and plaque severity was assessed using ordinal regression analysis. Mendelian randomisation (MR) analysis was used to determine the causal links between low-density lipoprotein cholesterol (LDL-C) and atherosclerosis. After matching analysis of the single-cell transcriptome and microarray data of carotid plaques, NECTIN2 was identified as a key factor affecting CA. The importance of NECTIN2 was further verified by immunofluorescence staining of CEA and APOE-/- rat specimens.

Results:

A total of 108 patients were included. The traditional lipid indices did not correlate significantly with the plaque severity (P > 0.05). NECTIN2 provided a strong causal link between LDL-C level and CA (MR effect size >0). Deep-sequencing data illustrated that NECTIN2 expression was cell specific. In early-stage CA, NECTIN2 expression was increased in endothelial cells; however, in advanced-stage CA, NECTIN2 was overexpressed in macrophages located in fibrous caps. APOE-/- rat carotid artery and human carotid plaques modelled the entire atherosclerotic process, showing an upregulation of NECTIN2 expression in CA.

Conclusions:

Lipid-related protein NECTIN2 is a potential marker in CA progression and can potentially be a new therapeutic target for clinical prevention.

Spatial mapping of cancer tissues by OMICS technologies

Spatial mapping of heterogeneity in gene expression in cancer tissues can improve our understanding of cancers and help in the rapid detection of cancers with high accuracy and reliability. Significant advancements have been made in recent years in OMICS technologies, which possess the strong potential to be applied in the spatial mapping of biopsy tissue samples and their molecular profiling to a single-cell level. The clinical application of OMICS technologies in spatial profiling of cancer tissues is also advancing. The current review presents recent advancements and prospects of applying OMICS technologies to the spatial mapping of various analytes in cancer tissues. We benchmark the current state of the art in the field to advance existing OMICS technologies for high throughput spatial profiling. The factors taken into consideration include spatial resolution, types of biomolecules, number of different biomolecules that can be detected from the same assay, labeled versus label-free approaches, and approximate time required for each assay. Further advancements are still needed for the widespread application of OMICs technologies in performing fast and high throughput spatial mapping of cancer tissues as well as their effective use in research and clinical applications.

Prognostic and immune infiltration signatures of proteasome 26S subunit, non-ATPase (PSMD) family genes in breast cancer patients

The complexity of breast cancer includes many interacting biological processes that make it difficult to find appropriate therapeutic treatments. Therefore, identifying potential diagnostic and prognostic biomarkers is urgently needed. Previous studies demonstrated that 26S proteasome delta subunit, non-ATPase (PSMD) family members significantly contribute to the degradation of damaged, misfolded, abnormal, and foreign proteins. However, transcriptional expressions of PSMD family genes in breast cancer still remain largely unexplored. Consequently, we used a holistic bioinformatics approach to explore PSMD genes involved in breast cancer patients by integrating several high-throughput databases, including The Cancer Genome Atlas (TCGA), cBioPortal, Oncomine, and Kaplan-Meier plotter. These data demonstrated that PSMD1, PSMD2, PSMD3, PSMD7, PSMD10, PSMD12, and PSMD14 were expressed at significantly higher levels in breast cancer tissue compared to normal tissues. Notably, the increased expressions of PSMD family genes were correlated with poor prognoses of breast cancer patients, which suggests their roles in tumorigenesis. Meanwhile, network and pathway analyses also indicated that PSMD family genes were positively correlated with ubiquinone metabolism, immune system, and cell-cycle regulatory pathways. Collectively, this study revealed that PSMD family members are potential prognostic biomarkers for breast cancer progression and possible promising clinical therapeutic targets.

Fabrication approaches for high-throughput and biomimetic disease modeling

There is often a tradeoff between in vitro disease modeling platforms that capture pathophysiologic complexity and those that are amenable to high-throughput fabrication and analysis. However, this divide is closing through the application of a handful of fabrication approaches-parallel fabrication, automation, and flow-driven assembly-to design sophisticated cellular and biomaterial systems. The purpose of this review is to highlight methods for the fabrication of high-throughput biomaterial-based platforms and showcase examples that demonstrate their utility over a range of throughput and complexity. We conclude with a discussion of future considerations for the continued development of higher-throughput in vitro platforms that capture the appropriate level of biological complexity for the desired application. STATEMENT OF SIGNIFICANCE: There is a pressing need for new biomedical tools to study and understand disease. These platforms should mimic the complex properties of the body while also permitting investigation of many combinations of cells, extracellular cues, and/or therapeutics in high-throughput. This review summarizes emerging strategies to fabricate biomimetic disease models that bridge the gap between complex tissue-mimicking microenvironments and high-throughput screens for personalized medicine.

Battling Chemoresistance in Cancer: Root Causes and Strategies to Uproot Them

With nearly 10 million deaths, cancer is the leading cause of mortality worldwide. Along with major key parameters that control cancer treatment management, such as diagnosis, resistance to the classical and new chemotherapeutic reagents continues to be a significant problem. Intrinsic or acquired chemoresistance leads to cancer recurrence in many cases that eventually causes failure in the successful treatment and death of cancer patients. Various determinants, including tumor heterogeneity and tumor microenvironment, could cause chemoresistance through a diverse range of mechanisms. In this review, we summarize the key determinants and the underlying mechanisms by which chemoresistance appears. We then describe which strategies have been implemented and studied to combat such a lethal phenomenon in the management of cancer treatment, with emphasis on the need to improve the early diagnosis of cancer complemented by combination therapy.

Prognoses and genomic analyses of proteasome 26S subunit, ATPase (PSMC) family genes in clinical breast cancer

Breast cancer is a complex disease, and several processes are involved in its development. Therefore, potential therapeutic targets need to be discovered for these patients. Proteasome 26S subunit, ATPase gene (PSMC) family members are well reported to be involved in protein degradation. However, their roles in breast cancer are still unknown and need to be comprehensively researched. Leveraging publicly available databases, such as cBioPortal and Oncomine, for high-throughput transcriptomic profiling to provide evidence-based targets for breast cancer is a rapid and robust approach. By integrating the aforementioned databases with the Kaplan–Meier plotter database, we investigated potential roles of six PSMC family members in breast cancer at the messenger RNA level and their correlations with patient survival. The present findings showed significantly higher expression profiles of PSMC2, PSMC3, PSMC4, PSMC5, and PSMC6 in breast cancer compared to normal breast tissues. Besides, positive correlations were also revealed between PSMC family genes and ubiquinone metabolism, cell cycle, and cytoskeletal remodeling. Meanwhile, we discovered that high levels of PSMC1, PSMC3, PSMC4, PSMC5, and PSMC6 transcripts were positively correlated with poor survival, which likely shows their importance in breast cancer development. Collectively, PSMC family members have the potential to be novel and essential prognostic biomarkers for breast cancer development.

Identification of Dipeptidyl Peptidase (DPP) Family Genes in Clinical Breast Cancer Patients via an Integrated Bioinformatics Approach

Breast cancer is a heterogeneous disease involving complex interactions of biological processes; thus, it is important to develop therapeutic biomarkers for treatment. Members of the dipeptidyl peptidase (DPP) family are metalloproteases that specifically cleave dipeptides. This family comprises seven members, including DPP3, DPP4, DPP6, DPP7, DPP8, DPP9, and DPP10; however, information on the involvement of DPPs in breast cancer is lacking in the literature. As such, we aimed to study their roles in this cancerous disease using publicly available databases such as cBioportal, Oncomine, and Kaplan–Meier Plotter. These databases comprise comprehensive high-throughput transcriptomic profiles of breast cancer across multiple datasets. Furthermore, together with investigating the messenger RNA expression levels of these genes, we also aimed to correlate these expression levels with breast cancer patient survival. The results showed that DPP3 and DPP9 had significantly high expression profiles in breast cancer tissues relative to normal breast tissues. High expression levels of DPP3 and DPP4 were associated with poor survival of breast cancer patients, whereas high expression levels of DPP6, DPP7, DPP8, and DPP9 were associated with good prognoses. Additionally, positive correlations were also revealed of DPP family genes with the cell cycle, transforming growth factor (TGF)-beta, kappa-type opioid receptor, and immune response signaling, such as interleukin (IL)-4, IL6, IL-17, tumor necrosis factor (TNF), and interferon (IFN)-alpha/beta. Collectively, DPP family members, especially DPP3, may serve as essential prognostic biomarkers in breast cancer.

MP3RNA-seq: Massively parallel 3' end RNA sequencing for high-throughput gene expression profiling and genotyping

Transcriptome deep sequencing (RNA-seq) has become a routine method for global gene expression profiling. However, its application to large-scale experiments remains limited by cost and labor constraints. Here we describe a massively parallel 3' end RNA-seq (MP3RNA-seq) method that introduces unique sample barcodes during reverse transcription to permit sample pooling immediately following this initial step. MP3RNA-seq allows for handling of hundreds of samples in a single experiment, at a cost of about $6 per sample for library construction and sequencing. MP3RNA-seq is effective for not only high-throughput gene expression profiling, but also genotyping. To demonstrate its utility, we applied MP3RNA-seq to 477 double haploid lines of maize. We identified 19,429 genes expressed in at least 50% of the lines and 35,836 high-quality single nucleotide polymorphisms for genotyping analysis. Armed with these data, we performed expression and agronomic trait quantitative trait locus (QTL) mapping and identified 25,797 expression QTLs for 15,335 genes and 21 QTLs for plant height, ear height, and relative ear height. We conclude that MP3RNA-seq is highly reproducible, accurate, and sensitive for high-throughput gene expression profiling and genotyping, and should be generally applicable to most eukaryotic species.

Highly Sensitive and Multiplexed In Situ RNA Profiling with Cleavable Fluorescent Tyramide

Understanding the composition, regulation, and function of complex biological systems requires tools that quantify multiple transcripts at their native cellular locations. However, the current multiplexed RNA imaging technologies are limited by their relatively low sensitivity or specificity, which hinders their applications in studying highly autofluorescent tissues, such as formalin-fixed paraffin-embedded (FFPE) tissues. To address this issue, here we develop a multiplexed in situ RNA profiling approach with a high sensitivity and specificity. In this approach, transcripts are first hybridized by target-specific oligonucleotide probes in pairs. Only when these two independent probes hybridize to the target in tandem will the subsequent signal amplification by oligonucleotide hybridization occur. Afterwards, horseradish peroxidase (HRP) is applied to further amplify the signal and stain the target with cleavable fluorescent tyramide (CFT). After imaging, the fluorophores are chemically cleaved and the hybridized probes are stripped by DNase and formamide. Through cycles of RNA staining, fluorescence imaging, signal cleavage, and probe stripping, many different RNA species can be profiled at the optical resolution. In applying this approach, we demonstrated that multiplexed in situ RNA analysis can be successfully achieved in both fixed, frozen, and FFPE tissues.

A fluorescent microarray platform based on catalytic hairpin assembly for MicroRNAs detection

The DNA microarray has distinctive advantages of high-throughput and less complicated operations, but tends to have a relatively low sensitivity. Catalytic hairpin assembly (CHA) is one of the most promising enzyme-free, isothermal DNA circuit for high efficient signal amplification. Here, a microarray-based catalytic hairpin assembly (mi-CHA) biosensing method has been developed to detect various miRNAs in a single test simultaneously. The target miRNA can trigger conformational transformations of hairpin-structured DNA probes on the chip surface and lead to the specific signal amplification. A significant advantage of this approach is that each duplex produced by the solid-phase CHA will be immobilized on the certain location of the chip and release fluorescent signal via the universal domain, eliminating the requirement of different fluorophores. This method has manifested a high detection sensitivity of human cancer-associated miRNAs (miR-21 and miR-155) down to 1.33 fM and promised a high specificity to distinguish single-base mismatches. Furthermore, the practicability of this method was demonstrated by analyzing target miRNAs in human serum and cancer cells. The experimental results suggest that the proposed method has high-throughput analytical potential and could be applied to many other clinical diagnosis.

K-seq, an affordable, reliable, and open Klenow NGS-based genotyping technology

BACKGROUND

K-seq, a new genotyping methodology based on the amplification of genomic regions using two steps of Klenow amplification with short oligonucleotides, followed by standard PCR and Illumina sequencing, is presented. The protocol was accompanied by software developed to aid with primer set design.

RESULTS

As the first examples, K-seq in species as diverse as tomato, dog and wheat was developed. K-seq provided genetic distances similar to those based on WGS in dogs. Experiments comparing K-seq and GBS in tomato showed similar genetic results, although K-seq had the advantage of finding more SNPs for the same number of Illumina reads. The technology reproducibility was tested with two independent runs of the tomato samples, and the correlation coefficient of the SNP coverages between samples was 0.8 and the genotype match was above 94%. K-seq also proved to be useful in polyploid species. The wheat samples generated specific markers for all subgenomes, and the SNPs generated from the diploid ancestors were located in the expected subgenome with accuracies greater than 80%.

CONCLUSION

K-seq is an open, patent-unencumbered, easy-to-set-up, cost-effective and reliable technology ready to be used by any molecular biology laboratory without special equipment in many genetic studies.

Atg16L1 as a Novel Biomarker and Autophagy Gene for Diabetic Retinopathy

OBJECTIVE

Accumulating evidence suggests the critical role of autophagy in the pathogenesis of diabetic retinopathy (DR). In the current study, we aim to identify autophagy genes involved in DR via microarray analyses.

METHODS

Gene microarrays were performed to identify differentially expressed lncRNAs/mRNAs between normal and DR retinas. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses of lncRNA-coexpressed mRNAs were used to determine the related pathological pathways and biological modules. Real-time polymerase chain reactions (PCR) were conducted to validate the microarray analyses.

RESULTS

A total of 2474 significantly dysregulated lncRNAs and 959 differentially expressed mRNAs were identified in the retina of DR. Based upon Signalnet analysis, Bcl2, Gabarapl2, Atg4c, and Atg16L1 participated the process of cell death in DR. Moreover, real-time PCR revealed significant upregulation of Atg16L1.

CONCLUSION

This study indicated the importance and potential role of Atg16L1, one of the autophagy genes, as a biomarker in DR development and progression.

Recent progress in non-native nucleic acid modifications

While Nature harnesses RNA and DNA to store, read and write genetic information, the inherent programmability, synthetic accessibility and wide functionality of these nucleic acids make them attractive tools for use in a vast array of applications.

A flexible, interpretable, and accurate approach for imputing the expression of unmeasured genes

While there are >2 million publicly-available human microarray gene-expression profiles, these profiles were measured using a variety of platforms that each cover a pre-defined, limited set of genes. Therefore, key to reanalyzing and integrating this massive data collection are methods that can computationally reconstitute the complete transcriptome in partially-measured microarray samples by imputing the expression of unmeasured genes. Current state-of-the-art imputation methods are tailored to samples from a specific platform and rely on gene-gene relationships regardless of the biological context of the target sample. We show that sparse regression models that capture sample-sample relationships (termed SampleLASSO), built on-the-fly for each new target sample to be imputed, outperform models based on fixed gene relationships. Extensive evaluation involving three machine learning algorithms (LASSO, k-nearest-neighbors, and deep-neural-networks), two gene subsets (GPL96–570 and LINCS), and multiple imputation tasks (within and across microarray/RNA-seq datasets) establishes that SampleLASSO is the most accurate model. Additionally, we demonstrate the biological interpretability of this method by showing that, for imputing a target sample from a certain tissue, SampleLASSO automatically leverages training samples from the same tissue. Thus, SampleLASSO is a simple, yet powerful and flexible approach for harmonizing large-scale gene-expression data.

Highly Multiplexed Single-Cell In Situ RNA and DNA Analysis by Consecutive Hybridization

The ability to comprehensively profile nucleic acids in individual cells in their natural spatial contexts is essential to advance our understanding of biology and medicine. Here, we report a novel method for spatial transcriptomics and genomics analysis. In this method, every nucleic acid molecule is detected as a fluorescent spot at its natural cellular location throughout the cycles of consecutive fluorescence in situ hybridization (C-FISH). In each C-FISH cycle, fluorescent oligonucleotide probes hybridize to the probes applied in the previous cycle, and also introduce the binding sites for the next cycle probes. With reiterative cycles of hybridization, imaging and photobleaching, the identities of the varied nucleic acids are determined by their unique color sequences. To demonstrate the feasibility of this method, we show that transcripts or genomic loci in single cells can be unambiguously quantified with 2 fluorophores and 16 C-FISH cycles or with 3 fluorophores and 9 C-FISH cycles. Without any error correction, the error rates obtained using the raw data are close to zero. These results indicate that C-FISH potentially enables tens of thousands (2¹⁶ = 65,536 or 3⁹ = 19,683) of different transcripts or genomic loci to be precisely profiled in individual cells in situ.

Towards a Systems Immunology Approach to Unravel Responses to Cancer Immunotherapy

Immunotherapy, particularly immune checkpoint blockade and chimeric antigen receptor (CAR)-T cells, holds a great promise against cancer. These treatments have markedly improved survival in solid as well as in hematologic tumors previously considered incurable. However, durable responses occur in a fraction of patients, and existing biomarkers (e.g. PD-L1) have shown limited prediction power. This scenario highlights the need to dissect the complex interplay between immune and tumor cells to identify reliable biomarkers of response to be used for patients’ selection. In this context, systems immunology represents indeed the new frontier to address important clinical challenges in biomarker discovery. Through the integration of multiple layers of data obtained with several high-throughput approaches, systems immunology may give insights on the vast range of inter-individual differences and on the influences of genes and factors that cooperatively shape the individual immune response to a given treatment. In this Mini Review, we give an overview of the current high-throughput methodologies, including genomics, epigenomics, transcriptomics, metabolomics, proteomics, and multi-parametric phenotyping suitable for systems immunology as well as on the key steps of data integration and biological interpretation. Additionally, we review recent studies in which multi-omics technologies have been used to characterize mechanisms of response and to identify powerful biomarkers of response to checkpoint inhibitors, CAR-T cell therapy, dendritic cell-based and peptide-based cancer vaccines. We also highlight the need of favoring the collaboration of researchers with complementary expertise and of integrating multi-omics data into biological networks with the final goal of developing accurate markers of therapeutic response.

RNA sequence analysis of dermal papilla cells' regeneration in 3D culture

It is well known that dermal papilla cells (DPCs) are crucial for hair follicle growth and regeneration. However, dermal papilla cells in 2D culture could lose their ability of regeneration after several passage intervals. As opposed to DPCs in 2D culture, the DPCs in 3D culture could passage extensively. However, the molecular mechanisms of DPCs’ regeneration in 3D culture remain unclear. Accordingly, gene sequencing is recommended for the investigation of hair regeneration between 2D and 3D culture, the three groups were established including DPCs in passage 2 in 2D culture, DPCs in passage 8 in 2D culture and DPCs in passage 8 in 3D culture. The differentially expressed genes (DEGs) were identified using the Venn diagram of these three groups, which included 1642 known and 359 novel genes, respectively. A total of 1642 known genes were used for Gene Ontology (GO), Kyoto Gene, Genomic Encyclopedia (KEGG) pathway enrichment and protein‐protein interaction (PPI) analyses, respectively. The functions and pathways of DEGs were enriched in biological regulation, signal transduction and immune system, etc. The key module and the top 10 hub genes (IL1B, CXCL12, HGF, EGFR, APP, CCL2, PTGS2, MMP9, NGF and SPP1) were also identified using the Cytoscape application. Furthermore, the qRT‐PCR results of the three groups validated that the hub genes were crucial for hair growth. In conclusion, the ten identified hub genes and related pathways in the current study can be used to understand the molecular mechanism of hair growth, and those provided a possibility for hair regeneration.

Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscape

Single-cell RNA sequencing is revealing an unexpectedly large degree of heterogeneity in gene expression levels across cell populations. However, little is known on the functional consequences of this heterogeneity and the contribution of individual cell fate decisions to the collective behavior of the tissues these cells are part of. Here, we use mechanistic modeling of signaling circuits, which reveals a complex functional landscape at single-cell level. Different clusters of neoplastic glioblastoma cells have been defined according to their differences in signaling circuit activity profiles triggering specific cancer hallmarks, which suggest different functional strategies with distinct degrees of aggressiveness. Moreover, mechanistic modeling of effects of targeted drug inhibitions at single-cell level revealed, how in some cells, the substitution of VEGFA, the target of bevacizumab, by other expressed proteins, like PDGFD, KITLG and FGF2, keeps the VEGF pathway active, insensitive to the VEGFA inhibition by the drug. Here, we describe for the first time mechanisms that individual cells use to avoid the effect of a targeted therapy, providing an explanation for the innate resistance to the treatment displayed by some cells. Our results suggest that mechanistic modeling could become an important asset for the definition of personalized therapeutic interventions.

Gene expression-based analysis identified NTNG1 and HGF as biomarkers for diabetic kidney disease

Diabetic kidney disease (DKD) is a leading cause of end-stage renal disease. Because the molecular mechanisms of DKD are not fully understood, exploration of hub genes and the mechanisms underlying this disease are essential for elucidating the pathogenesis and progression of DKD. Accordingly, in this study, we performed an analysis of gene expression in DKD. The differentially expressed genes (DEGs) included 39 upregulated genes and 113 downregulated genes in the GSE30528 dataset and 127 upregulated genes and 18 downregulated genes in the GSE30529 dataset. Additionally, functional analyses were performed to determine the roles of DEGs using glomeruli samples from patients with DKD and healthy controls from the GSE30528 dataset and using tubule samples from patients with DKD and healthy controls from the GSE30529 dataset. These DEGs were enriched in pathways such as the Wnt signaling pathway, metabolic pathways, and the mammalian target of rapamycin signaling pathway in the GSE30528 dataset and the longevity regulating pathway and Ras signaling pathway in the GSE30529 dataset. Moreover, a protein-protein interaction network was constructed using the identified DEGs, and hub gene analysis was performed. Furthermore, correlation analyses between key genes and pathological characteristics of DKD indicated that CCR4, NTNG1, HGF and ISL1 are related to DKD, and NTNG1 and HGF may server as diagnostic biomarkers in DKD using the receiver-operator characteristic (ROC) curve. Collectively, our findings established 2 reliable biomarkers for DKD.

Integration of Multi-omics Data for Expression Quantitative Trait Loci (eQTL) Analysis and eQTL Epistasis

Expression quantitative trait loci (eQTL) mapping studies identify genetic loci that regulate gene expression. eQTL mapping studies can capture gene regulatory interactions and provide insight into the genetic mechanism of biological systems. Recently, the integration of multi-omics data, such as single-nucleotide polymorphisms (SNPs), copy number variations (CNVs), DNA methylation, and gene expression, plays an important role in elucidating complex biological systems, since biological systems involve a sequence of complex interactions between various biological processes. This chapter introduces multi-omics data that have been used in many eQTL studies and integrative methodologies that incorporate multi-omics data for eQTL studies. Furthermore, we describe a statistical approach that can detect nonlinear causal relationships between eQTLs, called eQTL epistasis, and its importance.

Gene signatures and prognostic analyses of the Tob/BTG pituitary tumor-transforming gene (PTTG) family in clinical breast cancer patients

Breast cancer is the most common cancer type in females, and exploring the mechanisms of disease progression is playing a crucial role in the development of potential therapeutics. Pituitary tumor-transforming gene (PTTG) family members are well documented to be involved in cell-cycle regulation and mitosis, and contribute to cancer development by their involvement in cellular transformation in several tumor types. The critical roles of PTTG family members as crucial transcription factors in diverse types of cancers are recognized, but how they regulate breast cancer development still remains mostly unknown. Meanwhile, a holistic genetic analysis exploring whether PTTG family members regulate breast cancer progression via the cell cycle as well as the energy metabolism-related network is lacking. To comprehensively understand the messenger RNA expression profiles of PTTG proteins in breast cancer, we herein conducted a high-throughput screening approach by integrating information from various databases such as Oncomine, Kaplan-Meier Plotter, Metacore, ClueGo, and CluePedia. These useful databases and tools provide expression profiles and functional analyses. The present findings revealed that PTTG1 and PTTG3 are two important genes with high expressions in breast cancer relative to normal breast cells, implying their unique roles in breast cancer progression. Results of our coexpression analysis demonstrated that PTTG family genes were positively correlated with thiamine triphosphate (TTP), deoxycytidine triphosphate (dCTP) metabolic, glycolysis, gluconeogenesis, and cell-cycle related pathways. Meanwhile, through Cytoscape analyzed indicated that in addition to the metastasis markers AURKA, AURKB, and NDC80, many of the kinesin superfamily (KIF) members including KIFC1, KIF2C, KIF4A, KIF14, KIF20A, KIF23, were also correlated with PTTG family transcript expression. Finally, we revealed that high levels of PTTG1 and PTTG3 transcription predicted poor survival, which provided useful insights into prospective research of cancer associated with the PTTG family. Therefore, these members of the PTTG family would serve as distinct and essential prognostic biomarkers in breast cancer.

Global Transcriptional Analysis of Nontransformed Human Intestinal Epithelial Cells (FHs 74 Int) after Exposure to Selected Drinking Water Disinfection By-Products

BACKGROUND

Drinking water disinfection inadvertently leads to the formation of numerous disinfection by-products (DBPs), some of which are cytotoxic, mutagenic, genotoxic, teratogenic, and potential carcinogens both in vitro and in vivo.

OBJECTIVES

We investigated alterations to global gene expression (GE) in nontransformed human small intestine epithelial cells (FHs 74 Int) after exposure to six brominated and two chlorinated DBPs: bromoacetic acid (BAA), bromoacetonitrile (BAN), 2,6-dibromo-p-benzoquinone (DBBQ), bromoacetamide (BAM), tribromoacetaldehyde (TBAL), bromate (BrO3-), trichloroacetic acid (TCAA), and trichloroacetaldehyde (TCAL).

METHODS

Using whole-genome cDNA microarray technology (Illumina), we examined GE in nontransformed human cells after 4h exposure to DBPs at predetermined equipotent concentrations, identified significant changes in gene expression (p≤0.01), and investigated the relevance of these genes to specific toxicity pathways via gene and pathway enrichment analysis.

RESULTS

Genes related to activation of oxidative stress-responsive pathways exhibited fewer alterations than expected based on prior work, whereas all DBPs induced notable effects on transcription of genes related to immunity and inflammation.

DISCUSSION

Our results suggest that alterations to genes associated with immune and inflammatory pathways play an important role in the potential adverse health effects of exposure to DBPs. The interrelationship between these pathways and the production of reactive oxygen species (ROS) may explain the common occurrence of oxidative stress in other studies exploring DBP toxicity. Finally, transcriptional changes and shared induction of toxicity pathways observed for all DBPs caution of additive effects of mixtures and suggest further assessment of adverse health effects of mixtures is warranted. https://doi.org/10.1289/EHP4945.

Single Primer Enrichment Technology (SPET) for High-Throughput Genotyping in Tomato and Eggplant Germplasm

Single primer enrichment technology (SPET) is a new, robust, and customizable solution for targeted genotyping. Unlike genotyping by sequencing (GBS), and like DNA chips, SPET is a targeted genotyping technology, relying on the sequencing of a region flanking a primer. Its reliance on single primers, rather than on primer pairs, greatly simplifies panel design, and allows higher levels of multiplexing than PCR-based genotyping. Thanks to the sequencing of the regions surrounding the target SNP, SPET allows the discovery of thousands of closely linked, novel SNPs. In order to assess the potential of SPET for high-throughput genotyping in plants, a panel comprising 5k target SNPs, designed both on coding regions and introns/UTRs, was developed for tomato and eggplant. Genotyping of two panels composed of 400 tomato and 422 eggplant accessions, comprising both domesticated material and wild relatives, generated a total of 12,002 and 30,731 high confidence SNPs, respectively, which comprised both target and novel SNPs in an approximate ratio of 1:1.6, and 1:5.5 in tomato and eggplant, respectively. The vast majority of the markers was transferrable to related species that diverged up to 3.4 million years ago (Solanum pennellii for tomato and S. macrocarpon for eggplant). Maximum Likelihood phylogenetic trees and PCA outputs obtained from the whole dataset highlighted genetic relationships among accessions and species which were congruent with what was previously reported in literature. Better discrimination among domesticated accessions was achieved by using the target SNPs, while better discrimination among wild species was achieved using the whole SNP dataset. Our results reveal that SPET genotyping is a robust, high-throughput technology for genetic fingerprinting, with a high degree of cross-transferability between crops and their cultivated and wild relatives, and allows identification of duplicates and mislabeled accessions in genebanks.

Genetic algorithm for assigning weights to gene expressions using functional annotations

A method, named genetic algorithm for assigning weights to gene expressions using functional annotations (GAAWGEFA), is developed to assign proper weights to the gene expressions at each time point. The weights are estimated using functional annotations of the genes in a genetic algorithm framework. The method shows gene similarity in an improved manner as compared with other existing methods because it takes advantage of the existing functional annotations of the genes. The weight combination for the expressions at different time points is determined by maximizing the fitness function of GAAWGEFA in terms of the positive predictive value (PPV) for the top 10,000 gene pairs. The performance of the proposed method is primarily compared with Biweight mid correlation (BICOR) and original expression values for the six Saccharomyces cerevisiae datasets and one Bacillus subtilis dataset. The utility of GAAWGEFA is shown in predicting the functions of 48 unclassified genes (using p-value cutoff 10^-13) from Saccharomyces cerevisiae microarray data where the expressions are weighted using GAAWGEFA and are clustered using k-medoids algorithm. The related code along with various parameters is available at http://sampa.droppages.com/GAAWGEFA.html.

High-Density RNA Microarrays Synthesized In Situ by Photolithography

While high-density DNA microarrays have been available for over three decades, the synthesis of equivalent RNA microarrays has proven intractable until now. Herein we describe the first in situ synthesis of mixed-based, high-density RNA microarrays using photolithography and light-sensitive RNA phosphoramidites. With coupling efficiencies comparable to those of DNA monomers, RNA oligonucleotides at least 30 nucleotides long can now efficiently be prepared using modified phosphoramidite chemistry. A two-step deprotection route unmasks the phosphodiester, the exocyclic amines and the 2' hydroxyl. Hybridization and enzymatic assays validate the quality and the identity of the surface-bound RNA. We show that high-density is feasible by synthesizing a complex RNA permutation library with 262144 unique sequences. We also introduce DNA/RNA chimeric microarrays and explore their applications by mapping the sequence specificity of RNase HII.

Clinical biomarkers for cancer recognition and prevention: A novel approach with optical measurements

Cancer is the most important cause of death worldwide, and early cancer detection is the most fundamental factor for efficacy of treatment, prognosis, and increasing survival rate. Over the years great effort has been devoted to discovering and testing new biomarkers that can improve its diagnosis, especially at an early stage. Here we report the potential usefulness of new, easily applicable, non-invasive and relatively low-cost clinical biomarkers, based on abnormalities of oral mucosa spectral reflectance and fractal geometry of the vascular networks in several different tissues, for identification of hereditary non-polyposis colorectal cancer carriers as well for detection of other tumors, even at an early stage. In the near future the methodology/technology of these procedures should be improved, thus making possible their applicability worldwide as screening tools for early recognition and prevention of cancer.

Application of "Omics" and Systems Biology to Sarcoidosis Research

Sarcoidosis is a complex, polygenic disease of unknown cause with diverse clinical phenotypes, ranging from self-limited, asymptomatic disease to life-altering symptoms and early disease-related mortality. It is unlikely that a single common environmental exposure (e.g., infection, antigen) entirely explains the disease, and numerous genetic mutations are associated with the disease. As such, it is reasonable to assume, as with other phenotypically diverse diseases, that distinct genetic mechanisms and related biological biomarkers will serve to further define sarcoidosis subphenotypes, mechanisms, and possibly etiology, thus guiding personalized care. The fields of "omics" and systems biology research are widely applied to understand polygenic and phenotypically diverse diseases, such as sarcoidosis. "Omics" refers to technologies that allow comprehensive profiling of sets of molecules in an organism. Systems biology applies advanced computational approaches to make sense of the enormous data sets that are typically generated from "omics" platforms. The primary objectives of this article are to review the available "omics" tools, assess the current status of "omics" and systems biology research in the field of sarcoidosis, and consider how this technology could be applied to advance our understanding of the mechanistic underpinnings of disease and to develop novel treatments.