Analysis of microarray experiments of gene expression profiling

Amplification of Hippo Signaling Pathway Genes Is Governed and Implicated in the Serous Subtype-Specific Ovarian Carcino-Genesis

Among women, ovarian cancer ranks as the fifth most common cause of cancer-related deaths. This study examined the impact of Hippo signaling pathway on ovarian carcinogenesis. Therefore, the signatures related to Hippo signaling pathway were derived from the molecular signatures database (MSigDB) and were used for further analysis. The Z score-based pathway activation scoring method was employed to investigate the expression patterns of these signatures in the mRNA expression profiles of ovarian cancer cohorts. Compared to other subtype tumors, the results of this study show that the Hippo signaling pathway signatures are dysregulated prominently in serous subtype-specific ovarian carcinogenesis. A receiver operating characteristic (ROC) curve-based results of the Hippo gene set, yes-associated protein 1 (YAP1), and mammalian sterile 20-like kinases 1 (MST1) genes can predict the serous subtype tumors by higher specificity and sensitivity with significant areas under the curve values also further reconfirmed these signaling dysregulations. Moreover, these gene sets were studied further for mutation analysis in the profile of high-grade serous ovarian adenocarcinoma in the cBioPortal database. The OncoPrint results reveal that these Hippo signaling pathway genes are amplified highly during the grade three and stage third or fourth of serous type ovarian tumors. In addition, the results of the Dependency Map (DepMap) plot also clearly show that these genes are amplified significantly across the ovarian cancer cell lines. Finally, overall survival (OS) curve plot investigations also revealed that these gene expressions show poor survival patterns linked to highly expressed conditions in serous subtypes of ovarian cancer patients with significant p-values (p < 0.05). Thus, the current finding would help to develop the targeted therapies treatment for serous subtype ovarian carcinogenesis.

Omics-based novel strategies in the diagnosis of endometriosis

Endometriosis, an enigmatic and chronic disorder, is considered a debilitating condition despite being benign. Globally, this gynecologic disorder affects up to 10% of females of reproductive age, impacting almost 190 million individuals. A variety of genetic and environmental factors are involved in endometriosis development, hence the pathophysiology and etiology of endometriosis remain unclear. The uncertainty of the etiology of the disease and its complexity along with nonspecific symptoms have led to misdiagnosis or lack of diagnosis of affected people. Biopsy and laparoscopy are referred to as the gold standard for endometriosis diagnosis. However, the invasiveness of the procedure, the unnecessary operation in disease-free women, and the dependence of the reliability of diagnosis on experience in this area are considered the most significant limitations. Therefore, continuous studies have attempted to offer a noninvasive and reliable approach. The recent advances in modern technologies have led to the generation of large-scale biological data sets, known as -omics data, resulting in the proceeding of the -omics century in biomedical sciences. Thereby, the present study critically reviews novel and noninvasive biomarkers that are based on -omics approaches from 2020 onward. The findings reveal that biomarkers identified based on genomics, epigenomics, transcriptomics, proteomics, and metabolomics are potentially able to diagnose endometriosis, predict prognosis, and stage patients, and potentially, in the near future, a multi-panel of these biomarkers will generate clinical benefits.

Precision Medicine for Nasopharyngeal Cancer-A Review of Current Prognostic Strategies

Nasopharyngeal carcinoma (NPC) is an Epstein-Barr virus (EBV) driven malignancy arising from the nasopharyngeal epithelium. Current treatment strategies depend on the clinical stage of the disease, including the extent of the primary tumour, the extent of nodal disease, and the presence of distant metastasis. With the close association of EBV infection with NPC development, EBV biomarkers have shown promise in predicting treatment outcomes. Among the omic technologies, RNA and miRNA signatures have been widely studied, showing promising results in the research setting to predict treatment response. The transformation of radiology images into measurable features has facilitated the use of radiomics to generate predictive models for better prognostication and treatment selection. Nonetheless, much of this work remains in the research realm, and challenges remain in clinical implementation.

The toxic mechanism of 6:2 Cl-PFESA in adolescent male rats: Endocrine disorders and liver inflammation regulated by the gut microbiota-gut-testis/liver axis

In previous studies, 6:2 chlorinated polyfluorinated ether sulfonic acid (6:2 Cl-PFESA), a perfluorooctanesulfonate alternative, has been demonstrated to be toxic to mammals. However, the toxic mechanism of 6:2 Cl-PFESA in mammals is unknown. Herein, adolescent male rats were administered 50 μg/kg/Day 6:2 Cl-PFESA for 28 days (oral gavage) to estimate the toxicity of 6:2 Cl-PFESA and investigate its toxic mechanism. Significant changes in some hematological indicators (e.g., aspartate transaminase and neutrophils) and liver sections (inflammatory cell infiltration) indicated that 6:2 Cl-PFESA exposure caused rat hepatotoxicity. Six steroid hormones (e.g., testosterone, progesterone, and cortisol) in serum and thirteen genes in testicles (related to the pathway of steroid hormone biosynthesis) were significantly regulated in 6:2 Cl-PFESA-treated rats. This suggested that 6:2 Cl-PFESA induced rat endocrine disorders. Compared to the controls, the mean relative abundance of Ruminococcaceae, Pasteurellaceae, Micrococcaceae, and Desulfovibrionaceae was significantly regulated by 1.3-, 0.40-, 0.32-, and 3.2-fold in the 6:2 Cl-PFESA rats, respectively. The 6:2 Cl-PFESA treatment also significantly disturbed 47 gut metabolites (29 upregulated and 18 downregulated), mainly bile acids, short-chain fatty acids, and amino acids. In summary, 6:2 Cl-PFESA induced endocrine disorders and liver inflammation in rats by altering the gut microbiota-gut-testis/liver axis. This study first reveals the toxic mechanism of 6:2 Cl-PFESA in mammals through a multiomics approach and provides comprehensive insight into the toxic mechanism of 6:2 Cl-PFESA.

Data Mining of Microarray Datasets in Translational Neuroscience

Data mining involves the computational analysis of a plethora of publicly available datasets to generate new hypotheses that can be further validated by experiments for the improved understanding of the pathogenesis of neurodegenerative diseases. Although the number of sequencing datasets is on the rise, microarray analysis conducted on diverse biological samples represent a large collection of datasets with multiple web-based programs that enable efficient and convenient data analysis. In this review, we first discuss the selection of biological samples associated with neurological disorders, and the possibility of a combination of datasets, from various types of samples, to conduct an integrated analysis in order to achieve a holistic understanding of the alterations in the examined biological system. We then summarize key approaches and studies that have made use of the data mining of microarray datasets to obtain insights into translational neuroscience applications, including biomarker discovery, therapeutic development, and the elucidation of the pathogenic mechanisms of neurodegenerative diseases. We further discuss the gap to be bridged between microarray and sequencing studies to improve the utilization and combination of different types of datasets, together with experimental validation, for more comprehensive analyses. We conclude by providing future perspectives on integrating multi-omics, to advance precision phenotyping and personalized medicine for neurodegenerative diseases.

Differential gene expression analysis combined with molecular dynamics simulation study to elucidate the novel potential biomarker involved in pulmonary TB

Tuberculosis (TB) is a lethal multisystem disease that attacks the lungs' first line of defense. A substantial threat to public health and a primary cause of death is pulmonary TB. This study aimed to identify and investigate the probable differentially expressed genes (DEGs) primarily involved in Pulmonary TB. Accordingly, three independent gene expression data sets, numbered GSE139825, GSE139871, and GSE54992, were utilized for this purpose. The identified DEGs were used for bioinformatics-based analysis, including physical gene interaction, Gene Ontology (GO), network analysis and pathway studies using the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG). The computational analysis predicted that TNFAIP6 is the significant DEG in the gene expression profiling of TB datasets. According to gene ontology analysis, TNFAIP6 is also essential in injury and inflammation. Further, TNFA1P6 is strongly linked to arsenic poisoning, evident from the results of NetworkAnalyst, a comprehensive and interactive platform for gene expression profiling via network visual analytics. As a result, the TNFAIP6 gene was ultimately chosen as a candidate DEG and subsequently employed for in silico structural characterization studies. The tertiary structure of TNFAIP6 was modelled using the ROBETTA server, followed by validation with SAVES and ProSA webserver. Additionally, structural dynamic studies, including molecular dynamics simulation (MDS) and essential dynamics analysis, including principal component (PC) based free energy landscape (FEL) analysis, was used for checking the stability of TNFAIP6 models. The dynamics result established the structural rigidity of modelled TNFAIP6 through RMSD, RMSF and RoG results. The FEL analysis revealed the restricted conformational flexibility of TNFAIP6 by displaying a single minimum energy basin in the contour plot. The comprehensive computational analysis established that TNFAIP6 could serve as a viable biomarker to assess the severity of pulmonary TB.

Bone mineral density loci specific to the skull portray potential pleiotropic effects on craniosynostosis

Skull bone mineral density (SK-BMD) provides a suitable trait for the discovery of key genes in bone biology, particularly to intramembranous ossification, not captured at other skeletal sites. We perform a genome-wide association meta-analysis (n ~ 43,800) of SK-BMD, identifying 59 loci, collectively explaining 12.5% of the trait variance. Association signals cluster within gene-sets involved in skeletal development and osteoporosis. Among the four novel loci (ZIC1, PRKAR1A, AZIN1/ATP6V1C1, GLRX3), there are factors implicated in intramembranous ossification and as we show, inherent to craniosynostosis processes. Functional follow-up in zebrafish confirms the importance of ZIC1 on cranial suture patterning. Likewise, we observe abnormal cranial bone initiation that culminates in ectopic sutures and reduced BMD in mosaic atp6v1c1 knockouts. Mosaic prkar1a knockouts present asymmetric bone growth and, conversely, elevated BMD. In light of this evidence linking SK-BMD loci to craniofacial abnormalities, our study provides new insight into the pathophysiology, diagnosis and treatment of skeletal diseases.

Dysregulated early transcriptional signatures linked to mast cell and interferon responses are implicated in COVID-19 severity

Background

Dysregulated immune responses to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection are thought to underlie the progression of coronavirus disease 2019 (COVID-19) to severe disease. We sought to determine whether early host immune-related gene expression could predict clinical progression to severe disease.

Methods

We analysed the expression of 579 immunological genes in peripheral blood mononuclear cells taken early after symptom onset using the NanoString nCounter and compared SARS-CoV-2 negative controls with SARS-CoV-2 positive subjects with mild (SARS+ Mild) and Moderate/Severe disease to evaluate disease outcomes. Biobanked plasma samples were also assessed for type I (IFN-α2a and IFN-β), type II (IFN-γ) and type III (IFN-λ1) interferons (IFNs) as well as 10 additional cytokines using multiplex immunoassays.

Results

We identified 19 significantly deregulated genes in 62 SARS-CoV-2 positive subject samples within 5 days of symptom onset and 58 SARS-CoV-2 negative controls and found that type I interferon (IFN) signalling (MX1, IRF7, IFITM1, IFI35, STAT2, IRF4, PML, BST2, STAT1) and genes encoding proinflammatory cytokines (TNF, TNFSF4, PTGS2 and IL1B) were upregulated in both SARS+ groups. Moreover, we found that FCER1, involved in mast cell activation, was upregulated in the SARS+ Mild group but significantly downregulated in the SARS+ Moderate/Severe group. In both SARS+ groups we discovered elevated interferon type I IFN-α2a, type II IFN and type III IFN λ1 plasma levels together with higher IL-10 and IL-6. These results indicate that those with moderate or severe disease are characterised by deficiencies in a mast cell response together with IFN hyper-responsiveness, suggesting that early host antiviral immune responses could be a cause and not a consequence of severe COVID-19.

Conclusions

This study suggests that early host immune responses linking defects in mast cell activation with host interferon responses correlates with more severe outcomes in COVID-19. Further characterisation of this pathway could help inform better treatment for vulnerable individuals.

Identification and validation of reference genes in vetiver (Chrysopogon zizanioides) root transcriptome

Vetiver [Vetiveria zizanioides (L.) Roberty] is a perennial C-4 grass traditionally valued for its aromatic roots/root essential oil. Owing to its deep penetrating web-forming roots, the grass is now widely used across the globe for phytoremediation and the conservation of soil and water. This study has used the transcriptome data of vetiver roots in its two distinct geographic morphotypes (North Indian type A and South Indian type B) for reference gene(s) identification. Further, validation of reference genes using various abiotic stresses such as heat, cold, salt, and drought was carried out. The de novo assembly based on differential genes analysis gave 1,36,824 genes (PRJNA292937). Statistical tests like RefFinder, NormFinder, BestKeeper, geNorm, and Delta-Ct software were applied on 346 selected contigs. Eleven selected genes viz., GAPs, UBE2W, RP, OSCam2, MUB, RPS, Core histone 1, Core histone 2, SAMS, GRCWSP, PLDCP along with Actin were used for qRT-PCR analysis. Finally, the study identified the five best reference genes GAPs, OsCam2, MUB, Core histone 1, and SAMS along with Actin. The two optimal reference genes SAMS and Core histone 1 were identified with the help of qbase + software. The findings of the present analyses have value in the identification of suitable reference gene(s) in transcriptomic and molecular data analysis concerning various phenotypes related to abiotic stress and developmental aspects, as well as a quality control measure in gene expression experiments. Identifying reference genes in vetiver appears important as it allows for accurate normalization of gene expression data in qRT-PCR experiments.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12298-023-01315-7.

Genetic algorithm-based feature selection with manifold learning for cancer classification using microarray data

BACKGROUND

Microarray data have been widely utilized for cancer classification. The main characteristic of microarray data is "large p and small n" in that data contain a small number of subjects but a large number of genes. It may affect the validity of the classification. Thus, there is a pressing demand of techniques able to select genes relevant to cancer classification.

RESULTS

This study proposed a novel feature (gene) selection method, Iso-GA, for cancer classification. Iso-GA hybrids the manifold learning algorithm, Isomap, in the genetic algorithm (GA) to account for the latent nonlinear structure of the gene expression in the microarray data. The Davies-Bouldin index is adopted to evaluate the candidate solutions in Isomap and to avoid the classifier dependency problem. Additionally, a probability-based framework is introduced to reduce the possibility of genes being randomly selected by GA. The performance of Iso-GA was evaluated on eight benchmark microarray datasets of cancers. Iso-GA outperformed other benchmarking gene selection methods, leading to good classification accuracy with fewer critical genes selected.

CONCLUSIONS

The proposed Iso-GA method can effectively select fewer but critical genes from microarray data to achieve competitive classification performance.

Recent advances in sensing the inter-biomolecular interactions at the nanoscale - A comprehensive review of AFM-based force spectroscopy

Biomolecular interactions underpin most processes inside the cell. Hence, a precise and quantitative understanding of molecular association and dissociation events is crucial, not only from a fundamental perspective, but also for the rational design of biomolecular platforms for state-of-the-art biomedical and industrial applications. In this context, atomic force microscopy (AFM) appears as an invaluable experimental technique, allowing the measurement of the mechanical strength of biomolecular complexes to provide a quantitative characterization of their interaction properties from a single molecule perspective. In the present review, the most recent methodological advances in this field are presented with special focus on bioconjugation, immobilization and AFM tip functionalization, dynamic force spectroscopy measurements, molecular recognition imaging and theoretical modeling. We expect this work to significantly aid in grasping the principles of AFM-based force spectroscopy (AFM-FS) technique and provide the necessary tools to acquaint the type of data that can be achieved from this type of experiments. Furthermore, a critical assessment is done with other nanotechnology techniques to better visualize the future prospects of AFM-FS.

Molecular subclasses of preeclampsia characterized by a longitudinal maternal proteomics study: distinct biomarkers, disease pathways and options for prevention

OBJECTIVES

The heterogeneous nature of preeclampsia is a major obstacle to early screening and prevention, and a molecular taxonomy of disease is needed. We have previously identified four subclasses of preeclampsia based on first-trimester plasma proteomic profiles. Herein, we expanded this approach by using a more comprehensive panel of proteins profiled in longitudinal samples.

METHODS

Proteomic data collected longitudinally from plasma samples of women who developed preeclampsia (n=109) and of controls (n=90) were available from our previous report on 1,125 proteins. Consensus clustering was performed to identify subgroups of patients with preeclampsia based on data from five gestational-age intervals by using select interval-specific features. Demographic, clinical, and proteomic differences among clusters were determined. Differentially abundant proteins were used to identify cluster-specific perturbed KEGG pathways.

RESULTS

Four molecular clusters with different clinical phenotypes were discovered by longitudinal proteomic profiling. Cluster 1 involves metabolic and prothrombotic changes with high rates of early-onset preeclampsia and small-for-gestational-age neonates; Cluster 2 includes maternal anti-fetal rejection mechanisms and recurrent preeclampsia cases; Cluster 3 is associated with extracellular matrix regulation and comprises cases of mostly mild, late-onset preeclampsia; and Cluster 4 is characterized by angiogenic imbalance and a high prevalence of early-onset disease.

CONCLUSIONS

This study is an independent validation and further refining of molecular subclasses of preeclampsia identified by a different proteomic platform and study population. The results lay the groundwork for novel diagnostic and personalized tools of prevention.

Transcriptomics of the Carotid Body

The carotid body (CB) has emerged as a potential therapeutic target for treating sympathetically mediated cardiovascular, respiratory, and metabolic diseases. In adjunct to its classical role as an arterial O₂ sensor, the CB is a multimodal sensor activated by a range of stimuli in the circulation. However, consensus on how CB multimodality is achieved is lacking; even the best studied O₂-sensing appears to involve multiple convergent mechanisms. A strategy to understand multimodal sensing is to adopt a hypothesis-free, high-throughput transcriptomic approach. This has proven instrumental for understanding fundamental mechanisms of CB response to hypoxia and other stimulants, its developmental niche, cellular heterogeneity, laterality, and pathophysiological remodeling in disease states. Herein, we review this published work that reveals novel molecular mechanisms underpinning multimodal sensing and reveals numerous gaps in knowledge that require experimental testing.

Oxaliplatin-Resistant Hepatocellular Carcinoma Drives Immune Evasion Through PD-L1 Up-Regulation and PMN-Singular Recruitment

BACKGROUND & AIMS

Previously, we showed the inhibitor of differentiation or DNA binding 1 (ID1)/Myc signaling is highly expressed in oxaliplatin-resistant hepatocellular carcinoma (HCC). This study sought to investigate the role of ID1/Myc signaling on immune evasion in oxaliplatin-resistant HCC.

METHODS

The oxaliplatin (OXA)-resistant HCC cell lines (Hepa 1-6-OXA, 97H-OXA, and 3B-OXA) were established and their oxaliplatin tolerance was confirmed in vitro and in vivo. The relationship between ID1/Myc and programmed death-ligand 1 (PD-L1) up-regulation and polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) accumulation was explored. The underlying mechanism in which ID1/Myc signaling regulated PD-L1 expression and PMN-MDSC accumulation was investigated in vitro and vivo.

RESULTS

Increased ID1/Myc expression was identified in oxaliplatin-resistant HCC and correlated with PD-L1 up-regulation and PMN-MDSC accumulation. The knockdown of Myc sensitized oxaliplatin-resistant HCC cells to oxaliplatin and resulted in a decrease of PMN-MDSCs and an increase of interferon-γ⁺ CD8⁺ T cells in a tumor microenvironment. Polymerase chain reaction array, enzyme-linked immunosorbent assay, and MDSC Transwell migration assay indicated that oxaliplatin-resistant HCC cells recruited PMN-MDSCs through chemokine (C-C motif) ligand 5 (CCL5). The dual luciferase reporter assay and chromatin immunoprecipitation assay indicated that Myc could directly increase the transcriptions of PD-L1 and CCL5. Furthermore, anti-PD-L1 antibody combined with CCL5 blockade showed significant antitumor effects in oxaliplatin-resistant HCC.

CONCLUSIONS

ID1/Myc signaling drives immune evasion in oxaliplatin-resistant HCC via PD-L1 up-regulation and PMN-MDSC recruitment. Blocking the ID1/Myc-induced immune tolerance represents a promising treatment target to conquer chemoresistance in HCC.

Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer's disease

Background

A factor leading to Alzheimer's Disease (AD), portrayed by peripheral insulin resistance, is Type 2 diabetes mellitus (T2D). The likelihood of T2D cases would be at boosted danger in alternating AD cases has severe social consequences. Several genes have been detected via gene expression profiling or different techniques; despite the consideration of the utility of numerous of these genes stays insufficient.

Methods

This project is designed to uncover the mutual genomics motifs between AD and T2D via non-negative matrix factorization (NMF) of differentially expressed genes (DEGs) of T2D Mellitus of human cortical neurons of the neurovascular unit gene expression data. A rank factorization value is calculated by employing the combination of the NMF model with the unit invariant knee (UIK) point method. The metagenes are further determined by remarking the enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) enrichment tools. In this study, the most highly expressed genes of metagenes are subjected to protein-protein interaction (PPI) network study to discover the most significant biomarkers of T2D Mellitus in the ageing brain.

Results

We screened the most important shared genes (CDKN1A, COL22A1, EIF4A, GFAP, SLC1A1, and VIM) and essential human molecular pathways that motivate these diseases. The study aimed to validate the most significant hub genes using network-based methods which detected the corresponding relationship between AD and T2D.

Conclusions

Using in silico tools, the computational pipeline has broadly examined transformed pathways and discovered promising biomarkers and drug targets. We validated the most significant hub genes using network-based methods which detected the corresponding relationship between AD and T2D. These consequences on brain cells hypothetically reserve to diabetic Alzheimer's so-called type 3 diabetes (T3D) and may offer promising methodologies for curative intrusion.

Comparison of reference gene expression stability in mouse skeletal muscle via five algorithms

Real-time quantitative PCR (RT-qPCR) is a widely applied technique for relative quantification of gene expression. In this context, the selection of a suitable reference gene (RG) is an essential step for obtaining reliable and biologically relevant RT-qPCR results. The present study aimed to determine the expression stability of commonly used RGs in mouse skeletal muscle tissue. The expression pattern of eight RGs (ACTB, GAPDH, HPRT, YWHAZ, B2M, PPIA, TUBA and 18S) were evaluated by RT-qPCR in different sample groups classified based on genetic background, muscle tissue type, and growth stage, as well as in a C2C12 myoblast cell line model. Five computational programs were included in the study (comparative ΔCq value, NormFinder, BestKeeper, geNorm, RefFinder) to evaluate the expression stability of RGs. Furthermore, the normalization effects of RGs in soleus (SOL) and gastrocnemius (GAS) muscle tissue were evaluated. Collectively, ACTB, HPRT and YWHAZ were shown to be the most stable RGs, while GADPH and 18S were the least stable. Therefore, the combined use of ACTB, HPRT and YWHAZ is recommended for the normalization of gene expression results in experiments with murine skeletal muscle. The results discussed herein provide a foundation for gene expression analysis by RT-qPCR in mammalian skeletal muscle.

Hepatocellular carcinoma stage: an almost loss of fatty acid metabolism and gain of glucose metabolic pathways dysregulation

Cancer cells rewire the metabolic processes beneficial for cancer cell proliferation, survival, and their progression. In this study, metabolic processes related to glucose, glutamine, and fatty acid metabolism signatures were collected from the molecular signatures database and investigated in the context of energy metabolic pathways through available genome-wide expression profiles of liver cancer cohorts by gene sets-based pathway activation scoring analysis. The outcomes of this study portray that the fatty acid metabolism, transport, and its storage related signatures are highly expressed across early stages of liver tumors and on the contrary, the gene sets related to glucose transport and glucose metabolism are prominently activated in the hepatocellular carcinoma (HCC) stage. Based on the results, these metabolic pathways are clearly dysregulated across specific stages of carcinogenesis. The identified dimorphic metabolic pathway dysregulation patterns are further reconfirmed by examining corresponding metabolic pathway genes expression patterns across various stages encompassing profiles. Recurrence is the primary concern in the carcinogenesis of liver tumors due to liver tissues regeneration. Hence, to further explore these dysregulation effects on recurrent cirrhosis and recurrent HCC sample containing profile GSE20140 was examined and interestingly, this result also reiterated these differential metabolic pathways dysregulation. In addition, a recently established metabolome profile for the massive panel of cancer cell-lines, including liver cancer cell-lines, was used for further exploration. These findings also reassured those differential metabolites abundance of the fatty acid and glucose metabolic pathways enlighten those dimorphic metabolic pathways dysregulation. Moreover, ROC curves of fatty acid metabolic pathway genes such as acetyl-CoA carboxylase (ACACB), acyl-CoA dehydrogenase long chain (ACADL), and acyl-CoA dehydrogenase medium chain (ACADM) as well as glucose metabolic pathway genes such as phosphoglycerate kinase (PGK1), pyruvate dehydrogenase (PDHA1), pyruvate dehydrogenase kinase (PDK1) demonstrated greater sensitivity and specificity in the corresponding stage-specific tumors with significant p-values (p < 0.05). Furthermore, overall survival (OS) and recurrence-free survival (RFS) studies also reconfirmed that the rate-limiting genes expression of fatty acid and glucose metabolic pathways reveal better and poor survival in HCC patient cohorts, respectively. In conclusion, all these results clearly show that metabolic rewiring and the existence of two diverse metabolic pathways dysregulation involving fatty acid and glucose metabolism across the stages of liver tumors have been identified. These findings might be useful for developing therapeutic target treatments in stage-specific tumors.

Toward a new taxonomy of obstetrical disease: improved performance of maternal blood biomarkers for the great obstetrical syndromes when classified according to placental pathology

BACKGROUND

The major challenge for obstetrics is the prediction and prevention of the great obstetrical syndromes. We propose that defining obstetrical diseases by the combination of clinical presentation and disease mechanisms as inferred by placental pathology will aid in the discovery of biomarkers and add specificity to those already known.

OBJECTIVE

To describe the longitudinal profile of placental growth factor (PlGF), soluble fms-like tyrosine kinase-1 (sFlt-1), and the PlGF/sFlt-1 ratio throughout gestation, and to determine whether the association between abnormal biomarker profiles and obstetrical syndromes is strengthened by information derived from placental examination, eg, the presence or absence of placental lesions of maternal vascular malperfusion.

STUDY DESIGN

This retrospective case cohort study was based on a parent cohort of 4006 pregnant women enrolled prospectively. The case cohort of 1499 pregnant women included 1000 randomly selected patients from the parent cohort and all additional patients with obstetrical syndromes from the parent cohort. Pregnant women were classified into six groups: 1) term delivery without pregnancy complications (n=540; control); 2) preterm labor and delivery (n=203); 3) preterm premature rupture of the membranes (n=112); 4) preeclampsia (n=230); 5) small-for-gestational-age neonate (n=334); and 6) other pregnancy complications (n=182). Maternal plasma concentrations of PlGF and sFlt-1 were determined by enzyme-linked immunosorbent assays in 7560 longitudinal samples. Placental pathologists, masked to clinical outcomes, diagnosed the presence or absence of placental lesions of maternal vascular malperfusion. Comparisons between mean biomarker concentrations in cases and controls were performed by utilizing longitudinal generalized additive models. Comparisons were made between controls and each obstetrical syndrome with and without subclassifying cases according to the presence or absence of placental lesions of maternal vascular malperfusion.

RESULTS

1) When obstetrical syndromes are classified based on the presence or absence of placental lesions of maternal vascular malperfusion, significant differences in the mean plasma concentrations of PlGF, sFlt-1, and the PlGF/sFlt-1 ratio between cases and controls emerge earlier in gestation; 2) the strength of association between an abnormal PlGF/sFlt-1 ratio and the occurrence of obstetrical syndromes increases when placental lesions of maternal vascular malperfusion are present (adjusted odds ratio [aOR], 13.6 vs 6.7 for preeclampsia; aOR, 8.1 vs 4.4 for small-for-gestational-age neonates; aOR, 5.5 vs 2.1 for preterm premature rupture of the membranes; and aOR, 3.3 vs 2.1 for preterm labor (all P<0.05); and 3) the PlGF/sFlt-1 ratio at 28 to 32 weeks of gestation is abnormal in patients who subsequently delivered due to preterm labor with intact membranes and in those with preterm premature rupture of the membranes if both groups have placental lesions of maternal vascular malperfusion. Such association is not significant in patients with these obstetrical syndromes who do not have placental lesions.

CONCLUSION

Classification of obstetrical syndromes according to the presence or absence of placental lesions of maternal vascular malperfusion allows biomarkers to be informative earlier in gestation and enhances the strength of association between biomarkers and clinical outcomes. We propose that a new taxonomy of obstetrical disorders informed by placental pathology will facilitate the discovery and implementation of biomarkers as well as the prediction and prevention of such disorders.

BioTEA: Containerized Methods of Analysis for Microarray-Based Transcriptomics Data

Tens of thousands of gene expression data sets describing a variety of model organisms in many different pathophysiological conditions are currently stored in publicly available databases such as the Gene Expression Omnibus (GEO) and ArrayExpress (AE). As microarray technology is giving way to RNA-seq, it becomes strategic to develop high-level tools of analysis to preserve access to this huge amount of information through the most sophisticated methods of data preparation and processing developed over the years, while ensuring, at the same time, the reproducibility of the results. To meet this need, here we present bioTEA (biological Transcript Expression Analyzer), a novel software tool that combines ease of use with the versatility and power of an R/Bioconductor-based differential expression analysis, starting from raw data retrieval and preparation to gene annotation. BioTEA is an R-coded pipeline, wrapped in a Python-based command line interface and containerized with Docker technology. The user can choose among multiple options—including gene filtering, batch effect handling, sample pairing, statistical test type—to adapt the algorithm flow to the structure of the particular data set. All these options are saved in a single text file, which can be easily shared between different laboratories to deterministically reproduce the results. In addition, a detailed log file provides accurate information about each step of the analysis. Overall, these features make bioTEA an invaluable tool for both bioinformaticians and wet-lab biologists interested in transcriptomics. BioTEA is free and open-source.

Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease

Background

Cardiovascular disease (CVD) is the combination of coronary heart disease, myocardial infarction, rheumatic heart disease, and peripheral vascular disease of the heart and blood vessels. It is one of the leading deadly diseases that causes one-third of the deaths yearly in the globe. Additionally, the risk factors associated with it make the situation more complex for cardiovascular patients, which lead them towards mortality, but the genetic association between CVD and its risk factors is not clearly explored in the global literature. We addressed this issue and explored the linkage between CVD and its risk factors.

Methods

We developed an analytical approach to reveal the risk factors and their linkages with CVD. We used GEO microarray datasets for the CVD and other risk factors in this study. We performed several analyses including gene expression analysis, diseasome analysis, protein-protein interaction (PPI) analysis, and pathway analysis for discovering the relationship between CVD and its risk factors. We also examined the validation of our study using gold benchmark databases OMIM, dbGAP, and DisGeNET.

Results

We observed that the number of 32, 17, 53, 70, and 89 differentially expressed genes (DEGs) is overlapped between CVD and its risk factors of hypertension (HTN), type 2 diabetes (T2D), hypercholesterolemia (HCL), obesity, and aging, respectively. We identified 10 major hub proteins (FPR2, TNF, CXCL8, CXCL1, IL1B, VEGFA, CYBB, PTGS2, ITGAX, and CCR5), 12 significant functional pathways, and 11 gene ontological pathways that are associated with CVD. We also found the connection of CVD with its risk factors in the gold benchmark databases. Our experimental outcomes indicate a strong association of CVD with its risk factors of HTN, T2D, HCL, obesity, and aging.

Conclusions

Our computational approach explored the genetic association of CVD with its risk factors by identifying the significant DEGs, hub proteins, and signaling and ontological pathways. The outcomes of this study may be further used in the lab-based analysis for developing the effective treatment strategies of CVD.

Differential Transcriptomic Profiles Following Stimulation with Lipopolysaccharide in Intestinal Organoids from Dogs with Inflammatory Bowel Disease and Intestinal Mast Cell Tumor

Lipopolysaccharide (LPS) is associated with chronic intestinal inflammation and promotes intestinal cancer progression in the gut. While the interplay between LPS and intestinal immune cells has been well-characterized, little is known about LPS and the intestinal epithelium interactions. In this study, we explored the differential effects of LPS on proliferation and the transcriptome in 3D enteroids/colonoids obtained from dogs with naturally occurring gastrointestinal (GI) diseases including inflammatory bowel disease (IBD) and intestinal mast cell tumor. The study objective was to analyze the LPS-induced modulation of signaling pathways involving the intestinal epithelia and contributing to colorectal cancer development in the context of an inflammatory (IBD) or a tumor microenvironment. While LPS incubation resulted in a pro-cancer gene expression pattern and stimulated proliferation of IBD enteroids and colonoids, downregulation of several cancer-associated genes such as Gpatch4, SLC7A1, ATP13A2, and TEX45 was also observed in tumor enteroids. Genes participating in porphyrin metabolism (CP), nucleocytoplasmic transport (EEF1A1), arachidonic acid, and glutathione metabolism (GPX1) exhibited a similar pattern of altered expression between IBD enteroids and IBD colonoids following LPS stimulation. In contrast, genes involved in anion transport, transcription and translation, apoptotic processes, and regulation of adaptive immune responses showed the opposite expression patterns between IBD enteroids and colonoids following LPS treatment. In brief, the crosstalk between LPS/TLR4 signal transduction pathway and several metabolic pathways such as primary bile acid biosynthesis and secretion, peroxisome, renin-angiotensin system, glutathione metabolism, and arachidonic acid pathways may be important in driving chronic intestinal inflammation and intestinal carcinogenesis.

Non-negative matrix factorization and differential expression analyses identify hub genes linked to progression and prognosis of glioblastoma multiforme

One of the most prevailing primary brain tumors in adult human male is glioblastoma multiforme (GBM), which is categorized by rapid cellular growth. Even though the combination therapy comprises surgery, chemotherapy, and adjuvant therapies, the survival rate, on average, is 14.6 months. Glioma stem cells (GSCs) have key roles in tumorigenesis, progression, and defiance against chemotherapy and radiotherapy. In our study, firstly, the gene expression dataset GSE124145 was retrieved; the non-negative matrix factorization (NMF) method was applied on GBM dataset, and differentially expressed genes analysis (DEGs) was performed. After which, overlapping genes between metagenes and DEGs were detected to examine the Gene Ontology (GO) categories in the biological process (BP) in the stemness of GBM. The common hub genes were used to construct protein-protein interaction (PPI) network and further GO, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was utilized to pinpoint the real hub genes. The analysis of hub genes particular for the same GO categories demonstrated that specific hub genes triggered distinct features of the same biological processes. After utilizing GSE124145 and The Cancer Genome Atlas (TCGA) dataset for survival analysis, we screened five real hub genes: GUCA1A, RFC2, GNG11, MMP19, and NRG1, which are strongly associated with the progression and prognosis of GBM. The DEGs analysis revealed that all real hub genes were overexpressed in GBM and TCGA datasets, which further validates our results. The constructed study of PPI, GO, and KEGG pathway on common hub genes was performed. Finally, the KEGG pathways performed on the top 15 candidate hub genes (including six real hub genes) of the PPI network in the GBM gene expression dataset study found mitogen-activated protein kinase (Mapk) signaling pathway to be the most significant pathway. The rest of the hub genes reviewed throughout the analysis might be favorable targets for diagnosing and treating GBM and lower-grade gliomas.

Investigation of genes and pathways involved in breast cancer subtypes through gene expression meta-analysis

BACKGROUND

Molecular-based studies have revealed heterogeneity in Breast cancer BC while also improving classification and treatment. However, efforts are underway to distinguish between distinct subtypes of breast cancer. In this study, the results of several microarray studies were combined to identify genes and pathways specific to each BC subtype.

METHODS

Meta-analysis of multiple gene expression profile datasets was screened to find differentially expressed genes (DEGs) across subtypes of BC and normal breast tissue samples. Protein-protein interaction network and gene set enrichment analysis were used to identify critical genes and pathways associated with BC subtypes. The differentially expressed genes from meta-analysis was validated using an independent comprehensive breast cancer RNA-sequencing dataset obtained from the Cancer Genome Atlas (TCGA).

RESULTS

We identified 110 DEGs (13 DEGs in all and 97 DEGs in each subtype) across subtypes of BC. All subtypes had a small set of shared DEGs enriched in the Chemokine receptor bind chemokine pathway. Luminal A specific were enriched in the translational elongation process in mitochondria, and the enhanced process in luminal B subtypes was interferon-alpha/beta signaling. Cell cycle and mitotic DEGs were enriched in the basal-like group. All subtype-specific DEG genes (100%) were successfully validated for Luminal A, Luminal B, ERBB2, and Normal-like. However, the validation percentage for Basal-like group was 77.8%.

CONCLUSION

Integrating researches such as a meta-analysis of gene expression might be more effective in uncovering subtype-specific DEGs and pathways than a single-study analysis. It would be more beneficial to increase the number of studies that use matched BC subtypes along with GEO profiling approaches to reach a better result regarding DEGs and reduce probable biases. However, achieving 77.8% overlap in basal-specific genes and complete concordance in specific genes related to other subtypes can implicate the strength of our analysis for discovering the subtype-specific genes.

A computational model revealing the immune-related hub genes and key pathways involved in rheumatoid arthritis (RA)

Rheumatoid arthritis (RA) has one of the highest disability rates among inflammatory joint disorders. However, the reason and possible molecular events are still unclear. There are various treatment options available, but no complete cure. To obtain early diagnosis and successful medication in RA, it is necessary to explore gene susceptibility and pathogenic factors. The main intend of our work is to explore the immune-related hub genes with similar functions that are differentially expressed in RA patients. Three datasets such as GSE21959, GSE55457, and GSE77298, were taken to analyze the differently expressed genes (DEGs) among 55 RA and 33 control samples. We obtained 331 upregulated and 275 downregulated DEGs from three Gene Expression Omnibus (GEO) datasets using the R package. Furthermore, a protein-protein interaction network was built for upregulated and downregulated DEGs using Cytoscape. Subsequently, MCODE analysis was performed and obtained the top two modules in each DEG's upregulated and downregulated protein-protein interactions (PPIs) network. CytoNCA and cytoHubba were performed and identified overlapping DEGs. In addition, we narrowed down DEGs by filtering with immune-related genes and identified DE-IRGs. Gene ontology (GO) and KEGG pathway analysis in upregulated and downregulated DEGs were executed with the DAVID platform. Our study obtained the nine most significant DE-IRGs in RA such as CXCR4, CDK1, BUB1, BIRC5, AGTR1, EGFR, EDNRB, KALRN, and GHSR. Among them, CXCR4, CDK1, BUB1, and BIRC5 are overexpressed in RA and may contribute to the pathophysiology of the disease. Similarly, AGTR1, EGFR, EDNRB, KALRN, and GHSR are all low expressed in RA and may have a contribution to pathogenesis. GO, KEGG functional enrichment, and GeneMANIA showed that the dysregulated process of DE-IRGs causes RA development and progression. These findings may be helpful in future studies in RA diagnosis and therapy.

Gene network profiling in muscle-invasive bladder cancer: A systematic review and meta-analysis

BACKGROUND

Determining meta-analysis of transcriptional profiling of muscle-invasive bladder cancer (MIBC) through Gene Expression Omnibus (GEO) datasets has not been investigated. This study aims to define gene expression profiles in MIBC and to identify potential candidate genes and pathways.

OBJECTIVES

To review and evaluate gene expression studies in MIBC through publicly available RNA sequencing (RNA-Seq) and microarray data in order to identify potential prognostic and therapeutic targets for MIBC.

METHODS

A systematic literature search of the Ovid MEDLINE, PubMed, and Wiley Cochrane Central Register of Controlled Trials databases was performed using the terms "gene," "gene expression," and "bladder cancer" January 1, 1990 through March 2021 focused on populations with MIBC.

RESULTS

In the final analysis, GEO datasets were included. Fixed effect model was employed in the meta-analysis. Gene networking connections and gene-set functional analyses of the identified genes as differentially expressed in MIBC were performed using ImaGEO and GeneMANIA software. A heatmap for the upregulated and downregulated genes was generated along with the correlated pathways.

CONCLUSION

A total of 9 genes were reported in this analysis. Six genes were reported as upregulated (ProTα, SPINT1, UBE2E1, RAB25, KPNB1, HDAC1) and 3 genes as downregulated (NUP188, IPO13, NUP124). Genes were found to be involved in "ubiquitin mediated proteolysis," "protein processing in endoplasmic reticulum," "transcriptional misregulation in cancer," and "RNA transport" pathways.

Integrative Gene Expression and Metabolic Analysis Tool IgemRNA

Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has implications when researching the genotype–phenotype responses to environmental changes. Several algorithms for transcriptome analysis using genome-scale metabolic modeling have been proposed. Still, they are restricted to specific objectives and conditions and lack flexibility, have software compatibility issues, and require advanced user skills. We classified previously published algorithms, summarized transcriptome pre-processing, integration, and analysis methods, and implemented them in the newly developed transcriptome analysis tool IgemRNA, which (1) has a user-friendly graphical interface, (2) tackles compatibility issues by combining previous data input and pre-processing algorithms in MATLAB, and (3) introduces novel algorithms for the automatic comparison of different transcriptome datasets with or without Cobra Toolbox 3.0 optimization algorithms. We used publicly available transcriptome datasets from Saccharomyces cerevisiae BY4741 and H4-S47D strains for validation. We found that IgemRNA provides a means for transcriptome and environmental data validation on biochemical network topology since the biomass function varies for different phenotypes. Our tool can detect problematic reaction constraints.

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.

A review on epidermal growth factor receptor's role in breast and non-small cell lung cancer

Epithelial growth factor receptor (EGFR) is a cell surface transmembrane receptor that mediates the tyrosine signaling pathway to carry the extracellular messages inside the cell and thereby alter the function of nucleus. This leads to the generation of various protein products to up or downregulate the cellular function. It is encoded by cell erythroblastosis virus oncogene B1, so called C-erb B1/ERBB2/HER-2 gene that acts as a proto-oncogene. It belongs to the HER-2 receptor-family in breast cancer and responds best with anti-Herceptin therapy (anti-tyrosine kinase monoclonal antibody). HER-2 positive breast cancer patient exhibits worse prognosis without Herceptin therapy. Similar incidence and prognosis are reported in other epithelial neoplasms like EGFR + lung non-small cell carcinoma and glioblastoma (grade IV brain glial tumor). Present study highlights the role and connectivity of EGF with various cancers via signaling pathways, cell surface receptors mechanism, macromolecules, mitochondrial genes and neoplasm. Present study describes the EGFR associated gene expression profiling (in breast cancer and NSCLC), relation between mitrochondrial genes and carcinoma, and several in vitro and in vivo models to screen the synergistic effect of various combination treatments. According to this study, although clinical studies including targeted treatments, immunotherapies, radiotherapy, TKi-EGFR combined targeted therapy have been carried out to investigate the synergism of combination therapy; however still there is a gap to apply the scenarios of experimental and clinical studies for further developments. This review will give an idea about the transition from experimental to most advanced clinical studies with different combination drug strategies to treat cancer.

Odontogenic gene expression profile of human dental pulp-derived cells under high glucose influence: a microarray analysis

Hyperglycemia, a major characteristic of diabetes, is considered to play a vital role in diabetic complications. High glucose levels have been found to inhibit the mineralization of dental pulp cells. However, gene expression associated with this phenomenon has not yet been reported. This is important for future dental therapeutic application.

OBJECTIVE

Our study aimed to investigate the effect of high glucose levels on mineralization of human dental pulp-derived cells (hDPCs) and identify the genes involved.

METHODOLOGY

hDPCs were cultured in mineralizing medium containing 25 or 5.5 mM D-glucose. On days 1 and 14, RNA was extracted and expression microarray performed. Then, differentially expressed genes (DEGs) were selected for further validation using the reverse transcription quantitative polymerase chain reaction (RT-qPCR) method. Cells were fixed and stained with alizarin red on day 21 to detect the formation of mineralized nodules, which was further quantified by acetic acid extraction.

RESULTS

Comparisons between high-glucose and low-glucose conditions showed that on day 1, there were 72 significantly up-regulated and 75 down-regulated genes in the high-glucose condition. Moreover, 115 significantly up- and 292 down-regulated genes were identified in the high-glucose condition on day 14. DEGs were enriched in different GO terms and pathways, such as biological and cellular processes, metabolic pathways, cytokine-cytokine receptor interaction and AGE-RAGE signaling pathways. RT-qPCR results confirmed the significant expression of pyruvate dehydrogenase kinase 3 (PDK3), cyclin-dependent kinase 8 (CDK8), activating transcription factor 3 (ATF3), fibulin-7 (Fbln-7), hyaluronan synthase 1 (HAS1), interleukin 4 receptor (IL-4R) and apolipoprotein C1 (ApoC1).

CONCLUSIONS

The high-glucose condition significantly inhibited the mineralization of hDPCs. DEGs were identified, and interestingly, HAS1 and Fbln-7 genes may be involved in the glucose inhibitory effect on hDPC mineralization.

What's Wrong in a Jump? Prediction and Validation of Splice Site Variants

Alternative splicing (AS) is a crucial process to enhance gene expression driving organism development. Interestingly, more than 95% of human genes undergo AS, producing multiple protein isoforms from the same transcript. Any alteration (e.g., nucleotide substitutions, insertions, and deletions) involving consensus splicing regulatory sequences in a specific gene may result in the production of aberrant and not properly working proteins. In this review, we introduce the key steps of splicing mechanism and describe all different types of genomic variants affecting this process (splicing variants in acceptor/donor sites or branch point or polypyrimidine tract, exonic, and deep intronic changes). Then, we provide an updated approach to improve splice variants detection. First, we review the main computational tools, including the recent Machine Learning-based algorithms, for the prediction of splice site variants, in order to characterize how a genomic variant interferes with splicing process. Next, we report the experimental methods to validate the predictive analyses are defined, distinguishing between methods testing RNA (transcriptomics analysis) or proteins (proteomics experiments). For both prediction and validation steps, benefits and weaknesses of each tool/procedure are accurately reported, as well as suggestions on which approaches are more suitable in diagnostic rather than in clinical research.

Up regulated virulence genes in M. tuberculosis H37Rv gleaned from genome wide expression profiles

Identification of up regulated virulence genes in M. tuberculosis H37Rv using genome wide expression profiles is of interest in drug discovery for the disease. Hence, we report 17 up-regulated PPIN (Protein-Protein Interaction Network) enriched potential virulence linked genes using expression data available at the Gene Expression Omnibus (GEO) database for further consideration.

Gene expression analysis of MCF7 cell lines of breast cancer treated with herbal extract of Cissampelos pareira revealed association with viral diseases

BACKGROUND

It is necessary to assess the cellular, molecular, and pathogenetic characteristics of COVID-19 and attention is required to understand highly effective gene targets and mechanisms. In this study, we suggest understandings into the fundamental pathogenesis of COVID-19 through gene expression analyses using the microarray data set GSE156445 publicly reachable at NIH/NCBI Gene Expression Omnibus database. The data set consists of MCF7 which is a human breast cancer cell line with estrogen, progesterone and glucocorticoid receptors. The cell lines treated with different quantities of Cissampelos pareira (Cipa). Cipa is a traditional medicinal plant which would possess an antiviral potency in preventing viral diseases such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

METHODS

Utilizing Biobase, GEOquery, gplots packages in R studio, the differentially expressed genes (DEGs) were identified. The gene ontology (GO) of pathway enrichments employed by utilizing DAVID and KEGG enrichment analyses were studied. We further constructed a human protein-protein interaction (PPI) network and performed, based upon that, a subnetwork module analysis for significant signaling pathways.

RESULTS

The study identified 418 differentially expressed genes (DEGs) using bioinformatics tools. The gene ontology of pathway enrichments employed by GO and KEGG enrichment analyses of down-regulated and up-regulated DEGs were studied. Gene expression analysis utilizing gene ontology and KEGG results uncovered biological and signaling pathways such as "cell adhesion molecules", "plasma membrane adhesion molecules", "synapse assembly", and "Interleukin-3-mediated signaling" which are mostly linked to COVID-19. Our results provide in silico evidence for candidate genes which are vital for the inhibition, adhesion, and encoding cytokine protein including LYN, IGFBP5, IL-1R1, and IL-13RA1 that may have strong biomarker potential for infectious diseases such as COVID-19 related therapy targets.

Big data, integrative omics and network biology

A cell integrates various signals through a network of biomolecules that crosstalk to synergistically regulate the replication, transcription, translation and other metabolic activities of a cell. These networks regulate signal perception and processing that drives biological functions. The biological complexity cannot be fully captured by a single -omics discipline. The holistic study of an organism-in health, perturbation, exposure to environment and disease, is studied under systems biology. The bottom-up molecular approaches (genes, mRNA, protein, metabolite, etc.) have laid the foundation of current biological knowledge covering the horizon from viruses, bacteria, fungi, plants and animals. Yet, these techniques provide a rather myopic view of biology at the molecular level. To understand how the interconnected molecular components are formed and rewired in disease or exposure to environmental stimuli is the holy grail of modern biology. The omics era was heralded by the genomics revolution but advanced sequencing techniques are now also ubiquitous in transcriptomics, proteomics, metabolomics and lipidomics. Multi-omics data analysis and integration techniques are driving the quest for deeper insights into how the different layers of biomolecules talk to each other in diverse contexts.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target “ERBB4” and candidate drug “Wortmannin” provide insights on the possible personalized therapeutics for emerging COVID-19.

Molecular Analysis of Renal Allograft Biopsies: Where Do We Stand and Where Are We Going?

A renal core biopsy for histological evaluation is the gold standard for diagnosing renal transplant pathology. However, renal biopsy interpretation is subjective and can render insufficient precision, making it difficult to apply a targeted therapeutic regimen for the individual patient. This warrants a need for additional methods assessing disease state in the renal transplant. Significant research activity has been focused on the role of molecular analysis in the diagnosis of renal allograft rejection. The identification of specific molecular expression patterns in allograft biopsies related to different types of allograft injury could provide valuable information about the processes underlying renal transplant dysfunction and can be used for the development of molecular classifier scores, which could improve our diagnostic and prognostic ability and could guide treatment. Molecular profiling has the potential to be more precise and objective than histological evaluation and may identify injury even before it becomes visible on histology, making it possible to start treatment at the earliest time possible. Combining conventional diagnostics (histology, serology, and clinical data) and molecular evaluation will most likely offer the best diagnostic approach. We believe that the use of state-of-the-art molecular analysis will have a significant impact in diagnostics after renal transplantation. In this review, we elaborate on the molecular phenotype of both acute and chronic T cell-mediated rejection and antibody-mediated rejection and discuss the additive value of molecular profiling in the setting of diagnosing renal allograft rejection and how this will improve transplant patient care.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

Multiclass spectral feature scaling method for dimensionality reduction

Irregular features disrupt the desired classification. In this paper, we consider aggressively modifying scales of features in the original space according to the label information to form well-separated clusters in low-dimensional space. The proposed method exploits spectral clustering to derive scaling factors that are used to modify the features. Specifically, we reformulate the Laplacian eigenproblem of the spectral clustering as an eigenproblem of a linear matrix pencil whose eigenvector has the scaling factors. Numerical experiments show that the proposed method outperforms well-established supervised dimensionality reduction methods for toy problems with more samples than features and real-world problems with more features than samples.

Integrative Computational Approach Revealed Crucial Genes Associated With Different Stages of Diabetic Retinopathy

The increased incidence of diabetic retinopathy (DR) and the legacy effect associated with it has raised a great concern toward the need to find early diagnostic and treatment strategies. Identifying alterations in genes and microRNAs (miRNAs) is one of the most critical steps toward understanding the mechanisms by which a disease progresses, and this can be further used in finding potential diagnostic and prognostic biomarkers and treatment methods. We selected different datasets to identify altered genes and miRNAs. The integrative analysis was employed to find potential candidate genes (differentially expressed and aberrantly methylated genes that are also the target of altered miRNAs) and early genes (genes showing altered expression and methylation pattern during early stage of DR) for DR. We constructed a protein-protein interaction (PPI) network to find hub genes (potential candidate genes showing a greater number of interactions) and modules. Gene ontologies and pathways associated with the identified genes were analyzed to determine their role in DR progression. A total of 271 upregulated-hypomethylated genes, 84 downregulated-hypermethylated genes, 11 upregulated miRNA, and 30 downregulated miRNA specific to DR were identified. 40 potential candidate genes and 9 early genes were also identified. PPI network analysis revealed 7 hub genes (number of interactions >5) and 1 module (score = 5.67). Gene ontology and pathway analysis predicted enrichment of genes in oxidoreductase activity, binding to extracellular matrix, immune responses, leukocyte migration, cell adhesion, PI3K-Akt signaling pathway, ECM receptor interaction, etc., and thus their association with DR pathogenesis. In conclusion, we identified 7 hub genes and 9 early genes that could act as a potential prognostic, diagnostic, or therapeutic target for DR, and a few early genes could also play a role in metabolic memory phenomena.

Occurrence of differing metabolic dysregulations, a glucose driven and another fatty acid centric in gastric cancer subtypes

Gastric cancer is one of the most common cancers and ranks third in cancer-related deaths across globe. Cancer cells are known to take advantage of the altered metabolic processes to sustain their survival, proliferation, and cancer progression. In this investigation, we explored the available genome-wide expression profiles of few hundreds of gastric tumors and non-cancerous gastric tissues and analyzed in the context of metabolic pathways. Gastric tumors were investigated for the metabolic processes related to glucose metabolism, glucose transport, glutamine metabolism, and fatty acid metabolism, by metabolic pathway-focused gene set enrichment analysis. Notably, all glucose metabolism and glutamine metabolism-related gene sets were found enriched in intestinal subtype gastric tumors. On the other hand, the gene sets related to glucose transport and glucan (glycan) metabolisms are enriched in diffuse subtype gastric tumors. Strikingly, fatty acid metabolisms, fatty acid transport, and fat differentiation-related signatures are also highly activated in diffuse subtype gastric tumors. Exploration of the recently established metabolome profile of the massive panel of cell lines also revealed the metabolites of glucose and fatty acid metabolic pathways to show the differing abundance across gastric cancer subtypes. The subtype-specific metabolic rewiring and the existence of two distinct metabolic dysregulations involving glucose and fatty acid metabolism in gastric cancer subtypes have been identified. The identified differing metabolic dysregulations would pave way for the development of targeted therapeutic strategies for the gastric cancer subtypes.

Identifying the Salient Genes in Microarray Data: A Novel Game Theoretic Model for the Co-Expression Network

Microarray techniques are used to generate a large amount of information on gene expression. This information can be statistically processed and analyzed to identify the genes useful for the diagnosis and prognosis of genetic diseases. Game theoretic tools are applied to analyze the gene expression data. Gene co-expression networks are increasingly used to explore the system-level functionality of genes, where the roles of the genes in building networks in addition to their independent activities are also considered. In this paper, we develop a novel microarray network game by constructing a gene co-expression network and defining a game on this network. The notion of the Link Relevance Index (LRI) for this network game is introduced and characterized. The LRI successfully identifies the relevant cancer biomarkers. It also enables identifying salient genes in the colon cancer dataset. Network games can more accurately describe the interactions among genes as their basic premises are to consider the interactions among players prescribed by a network structure. LRI presents a tool to identify the underlying salient genes involved in cancer or other metabolic syndromes.

Discovery of a transcriptomic core of genes shared in 8 primary retinoblastoma with a novel detection score analysis

PURPOSE

Expression microarrays are powerful technology that allows large-scale analysis of RNA profiles in a tissue; these platforms include underexploited detection scores outputs. We developed an algorithm using the detection score, to generate a detection profile of shared elements in retinoblastoma as well as to determine its transcriptomic size and structure.

METHODS

We analyzed eight briefly cultured primary retinoblastomas with the Human transcriptome array 2.0 (HTA2.0). Transcripts and genes detection scores were determined using the Detection Above Background algorithm (DABG). We used unsupervised and supervised computational tools to analyze detected and undetected elements; WebGestalt was used to explore functions encoded by genes in relevant clusters and performed experimental validation.

RESULTS

We found a core cluster with 7,513 genes detected and shared by all samples, 4,321 genes in a cluster that was commonly absent, and 7,681 genes variably detected across the samples accounting for tumor heterogeneity. Relevant pathways identified in the core cluster relate to cell cycle, RNA transport, and DNA replication. We performed a kinome analysis of the core cluster and found 4 potential therapeutic kinase targets. Through analysis of the variably detected genes, we discovered 123 differentially expressed transcripts between bilateral and unilateral cases.

CONCLUSIONS

This novel analytical approach allowed determining the retinoblastoma transcriptomic size, a shared active transcriptomic core among the samples, potential therapeutic target kinases shared by all samples, transcripts related to inter tumor heterogeneity, and to determine transcriptomic profiles without the need of control tissues. This approach is useful to analyze other cancer or tissue types.

CORAZON: a web server for data normalization and unsupervised clustering based on expression profiles

Objective

Data normalization and clustering are mandatory steps in gene expression and downstream analyses, respectively. However, user-friendly implementations of these methodologies are available exclusively under expensive licensing agreements, or in stand-alone scripts developed, reflecting on a great obstacle for users with less computational skills.

Results

We developed an online tool called CORAZON (Correlations Analyses Zipper Online), which implements three unsupervised learning methods to cluster gene expression datasets in a friendly environment. It allows the usage of eight gene expression normalization/transformation methodologies and the attribute’s influence. The normalizations requiring the gene length only could be performed to RNA-seq, meanwhile the others can be used with microarray and/or NanoString data. Clustering methodologies performances were evaluated through five models with accuracies between 92 and 100%. We applied our tool to obtain functional insights of non-coding RNAs (ncRNAs) based on Gene Ontology enrichment of clusters in a dataset generated by the ENCODE project. The clusters where the majority of transcripts are coding genes were enriched in Cellular, Metabolic, Transports, and Systems Development categories. Meanwhile, the ncRNAs were enriched in the Detection of Stimulus, Sensory Perception, Immunological System, and Digestion categories. CORAZON source-code is freely available at https://gitlab.com/integrativebioinformatics/corazon and the web-server can be accessed at http://corazon.integrativebioinformatics.me.

Human Monocyte Subset Distinctions and Function: Insights From Gene Expression Analysis

Monocytes are a highly plastic innate immune cell population that displays significant heterogeneity within the circulation. Distinct patterns of surface marker expression have become accepted as a basis for distinguishing three monocyte subsets in humans. These phenotypic subsets, termed classical, intermediate and nonclassical, have also been demonstrated to differ in regard to their functional properties and disease associations when studied in vitro and in vivo. Nonetheless, for the intermediate monocyte subset in particular, functional experiments have yielded conflicting results and some studies point to further levels of heterogeneity. Developments in genetic sequencing technology have provided opportunities to more comprehensively explore the phenotypic and functional differences among conventionally-recognized immune cell subtypes as well as the potential to identify novel subpopulations. In this review, we summarize the transcriptomic evidence in support of the existence of three separate monocyte subsets. We also critically evaluate the insights into subset functional distinctions that have been garnered from monocyte gene expression analysis and the potential utility of such studies to unravel subset-specific functional changes which arise in disease states.

Gene expression signatures: A tool for analysis of breast cancer prognosis and therapy

Breast Cancer is the most predominant female cancer in developed as well as developing countries. The treatment strategies of breast cancers depends on an array of factors like age at diagnosis, menstrual status, dietary pattern, immunological response, genetic variations of the cancer cells etc. Recent technological advancements in cancer diagnosis lead to the emergence of gene expression pattern for better understanding of the tumor behavior. It has not only bolstered the prognosis, but also the early diagnosis and therapy. The accuracy in disease prognosis can be boosted when gene expression signatures are combined with traditional clinicopathological features. This review explains how the evolution of gene expression signatures for breast cancers, its advantages and future prospects. In addition, an overview of currently available gene expression signature analysis tools and consolidated information on their current status and specific benefits, that can be availed for breast cancer diagnosis are also discussed.