Psoriasis is the most common and chronic skin disease that affects individuals from every age group. The rate of psoriasis is increasing over the time in both developed and developing countries. Studies have revealed the possibility of association of psoriasis with skin cancers, particularly non-melanoma skin cancers (NMSC), which, include basal cell carcinoma and cutaneous squamous cell carcinoma (cSCC). There is a need to analyze the disease at molecular level to propose potential biomarkers and therapeutic targets in comparison to cSCC. Therefore, the second analyzed disease of this study is cSCC. It is the second most common prevalent skin cancer all over the world with the potential to metastasize and recur. There is an urge to validate the proposed biomarkers and discover new potential biomarkers as well. In order to achieve the goals and objectives of the study, microarray and RNA-sequencing data analyses were performed followed by network analysis. Afterwards, quantitative systems biology was implemented to analyze the results at a holistic level. The aim was to predict the molecular patterns that can lead psoriasis to cancer. The current study proposed potential biomarkers and therapeutic targets for psoriasis and cSCC. IL-17 signaling pathway is also identified as significant pathway in both diseases. Moreover, the current study proposed that autoimmune pathology, neutrophil recruitment, and immunity to extracellular pathogens are sensitive towards MAPKs (MAPK13 and MAPK14) and genes for AP-1 (FOSL1 and FOS). Therefore, these genes should be further studied in gene knock down based studies as they may play significant role in leading psoriasis towards cancer.

Numerous studies have highlighted the utility of glycan microarray analysis for the elucidation of protein-glycan interactions. However, most current glycan microarray studies analyze glycan binding protein (GBP)-glycan interactions at a single protein concentration. While this approach provides useful information related to a GBP’s overall binding capabilities, extrapolation of true glycan binding preferences using this method fails to account for printing variations or other factors that may confound relative binding. To overcome this limitation, we examined glycan array binding of three galectins over a range of concentrations to allow for a more complete assessment of binding preferences. This approach produced a richer data set than single concentration analysis and provided more accurate identification of true glycan binding preferences. However, while this approach can be highly informative, currently available data analysis approaches make it impractical to perform binding isotherms for each glycan present on currently available platforms following GBP evaluation. To overcome this limitation, we developed a method to directly optimize the efficiency of assessing association constants following multi-GBP concentration glycan array analysis. To this end, we developed programs that automatically analyze raw array data (kdMining) to generate output graphics (kaPlotting) following array analysis at multiple doses. These automatic programing methods reduced processing time from 32.8 h to 1.67 min. Taken together, these results demonstrate an effective approach to glycan array analysis that provides improved detail and efficiency when compared to previous methods.

Alzheimer's disease (AD) has been categorized by the Centers for Disease Control and Prevention (CDC) as the 6th leading cause of death in the United States. AD is a significant health-care burden because of its increased occurrence (specifically in the elderly population), and the lack of effective treatments and preventive methods. With an increase in life expectancy, the CDC expects AD cases to rise to 15 million by 2060. Aging has been previously associated with susceptibility to AD, and there are ongoing efforts to effectively differentiate between normal and AD age-related brain degeneration and memory loss. AD targets neuronal function and can cause neuronal loss due to the buildup of amyloid-beta plaques and intracellular neurofibrillary tangles. Our study aims to identify temporal changes within gene expression profiles of healthy controls and AD subjects. We conducted a meta-analysis using publicly available microarray expression data from AD and healthy cohorts. For our meta-analysis, we selected datasets that reported donor age and gender, and used Affymetrix and Illumina microarray platforms (8 datasets, 2,088 samples). Raw microarray expression data were re-analyzed, and normalized across arrays. We then performed an analysis of variance, using a linear model that incorporated age, tissue type, sex, and disease state as effects, as well as study to account for batch effects, and included binary interactions between factors. Our results identified 3,735 statistically significant (Bonferroni adjusted p < 0.05) gene expression differences between AD and healthy controls, which we filtered for biological effect (10% two-tailed quantiles of mean differences between groups) to obtain 352 genes. Interesting pathways identified as enriched comprised of neurodegenerative diseases pathways (including AD), and also mitochondrial translation and dysfunction, synaptic vesicle cycle and GABAergic synapse, and gene ontology terms enrichment in neuronal system, transmission across chemical synapses and mitochondrial translation. Overall our approach allowed us to effectively combine multiple available microarray datasets and identify gene expression differences between AD and healthy individuals including full age and tissue type considerations. Our findings provide potential gene and pathway associations that can be targeted to improve AD diagnostics and potentially treatment or prevention.

IntroductionTo decode the pathology of Alzheimer’s disease (AD), this study employs multi-omics approaches and bioinformatics analyses to explore AD-associated differentially expressed genes (DEGs), dissect the underlying mechanisms, and thereby facilitate the identification of core genes as well as the development of targeted therapeutic strategies.MethodsSix independent AD datasets were collected from the Gene Expression Omnibus (GEO) database, and data were processed and normalized using the R software. The evaluation of relationships between differentially expressed genes (DEGs) and AD encompassed differential expression analysis, expression quantitative trait loci (eQTL) analysis, and Mendelian randomization (MR) analysis. Additionally, gene set enrichment analysis (GSEA), immune cell correlation analysis, and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were employed to investigate the functional roles and pathways of these genes. Machine learning approaches were applied to identify potential genes from differentially expressed genes (DEGs) associated with AD. The diagnostic performance of these candidate genes was assessed using a nomogram and receiver operating characteristic curves. The expression levels of the identified genes were further validated via quantitative real-time polymerase chain reaction (qRT-PCR).ResultsDifferential gene analysis identified 294 highly expressed genes and 330 lowly expressed genes, and MR analysis identified 10 significantly co-expressed genes associated with AD, specifically METTL7A, SERPINB6, VASP, ENTPD2, CXCL1, FIBP, FUCA1, TARBP1, SORCS3, and DMXL2. Noteworthy observations naive CD4+ T cells in AD, with this distinct from CIBERSORT analysis included the presence of unique immune cell subset further underscoring the critical role of immune processes in the pathogenesis and progression of the disease. METTL7A, SERPINB6, VASP, ENTPD2, FIBP, FUCA1, TARBP1, SORCS3, and DMXL2 were selected for nomogram construction and machine learning-based assessment of diagnostic value, demonstrating considerable diagnostic potential. Furthermore, the significance of the identified key genes was corroborated using both the GEO validation set and qRT-PCR.ConclusionMETTL7A, SERPINB6, VASP, ENTPD2, FIBP, FUCA1, TARBP1, SORCS3, and DMXL2 may regulate the progression of AD. These findings not only deepen our mechanistic understanding of AD pathology but also provide potential candidate genes for the development of targeted therapeutic strategies against AD.

Mammals express thousands of long noncoding (lnc) RNAs, a few of which are shown to function in tissue development. However, the entire repertoire of lncRNAs and the extent to which they regulate biological processes in different tissues and species are not defined. Indeed, most lncRNAs are not conserved between species, raising questions about function. We used RNA-Seq to identify lncRNAs in primary murine fetal liver erythroblasts expressing the lineage marker TER119, megakaryocytes (CD41+) cultured from embryonic day (E) 14.5 murine fetal liver and megakaryocyte erythroid progenitors (MEPs) isolated from mouse bone marrow. We identified 683 and 594 polyadenylated lncRNAs expressed in red blood cell (erythroid) precursors of mice and humans, respectively. More than one half of erythroid lncRNAs are un-annotated, emphasizing the opportunity for new discovery through studies of specialized cell types. We analyzed the expression of these identified lncRNAs in several hematopoietic compartments using a custom microarray to identify erythroid-specific lncRNAs that were robustly expressed in both fetal liver and adult erythroid cells as targets for knockdown. Over 90% of fetal liver erythroid lncRNAs detected using RNA-seq were expressed in adult erythroblasts measured on the microarray. Analysis of the murine erythroid lncRNA transcriptome indicates that ~75% arise from promoters and 25% from enhancers, many of which are regulated by the key erythroid transcription factors GATA1 and SCL/TAL1. Erythroid lncRNA expression is largely conserved among 8 different mouse strains, yet only 15% of mouse lncRNAs are expressed in humans and vice versa, reflecting dramatically greater species-specificity than coding genes. We investigated potential functions of 21 relatively abundant erythroid-specific murine lncRNAs (both conserved and non-conserved) by RNA interference in primary mouse erythroid precursors, and identified 7 whose knockdown inhibited features of terminal erythroid maturation including cell size reduction and enucleation. Strikingly, at least 6 of the 7 lncRNAs have no detectable expression in human erythroblasts, demonstrating that lack of conservation between mammalian species does not predict lack of function. These results reflect marked evolutionary differences between protein-coding genes and lncRNAs and indicate that the latter exert tissue- and species-specific roles in development. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Overall design: A custom Agilent microarray was designed to interrogate expression levels of long noncoding RNAs identified using RNA-seq in several different hematopoietic progenitor and differentiated cell populations. LncRNA gene definitions and RNA-seq data used to identify long noncoding RNAs are deposited in GEO with the accession numbers GSE51667 and GSE40522 respectively. The Agilent eArray platform was used to customize the SurePrint G3 Mouse GE 8x60K microarray to add 3 custom-designed probes (60nt length) each against several categories of genes, namely pseudogene, genes with small RNA overlap, low stringency lncRNAs and high stringency lncRNAs . We interrogated 7 types of hematopoietic cells, HSC, CMP, MEP, GMP, early and late erythroblasts and granulocytes. Expression measurements were determined at least in duplicate for all samples and in triplicate for most samples.

Tuberculosis (TB) is one of deadly transmissible disease that causes death worldwide; however, only 10% of people infected with Mycobacteriumtuberculosis develop disease, indicating that host genetic factors may play key role in determining susceptibility to TB disease. In this way, the analysis of gene expression profiling of TB infected individuals can give us a snapshot of actively expressed genes and transcripts under various conditions. In the present study, we have analyzed microarray data set and compared the gene expression profiles of patients with different datasets of healthy control, latent infection, and active TB. We observed the transition of genes from normal condition to different stages of the TB and identified and annotated those genes/pathways/processes that have important roles in TB disease during its cyclic interventions in the human body. We identified 488 genes that were differentially expressed at various stages of TB and allocated to pathways and gene set enrichment analysis. These pathways as well as GSEA’s importance were evaluated according to the number of DEGs presents in both. In addition, we studied the gene regulatory networks that may help to further understand the molecular mechanism of immune response against the TB infection and provide us a new angle for future biomarker and therapeutic targets. In this study, we identified 26 leading hubs which are deeply rooted from top to bottom in the gene regulatory network and work as the backbone of the network. These leading hubs contains 31 key regulator genes, of which 14 genes were up-regulated and 17 genes were down-regulated. The proposed approach is based on gene-expression profiling, and network analysis approaches predict some unknown TB-associated genes, which can be considered (or can be tested) as reliable candidates for further (in vivo/in vitro) studies.

For whole genome expression analysis, we used Affymetrix GeneChip® microarray analysis. For this purpose, total RNA was extracted from callus and leaves of wildtype, mutants, AtCRK1-overexpression (OX) line, and AtCRK1 KO line using a RNeasy Plant Mini Kit (Qiagen). RNA was treated with RT-grade DNase and quantified through a bioanalyzer (Agilent 2100). cDNA was prepared using 2 μg of RNA following the standard GeneChip® protocol for eukaryotic target preparation (Affymetric GeneChip® Expression Analysis Technical Manual) (http://tools.thermofisher.com/content/sfs/manuals/expression_analysis_manual.pdf). All cDNA cleaning, cRNA preparation, cRNA quantification, cRNA fragmentation, hybridization, staining, washing, and scanning were performed using standard GeneChip® expression protocols. Microarray data were analyzed with Partek Genomics Suite® software.

• The global industry was valued at US$ 919.9 Mn in 2022
• It is estimated to grow at a CAGR of 7.5% from 2023 to 2031 and reach US$ 1.7 Bn by the end of 2031

BackgroundIdiopathic pulmonary fibrosis (IPF) is a chronic, progressive, and fatal fibrotic pulmonary disease with unknow etiology. Owing to lack of reliable prognostic biomarkers and effective treatment measures, patients with IPF usually exhibit poor prognosis. The aim of this study is to establish a risk score prognostic model for predicting the prognosis of patients with IPF based on autophagy-related genes.MethodsThe GSE70866 dataset was obtained from the gene expression omnibus (GEO) database. The autophagy-related genes were collected from the Molecular Signatures Database (MSigDB). Gene enrichment analysis for differentially expressed genes (DEGs) was performed to explore the function of DEGs. Univariate, least absolute shrinkage and selection operator (LASSO), as well as multivariate Cox regression analyses were conducted to identify a multi-gene prognostic model. Receiver operating characteristic (ROC) curve was applied to assess the prediction accuracy of the model. The expression of genes screened from the prognostic model was validated in clinical samples and human lung fibroblasts by qPCR and western blot assays.ResultsAmong the 514 autophagy-related genes, a total of 165 genes were identified as DEGs. These DEGs were enriched in autophagy-related processes and pathways. Based on the univariate, LASSO, and multivariate Cox regression analyses, two genes (MET and SH3BP4) were included for establishing the risk score prognostic model. According to the median value of the risk score, patients with IPF were stratified into high-risk and low-risk groups. Patients in high-risk group had shorter overall survival (OS) than low-risk group in both training and test cohorts. Multivariate regression analysis indicated that prognostic model can act as an independent prognostic indicator for IPF. ROC curve analysis confirmed the reliable predictive value of prognostic model. In the validation experiments, upregulated MET expression and downregulated SH3BP4 expression were observed in IPF lung tissues and TGF-β1-activated human lung fibroblasts, which is consistent with results from microarray data analysis.ConclusionThese findings indicated that the risk score prognostic model based on two autophagy-related genes can effectively predict the prognosis of patients with IPF.

BackgroundHeart failure (HF) is the end stage of various cardiovascular diseases with a high mortality rate. Novel diagnostic and therapeutic biomarkers for HF are urgently required. Our research aims to identify HF-related hub genes and regulatory networks using bioinformatics and validation assays.MethodsUsing four RNA-seq datasets in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of HF using Removal of Unwanted Variation from RNA-seq data (RUVSeq) and the robust rank aggregation (RRA) method. Then, hub genes were recognized using the STRING database and Cytoscape software with cytoHubba plug-in. Furthermore, reliable hub genes were validated by the GEO microarray datasets and quantitative reverse transcription polymerase chain reaction (qRT-PCR) using heart tissues from patients with HF and non-failing donors (NFDs). In addition, R packages “clusterProfiler” and “GSVA” were utilized for enrichment analysis. Moreover, the transcription factor (TF)–DEG regulatory network was constructed by Cytoscape and verified in a microarray dataset.ResultsA total of 201 robust DEGs were identified in patients with HF and NFDs. STRING and Cytoscape analysis recognized six hub genes, among which ASPN, COL1A1, and FMOD were confirmed as reliable hub genes through microarray datasets and qRT-PCR validation. Functional analysis showed that the DEGs and hub genes were enriched in T-cell-mediated immune response and myocardial glucose metabolism, which were closely associated with myocardial fibrosis. In addition, the TF–DEG regulatory network was constructed, and 13 significant TF–DEG pairs were finally identified.ConclusionOur study integrated different RNA-seq datasets using RUVSeq and the RRA method and identified ASPN, COL1A1, and FMOD as potential diagnostic biomarkers for HF. The results provide new insights into the underlying mechanisms and effective treatments of HF.

BackgroundNon-obstructive azoospermia (NOA) is a major contributor of male infertility. Herein, we used existing datasets to identify novel biomarkers for the diagnosis and prognosis of NOA, which could have great significance in the field of male infertility.MethodsNOA datasets were obtained from the Gene Expression Omnibus (GEO) database. CIBERSORT was utilized to analyze the distributions of 22 immune cell populations. Hub genes were identified by applying weighted gene co-expression network analysis (WGCNA), machine learning methods, and protein–protein interaction (PPI) network analysis. The expression of hub genes was verified in external datasets and was assessed by receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis (GSEA) was applied to explore the important functions and pathways of hub genes. The mRNA–microRNA (miRNA)–transcription factors (TFs) regulatory network and potential drugs were predicted based on hub genes. Single-cell RNA sequencing data from the testes of patients with NOA were applied for analyzing the distribution of hub genes in single-cell clusters. Furthermore, testis tissue samples were obtained from patients with NOA and obstructive azoospermia (OA) who underwent testicular biopsy. RT-PCR and Western blot were used to validate hub gene expression.ResultsTwo immune-related oxidative stress hub genes (SHC1 and FGFR1) were identified. Both hub genes were highly expressed in NOA samples compared to control samples. ROC curve analysis showed a remarkable prediction ability (AUCs > 0.8). GSEA revealed that hub genes were predominantly enriched in toll-like receptor and Wnt signaling pathways. A total of 24 TFs, 82 miRNAs, and 111 potential drugs were predicted based on two hub genes. Single-cell RNA sequencing data in NOA patients indicated that SHC1 and FGFR1 were highly expressed in endothelial cells and Leydig cells, respectively. RT-PCR and Western blot results showed that mRNA and protein levels of both hub genes were significantly upregulated in NOA testis tissue samples, which agree with the findings from analysis of the microarray data.ConclusionIt appears that SHC1 and FGFR1 could be significant immune-related oxidative stress biomarkers for detecting and managing patients with NOA. Our findings provide a novel viewpoint for illustrating potential pathogenesis in men suffering from infertility.

The expression of maps of extended RAAS, including 37 genes, in 23 different tissues/organs.

The goals of the study were to assess array platform performance and analysis algorithm performance. The most widely used method for detecting genome-wide protein-DNA interactions is chromatin immunoprecipitation on tiling microarrays, commonly known as ChIP-chip. Here, we conducted the first objective analysis of tiling array platforms, amplification procedures, and signal detection algorithms in a simulated ChIP-chip experiment. Mixtures of human genomic DNA and 'spike-ins' comprised of nearly 100 human sequences at various concentrations were hybridized to four tiling array platforms by eight independent groups. Blind to the number of spike-ins, their locations, and the range of concentrations, each group made predictions of the spike-in locations. We found that microarray platform choice is not the primary determinant of overall performance. In fact, variation in performance between labs, protocols and algorithms within the same array platform was greater than the variation in performance between array platforms. However, each array platform had unique performance characteristics that varied with tiling resolution and the number of replicates, which have implications for cost versus detection power. Long oligonucleotide arrays were slightly more sensitive at detecting very low enrichment. On all platforms, simple sequence repeats and genome redundancy tended to result in false positives. LM-PCR and WGA, the most popular sample amplification techniques, reproduced relative enrichment levels with high fidelity. Performance among signal detection algorithms was heavily dependent on array platform. The spike-in DNA samples and the data presented here provide a stable benchmark against which future ChIP platforms, protocol improvements, and analysis methods can be evaluated. Keywords: ChIP-chip, competition For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf DNA was spiked-in to a background DNA sample. Same DNA samples used in the arrays in this Series; these are technical array replicates.

Studies describing the expression patterns and biomarkers for the tumoral process increase in number every year. The availability of new datasets, although essential, also creates a confusing landscape where common or critical mechanisms are obscured amidst the divergent and heterogeneous nature of such results. In this work, we manually curated the Gene Expression Omnibus using rigorous filtering criteria to select the most homogeneous and highest quality microarray and RNA-seq datasets from multiple types of cancer. By applying systems biology approaches, combined with machine learning analysis, we investigated possible frequently deregulated molecular mechanisms underlying the tumoral process. Our multi-approach analysis of 99 curated datasets, composed of 5,406 samples, revealed 47 differentially expressed genes in all analyzed cancer types, which were all in agreement with the validation using TCGA data. Results suggest that the tumoral process is more related to the overexpression of core deregulated machinery than the underexpression of a given gene set. Additionally, we identified gene expression similarities between different cancer types not described before and performed an overall survival analysis using 20 cancer types. Finally, we were able to suggest a core regulatory mechanism that could be frequently deregulated.

IntroductionMeningiomas are the most common brain tumor, with prevalence of approximately 3%. Histological grading has a major role in determining treatment choice and predicting outcome. While indolent grade 1 and aggressive grade 3 meningiomas exhibit relatively homogeneous clinical behavior, grade 2 meningiomas are far more heterogeneous, making outcome prediction challenging. We hypothesized two subgroups of grade 2 meningiomas which biologically resemble either World Health Organization (WHO) grade 1 or WHO grade 3. Our aim was to establish gene expression signatures that separate grade 2 meningiomas into two homogeneous subgroups: a more indolent subtype genetically resembling grade 1 and a more aggressive subtype resembling grade 3.MethodsWe carried out an observational meta-analysis on 212 meningiomas from six distinct studies retrieved from the open-access platform Gene Expression Omnibus. Microarray data was analyzed with systems-level gene co-expression network analysis. Fuzzy C-means clustering was employed to reclassify 34 of the 46 grade 2 meningiomas (74%) into a benign “grade 1-like” (13/46), and malignant “grade 3-like” (21/46) subgroup based on transcriptomic profiles. We verified shared biology between matching subgroups based on meta-gene expression and recurrence rates. These results were validated further using an independent RNA-seq dataset with 160 meningiomas, with similar results.ResultsRecurrence rates of “grade 1-like” and “grade 3- like” tumors were 0 and 75%, respectively, statistically similar to recurrence rates of grade 1 (17%) and 3 (85%). We also found overlapping biological processes of new subgroups with their adjacent grades 1 and 3.ConclusionThese results underpin molecular signatures as complements to histological grading systems. They may help reshape prediction, follow-up planning, treatment decisions and recruitment protocols for future and ongoing clinical trials.

IntroductionLupus nephritis (LN) is a major risk factor of morbidity and mortality. Glomerular injury is associated with different pathogeneses and clinical presentations in LN patients. However, the molecular mechanisms involved are not well understood. This study aimed to explore the molecular characteristics and mechanisms of this disease using bioinformatics analysis.MethodsTo characterize glomeruli in LN, microarray datasets GSE113342 and GSE32591 were downloaded from the Gene Expression Omnibus database and analyzed to determine the differentially expressed genes (DEGs) between LN glomeruli and normal glomeruli. Functional enrichment analyses and protein–protein interaction network analyses were then performed. Module analysis was performed using the Search Tool for the Retrieval of Interacting Genes/Proteins and Cytoscape software. Immunofluorescence staining was performed to identify the glomerular expression of S100A8 in various International Society of Nephrology/Renal Pathology Society (ISN/RPS) class LN patients. The image of each glomerulus was acquired using a digital imaging system, and the green fluorescence intensity was quantified using Image-Pro Plus software.ResultsA total of 13 DEGs, consisting of 12 downregulated genes and one upregulated gene (S100A8), were identified in the microarray datasets. The functions and pathways associated with the DEGs mainly include inflammatory response, innate immune response, neutrophil chemotaxis, leukocyte migration, cell adhesion, cell–cell signaling, and infection. We also found that monocytes and activated natural killer cells were upregulated in both GSE113342 and GSE32591. Glomerular S100A8 staining was significantly enhanced compared to that in the controls, especially in class IV.ConclusionsThe DEGs identified in the present study help us understand the underlying molecular mechanisms of LN. Our results show that glomerular S100A8 expression varies in different pathological types; however, further research is required to confirm the role of S100A8 in LN.

BackgroundAmyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by the loss of upper and lower motor neurons, leading to progressive weakness of voluntary muscles, with death following from neuromuscular respiratory failure, typically within 3 to 5 years. There is a strong genetic contribution to ALS risk. In 10% or more, a family history of ALS or frontotemporal dementia is obtained, and the Mendelian genes responsible for ALS in such families have now been identified in about 50% of cases. Only about 14% of apparently sporadic ALS is explained by known genetic variation, suggesting that other forms of genetic variation are important. Telomeres maintain DNA integrity during cellular replication, differ between sexes, and shorten naturally with age. Sex and age are risk factors for ALS and we therefore investigated telomere length in ALS.MethodsSamples were from Project MinE, an international ALS whole genome sequencing consortium that includes phenotype data. For validation we used donated brain samples from motor cortex from people with ALS and controls. Ancestry and relatedness were evaluated by principal components analysis and relationship matrices of DNA microarray data. Whole genome sequence data were from Illumina HiSeq platforms and aligned using the Isaac pipeline. TelSeq was used to quantify telomere length using whole genome sequence data. We tested the association of telomere length with ALS and ALS survival using Cox regression.ResultsThere were 6,580 whole genome sequences, reducing to 6,195 samples (4,315 from people with ALS and 1,880 controls) after quality control, and 159 brain samples (106 ALS, 53 controls). Accounting for age and sex, there was a 20% (95% CI 14%, 25%) increase of telomere length in people with ALS compared to controls (p = 1.1 × 10−12), validated in the brain samples (p = 0.03). Those with shorter telomeres had a 10% increase in median survival (p = 5.0×10−7). Although there was no difference in telomere length between sporadic ALS and familial ALS (p=0.64), telomere length in 334 people with ALS due to expanded C9orf72 repeats was shorter than in those without expanded C9orf72 repeats (p = 5.0×10−4).DiscussionAlthough telomeres shorten with age, longer telomeres are a risk factor for ALS and worsen prognosis. Longer telomeres are associated with ALS.

ObjectiveTo investigate the potential pathogenic mechanism of temporal lobe epilepsy with hippocampal sclerosis (TLE+HS) by analyzing the expression profiles of microRNA/ mRNA/ lncRNA/ DNA methylation in brain tissues.MethodsBrain tissues of six patients with TLE+HS and nine of normal temporal or parietal cortices (NTP) of patients undergoing internal decompression for traumatic brain injury (TBI) were collected. The total RNA was dephosphorylated, labeled, and hybridized to the Agilent Human miRNA Microarray, Release 19.0, 8 × 60K. The cDNA was labeled and hybridized to the Agilent LncRNA+mRNA Human Gene Expression Microarray V3.0,4 × 180K. For methylation detection, the DNA was labeled and hybridized to the Illumina 450K Infinium Methylation BeadChip. The raw data was extracted from hybridized images using Agilent Feature Extraction, and quantile normalization was performed using the Agilent GeneSpring. P-value < 0.05 and absolute fold change >2 were considered the threshold of differential expression data. Data analyses were performed using R and Bioconductor. BrainSpan database was used to screen for signatures that were not differentially expressed in normal human hippocampus and cortex (data from BrainSpan), but differentially expressed in TLE+HS’ hippocampus and NTP’ cortex (data from our cohort). The strategy “Guilt by association” was used to predict the prospective roles of each important hub mRNA, miRNA, or lncRNA.ResultsA significantly negative correlation (r < −0.5) was found between 116 pairs of microRNA/mRNA, differentially expressed in six patients with TLE+HS and nine of NTP. We examined this regulation network’s intersection with target gene prediction results and built a lncRNA-microRNA-Gene regulatory network with structural, and functional significance. Meanwhile, we found that the disorder of FGFR3, hsa-miR-486-5p, and lnc-KCNH5-1 plays a key vital role in developing TLE+HS.

Endometriosis is a common inflammatory estrogen-dependent gynecological disorder, associated with pelvic pain and reduced fertility in women. Several aspects of this disorder and its cellular and molecular etiology remain unresolved. We have analyzed the global gene expression patterns in the endometrium, peritoneum and in endometriosis lesions of endometriosis patients and in the endometrium and peritoneum of healthy women. In this report, we present the EndometDB, an interactive web-based user interface for browsing the gene expression database of collected samples without the need for computational skills. The current database incorporates the expression data from 115 patients and 53 controls, with over 24000 genes and clinical features, such as their age, disease stages, hormonal medication, menstrual cycle phase, and the different endometriosis lesion types. Using the web-tool, the end-user can easily generate various plot outputs, including boxplots, heatmaps and scatterplots, and the generated plot outputs can be downloaded in pdf-format. Availability and implementation: The web-based user interface is implemented using HTML5, JavaScript, CSS, Plotly and R. It is freely available from URL: https://endometdb.utu.fi/gene_analysis.Endomet Database dump and schema.Number of samples used in quantitative real-time PCR.Pie chart showing the percentage of the different types of samples analyzed for global gene expression by microarrays.Multidimensional scaling of WNT pathway genes of the endometrium vs endometriosis samples using Canberra distance metric and colored by tissues. The analysis separates endometrial specimens from the different lesions.Result of scree test for PCA analysis of WNT pathway genes. The scree plot explains how much the principal components account for the total variance in the expression data.

Influenza, a communicable disease, affects thousands of people worldwide. Young children, elderly, immunocompromised individuals and pregnant women are at higher risk for being infected by the influenza virus. Our study aims to highlight differentially expressed genes in influenza disease compared to influenza vaccination, including variability due to age and sex. To accomplish our goals, we conducted a meta-analysis using publicly available microarray expression data. Our inclusion criteria included subjects with influenza, subjects who received the influenza vaccine and healthy controls. We curated 18 microarray datasets for a total of 3,481 samples (1,277 controls, 297 influenza infection, 1,907 influenza vaccination). We pre-processed the raw microarray expression data in R using packages available to pre-process Affymetrix and Illumina microarray platforms. We used a Box-Cox power transformation of the data prior to our down-stream analysis to identify differentially expressed genes. Statistical analyses were based on linear mixed effects model with all study factors and successive likelihood ratio tests (LRT) to identify differentially-expressed genes. We filtered LRT results by disease (Bonferroni adjusted p < 0.05) and used a two-tailed 10% quantile cutoff to identify biologically significant genes. Furthermore, we assessed age and sex effects on the disease genes by filtering for genes with a statistically significant (Bonferroni adjusted p < 0.05) interaction between disease and age, and disease and sex. We identified 4,889 statistically significant genes when we filtered the LRT results by disease factor, and gene enrichment analysis (gene ontology and pathways) included innate immune response, viral process, defense response to virus, Hematopoietic cell lineage and NF-kappa B signaling pathway. Our quantile filtered gene lists comprised of 978 genes each associated with influenza infection and vaccination. We also identified 907 and 48 genes with statistically significant (Bonferroni adjusted p < 0.05) disease-age and disease-sex interactions, respectively. Our meta-analysis approach highlights key gene signatures and their associated pathways for both influenza infection and vaccination. We also were able to identify genes with an age and sex effect. This gives potential for improving current vaccines and exploring genes that are expressed equally across ages when considering universal vaccinations for influenza.