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RNA sequencing is the gold-standard method to quantify transcriptomic changes between two conditions. The overwhelming majority of data analysis methods available are focused on polyadenylated RNA transcribed from single-copy genes and overlook transcripts from repeated sequences such as transposable elements (TEs). These self-autonomous genetic elements are increasingly studied, and specialized tools designed to handle multimapping sequencing reads are available. Transfer RNAs are transcribed by RNA polymerase III and are essential for protein translation. There is a need for integrated software that is able to analyze multiple types of RNA. Here, we present 3t-seq, a Snakemake pipeline for integrated differential expression analysis of transcripts from single-copy genes, TEs, and tRNA. 3t-seq produces an accessible report and easy-to-use results for downstream analysis starting from raw sequencing data and performing quality control, genome mapping, gene expression quantification, and statistical testing. It implements three methods to quantify TEs expression and one for tRNA genes. It provides an easy-to-configure method to manage software dependencies that lets the user focus on results. 3t-seq is released under MIT license and is available at https://github.com/boulardlab/3t-seq [ABSTRACT FROM AUTHOR]

In recent years, the role of bioinformatics and computational biology together with omics techniques and transcriptomics has gained tremendous importance in biomedicine and healthcare, particularly for the identification of biomarkers for precision medicine and drug discovery. Differential gene expression (DGE) analysis is one of the most used techniques for RNA-sequencing (RNA-seq) data analysis. This tool, which is typically used in various RNA-seq data processing applications, allows the identification of differentially expressed genes across two or more sample sets. Functional enrichment analyses can then be performed to annotate and contextualize the resulting gene lists. These studies provide valuable information about disease-causing biological processes and can help in identifying molecular targets for novel therapies. This review focuses on differential gene expression (DGE) analysis pipelines and bioinformatic techniques commonly used to identify specific biomarkers and discuss the advantages and disadvantages of these techniques.

Abstract Background Phytophthora palmivora is a devastating oomycete pathogen in durian, one of the most economically important crops in Southeast Asia. The use of fungicides in Phytophthora management may not be a long-term solution because of emerging chemical resistance issues. It is crucial to develop Phytophthora-resistant durian cultivars, and information regarding the underlying resistance mechanisms is valuable for smart breeding programs. Results In this study, we conducted RNA sequencing (RNA-seq) to investigate early gene expression responses (at 8, 24, and 48 h) after the P. palmivora infection in three durian cultivars, which included one resistant cultivar (Puangmanee; PM) and two susceptible cultivars (Monthong; MT and Kradumthong; KD). We performed co-expression and differential gene expression analyses to capture gene expression patterns and identify the differentially expressed genes. The results showed that genes encoding heat shock proteins (HSPs) were upregulated in all infected durians. The expression levels of genes encoding HSPs, such as ERdj3B, were high only in infected PM. A higher level of P. palmivora resistance in PM appeared to be associated with higher expression levels of various genes encoding defense and chitin response proteins, such as lysM domain receptor-like kinases. MT had a lower resistance level than PM, although it possessed more upregulated genes during P. palmivora infection. Many photosynthetic and defense genes were upregulated in the infected MT, although their expression levels were lower than those in the infected PM. KD, the least resistant cultivar, showed downregulation of genes involved in cell wall organization or biogenesis during P. palmivora infection. Conclusions Our results showed that the three durian cultivars exhibited significantly different gene expression patterns in response to P. palmivora infection. The upregulation of genes encoding HSPs was common in all studied durians. The high expression of genes encoding chitin response proteins likely contributed to P. palmivora resistance in durians. Durian susceptibility was associated with low basal expression of defense genes and downregulation of several cell wall-related genes. These findings enhance our understanding of durian resistance to Phytophthora infection and could be useful for the development of elite durian cultivars.

Abstract Background Conventional differential gene expression analysis pipelines for non-model organisms require computationally expensive transcriptome assembly. We recently proposed an alternative strategy of directly aligning RNA-seq reads to a protein database, and demonstrated drastic improvements in speed, memory usage, and accuracy in identifying differentially expressed genes. Result Here we report a further speed-up by replacing DNA-protein alignment by quasi-mapping, making our pipeline > 1000× faster than assembly-based approach, and still more accurate. We also compare quasi-mapping to other mapping techniques, and show that it is faster but at the cost of sensitivity. Conclusion We provide a quick-and-dirty differential gene expression analysis pipeline for non-model organisms without a reference transcriptome, which directly quasi-maps RNA-seq reads to a reference protein database, avoiding computationally expensive transcriptome assembly.

Background: Conventional differential gene expression analysis pipelines for non-model organisms require computationally expensive transcriptome assembly. We recently proposed an alternative strategy of directly aligning RNA-seq reads to a protein database, and demonstrated drastic improvements in speed, memory usage, and accuracy in identifying differentially expressed genes. Result: Here we report a further speed-up by replacing DNA-protein alignment by quasi-mapping, making our pipeline > 1000× faster than assembly-based approach, and still more accurate. We also compare quasi-mapping to other mapping techniques, and show that it is faster but at the cost of sensitivity. Conclusion: We provide a quick-and-dirty differential gene expression analysis pipeline for non-model organisms without a reference transcriptome, which directly quasi-maps RNA-seq reads to a reference protein database, avoiding computationally expensive transcriptome assembly. [ABSTRACT FROM AUTHOR]

Background: Phytophthora palmivora is a devastating oomycete pathogen in durian, one of the most economically important crops in Southeast Asia. The use of fungicides in Phytophthora management may not be a long-term solution because of emerging chemical resistance issues. It is crucial to develop Phytophthora-resistant durian cultivars, and information regarding the underlying resistance mechanisms is valuable for smart breeding programs. Results: In this study, we conducted RNA sequencing (RNA-seq) to investigate early gene expression responses (at 8, 24, and 48 h) after the P. palmivora infection in three durian cultivars, which included one resistant cultivar (Puangmanee; PM) and two susceptible cultivars (Monthong; MT and Kradumthong; KD). We performed co-expression and differential gene expression analyses to capture gene expression patterns and identify the differentially expressed genes. The results showed that genes encoding heat shock proteins (HSPs) were upregulated in all infected durians. The expression levels of genes encoding HSPs, such as ERdj3B, were high only in infected PM. A higher level of P. palmivora resistance in PM appeared to be associated with higher expression levels of various genes encoding defense and chitin response proteins, such as lysM domain receptor-like kinases. MT had a lower resistance level than PM, although it possessed more upregulated genes during P. palmivora infection. Many photosynthetic and defense genes were upregulated in the infected MT, although their expression levels were lower than those in the infected PM. KD, the least resistant cultivar, showed downregulation of genes involved in cell wall organization or biogenesis during P. palmivora infection. Conclusions: Our results showed that the three durian cultivars exhibited significantly different gene expression patterns in response to P. palmivora infection. The upregulation of genes encoding HSPs was common in all studied durians. The high expression of genes encoding chitin response proteins likely contributed to P. palmivora resistance in durians. Durian susceptibility was associated with low basal expression of defense genes and downregulation of several cell wall-related genes. These findings enhance our understanding of durian resistance to Phytophthora infection and could be useful for the development of elite durian cultivars. [ABSTRACT FROM AUTHOR]

Gene ontology phrases are a widely used set of hierarchical terms that describe the biological properties of genes. These terms are then used to annotate individual genes, making it possible to determine the likely physiological properties of groups of genes such as a list of differentially expressed genes. Consequently, their ability to predict changes in biological features and functions based on alterations in gene expression has made gene ontology terms popular in the wide range of bioinformatic fields, such as differential gene expression and evolutionary biology. However, while they make the analysis easier, it is seldom easy to convey the results in a readily understandable manner. A number of applications have been developed to visualize gene ontology (GO) term enrichment; however, these solutions tend to focus on the display of aggregated results from a single analysis, making them unsuitable for the analysis of a series of experiments such as a time course or response to different drug treatments. As multiple pair wise comparisons are becoming a common feature of RNA profiling experiments, the absence of a mechanism to easily compare them is a significant problem. Consequently, to overcome this obstacle, we have developed GOTermViewer, an application that displays GO term enrichment data as determined by GOstats such that changes in physiological response across a number of individual analyses across a time course or range of drug treatments can be visualized. [ABSTRACT FROM AUTHOR]

Introduction. Rubella virus is an RNA-containing virus capable of infecting human cells and causing infectious disease. Infection of pregnant women with rubella virus can lead to abortion or congenital rubella syndrome (CRS), a set of long-term birth defects including incomplete fetal organ development and mental retardation. There is no specific treatment for rubella and CRS. The regulation of antiviral immune response and viral reproduction by long non-coding RNAs is currently under active investigation. In this study, we evaluated the changes in the expression profile of long non-coding RNAs in rubella virus-infected A549 epithelial by RNA sequencing. Materials and Methods. A549 cells were infected with a wild-type variant of laboratory strain C-77 of rubella virus with a multiplicity of infection of 1.0 infectious units per cell and incubated for 72 hours. Virus titres were determined by the CCID method in the sensitive RK-13 cell culture. 48 h after infection, the cell monolayer was lysed, RNA was isolated, and libraries were prepared for sequencing. Sequencing was performed on the NextSeq500 platform (Illumina, USA) in paired-end reading mode. Validation of the obtained RNA sequencing data was performed using quantitative real-time PCR. Results. Rubella virus replication affects the production of some long non-coding RNAs by altering their expression profile. Thus, upon infection of A549 epithelial cells with rubella virus, there was a significant increase in the expression of such long non-coding RNAs as GAS5, NEAT1, LUCAT1, MIR210HG, MEG3, EPB41L4A-AS1, ZFAS1, and SNHG 1, 7, 12, 29, 32. DANCR, IGFL2-AS1, IGFL2-AS1, MIR1915HG, and SNHG14 were most significantly decreased in expression. Gene ontology (GO)-analysis revealed that long non-coding RNAs are involved at different levels in the mechanisms of immune response, in particular, RNA processing and nucleic acid metabolism; therefore, up- and down-regulation of these molecules leads to modulation of human antiviral immune response in response to rubella virus infection. Conclusion. Thus, the regulation of long non-coding RNA production by rubella virus has been shown for the first time. Differentially expressed long non-coding RNAs can be used as prognostic and diagnostic biomarkers of viral diseases.

Although the hemostatically efficacious nature of cellulose nanofibers (CNFs) is well established, data regarding their hemostatic performance in large animal systems and safety toward human skin cells, which are critical factors for clinical translation, are lacking. This study evaluated the clinical potential of CNF-coated gauze by testing its hemostatic efficacy in rats, rabbits, and pigs, and assessing its safety in human primary cells through differential gene expression (DEG) analysis. In-vitro studies using rabbit blood revealed that the CNF-coated gauze exhibited a significantly lower blood clotting index (BCI) value than gauze coated with chitosan, a commonly used hemostatic agent, indicating superior blood clotting. In-vivo tests on bleeding-induced pig livers revealed that the CNF-coated gauze delivered a four-fold higher hemostatic success rate reduced bleeding volume compared to regular gauze. Moreover, a rat wound-healing model revealed improved healing with CNF-coated gauze. DEG analysis of human dermal fibroblast primary cells showed no statistically significant differences between the CNF and control groups, indicating its human safety. Overall, our results suggest that CNF-coated hemostatic gauze absorbs blood rapidly, delivers superior hemostatic performance compared to regular gauze, is safe from both histological and DEG perspectives, and is therefore suitable for use in healthcare and clinical settings.

The poultry's immune system is regulated by various genes involved in both innate and acquired immune responses. Understanding the changes in gene expression post-infection is essential for developing effective intervention strategies and improving disease management in the poultry industry. This study aimed to employ RNA-Seq to investigate the differential gene expression profiles and to identify the essential genes and pathways involved in the host response of poultry infected with the avian leukosis virus. For this purpose, RNA-Seq data of healthy and avian leukosis virus-infected birds on days 24 (n=6) and 40 (n=6) post-infection were used. After quality control and preprocessing, we aligned the reads to the chicken reference genome using STAR software and quantified gene expression using HTSeq-Count. Differential gene expression analysis was performed using the edgeR package in R. The results of this study showed that the uniquely mapped read percentage ranged from 78.07% to 87.74%, and the mismatch rate per base was found to vary between 0.77% and 1.45%. A total of 2,213 and 1165 genes exhibited significant differential expression compared to the control group on day 24 and 40 post-infection, respectively (p<0.05). The gene ontology enrichment and pathway analysis revealed that six candidate genes, AvBD1, AvBD6, CATH1, CATH2, CATH3, and DEFB4A, are associated with the immune response on days 24 and 40. Additionally, on day 24, two more candidate genes, AvBD5 and LYG2, were found to be involved in the immune response. [ABSTRACT FROM AUTHOR]

The TERT gene encodes the reverse transcriptase subunit of telomerase and is normally transcriptionally suppressed in differentiated human cells but reactivated in cancers where its expression is frequently associated with poor survival prognosis. Here we experimentally assessed the RNA sequencing expression patterns associated with TERT transcription in 1039 human cancer samples of 27 tumor types. We observed a bimodal distribution of TERT expression where ~27% of cancer samples did not express TERT and the rest showed a bell-shaped distribution. Expression of TERT strongly correlated with 1443 human genes including 103 encoding transcriptional factor proteins. Comparison of TERT- positive and negative cancers showed the differential activation of 496 genes and 1975 molecular pathways. Therein, 32/38 (84%) of DNA repair pathways were hyperactivated in TERT+ cancers which was also connected with accelerated replication, transcription, translation, and cell cycle progression. In contrast, the level of 40 positive cell cycle regulator proteins and a set of epithelial-to-mesenchymal transition pathways was specific for the TERTgroup suggesting different proliferation strategies for both groups of cancer. Our pilot study showed that the TERT+ group had ~13% of cancers with C228T or C250T mutated TERT promoter. However, the presence of promoter mutations was not associated with greater TERT expression compared with other TERT+ cancers, suggesting parallel mechanisms of its transcriptional activation in cancers. In addition, we detected a decreased expression of L1 retrotransposons in the TERT+ group, and further decreased L1 expression in promoter mutated TERT+ cancers. TERT expression was correlated with 17 genes encoding molecular targets of cancer therapeutics and may relate to differential survival patterns of TERT- positive and negative cancers. [ABSTRACT FROM AUTHOR]

Normal tissues are essential for studying disease-specific differential gene expression. However, healthy human controls are typically available only in postmortal/autopsy settings. In cancer research, fragments of pathologically normal tissue adjacent to tumor site are frequently used as the controls. However, it is largely underexplored how cancers can systematically influence gene expression of the neighboring tissues. Here we performed a comprehensive pan-cancer comparison of molecular profiles of solid tumor-adjacent and autopsy-derived healthy normal tissues. We found a number of systemic molecular differences related to activation of the immune cells, intracellular transport and autophagy, cellular respiration, telomerase activation, p38 signaling, cytoskeleton remodeling, and reorganization of the extracellular matrix. The tumor-adjacent tissues were deficient in apoptotic signaling and negative regulation of cell growth including G2/M cell cycle transition checkpoint. We also detected an extensive rearrangement of the chemical perception network. Molecular targets of 32 and 37 cancer drugs were over- or underexpressed, respectively, in the tumor-adjacent norms. These processes may be driven by molecular events that are correlated between the paired cancer and adjacent normal tissues, that mostly relate to inflammation and regulation of intracellular molecular pathways such as the p38, MAPK, Notch, and IGF1 signaling. However, using a model of macaque postmortal tissues we showed that for the 30 min - 24-hour time frame at 4ºC, an RNA degradation pattern in lung biosamples resulted in an artifact differential expression profile for 1140 genes, although no differences could be detected in liver. Thus, such concerns should be addressed in practice.

Rapeseed-mustard, the oleiferous Brassica species are important oilseed crops cultivated all over the globe. Mustard aphid Lipaphis erysimi (L.) Kaltenbach is a major threat to the cultivation of rapeseed-mustard. Wild mustard Rorippa indica (L.) Hiern shows tolerance to mustard aphids as a nonhost and hence is an important source for the bioprospecting of potential resistance genes and defense measures to manage mustard aphids sustainably. We performed mRNA sequencing of the R. indica plant uninfested and infested by the mustard aphids, harvested at 24 hours post-infestation. Following quality control, the high-quality reads were subjected to de novo assembly of the transcriptome. As there is no genomic information available for this potential wild plant, the raw reads will be useful for further bioinformatics analysis and the sequence information of the assembled transcripts will be helpful to design primers for the characterization of specific gene sequences. In this study, we also used the generated resource to comprehensively analyse the global profile of differential gene expression in R. indica in response to infestation by mustard aphids. The functional enrichment analysis of the differentially expressed genes reveals a significant immune response and suggests the possibility of chitin-induced defense signaling. [ABSTRACT FROM AUTHOR]

Intellectual disability (ID) is an early childhood neurodevelopmental disorder that is characterized by impaired intellectual functioning and adaptive behavior. It is one of the major concerns in the field of neurodevelopmental disorders across the globe. Diversified approaches have been put forward to overcome this problem. Among all these approaches, high throughput transcriptomic analysis has taken an important dimension. The identification of genes causing ID rapidly increased over the past 3 to 5 years owing to the use of sophisticated high throughput sequencing platforms. Early monitoring and preventions are much important for such disorder as their progression occurs during fetal development. This study is an attempt to identify differentially expressed genes (DEGs) and upregulated biological processes involved in development of ID patients through comparative analysis of available transcriptomics data. A total of 7 transcriptomic studies were retrieved from National Center for Biotechnology Information (NCBI) and were subjected to quality check and trimming prior to alignment. The normalization and differential expression analysis were carried out using DESeq2 and EdgeR packages of Rstudio to identify DEGs in ID. In progression of the study, functional enrichment analysis of the results obtained from both DESeq2 and EdgeR was done using gene set enrichment analysis (GSEA) tool to identify major upregulated biological processes involved in ID. Our findings concluded that monitoring the level of E2F targets, estrogen, and genes related to oxidative phosphorylation, DNA repair, and glycolysis during the developmental stage of an individual can help in the early detection of ID disorder. [ABSTRACT FROM AUTHOR]

The TERT gene encodes the reverse transcriptase subunit of telomerase and is normally transcriptionally suppressed in differentiated human cells but reactivated in cancers where its expression is frequently associated with poor survival prognosis. Here we experimentally assessed the RNA sequencing expression patterns associated with TERT transcription in 1039 human cancer samples of 27 tumor types. We observed a bimodal distribution of TERT expression where ∼27% of cancer samples did not express TERT and the rest showed a bell-shaped distribution. Expression of TERT strongly correlated with 1443 human genes including 103 encoding transcriptional factor proteins. Comparison of TERT- positive and negative cancers showed the differential activation of 496 genes and 1975 molecular pathways. Therein, 32/38 (84%) of DNA repair pathways were hyperactivated in TERT+ cancers which was also connected with accelerated replication, transcription, translation, and cell cycle progression. In contrast, the level of 40 positive cell cycle regulator proteins and a set of epithelial-to-mesenchymal transition pathways was specific for the TERT- group suggesting different proliferation strategies for both groups of cancer. Our pilot study showed that the TERT+ group had ∼13% of cancers with C228T or C250T mutated TERT promoter. However, the presence of promoter mutations was not associated with greater TERT expression compared with other TERT+ cancers, suggesting parallel mechanisms of its transcriptional activation in cancers. In addition, we detected a decreased expression of L1 retrotransposons in the TERT+ group, and further decreased L1 expression in promoter mutated TERT+ cancers. TERT expression was correlated with 17 genes encoding molecular targets of cancer therapeutics and may relate to differential survival patterns of TERT- positive and negative cancers.

Biologics represent the fastest growing group of therapeutics, but many advanced recombinant protein moieties remain difficult to produce. Here, we identify metabolic engineering targets limiting expression of recombinant human proteins through a systems biology analysis of the transcriptomes of CHO and HEK293 during recombinant expression. In an expression comparison of 24 difficult to express proteins, one third of the challenging human proteins displayed improved secretion upon host cell swapping from CHO to HEK293. Guided by a comprehensive transcriptomics comparison between cell lines, especially highlighting differences in secretory pathway utilization, a co-expression screening of 21 secretory pathway components validated ATF4, SRP9, JUN, PDIA3 and HSPA8 as productivity boosters in CHO. Moreover, more heavily glycosylated products benefitted more from the elevated activities of the N- and O-glycosyltransferases found in HEK293. Collectively, our results demonstrate the utilization of HEK293 for expression rescue of human proteins and suggest a methodology for identification of secretory pathway components for metabolic engineering of HEK293 and CHO.

Abstract Deconvolution of RNA sequencing data is a computational method used to estimate the relative proportions of different cell types or subpopulations within a heterogeneous sample based on gene expression profiles. This technique is particularly useful in studies where the goal is to identify changes in gene expression that are specific to a particular cell type or subpopulation. The deconvolution process involves using reference gene expression profiles from known cell types or subpopulations to infer the relative abundance of these cells within a mixed sample. This is typically done using linear regression or other statistical methods to model the observed gene expression data as a linear combination of the reference profiles. Once the relative proportions of each cell type or subpopulation have been estimated, downstream analyses can be performed on each component separately, allowing for more precise identification of cell-type-specific changes in gene expression. Overall, deconvolution of RNA sequencing data is a powerful tool for dissecting complex biological systems and identifying cell-type-specific molecular signatures that may be relevant for disease diagnosis and treatment.

Zhaobing Pan,1– 3,&ast; Qiaoshan Yang,1– 3,&ast; Xiaojing Zhang,1– 3 Xiaoqing Xu,1– 3 Yao Sun,1– 3 Fusheng Zhou,1– 4 Leilei Wen1– 3 1Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 2Institute of Dermatology, Anhui Medical University, Hefei, Anhui, People’s Republic of China; 3Key Laboratory of Dermatology (Anhui Medical University), Ministry of Education, Hefei, Anhui, People’s Republic of China; 4Inflammation and Immune Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Fusheng Zhou; Leilei Wen, Department of Dermatology, The First Affiliated Hospital, Anhui Medical University, No. 218, Jixi Road, Shushan District, Hefei, 230000, Anhui, People’s Republic of China, Tel +86-138-5698-5934 ; +86-183-5609-9275, Fax +86-0551-63633742, Email biozhoufs@163.com; wenleilei_2012@163.comPurpose: Systemic lupus erythematosus (SLE) is a typical autoimmune disease characterized by the involvement of multiple organs and the production of antinuclear antibodies. This study aimed to investigate the molecular mechanism of SLE.Patients and Methods: We retrieved genome-wide gene expression levels from five public datasets with relatively large sample sizes from the Gene Expression Omnibus (GEO), and we compared the expression profiles of peripheral blood mononuclear cells (PBMCs) from SLE patients and healthy controls (HCs). The expression of seven target genes in PBMCs from 25 cases and 3 HCs was further validated by reverse-transcription quantitative PCR (RT‒qPCR). Flow cytometry was used for verifying the proportion of naive CD4(+) T cells and M2 macrophages in PBMCs from 5 cases and 4 HCs.Results: We found 14 genes (TRIM5, FAM8A1, SHFL, LHFPL2, PARP14, IFIT5, PARP12, DDX60, IRF7, IF144, OAS1, OAS3, RHBDF2, and RSAD2) that were differentially expressed among all five datasets. The heterogeneity test under the fixed effect model showed no obvious heterogeneity of TRIM5, FAM8A1, and SHFL across different populations. TRIM5 was positively correlated with the remaining 13 genes. By separating patient samples into TRIM5-high and TRIM5-low groups, we found that up-regulated genes in the TRIM5-high group were mainly enriched in virus-related pathways. Immune cell proportion analysis and flow cytometry revealed that naive CD4(+) T cells were significantly decreased while M2 macrophages were increased in the SLE group. TRIM5 expression levels were negatively correlated with naive CD4(+) T cells but positively correlated with M2 macrophages.Conclusion: Our data indicated that TRIM5 might be a key factor that modulates SLE etiology, possibly through naive CD4(+) T cells and M2 macrophages.Keywords: differential gene expression analysis, gene, virus, autoimmune disease

Frontal temporal dementia (FTD) is a neurological disorder known to have fewer therapeutic options. So far, only a few biomarkers are available for FTD that can be used as potential comorbidity targets. For example, genes such as VCP, which has a role in breast cancer, and WFS1, which has a role in COVID-19, are known to show a role in FTD as well. To this end, in the present study, we aim to identify potential biomarkers or susceptible genes for FTD that show comorbidities with diseases such as COVID-19 and breast cancer. A dataset from Gene Expression Omnibus containing FTD expression profiles from African American and white ethnicity backgrounds was included in our study. In FTD samples of the GSE193391 dataset, we identified 305 DEGs, with 168 genes being up-regulated and 137 genes being down-regulated. We conducted a comorbidity analysis for COVID-19 and breast cancer, followed by an analysis of potential drug interactions, pathogenicity, analysis of genetic variants, and functional enrichment analysis. Our results showed that the genes AKT3, GFAP, ADCYAP1R1, VDAC1, and C4A have significant transcriptomic alterations in FTD along with the comorbidity status with COVID-19 and breast cancer. Functional pathway analysis revealed that these comorbid genes were significantly enriched in the pathways such as glioma, JAK/STAT signaling, systematic lupus erythematosus, neurodegeneration-multiple diseases, and neuroactive ligand–receptor interaction. Overall, from these results, we concluded that these genes could be recommended as potential therapeutic targets for the treatment of comorbidities (breast cancer and COVID-19) in patients with FTD. [ABSTRACT FROM AUTHOR]

Gynaecological serous carcinomas (GSCs) constitute a distinctive entity among female tumours characterised by a very poor prognosis. In addition to late-stage diagnosis and a high rate of recurrent disease associated with massive peritoneal carcinomatosis, the systematic acquisition of resistance to first-line chemotherapy based on platinum determines the unfavourable outcome of GSC patients. To explore the molecular mechanisms associated with platinum resistance, we generated patient-derived organoids (PDOs) from liquid biopsies of GSC patients. PDOs are emerging as a relevant preclinical model system to assist in clinical decision making, mainly from tumoural tissue and particularly for personalised therapeutic options. To approach platinum resistance in a GSC context, proficient PDOs were generated from the ascitic fluid of ovarian, primary peritoneal and uterine serous carcinoma patients in platinum-sensitive and platinum-resistant clinical settings from the uterine aspirate of a uterine serous carcinoma patient, and we also induced platinum resistance in vitro in a representative platinum-sensitive PDO. Histological and immunofluorescent characterisation of these ascites-derived organoids showed resemblance to the corresponding original tumours, and assessment of platinum sensitivity in these preclinical models replicated the clinical setting of the corresponding GSC patients. Differential gene expression profiling of a panel of 770 genes representing major canonical cancer pathways, comparing platinum-sensitive and platinum-resistant PDOs, revealed cellular response to DNA damage stimulus as the principal biological process associated with the acquisition of resistance to the first-line therapy for GSC. Additionally, candidate genes involved in regulation of cell adhesion, cell cycles, and transcription emerged from this proof-of-concept study. In conclusion, we describe the generation of PDOs from liquid biopsies in the context of gynaecological serous carcinomas to explore the molecular determinants of platinum resistance. [ABSTRACT FROM AUTHOR]

Normal tissues are essential for studying disease-specific differential gene expression. However, healthy human controls are typically available only in postmortal/autopsy settings. In cancer research, fragments of pathologically normal tissue adjacent to tumor site are frequently used as the controls. However, it is largely underexplored how cancers can systematically influence gene expression of the neighboring tissues. Here we performed a comprehensive pan-cancer comparison of molecular profiles of solid tumor-adjacent and autopsy-derived “healthy” normal tissues. We found a number of systemic molecular differences related to activation of the immune cells, intracellular transport and autophagy, cellular respiration, telomerase activation, p38 signaling, cytoskeleton remodeling, and reorganization of the extracellular matrix. The tumor-adjacent tissues were deficient in apoptotic signaling and negative regulation of cell growth including G2/M cell cycle transition checkpoint. We also detected an extensive rearrangement of the chemical perception network. Molecular targets of 32 and 37 cancer drugs were over- or underexpressed, respectively, in the tumor-adjacent norms. These processes may be driven by molecular events that are correlated between the paired cancer and adjacent normal tissues, that mostly relate to inflammation and regulation of intracellular molecular pathways such as the p38, MAPK, Notch, and IGF1 signaling. However, using a model of macaque postmortal tissues we showed that for the 30 min – 24-hour time frame at 4ºC, an RNA degradation pattern in lung biosamples resulted in an artifact “differential” expression profile for 1140 genes, although no differences could be detected in liver. Thus, such concerns should be addressed in practice.

Introduction: Long non-coding ribonucleic acids (lncRNAs) are involved in the cellular damage response following exposure to ionizing radiation as applied in radiotherapy. However, the role of lncRNAs in radiation response concerning intrinsic susceptibility to late effects of radiation exposure has not been examined in general or in long-term survivors of childhood cancer with and without potentially radiotherapy-related second primary cancers, in particular. Methods: Primary skin fibroblasts (n=52 each) of long-term childhood cancer survivors with a first primary cancer only (N1), at least one second primary neoplasm (N2+), as well as tumor-free controls (N0) from the KiKme casecontrol study were matched by sex, age, and additionally by year of diagnosis and entity of the first primary cancer. Fibroblasts were exposed to 0.05 and 2 Gray (Gy) X-rays. Differentially expressed lncRNAs were identified with and without interaction terms for donor group and dose. Weighted co-expression networks of lncRNA and mRNA were constructed using WGCNA. Resulting gene sets (modules) were correlated to the radiation doses and analyzed for biological function. Results: After irradiation with 0.05Gy, few lncRNAs were differentially expressed (N0: AC004801.4; N1: PCCA-DT, AF129075.3, LINC00691, AL158206.1; N2+: LINC02315). In reaction to 2 Gy, the number of differentially expressed lncRNAs was higher (N0: 152, N1: 169, N2+: 146). After 2 Gy, AL109976.1 and AL158206.1 were prominently upregulated in all donor groups. The co-expression analysis identified two modules containing lncRNAs that were associated with 2 Gy (module1: 102 mRNAs and 4 lncRNAs: AL158206.1, AL109976.1, AC092171.5, TYMSOS, associated with p53-mediated reaction to DNA damage; module2: 390 mRNAs, 7 lncRNAs: AC004943.2, AC012073.1, AC026401.3, AC092718.4, MIR31HG, STXBP5-AS1, TMPO-AS1, associated with cell cycle regulation). Discussion: For the first time, we identified the lncRNAs AL158206.1 and AL109976.1 as involved in the radiation response in primary fibroblasts by differential expression analysis. The co-expression analysis revealed a role of these lncRNAs in the DNA damage response and cell cycle regulation post-IR. These transcripts may be targets in cancer therapy against radiosensitivity, as well as provide grounds for the identification of at-risk patients for immediate adverse reactions in healthy tissues. With this work we deliver a broad basis and new leads for the examination of lncRNAs in the radiation response. [ABSTRACT FROM AUTHOR]

Abstract Acute myeloid leukemia (AML) is a complex, heterogeneous malignant hematologic disease. Although multiple prognostic-related genes gave been explored in previous studies, there are still many genes whose prognostic value remains unclear. In this study, a total of 1532 AML patients from three GEO databases were included, five genes with potential prognostic value (DLC1, NF1B, DENND5B, TANC2 and ELAVL4) were screened by weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE). Based on this, we conducted survival analysis of the above five genes through the TCGA database and found that low level of DLC1 was detrimental to the long-term prognosis of AML patients. We also performed external validation in 48 AML patients from our medical center to analyze the impact of DLC1 level on prognosis. In conclusion, DLC1 may be a potential marker affecting the prognosis of AML, and its deficiency is associated with poor prognosis.

IntroductionAndrogenetic alopecia (AGA) is a common progressive scalp hair loss disorder that leads to baldness. This study aimed to identify core genes and pathways involved in premature AGA through an in-silico approach.MethodsGene expression data (GSE90594) from vertex scalps of men with premature AGA and men without pattern hair loss was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the bald and haired samples were identified using the limma package in R. Gene ontology and Reactome pathway enrichment analyses were conducted separately for the up-regulated and down-regulated genes. The DEGs were annotated with the AGA risk loci, and motif analysis in the promoters of the DEGs was also carried out. STRING Protein-protein interaction (PPI) and Reactome Functional Interaction (FI) networks were constructed using the DEGs, and the networks were analyzed to identify hub genes that play could play crucial roles in AGA pathogenesis.Results and discussionThe in-silico study revealed that genes involved in the structural makeup of the skin epidermis, hair follicle development, and hair cycle are down-regulated, while genes associated with the innate and adaptive immune systems, cytokine signaling, and interferon signaling pathways are up-regulated in the balding scalps of AGA. The PPI and FI network analyses identified 25 hub genes namely CTNNB1, EGF, GNAI3, NRAS, BTK, ESR1, HCK, ITGB7, LCK, LCP2, LYN, PDGFRB, PIK3CD, PTPN6, RAC2, SPI1, STAT3, STAT5A, VAV1, PSMB8, HLA-A, HLA-F, HLA-E, IRF4, and ITGAM that play crucial roles in AGA pathogenesis. The study also implicates that Src family tyrosine kinase genes such as LCK, and LYN in the up-regulation of the inflammatory process in the balding scalps of AGA highlighting their potential as therapeutic targets for future investigations.

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Down syndrome is a common genetic disorder caused by partial or complete triplication of chromosome 21. This syndrome shows an overall and progressive impairment of olfactory function, detected early in adulthood. The olfactory neuronal cells are located in the nasal olfactory mucosa and represent the first sensory neurons of the olfactory pathway. Herein, we applied the olfactory swabbing procedure to allow a gentle collection of olfactory epithelial cells in seven individuals with Down syndrome and in ten euploid controls. The aim of this research was to investigate the peripheral gene expression pattern in olfactory epithelial cells through RNAseq analysis. Validated tests (Sniffin' Sticks Extended test) were used to assess olfactory function. Olfactory scores were correlated with RNAseq results and cognitive scores (Vineland II and Leiter scales). All Down syndrome individuals showed both olfactory deficit and intellectual disability. Down syndrome individuals and euploid controls exhibited clear expression differences in genes located in and outside the chromosome 21. In addition, a significant correlation was found between olfactory test scores and gene expression, while a non-significant correlation emerged between olfactory and cognitive scores. This first preliminary step gives new insights into the Down syndrome olfactory system research, starting from the olfactory neuroepithelium, the first cellular step on the olfactory way. [ABSTRACT FROM AUTHOR]

Abstract Background RNA-seq is being increasingly adopted for gene expression studies in a panoply of non-model organisms, with applications spanning the fields of agriculture, aquaculture, ecology, and environment. For organisms that lack a well-annotated reference genome or transcriptome, a conventional RNA-seq data analysis workflow requires constructing a de-novo transcriptome assembly and annotating it against a high-confidence protein database. The assembly serves as a reference for read mapping, and the annotation is necessary for functional analysis of genes found to be differentially expressed. However, assembly is computationally expensive. It is also prone to errors that impact expression analysis, especially since sequencing depth is typically much lower for expression studies than for transcript discovery. Results We propose a shortcut, in which we obtain counts for differential expression analysis by directly aligning RNA-seq reads to the high-confidence proteome that would have been otherwise used for annotation. By avoiding assembly, we drastically cut down computational costs – the running time on a typical dataset improves from the order of tens of hours to under half an hour, and the memory requirement is reduced from the order of tens of Gbytes to tens of Mbytes. We show through experiments on simulated and real data that our pipeline not only reduces computational costs, but has higher sensitivity and precision than a typical assembly-based pipeline. A Snakemake implementation of our workflow is available at: https://bitbucket.org/project_samar/samar . Conclusions The flip side of RNA-seq becoming accessible to even modestly resourced labs has been that the time, labor, and infrastructure cost of bioinformatics analysis has become a bottleneck. Assembly is one such resource-hungry process, and we show here that it can be avoided for quick and easy, yet more sensitive and precise, differential gene expression analysis in non-model organisms.

IntroductionLong non-coding ribonucleic acids (lncRNAs) are involved in the cellular damage response following exposure to ionizing radiation as applied in radiotherapy. However, the role of lncRNAs in radiation response concerning intrinsic susceptibility to late effects of radiation exposure has not been examined in general or in long-term survivors of childhood cancer with and without potentially radiotherapy-related second primary cancers, in particular.MethodsPrimary skin fibroblasts (n=52 each) of long-term childhood cancer survivors with a first primary cancer only (N1), at least one second primary neoplasm (N2+), as well as tumor-free controls (N0) from the KiKme case-control study were matched by sex, age, and additionally by year of diagnosis and entity of the first primary cancer. Fibroblasts were exposed to 0.05 and 2 Gray (Gy) X-rays. Differentially expressed lncRNAs were identified with and without interaction terms for donor group and dose. Weighted co-expression networks of lncRNA and mRNA were constructed using WGCNA. Resulting gene sets (modules) were correlated to the radiation doses and analyzed for biological function.ResultsAfter irradiation with 0.05Gy, few lncRNAs were differentially expressed (N0: AC004801.4; N1: PCCA-DT, AF129075.3, LINC00691, AL158206.1; N2+: LINC02315). In reaction to 2 Gy, the number of differentially expressed lncRNAs was higher (N0: 152, N1: 169, N2+: 146). After 2 Gy, AL109976.1 and AL158206.1 were prominently upregulated in all donor groups. The co-expression analysis identified two modules containing lncRNAs that were associated with 2 Gy (module1: 102 mRNAs and 4 lncRNAs: AL158206.1, AL109976.1, AC092171.5, TYMSOS, associated with p53-mediated reaction to DNA damage; module2: 390 mRNAs, 7 lncRNAs: AC004943.2, AC012073.1, AC026401.3, AC092718.4, MIR31HG, STXBP5-AS1, TMPO-AS1, associated with cell cycle regulation).DiscussionFor the first time, we identified the lncRNAs AL158206.1 and AL109976.1 as involved in the radiation response in primary fibroblasts by differential expression analysis. The co-expression analysis revealed a role of these lncRNAs in the DNA damage response and cell cycle regulation post-IR. These transcripts may be targets in cancer therapy against radiosensitivity, as well as provide grounds for the identification of at-risk patients for immediate adverse reactions in healthy tissues. With this work we deliver a broad basis and new leads for the examination of lncRNAs in the radiation response.

Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress they are becoming one of the deadliest brain tumors. There is still great need for timely and accurate diagnosis and prognosis of LGG. Herein, we aimed to identify diagnostic and prognostic biomarkers associated with LGG, by employing diverse computational approaches. For this purpose, differential gene expression analysis on high-throughput transcriptomics data of LGG versus corresponding healthy brain tissue, derived from TCGA and GTEx, respectively, was performed. Weighted gene co-expression network analysis of the detected differentially expressed genes was carried out in order to identify modules of co-expressed genes significantly correlated with LGG clinical traits. The genes comprising these modules were further used to construct gene co-expression and protein-protein interaction networks. Based on the network analyses, we derived a consensus of eighteen hub genes, namely, CD74, CD86, CDC25A, CYBB, HLA-DMA, ITGB2, KIF11, KIFC1, LAPTM5, LMNB1, MKI67, NCKAP1L, NUSAP1, SLC7A7, TBXAS1, TOP2A, TYROBP, and WDFY4. All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. The findings of this study could be applicable in the clinical setting for diagnosing and monitoring LGG.

Drug-induced differential gene expression analysis (DGEA) is essential for uncovering the molecular basis of cell phenotypic changes and understanding individual tumor responses to anticancer drugs. Performing high throughput DGEA is challenging due to the high cost and labor-intensive multi-step sample preparation protocols. In particular, performing drug-induced DGEA on cancer cells derived from patient biopsies is even more challenging due to the scarcity of available cells. A novel, miniaturized, nanoliter-scale method for drug-induced DGEA is introduced, enabling high-throughput and parallel analysis of patient-derived cell drug responses, overcoming the limitations and laborious nature of traditional protocols. The method is based on the Droplet Microarray (DMA), a microscope glass slide with hydrophilic spots on a superhydrophobic background, facilitating droplet formation for cell testing. DMA allows microscopy-based phenotypic analysis, cDNA extraction, and DGEA. The procedure includes cell lysis for mRNA isolation and cDNA conversion followed by droplet pooling for qPCR analysis. In this study, the drug-induced DGEA protocol on the DMA platform is demonstrated using patient-derived chronic lymphocytic leukemia (CLL) cells. This methodology is critical for DGEA with limited cell numbers and promise for applications in functional precision oncology. This method enables molecular profiling of patient-derived samples after drug treatment, crucial for understanding individual tumor responses to anticancer drugs., (© 2024 The Author(s). Advanced Healthcare Materials published by Wiley‐VCH GmbH.)

Abstract Background Recent development of bioinformatics tools for Next Generation Sequencing data has facilitated complex analyses and prompted large scale experimental designs for comparative genomics. When combined with the advances in network inference tools, this can lead to powerful methodologies for mining genomics data, allowing development of pipelines that stretch from sequence reads mapping to network inference. However, integrating various methods and tools available over different platforms requires a programmatic framework to fully exploit their analytic capabilities. Integrating multiple genomic analysis tools faces challenges from standardization of input and output formats, normalization of results for performing comparative analyses, to developing intuitive and easy to control scripts and interfaces for the genomic analysis pipeline. Results We describe here NetSeekR, a network analysis R package that includes the capacity to analyze time series of RNA-Seq data, to perform correlation and regulatory network inferences and to use network analysis methods to summarize the results of a comparative genomics study. The software pipeline includes alignment of reads, differential gene expression analysis, correlation network analysis, regulatory network analysis, gene ontology enrichment analysis and network visualization of differentially expressed genes. The implementation provides support for multiple RNA-Seq read mapping methods and allows comparative analysis of the results obtained by different bioinformatics methods. Conclusion Our methodology increases the level of integration of genomics data analysis tools to network inference, facilitating hypothesis building, functional analysis and genomics discovery from large scale NGS data. When combined with network analysis and simulation tools, the pipeline allows for developing systems biology methods using large scale genomics data.

Understanding the molecular mechanism of clear cell renal cell carcinoma (ccRCC) is essential for predicting the prognosis and developing new targeted therapies. Our study is to identify hub genes related to ccRCC and to further analyze its prognostic significance. The ccRCC gene expression profiles of GSE46699 from the Gene Expression Omnibus (GEO) database and datasets from the Cancer Genome Atlas Database The Cancer Genome Atlas were used for the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We screened out 397 overlapping genes from the four sets of results, and then performed Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. In addition, the protein-protein interaction (PPI) network of 397 overlapping genes was mapped using the STRING database. We identified ten hub genes (KNG1, TIMP1, ALB, C3, GPC3, VCAN, P4HB, CHGB, LGALS1, EGF) using the CytoHubba plugin of Cytoscape based on the Maximal Clique Centrality (MCC) score. According to Kaplan-Meier survival analysis, higher expression of LGALS1 and TIMP1 was related to poorer overall survival (OS) in patients with ccRCC. Univariate and multivariate Cox proportional hazard analysis showed that the expression of LGALS1 was an independent risk factor for poor prognosis. Moreover, the higher the clinical grade and stage of ccRCC, the higher the expression of LGALS1. LGALS1 may play an important role in developing ccRCC and may be potential a biomarker for prognosis and treatment targets.

Acute myeloid leukemia (AML) is a complex, heterogeneous malignant hematologic disease. Although multiple prognostic-related genes gave been explored in previous studies, there are still many genes whose prognostic value remains unclear. In this study, a total of 1532 AML patients from three GEO databases were included, five genes with potential prognostic value (DLC1, NF1B, DENND5B, TANC2 and ELAVL4) were screened by weighted gene co-expression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE). Based on this, we conducted survival analysis of the above five genes through the TCGA database and found that low level of DLC1 was detrimental to the long-term prognosis of AML patients. We also performed external validation in 48 AML patients from our medical center to analyze the impact of DLC1 level on prognosis. In conclusion, DLC1 may be a potential marker affecting the prognosis of AML, and its deficiency is associated with poor prognosis. [ABSTRACT FROM AUTHOR]

The identification of genes that are expressed differentially in the diseased versus healthy individual give relevant information regarding the pathology of the disease. The identification of DEGs can be a significant step in the field of clinical and pharmaceutical research. They can act as a potent biomarker, therapeutic target, or gene signature for the early diagnosis of the disease. Intellectual disability is a neurodevelopmental disorder that affects those at the fetal stage. Timely diagnosis of the disease can help in preventing severe neurodevelopmental delay in the child. In the current study, a meta-analysis approach was applied for the identification of the DEGs in patients of intellectual disability disorder. Six intellectual disability datasets were retrieved from the GEO database of NCBI and were subjected to quality check, trimming, and alignment. Post-alignment, FeatureCounts was used to form a raw gene count file for differential analysis. The differentially expressed genes were analyzed using the EdgeR statistical package of R Studio. The genes which had an FDR p-value less than 0.05 and log2foldchange greater than 0 were considered upregulated and significantly expressed genes. The study found MTRNR2L1, PAPSS2, L1CAM, IGLV1-47, IGLV3-19, and IGKV1-16 genes to be upregulated in the patient sample. These genes can thus play an important role in the progression of intellectual disability disorder that facilitates early diagnosis of the disease. [ABSTRACT FROM AUTHOR]

Abstract Objectives This study aimed to identify novel targets in the carcinogenesis, therapy and prognosis of osteosarcoma from genomic level, together with screening ideal lead compounds with potential inhibition regarding MMP-9. Methods Gene expression profiles from GSE12865, GSE14359, GSE33382, GSE36001 and GSE99671 were obtained respectively from GEO database. Differentially expressed genes were identified, and functional enrichment analysis, such as GO, KEGG, GSEA, PPI were performed to make a comprehensive understanding of the hub genes. Next, a series of high-precision computational techniques were conducted to screen potential lead compounds targeting MMP9, including virtual screening, ADME, toxicity prediction, and accurate docking analysis. Results 10 genes, MMP9, CD74, SPP1, CXCL12, TYROBP, FCER1G, HCLS1, ARHGDIB, LAPTM5 and IGF1R were identified as hub genes in the initiation of osteosarcoma. Machine learning, multivariate Cox analysis, ssGSEA and survival analysis demonstrated that these genes had values in prognosis, immune-correlation and targeted treatment. Tow novel compounds, ZINC000072131515 and ZINC000004228235, were screened as potential inhibitor regarding MMP9, and they could bind to MMP9 with favorable interaction energy and high binding affinity. Meanwhile, they were precited to be efficient and safe drugs with low-ames mutagenicity, none weight evidence of carcinogenicity, as well as non-toxic with liver. Conclusions This study revealed the significance of 10-gene signature in the development of osteosarcoma. Besides, drug candidates identified in this study provided a solid basis on MMP9 inhibitors’ development.

Background: Acute myeloid leukemia (AML) is a malignant and molecularly heterogeneous disease. It is essential to clarify the molecular mechanisms of AML and develop targeted treatment strategies to improve patient prognosis. Methods: AML mRNA expression data and survival status were extracted from TCGA and GEO databases (GSE37642, GSE76009, GSE16432, GSE12417, GSE71014). Weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis were performed. Functional enrichment analysis and protein-protein interaction (PPI) network were used to screen out hub genes. In addition, we validated the expression levels of hub genes as well as the prognostic value and externally validated TRIM32 with clinical data from our center. AML cell lines transfected with TRIM32 shRNA were also established to detect the proliferation in vitro. Results: A total of 2192 AML patients from TCGA and GEO datasets were included in this study and 20 differentially co-expressed genes were screened by WGCNA and differential gene expression analysis methods. These genes were mainly enriched in phospholipid metabolic processes (biological processes, BP), secretory granule membranes (cellular components, CC), and protein serine/threonine kinase activity (molecular functions, MF). In addition, the protein-protein interaction (PPI) network contains 15 nodes and 15 edges and 10 hub genes (TLE1, GLI2, HDAC9, MICALL2, DOCK1, PDPN, RAB27B, SIX3, TRIM32 and TBX1) were identified. The expression of 10 central genes, except TLE1, was associated with survival status in AML patients (p<0.05). High expression of TRIM32 was tightly associated with poor relapse-free survival (RFS) and overall survival (OS) in AML patients, which was verified in the bone marrow samples from our center. In vitro, knockdown of TRIM32 can inhibit the proliferation of AML cell lines. Conclusion: TRIM32 was associated with the progression and prognosis of AML patients and could be a potential therapeutic target and biomarker for AML in the future. [ABSTRACT FROM AUTHOR]

Objective: To investigate the differential gene expression and function of peripheral blood monocytes in patients with coronary heart disease and acute myocardial infarction. Methods: Patients receiving coronary angiography in Kunming First People’s Hospital from September 2020 to September 2021 were collected and total RNA of peripheral blood monocytes was extracted. DNBSEQ platform was used for second-generation highthroughput sequencing. We tested and screened differentially expressed genes, and made KEGG and GO enrichment analysis. Results: There were 89 differentially expressed genes,67 up-regulated genes and 22 downregulated genes in acute myocardial infarction group compared with normal coronary artery group. Among them, there were 14 differential lncRNAs, 72 differential mRNAs, and 3 differential circRNAs. KEGG pathway enrichment analysis, KEGG disease enrichment analysis and KEGG molecular enrichment analysis were completed. GO cell composition enrichment analysis, GO cell function enrichment analysis and GO biological process molecular enrichment analysis were completed. Conclusions: 10 mRNAs,6 lncRNAs,and 2 circRNAs with significant differences were screened. The enrichment analysis involves infection,transcriptional disorder,PI3K Akt signaling pathway, lipid synthesis, phagocytic vesicle cavity, extracellular matrix, lipopolysaccharide binding, etc., which may be related to the occurrence and development of coronary atherosclerosis and the occurrence of acute thrombotic events. [ABSTRACT FROM AUTHOR]

Abstract Background Epigenome-wide association studies (EWAS) and differential gene expression analyses are generally performed on tissue samples, which consist of multiple cell types. Cell-type-specific effects of a trait, such as disease, on the omics expression are of interest but difficult or costly to measure experimentally. By measuring omics data for the bulk tissue, cell type composition of a sample can be inferred statistically. Subsequently, cell-type-specific effects are estimated by linear regression that includes terms representing the interaction between the cell type proportions and the trait. This approach involves two issues, scaling and multicollinearity. Results First, although cell composition is analyzed in linear scale, differential methylation/expression is analyzed suitably in the logit/log scale. To simultaneously analyze two scales, we applied nonlinear regression. Second, we show that the interaction terms are highly collinear, which is obstructive to ordinary regression. To cope with the multicollinearity, we applied ridge regularization. In simulated data, nonlinear ridge regression attained well-balanced sensitivity, specificity and precision. Marginal model attained the lowest precision and highest sensitivity and was the only algorithm to detect weak signal in real data. Conclusion Nonlinear ridge regression performed cell-type-specific association test on bulk omics data with well-balanced performance. The omicwas package for R implements nonlinear ridge regression for cell-type-specific EWAS, differential gene expression and QTL analyses. The software is freely available from https://github.com/fumi-github/omicwas

An improved understanding of the molecular mechanism of colorectal adenocarcinoma is necessary to predict the prognosis and develop new target gene therapy strategies. This study aims to identify hub genes associated with colorectal adenocarcinoma and further analyze their prognostic significance. In this study, The Cancer Genome Atlas (TCGA) COAD-READ database and the gene expression profiles of GSE25070 from the Gene Expression Omnibus were collected to explore the differentially expressed genes between colorectal adenocarcinoma and normal tissues. The weighted gene co-expression network analysis (WGCNA) and differential expression analysis identified 82 differentially co-expressed genes in the collected datasets. Enrichment analysis was applied to explore the regulated signaling pathway in colorectal adenocarcinoma. In addition, 10 hub genes were identified in the protein–protein interaction (PPI) network by using the cytoHubba plug-in of Cytoscape, where five genes were further proven to be significantly related to the survival rate. Compared with normal tissues, the expressions of the five genes were both downregulated in the GSE110224 dataset. Subsequently, the expression of the five hub genes was confirmed by the Human Protein Atlas database. Finally, we used Cox regression analysis to identify genes associated with prognosis, and a 3-gene signature (CLCA1–CLCA4–GUCA2A) was constructed to predict the prognosis of patients with colorectal cancer. In conclusion, our study revealed that the five hub genes and CLCA1–CLCA4–GUCA2A signature are highly correlated with the development of colorectal adenocarcinoma and can serve as promising prognosis factors to predict the overall survival rate of patients.

RNA-sequencing (RNA-seq) is a relatively new technology that lacks standardisation. RNA-seq can be used for Differential Gene Expression (DGE) analysis, however, no consensus exists as to which methodology ensures robust and reproducible results. Indeed, it is broadly acknowledged that DGE methods provide disparate results. Despite obstacles, RNA-seq assays are in advanced development for clinical use but further optimisation will be needed. Herein, five DGE models (DESeq2, voom + limma, edgeR, EBSeq, NOISeq) for gene-level detection were investigated for robustness to sequencing alterations using a controlled analysis of fixed count matrices. Two breast cancer datasets were analysed with full and reduced sample sizes. DGE model robustness was compared between filtering regimes and for different expression levels (high, low) using unbiased metrics. Test sensitivity estimated as relative False Discovery Rate (FDR), concordance between model outputs and comparisons of a ’population’ of slopes of relative FDRs across different library sizes, generated using linear regressions, were examined. Patterns of relative DGE model robustness proved dataset-agnostic and reliable for drawing conclusions when sample sizes were sufficiently large. Overall, the non-parametric method NOISeq was the most robust followed by edgeR, voom, EBSeq and DESeq2. Our rigorous appraisal provides information for method selection for molecular diagnostics. Metrics may prove useful towards improving the standardisation of RNA-seq for precision medicine.

Background and aims: As a major cause of liver disease worldwide, non-alcoholic fatty liver disease (NAFLD) comprises non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH). Due to the high prevalence and poor prognosis of NASH, it is critical to understand its mechanisms. However, the etiology and mechanisms remain largely unknown. In addition, the gold standard for the diagnosis of NASH is liver biopsy, which is an invasive procedure. Therefore, there is a pressing need to develop noninvasive tests for NASH diagnosis. The goal of the study is to discover key genes involved in NASH development and investigate their value as noninvasive biomarkers. Methods: The Gene Expression Omnibus (GEO) database was used to obtain two datasets encompassing NASH patients and healthy controls. We used weighted gene co-expression network analysis (WGCNA) and differential expression analysis in order to investigate the association between gene sets and clinical features, as well as to discover co-expression modules. A protein-protein interaction (PPI) network was created to extract hub genes. The results were validated using another publicly available dataset and mice treated with a high-fat diet (HFD) and carbon tetrachloride (CCl4). Results: A total of 24 differentially co-expressed genes were selected by WGCNA and differential expression analysis. KEGG analysis indicated most of them were enriched in the focal adhesion pathway. GO analysis showed these genes were mainly enriched in circadian rhythm, aging, angiogenesis and response to drug (biological process), endoplasmic reticulum lumen (cellular component), and protein binding (molecular function). As a result, eight genes (JUN, SERPINE1, GINS2, TYMS, HMMR, IGFBP2, BIRC3, TNFRSF12A) were identified as hub genes. Finally, three genes were found significantly changed in both the validation dataset and the mouse model. Conclusion: Our research discovered genes that have the potential to mediate the process of NASH and might be useful diagnostic biomarkers for the disorder. [ABSTRACT FROM AUTHOR]

Several S100 proteins have been shown to play an important role in the innate immune response to infection and in regenerative processes. However, they have scarcely been investigated during inflammation of the dental pulp. Therefore, in this study, we performed gene expression profiling of S100 proteins in healthy and inflamed human dental pulps. Tissue samples of human dental pulps were used, including 15 clinically diagnosed as symptomatic irreversible pulpitis (SIP), 7 as asymptomatic irreversible pulpitis (AIP), and 19 as healthy pulp (HP). S100 gene expression levels were quantitatively evaluated for S100A1, -A2, -A3, -A4, -A6, -A7, -A8, -A9, -A10, -A11, -A13, -A14 , and -A16 by the quantitative polymerase chain reaction technique. In order to monitor the status of inflammation and degradation of pulp tissues, IL-8 , COX-2 , and HMGB-1 gene expression was also analyzed with GAPDH serving as the reference gene. Differential expression rates for each target gene between SIP, AIP, and HP were evaluated by analysis of variance followed by the Bonferroni post hoc test. Significantly reduced gene expression levels could be detected in SIP compared with HP for S100A1, -A2, -A3, -A4, -A6, -A10, and -A13 and for HMGB-1 , whereas the gene expression of S100A8 and -A14 and IL-8 were significantly increased. In AIP, significantly increased expression levels compared with HP were only detected for S100A14 and -A16 and IL-8 , with other genes of interest not being altered. The present study revealed significant differences in gene expression profiles of S100 proteins comparing samples from healthy and inflamed dental pulp. More pronounced differences were observed for symptomatic than for asymptomatic pulpitis. [ABSTRACT FROM AUTHOR]

Studies on tick-pathogen-host interactions are helping to identify candidates for vaccines against ticks and tick-borne diseases and to discover potent bioactive tick molecules. The tick midgut is the main tissue involved in blood feeding and, moreover, the first organ to have contact with pathogens ingested through the blood meal. As little is known about the molecular biology of feeding and tick defence mechanisms against microorganisms, but important for understanding vector-pathogen interactions, we explored the early transcriptional changes in the midgut of Ornithodoros moubata after feeding and in response to challenge with the relapsing-fever spirochete Borrelia duttonii using the Ion S5XL platform. Besides transcripts with metabolic function and immune-related transcripts we discovered numerous putative and uncharacterized protein sequences. Overall, our analyses support previous studies and provides a valuable reference database for further functional proteomic analysis of midgut proteins of O. moubata. [ABSTRACT FROM AUTHOR]

Background: RNA-seq is being increasingly adopted for gene expression studies in a panoply of non-model organisms, with applications spanning the fields of agriculture, aquaculture, ecology, and environment. For organisms that lack a well-annotated reference genome or transcriptome, a conventional RNA-seq data analysis workflow requires constructing a de-novo transcriptome assembly and annotating it against a high-confidence protein database. The assembly serves as a reference for read mapping, and the annotation is necessary for functional analysis of genes found to be differentially expressed. However, assembly is computationally expensive. It is also prone to errors that impact expression analysis, especially since sequencing depth is typically much lower for expression studies than for transcript discovery. Results: We propose a shortcut, in which we obtain counts for differential expression analysis by directly aligning RNA-seq reads to the high-confidence proteome that would have been otherwise used for annotation. By avoiding assembly, we drastically cut down computational costs – the running time on a typical dataset improves from the order of tens of hours to under half an hour, and the memory requirement is reduced from the order of tens of Gbytes to tens of Mbytes. We show through experiments on simulated and real data that our pipeline not only reduces computational costs, but has higher sensitivity and precision than a typical assembly-based pipeline. A Snakemake implementation of our workflow is available at: https://bitbucket.org/project%5fsamar/samar. Conclusions: The flip side of RNA-seq becoming accessible to even modestly resourced labs has been that the time, labor, and infrastructure cost of bioinformatics analysis has become a bottleneck. Assembly is one such resource-hungry process, and we show here that it can be avoided for quick and easy, yet more sensitive and precise, differential gene expression analysis in non-model organisms. [ABSTRACT FROM AUTHOR]

Abstract Background Direct cDNA preamplification protocols developed for single-cell RNA-seq have enabled transcriptome profiling of precious clinical samples and rare cell populations without the need for sample pooling or RNA extraction. We term the use of single-cell chemistries for sequencing low numbers of cells limiting-cell RNA-seq (lcRNA-seq). Currently, there is no customized algorithm to select robust/low-noise transcripts from lcRNA-seq data for between-group comparisons. Methods Herein, we present CLEAR, a workflow that identifies reliably quantifiable transcripts in lcRNA-seq data for differentially expressed genes (DEG) analysis. Total RNA obtained from primary chronic lymphocytic leukemia (CLL) CD5+ and CD5− cells were used to develop the CLEAR algorithm. Once established, the performance of CLEAR was evaluated with FACS-sorted cells enriched from mouse Dentate Gyrus (DG). Results When using CLEAR transcripts vs. using all transcripts in CLL samples, downstream analyses revealed a higher proportion of shared transcripts across three input amounts and improved principal component analysis (PCA) separation of the two cell types. In mouse DG samples, CLEAR identifies noisy transcripts and their removal improves PCA separation of the anticipated cell populations. In addition, CLEAR was applied to two publicly-available datasets to demonstrate its utility in lcRNA-seq data from other institutions. If imputation is applied to limit the effect of missing data points, CLEAR can also be used in large clinical trials and in single cell studies. Conclusions lcRNA-seq coupled with CLEAR is widely used in our institution for profiling immune cells (circulating or tissue-infiltrating) for its transcript preservation characteristics. CLEAR fills an important niche in pre-processing lcRNA-seq data to facilitate transcriptome profiling and DEG analysis. We demonstrate the utility of CLEAR in analyzing rare cell populations in clinical samples and in murine neural DG region without sample pooling.

Background and aims: As a major cause of liver disease worldwide, non-alcoholic fatty liver disease (NAFLD) comprises non-alcoholic fatty liver (NAFL) and non-alcoholic steatohepatitis (NASH). Due to the high prevalence and poor prognosis of NASH, it is critical to understand its mechanisms. However, the etiology and mechanisms remain largely unknown. In addition, the gold standard for the diagnosis of NASH is liver biopsy, which is an invasive procedure. Therefore, there is a pressing need to develop noninvasive tests for NASH diagnosis. The goal of the study is to discover key genes involved in NASH development and investigate their value as noninvasive biomarkers.Methods: The Gene Expression Omnibus (GEO) database was used to obtain two datasets encompassing NASH patients and healthy controls. We used weighted gene co-expression network analysis (WGCNA) and differential expression analysis in order to investigate the association between gene sets and clinical features, as well as to discover co-expression modules. A protein-protein interaction (PPI) network was created to extract hub genes. The results were validated using another publicly available dataset and mice treated with a high-fat diet (HFD) and carbon tetrachloride (CCl4).Results: A total of 24 differentially co-expressed genes were selected by WGCNA and differential expression analysis. KEGG analysis indicated most of them were enriched in the focal adhesion pathway. GO analysis showed these genes were mainly enriched in circadian rhythm, aging, angiogenesis and response to drug (biological process), endoplasmic reticulum lumen (cellular component), and protein binding (molecular function). As a result, eight genes (JUN, SERPINE1, GINS2, TYMS, HMMR, IGFBP2, BIRC3, TNFRSF12A) were identified as hub genes. Finally, three genes were found significantly changed in both the validation dataset and the mouse model.Conclusion: Our research discovered genes that have the potential to mediate the process of NASH and might be useful diagnostic biomarkers for the disorder.