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RationaleQuantification of type 2 inflammation provided a molecular basis for heterogeneity in asthma. Non-type 2 pathways that contribute to asthma pathogenesis are not well understood.ObjectivesTo identify dysregulated pathways beyond type 2 inflammation.MethodsWe applied RNA sequencing to airway epithelial brushings obtained from subjects with stable mild asthma not on corticosteroids (n = 19) and healthy control subjects (n = 16). Sequencing reads were mapped to human and viral genomes. In the same cohort, and in a separate group with severe asthma (n = 301), we profiled blood gene expression with microarrays.Measurements and main resultsIn airway brushings from mild asthma on inhaled corticosteroids, RNA sequencing yielded 1,379 differentially expressed genes (false discovery rate

The novel coronavirus named SARS-CoV-2 has emerged at the end of 2019, which causes coronavirus disease (COVID-2019). Recent case reports of COVID-19 patients have revealed the onset of Parkinson's disease (PD) symptoms in patients who do not have a family history of the PD. However, till recently, no genetic impact or mechanisms that may induce Parkinsonism in COVID-19 patients or after COVID-19 have been found.. This study aimed to detect the commonly dysregulated genes, transcriptional regulators, and pathways between PD and COVID-19. We integrated genome-wide transcriptomic datasets from peripheral blood mononuclear cells (PBMC) samples from COVID-19 and PD and associated pathways. Our study revealed 81 upregulated and 48 downregulated differentially expressed genes (DEGs) shared between PD and COVID-19. These dysregulated genes were involved in key pathways "mitochondrion structure organization", "cell activation in immune response", and "signalling by interleukins". Our analysis showed RELA, TP53 and SP1 TFs that may regulate the upregulated DEGs. We have discovered key dysregulated genes and characterized the biological processes of commonly dysregulated in COVID-19 and PD, which could be used for the design of personalized treatment of PD following COVID-19. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by the newly discovered coronavirus, SARS-CoV-2. Increased severity of COVID-19 has been observed in patients with diabetes mellitus (DM). This study aimed to identify common transcriptional signatures, regulators and pathways between COVID-19 and DM. We have integrated human whole-genome transcriptomic datasets from COVID-19 and DM, followed by functional assessment with gene ontology (GO) and pathway analyses. In peripheral blood mononuclear cells (PBMCs), among the upregulated differentially expressed genes (DEGs), 32 were found to be commonly modulated in COVID-19 and type 2 diabetes (T2D), while 10 DEGs were commonly downregulated. As regards type 1 diabetes (T1D), 21 DEGs were commonly upregulated, and 29 DEGs were commonly downregulated in COVID-19 and T1D. Moreover, 35 DEGs were commonly upregulated in SARS-CoV-2 infected pancreas organoids and T2D islets, while 14 were commonly downregulated. Several GO terms were found in common between COVID-19 and DM. Prediction of the putative transcription factors involved in the upregulation of genes in COVID-19 and DM identified RELA to be implicated in both PBMCs and pancreas. Here, for the first time, we have characterized the biological processes and pathways commonly dysregulated in COVID-19 and DM, which could be in the next future used for the design of personalized treatment of COVID-19 patients suffering from DM as comorbidity. [ABSTRACT FROM AUTHOR]

Min Deng,1 Xiao-Dong Lv,2 Zhi-Xian Fang,2 Xin-Sheng Xie,1 Wen-Yu Chen2 1Department of Infectious Diseases, The First Hospital of Jiaxing, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China; 2Department of Respiration, The First Hospital of Jiaxing, The First Affiliated Hospital of Jiaxing University, Jiaxing 314000, China Background: Although the incidence of tuberculosis (TB) has dropped substantially, it still is a serious threat to human health. And in recent years, the emergence of resistant bacilli and inadequate disease control and prevention has led to a significant rise in the global TB epidemic. It is known that the cause of TB is Mycobacterium tuberculosis infection. But it is not clear why some infected patients are active while others are latent.Methods: We analyzed the blood gene expression profiles of 69 latent TB patients and 54 active pulmonary TB patients from GEO (Transcript Expression Omnibus) database.Results: By applying minimal redundancy maximal relevance and incremental feature selection, we identified 24 signature genes which can predict the TB activation. The support vector machine predictor based on these 24 genes had a sensitivity of 0.907, specificity of 0.913, and accuracy of 0.911, respectively. Although they need to be validated in a large independent dataset, the biological analysis of these 24 genes showed great promise.Conclusion: We found that cytokine production was a key process during TB activation and genes like CYBB, TSPO, CD36, and STAT1 worth further investigation. Keywords: tuberculosis, blood gene expression, support vector machine, minimal redundancy maximal relevance, incremental feature selection

Accumulating evidence has suggested a shared pathophysiology between Alzheimer’s disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases.

Alzheimer’s disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases.

Background: CrossFit is known as a functional fitness training high-intensity exercise to improve physical performance. The most studied polymorphisms are the ACTN3 R577X gene, known for speed, power, and strength, and ACE I/D, related to endurance and strength. The present investigation analyzed the effects of training on ACTN3 and ACE gene expression in CrossFit athletes for 12 weeks., Methods: the studies included 18 athletes from the Rx category, where ACTN3 (RR, RX, XX) and ACE (II, ID, DD) characterization of genotypes and tests of maximum strength (NSCA), power (T-Force), and aerobic endurance (Course Navette) were performed. The technique used was the reverse transcription-quantitative PCR real-time polymerase chain reaction (RT-qPCR) for the relative expression analysis., Results: the relative quantification (RQ) values for the ACTN3 gene increased their levels 2.3 times ( p = 0.035), and for ACE, they increased 3.0 times ( p = 0.049)., Conclusions: there is an overexpression of the ACTN3 and ACE genes due to the effect of training for 12 weeks. Additionally, the correlation of the expression of the ACTN3 ( p = 0.040) and ACE ( p = 0.030) genes with power was verified.

The aim of the research was to investigate the variation of gene expression in the blood of lactating cows in relation to the genetic merit for productive traits. For the study, 24 Italian Holstein (IH) cows from a single farm and 24 Italian Simmental (IS) cows from a second farm were selected. Cows were in mid-lactation, and farms had similar management and feeding conditions. For each breed, cows were separated into 3 classes of estimated breeding value (EBV) for milk protein yield (EBVp), namely, 8 cows with low EBVp (low group, LG), 8 cows with high EBV (high group, HG), and the 8 cows closest to the median (medium group, MG). Gene expression was measured on blood with a wholetranscriptome bovine microarray, and data of LG and HG were expressed relative to MG. The number of differentially expressed genes between the low and high EBVp groups were 443 for IS and 281 for IH cows. The IS cows had a greater number of genes (398 vs. 241 in IH cows) with opposite expression in the high and low EBV groups, compared with the MG. In IS cows, the largest number of pathways affected were within the KEGG subcategories of "energy metabolism," "lipid metabolism," and "metabolism of cofactors and vitamins." Furthermore, the "glutathione metabolism" pathway was markedly affected, with GSTM1 (from glutathione-S-transferase family), GSS (glutathione synthetase), and G6PD (glucose-6-phosphate dehydrogenase) upregulated in the IS HG but downregulated in the IS LG group. The "intestinal immune network for IgA production" pathway had 2 bovine leukocytes antigen (BoLA-DQ) genes, and the B-cell activating factor of the TNF superfamily (TNFSF13B) upregulated in the IS LG group and downregulated in the IS HG group. The IH cows had the highest number of affected pathways in the "metabolism" and "organismal systems" categories, the latter with 5 out of 10 subcategories relative to the immune system. Within the "T cell receptor signaling pathway," the IH HG cows had upregulation of the CD1d, CD4, and CD3 antigens and downregulation of the NFkB subunit ReLa gene. This study revealed that specific metabolic and immunological pathways are directly related to genetic merit, and could be considered a signature of quantitative selection. Starting from these preliminary observations, the comparison of differentially expressed genes among animals with different EBVp seems a promising approach to unravel molecular response at a blood level associated with productive traits. [ABSTRACT FROM AUTHOR]

Background: Decades of research strongly suggest that the genetic etiology of autism spectrum disorders (ASDs) is heterogeneous. However, most published studies focus on group differences between cases and controls. In contrast, we hypothesized that the heterogeneity of the disorder could be characterized by identifying pathways for which individuals are outliers rather than pathways representative of shared group differences of the ASD diagnosis. Methods: Two previously published blood gene expression data sets - the Translational Genetics Research Institute (TGen) dataset (70 cases and 60 unrelated controls) and the Simons Simplex Consortium (Simons) dataset (221 probands and 191 unaffected family members) - were analyzed. All individuals of each dataset were projected to biological pathways, and each sample's Mahalanobis distance from a pooled centroid was calculated to compare the number of case and control outliers for each pathway. Results: Analysis of a set of blood gene expression profiles from 70 ASD and 60 unrelated controls revealed three pathways whose outliers were significantly overrepresented in the ASD cases: neuron development including axonogenesis and neurite development (29% of ASD, 3% of control), nitric oxide signaling (29%, 3%), and skeletal development (27%, 3%). Overall, 50% of cases and 8% of controls were outliers in one of these three pathways, which could not be identified using group comparison or gene-level outlier methods. In an independently collected data set consisting of 221 ASD and 191 unaffected family members, outliers in the neurogenesis pathway were heavily biased towards cases (20.8% of ASD, 12.0% of control). Interestingly, neurogenesis outliers were more common among unaffected family members (Simons) than unrelated controls (TGen), but the statistical significance of this effect was marginal (Chi squared P < 0.09). Conclusions: Unlike group difference approaches, our analysis identified the samples within the case and control groups that manifested each expression signal, and showed that outlier groups were distinct for each implicated pathway. Moreover, our results suggest that by seeking heterogeneity, pathway-based outlier analysis can reveal expression signals that are not apparent when considering only shared group differences. [ABSTRACT FROM AUTHOR]

Background: Colonoscopy is widely regarded to be the gold standard for colorectal cancer (CRC) detection. Recent studies, however, suggest that the effectiveness of colonoscopy is mostly confined to tumors on the left side of the colon (descending, sigmoid, rectum), and that the technology has poor tumor detection for right-sided (cecum, ascending, transverse) lesions. A minimally invasive test that can detect both left-sided and right-sided lesions could increase the effectiveness of screening colonoscopy by revealing the potential presence of neoplasms in the right-sided "blind spot". Methods: We previously reported on a seven-gene, blood-based biomarker panel that effectively stratifies a patient's risk of having CRC. For the current study, we assessed the effectiveness of the seven-gene panel for the detection of left- and right-sided CRC lesions. Results were evaluated for 314 patients with CRC (left-sided: TNM I, 65; TNM II, 57; TNM III, 60; TNM IV, 17; unknown, 9. right-sided: TNM I, 28; TNM II, 29; TNM III, 38; TNM IV, 12; unknown, 1 and including two samples with both left and right lesions) and 328 control samples. Blood samples were obtained prior to clinical staging and therapy. Most CRC subjects had localized disease (stages I and II, 58%); regional (stage III) and systemic (stage IV) disease represented 32% and 9%, respectively, of the study population. Results: The panel detected left-sided (74%, 154/208) and right-sided (85%, 92/108) lesions with an overall sensitivity of 78% (215/316) at a specificity of 66% (215/328). Treatable cancer (stages I to III) was detected with left-sided lesion sensitivity of 76% (138/182) and right-sided sensitivity of 84% (80/95). Conclusion: This seven-gene biomarker panel detected right-sided CRC lesions across all cancer stages with a sensitivity that is at least equal to that for left-sided lesions. This study supports the use of this panel as the basis for a patient-friendly, blood-based test that can be easily incorporated into a routine physical examination in advance of colonoscopy to provide a convenient companion diagnostic and a pre-screening alert, ultimately leading to enhanced CRC screening effectiveness. [ABSTRACT FROM AUTHOR]

Accumulating evidence has suggested a shared pathophysiology between Alzheimer's disease (AD) and cardiovascular disease (CVD). Based on genome-wide transcriptomes, specifically those of blood samples, we identify the shared disease-related signatures between AD and CVD. In addition to gene expressions in blood, the following prior knowledge were utilized to identify several candidate disease-related gene (DRG) sets: protein–protein interactions, transcription factors, disease–gene relationship databases, and single nucleotide polymorphisms. We selected the respective DRG sets for AD and CVD that show a high accuracy for disease prediction in bulk and single-cell gene expression datasets. Then, gene regulatory networks (GRNs) were constructed from each of the AD and CVD DRG sets to identify the upstream regulating genes. Using the GRNs, we identified two common upstream genes (GPBP1 and SETDB2) between the AD and CVD GRNs. In summary, this study has identified the potential AD- and CVD-related genes and common hub genes between these sets, which may help to elucidate the shared mechanisms between these two diseases. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) and diabetes mellitus (DM) are known to have a shared molecular mechanism. We aimed to identify shared blood transcriptomic signatures between AD and DM. Blood expression datasets for each disease were combined and a co-expression network was used to construct modules consisting of genes with similar expression patterns. For each module, a gene regulatory network based on gene expression and protein-protein interactions was established to identify hub genes. We selected one module, where COPS4, PSMA6, GTF2B, GTF2F2, and SSB were identified as dysregulated transcription factors that were common between AD and DM. These five genes were also differentially co-expressed in disease-related tissues, such as the brain in AD and the pancreas in DM. Our study identified gene modules that were dysregulated in both AD and DM blood samples, which may contribute to reveal common pathophysiology between two diseases. [ABSTRACT FROM AUTHOR]

Background: Colonoscopy is widely regarded to be the gold standard for colorectal cancer (CRC) detection. Recent studies, however, suggest that the effectiveness of colonoscopy is mostly confined to tumors on the left side of the colon (descending, sigmoid, rectum), and that the technology has poor tumor detection for right-sided (cecum, ascending, transverse) lesions. A minimally invasive test that can detect both left-sided and right-sided lesions could increase the effectiveness of screening colonoscopy by revealing the potential presence of neoplasms in the right-sided “blind spot”. Methods: We previously reported on a seven-gene, blood-based biomarker panel that effectively stratifies a patient’s risk of having CRC. For the current study, we assessed the effectiveness of the seven-gene panel for the detection of left- and right-sided CRC lesions. Results were evaluated for 314 patients with CRC (left-sided: TNM I, 65; TNM II, 57; TNM III, 60; TNM IV, 17; unknown, 9. right-sided: TNM I, 28; TNM II, 29; TNM III, 38; TNM IV, 12; unknown, 1 and including two samples with both left and right lesions) and 328 control samples. Blood samples were obtained prior to clinical staging and therapy. Most CRC subjects had localized disease (stages I and II, 58%); regional (stage III) and systemic (stage IV) disease represented 32% and 9%, respectively, of the study population. Results: The panel detected left-sided (74%, 154/208) and right-sided (85%, 92/108) lesions with an overall sensitivity of 78% (215/316) at a specificity of 66% (215/328). Treatable cancer (stages I to III) was detected with left-sided lesion sensitivity of 76% (138/182) and right-sided sensitivity of 84% (80/95). Conclusion: This seven-gene biomarker panel detected right-sided CRC lesions across all cancer stages with a sensitivity that is at least equal to that for left-sided lesions. This study supports the use of this panel as the basis for a patient-friendly, blood-based test that can be easily incorporated into a routine physical examination in advance of colonoscopy to provide a convenient companion diagnostic and a pre-screening alert, ultimately leading to enhanced CRC screening effectiveness.