Blood Transcriptome Research Articles

Analysis of the influence of pyroptosis-related genes on molecular characteristics in patients with acute myocardial infarction.

Pyroptosis is a newly identified mode of programmed cell death, but the potential role in patients with acute myocardial infarction (AMI) remains unclear. In this study, bioinformatics methods were used to identify differentially expressed genes from peripheral blood transcriptome data between normal subjects and patients with AMI which were downloaded by the Gene Expression Omnibus database. Comparing Random Forest (RF) and Support Vector Machine (SVM) training algorithms were used to identify pyroptosis-related genes, predicting patients with AMI by nomogram based on informative genes. Moreover, clustering was used to amplify the feature of pyroptosis, in order to facilitate analysis distinct biological differences. Diversity analysis indicated that a majority of pyroptosis-related genes are expressed at higher levels in patients with AMI. The receiver operating characteristic curves show that the RF model is more responsive than the SVM machine learning model to the pyroptosis characteristics of these patients in vivo. We obtained a column line graph diagnostic model which was developed based on 19 genes established by the RF model. After the consensus clustering algorithm of single sample Gene Set Enrichment Analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) Enrichment Analysis, the results for them found that pyroptosis-related genes mediate the activation of multiple immune cells and many inflammatory pathways in the body. We used RF and SVM algorithms to determine 19 pyroptosis-related genes and evaluate their immunological effects in patients with AMI. We also constructed a series of by nomogram related to pyroptosis-related genes to predict the risk of developing AMI.

Developments in Post-Resuscitation Care for Out-of-Hospital Cardiac Arrests in Adults-A Narrative Review.

This review focuses on current developments in post-resuscitation care for adults with an out-of-hospital cardiac arrest (OHCA). As the incidence of OHCA is high and with a low percentage of survival, it remains a challenge to treat those who survive the initial phase and regain spontaneous circulation. Early titration of oxygen in the out-of-hospital phase is not associated with increased survival and should be avoided. Once the patient is admitted, the oxygen fraction can be reduced. To maintain an adequate blood pressure and urine output, noradrenaline is the preferred agent over adrenaline. A higher blood pressure target is not associated with higher rates of good neurological survival. Early neuro-prognostication remains a challenge, and prognostication bundles should be used. Established bundles could be extended by novel biomarkers and methods in the upcoming years. Whole blood transcriptome analysis has shown to reliably predict neurological survival in two feasibility studies. This needs further investigation in larger cohorts.

Prenatal Exposure to Metabolism-Disrupting Chemicals, Cord Blood Transcriptome Perturbations, and Birth Weight in a Belgian Birth Cohort.

Prenatal exposure to metabolism-disrupting chemicals (MDCs) has been linked to birth weight, but the molecular mechanisms remain largely unknown. In this study, we investigated gene expressions and biological pathways underlying the associations between MDCs and birth weight, using microarray transcriptomics, in a Belgian birth cohort. Whole cord blood measurements of dichlorodiphenyldichloroethylene (p,p'-DDE), polychlorinated biphenyls 153 (PCB-153), perfluorooctanoic acid (PFOA), perfluorooctane sulfonic acid (PFOS), and transcriptome profiling were conducted in 192 mother-child pairs. A workflow including a transcriptome-wide association study, pathway enrichment analysis with a meet-in-the-middle approach, and mediation analysis was performed to characterize the biological pathways and intermediate gene expressions of the MDC-birth weight relationship. Among 26,170 transcriptomic features, we successfully annotated five overlapping metabolism-related gene expressions associated with both an MDC and birth weight, comprising BCAT2, IVD, SLC25a16, HAS3, and MBOAT2. We found 11 overlapping pathways, and they are mostly related to genetic information processing. We found no evidence of any significant mediating effect. In conclusion, this exploratory study provides insights into transcriptome perturbations that may be involved in MDC-induced altered birth weight.

High diagnostic potential of short and long read genome sequencing with transcriptome analysis in exome-negative developmental disorders

Exome sequencing (ES) has become the method of choice for diagnosing rare diseases, while the availability of short-read genome sequencing (SR-GS) in a medical setting is increasing. In addition, new sequencing technologies, such as long-read genome sequencing (LR-GS) and transcriptome sequencing, are being increasingly used. However, the contribution of these techniques compared to widely used ES is not well established, particularly in regards to the analysis of non-coding regions. In a pilot study of five probands affected by an undiagnosed neurodevelopmental disorder, we performed trio-based short-read GS and long-read GS as well as case-only peripheral blood transcriptome sequencing. We identified three new genetic diagnoses, none of which affected the coding regions. More specifically, LR-GS identified a balanced inversion in NSD1, highlighting a rare mechanism of Sotos syndrome. SR-GS identified a homozygous deep intronic variant of KLHL7 resulting in a neoexon inclusion, and a de novo mosaic intronic 22-bp deletion in KMT2D, leading to the diagnosis of Perching and Kabuki syndromes, respectively. All three variants had a significant effect on the transcriptome, which showed decreased gene expression, mono-allelic expression and splicing defects, respectively, further validating the effect of these variants. Overall, in undiagnosed patients, the combination of short and long read GS allowed the detection of cryptic variations not or barely detectable by ES, making it a highly sensitive method at the cost of morecomplex bioinformatics approaches. Transcriptome sequencing is a valuable complement for the functional validation of variations, particularly in the non-coding genome.

The Effects of a Multi-Ingredient Supplement Containing Wasabia Japonica Extract, Theacrine, and Copper (I) Niacin Chelate on Peripheral Blood Mononuclear Cell DNA Methylation, Transcriptomics, and Sirtuin Activity

Herein, we determined if a multi-ingredient supplement (NAD3; 312 mg of combined Wasabia japonica extract, theacrine, and copper (I)niacin chelate) versus a placebo (CTL) affected peripheral blood mononuclear (PMBC) transcriptomic, DNA methylation, and sirtuin activity profiles in middle-aged adults after 12 weeks of supplementation. Several mRNAs demonstrated interactions (n = 148 at ±1.5-fold change, p < 0.01), and more stringent filtering indicated that 25 mRNAs were upregulated and 29 were downregulated in the NAD3 versus CTL group. Bioinformatics on these 64 mRNAs suggested that DNA conformational alterations may have been promoted with NAD3 supplementation, and this was corroborated with more CpG sites being hypermethylated (p < 0.001) in the CTL versus the NAD3 group when examining pre- to post-intervention changes (369 versus 35). PBMC SIRT activity decreased in CTL participants (p < 0.001), but not in NAD3 participants (p = 0.289), and values at 12 weeks trended higher in NAD3 participants (p = 0.057). Interestingly, the pre- to post- changes in SIRT activity values significantly correlated with changes in PBMC NAD+: NADH values obtained from a previous investigation in these participants (r = 0.534, p = 0.015). In conclusion, the current mRNA and DNA methylation data indirectly suggest that NAD3 supplementation may affect PBMC DNA conformation, while other direct assays suggest that NAD3 supplementation maintains SIRT activity through the potential maintenance of NAD+: NADH levels. However, these results are preliminary due to limited n-sizes and the study being performed in middle-aged adults.

Blood transcriptome sequencing identifies biomarkers able to track disease stages in spinocerebellar ataxia type 3.

Transcriptional dysregulation has been described in spinocerebellar ataxia type 3/Machado-Joseph disease (SCA3/MJD), an autosomal dominant ataxia caused by a polyglutamine expansion in the ataxin-3 protein. As ataxin-3 is ubiquitously expressed, transcriptional alterations in blood may reflect early changes that start before clinical onset and might serve as peripheral biomarkers in clinical and research settings. Our goal was to describe enriched pathways and report dysregulated genes, which can track disease onset, severity or progression in carriers of the ATXN3 mutation (pre-ataxic subjects and patients). Global dysregulation patterns were identified by RNA sequencing of blood samples from 40 carriers of ATXN3 mutation and 20 controls and further compared with transcriptomic data from post-mortem cerebellum samples of MJD patients and controls. Ten genes-ABCA1, CEP72, PTGDS, SAFB2, SFSWAP, CCDC88C, SH2B1, LTBP4, MEG3 and TSPOAP1-whose expression in blood was altered in the pre-ataxic stage and simultaneously, correlated with ataxia severity in the overt disease stage, were analysed by quantitative real-time PCR in blood samples from an independent set of 170 SCA3/MJD subjects and 57 controls. Pathway enrichment analysis indicated the Gαi signalling and the oestrogen receptor signalling to be similarly affected in blood and cerebellum. SAFB2, SFSWAP and LTBP4 were consistently dysregulated in pre-ataxic subjects compared to controls, displaying a combined discriminatory ability of 79%. In patients, ataxia severity was associated with higher levels of MEG3 and TSPOAP1. We propose expression levels of SAFB2, SFSWAP and LTBP4 as well as MEG3 and TSPOAP1 as stratification markers of SCA3/MJD progression, deserving further validation in longitudinal studies and in independent cohorts.

Indian food habit & food ingredients may have a role in lowering the severity & high death rate from COVID-19 in Indians: findings from the first nutrigenomic analysis.

During the COVID-19 pandemic, the death rate was reportedly 5-8 fold lower in India which is densely populated as compared to less populated western countries. The aim of this study was to investigate whether dietary habits were associated with the variations in COVID-19 severity and deaths between western and Indian population at the nutrigenomics level. In this study nutrigenomics approach was applied. Blood transcriptome of severe COVID-19 patients from three western countries (showing high fatality) and two datasets from Indian patients were used. Gene set enrichment analyses were performed for pathways, metabolites, nutrients, etc., and compared for western and Indian samples to identify the food- and nutrient-related factors, which may be associated with COVID-19 severity. Data on the daily consumption of twelve key food components across four countries were collected and a correlation between nutrigenomics analyses and per capita daily dietary intake was investigated. Distinct dietary habits of Indians were observed, which may be associated with low death rate from COVID-19. Increased consumption of red meat, dairy products and processed foods by western populations may increase the severity and death rate by activating cytokine storm-related pathways, intussusceptive angiogenesis, hypercapnia and enhancing blood glucose levels due to high contents of sphingolipids, palmitic acid and byproducts such as CO2 and lipopolysaccharide (LPS). Palmitic acid also induces ACE2 expression and increases the infection rate. Coffee and alcohol that are highly consumed in western countries may increase the severity and death rates from COVID-19 by deregulating blood iron, zinc and triglyceride levels. The components of Indian diets maintain high iron and zinc concentrations in blood and rich fibre in their foods may prevent CO2 and LPS-mediated COVID-19 severity. Regular consumption of tea by Indians maintains high high-density lipoprotein (HDL) and low triglyceride in blood as catechins in tea act as natural atorvastatin. Importantly, regular consumption of turmeric in daily food by Indians maintains strong immunity and curcumin in turmeric may prevent pathways and mechanisms associated with SARS-CoV-2 infection and COVID-19 severity and lowered the death rate. Our results suggest that Indian food components suppress cytokine storm and various other severity related pathways of COVID-19 and may have a role in lowering severity and death rates from COVID-19 in India as compared to western populations. However, large multi-centered case-control studies are required to support our current findings.

Adaptive Regulation of Stopover Refueling during Bird Migration: Insights from Whole Blood Transcriptomics.

Migration is one of the most energy-demanding tasks in avian life cycle. Many birds might not have sufficient fuel stores to cover long distances, so they must stop to rest and refuel at stopover sites, especially after the crossing of large ecological barriers. There, birds undergo several behavioral, morphological, and physiological trait adjustments to recover from and prepare for their journey; however, regulation of such processes at the molecular level remains largely unknown. In this study, we used transcriptomic information from the whole blood of migrating garden warblers (Sylvia borin) to identify key regulatory pathways related to adaptations for migration. Birds were temporarily caged during spring migration stopover and then sampled twice at different refueling states (lean vs. fat), reflecting different migratory stages (stopover arrival vs. departure) after the crossing of an extended ecological barrier. Our results show that top expressed genes during migration are involved in important pathways regarding adaptations to migration at high altitudes such as increase of aerobic capacity and angiogenesis. Gene expression profiles largely reflected the two experimental conditions with several enzymes involved in different aspects of metabolic activity being differentially expressed between states providing several candidate genes for future functional studies. Additionally, we identified several hub genes, upregulated in lean birds that could be involved in the extraordinary phenotypic flexibility in organ mass displayed by avian migrants. Finally, our approach provides novel evidence that regulation of water homeostasis may represent a significant adaptive mechanism, allowing birds to conserve water during long-distance flight, mainly through protein catabolism.

Profiling system-wide variations and similarities between Rheumatic Heart Disease and Acute Rheumatic Fever-A pilot analysis.

Rheumatic heart disease (RHD) continues to affect developing countries with low income due to the lack of resources and effective diagnostic techniques. Understanding the genetic basis common to both the diseases and that of progression from its prequel disease state, Acute Rheumatic Fever (ARF), would aid in developing predictive biomarkers and improving patient care. To gain system-wide molecular insights into possible causes for progression, in this pilot study, we collected blood transcriptomes from ARF (5) and RHD (5) patients. Using an integrated transcriptome and network analysis approach, we identified a subnetwork comprising the most significantly differentially expressed genes and most perturbed pathways in RHD compared to ARF. For example, the chemokine signaling pathway was seen to be upregulated, while tryptophan metabolism was found to be downregulated in RHD. The subnetworks of variation between the two conditions provide unbiased molecular-level insights into the host processes that may be linked with the progression of ARF to RHD, which has the potential to inform future diagnostics and therapeutic strategies. We also found a significantly raised neutrophil/lymphocyte ratio in both ARF and RHD cohorts. Activated neutrophils and inhibited Natural Killer cell gene signatures reflected the drivers of the inflammatory process typical to both disease conditions.

Association between Organophosphate Ester Exposure and Insulin Resistance with Glycometabolic Disorders among Older Chinese Adults 60-69 Years of Age: Evidence from the China BAPE Study.

Organophosphate esters (OPEs) are common endocrine-disrupting chemicals, and OPE exposure may be associated with type 2 diabetes (T2D). However, greater knowledge regarding the biomolecular intermediators underlying the impact of OPEs on T2D in humans are needed to understand biological etiology. We explored the associations between OPE exposure and glycometabolic markers among older Chinese adults 60-69 years of age to elucidate the underlying mechanisms using a multi-omics approach. This was a longitudinal panel study comprising 76 healthy participants 60-69 years of age who lived in Jinan city of northern China. The study was conducted once every month for 5 months, from September 2018 to January 2019. We measured a total of 17 OPEs in the blood, 11 OPE metabolites in urine, and 4 glycometabolic markers (fasting plasma glucose, glycated serum protein, fasting insulin, and homeostatic model assessment for insulin resistance). The blood transcriptome and serum/urine metabolome were also evaluated. The associations between individual OPEs and glycometabolic markers were explored. An adverse outcome pathway (AOP) was established to determine the biomolecules mediating the associations. Exposure to five OPEs and OPE metabolites (trimethylolpropane phosphate, triphenyl phosphate, tri-iso-butyl phosphate, dibutyl phosphate, and diphenyl phosphate) was associated with increased levels of glycometabolic markers. The mixture effect analysis further indicated the adverse effect of OPE mixtures. Multi-omics analyses revealed that the endogenous changes in the transcriptional and metabolic levels were associated with OPE exposure. The putative AOPs model suggested that triggers of molecular initiation events (e.g., insulin receptor and glucose transporter type 4) with subsequent key events, including disruptions in signal transduction pathways (e.g., phosphatidylinositol 3-kinase/protein kinase B and insulin secretion signaling) and biological functions (glucose uptake and insulin secretion), may constitute the diabetogenic effects of OPEs. OPEs are associated with the elevated risk of T2D among older Chinese adults 60-69 years of age. Implementing OPE exposure reduction strategies may help reduce the T2D burden among these individuals, if the relationship is causal. https://doi.org/10.1289/EHP11896.

Identification of immunodiagnostic blood biomarkers associated with spinal cord injury severity.

Blood always shows some immune changes after spinal cord injury (SCI), and detection of such changes in blood may be helpful for diagnosis and treatment of SCI. However, studies to date on blood immune changes after SCI in humans are not comprehensive. Therefore, to obtain the characteristics of blood immune changes and immunodiagnostic blood biomarkers of SCI and its different grades, a human blood transcriptome sequencing dataset was downloaded and analyzed to obtain differentially expressed immune-related genes (DEIGs), related functions and signaling pathways related to SCI and its various grades. Characteristic biomarkers of SCI and its different grades were identified by using weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) logistic regression. Expression of biomarkers was verified through experiments. The area under the curve (AUC) of biomarkers was calculated to evaluate their diagnostic value, and differences in immune cell content were examined. In this study, 17 kinds of immune cells with different contents between the SCI group and healthy control (HC) group were identified, with 7 immune cell types being significantly increased. Differences in the content of immune cells between different grades of SCI and the HC group were also discovered. DEIGs were identified, with alteration in some immune-related signaling pathways, vascular endothelial growth factor signaling pathways, and axon guidance signaling pathways. The SCI biomarkers identified and those of American Spinal Injury Society Impairment Scale (AIS) A and AIS D of SCI have certain diagnostic sensitivity. Analysis of the correlation of immune cells and biomarkers showed that biomarkers of SCI, AIS A grade and AIS D grade correlated positively or negatively with some immune cells. CKLF, EDNRB, FCER1G, SORT1, and TNFSF13B can be used as immune biomarkers for SCI. Additionally, GDF11and HSPA1L can be used as biomarkers of SCI AIS A grade; PRKCA and CMTM2 can be used as biomarkers of the SCI AIS D grade. Detecting expression of these putative biomarkers and changes in related immune cells may be helpful for predicting the severity of SCI.

X chromosome dosage and the genetic impact across human tissues

BackgroundSex chromosome aneuploidies (SCAs) give rise to a broad range of phenotypic traits and diseases. Previous studies based on peripheral blood samples have suggested the presence of ripple effects, caused by altered X chromosome number, affecting the methylome and transcriptome. Whether these alterations can be connected to disease-specific tissues, and thereby having clinical implication for the phenotype, remains to be elucidated.MethodsWe performed a comprehensive analysis of X chromosome number on the transcriptome and methylome in blood, fat, and muscle tissue from individuals with 45,X, 46,XX, 46,XY, and 47,XXY.ResultsX chromosome number affected the transcriptome and methylome globally across all chromosomes in a tissue-specific manner. Furthermore, 45,X and 47,XXY demonstrated a divergent pattern of gene expression and methylation, with overall gene downregulation and hypomethylation in 45,X and gene upregulation and hypermethylation in 47,XXY. In fat and muscle, a pronounced effect of sex was observed. We identified X chromosomal genes with an expression pattern different from what would be expected based on the number of X and Y chromosomes. Our data also indicate a regulatory function of Y chromosomal genes on X chromosomal genes.Fourteen X chromosomal genes were downregulated in 45,X and upregulated in 47,XXY, respectively, in all three tissues (AKAP17A, CD99, DHRSX, EIF2S3, GTPBP6, JPX, KDM6A, PP2R3B, PUDP, SLC25A6, TSIX, XIST, ZBED1, ZFX). These genes may be central in the epigenetic and genomic regulation of sex chromosome aneuploidies.ConclusionWe highlight a tissue-specific and complex effect of X chromosome number on the transcriptome and methylome, elucidating both shared and non-shared gene-regulatory mechanism between SCAs.

Rapid efficacy of anifrolumab across multiple subtypes of recalcitrant cutaneous lupus erythematosus parallels changes in discrete subsets of blood transcriptomic and cellular biomarkers.

Observations with rituximab suggest B-cell independent mechanisms of cutaneous lupus erythematosus (CLE) in systemic lupus erythematosus (SLE), especially discoid lupus erythematosus (DLE). Type-I interferon receptor blockade with anifrolumab shows efficacy in SLE, but efficacy for cutaneous disease of specific morphologies has not been studied. Interferon has pleotropic immune effects and it is unknown which of these are critical to therapeutic response. We evaluated clinical efficacy and quality-of-life impact of type-I interferon-blockade in: (i) rituximab-refractory CLE; (ii) DLE and other morphologies and (iii) transcriptomic and flow cytometric biomarkers. We conducted a prospective single-centre study of anifrolumab in refractory mucocutaneous SLE. CLE Disease Area and Severity Index (CLASI) activity score, health-related quality of life, 96-probe TaqMan® gene expression analysis capturing key SLE blood transcriptome signatures, and eight-colour flow cytometry were undertaken at baseline, 1, 3 and 6 months. Seven patients [DLE (n = 5), chilblain lupus erythematosus (n = 1), subacute CLE (n = 1)] were evaluated. The median number of prior therapies was six (range 3-15), including rituximab in six of seven patients. Median CLASI-A showed rapid and sustained improvement from 17 at baseline to 6 (P = 0.016) at 1 month and 0 (P < 0.001) by 3 months. The median percentage reduction in CLASI-A at 3 months was 60%. Significant improvements were observed in Dermatology Life Quality Index scores (P < 0.001), EuroQol 5D visual analogue scale (P = 0.002) and LupusQoL fatigue, image and planning domains (P ≤ 0.05). One patient discontinued treatment owing to severe herpes zoster. Clinical responses paralleled discrete suppression of interferon-stimulated genes (ISGs) from SLE blood transcriptome module M1.2 with more varied downregulation in other interferon modules. Myeloid and inflammation-annotated genes remained upregulated throughout treatment. Intermediate monocytes (CD14++CD16+) reduced to normal levels during therapy (P = 0.014), while other flow subsets showed no substantive changes. These data indicate rapid efficacy of anifrolumab in DLE and rituximab-resistant CLE. Response is associated with suppression of a subset of ISGs and decline in intermediate monocytes. Suppression of all ISGs or the wider SLE blood transcriptome is not required for response.

Integration of Mendelian randomisation and systems biology models to identify novel blood-based biomarkers for stroke

Stroke is the second largest cause of mortality in the world. Genome-wide association studies (GWAS) have identified some genetic variants associated with stroke risk, but their putative functional causal genes are unknown. Hence, we aimed to identify putative functional causal gene biomarkers of stroke risk. We used a summary-based Mendelian randomisation (SMR) approach to identify the pleiotropic associations of genetically regulated traits (i.e., gene expression and DNA methylation) with stroke risk. Using SMR approach, we integrated cis-expression quantitative loci (cis-eQTLs) and cis-methylation quantitative loci (cis-mQTLs) data with GWAS summary statistics of stroke. We also utilised heterogeneity in dependent instruments (HEIDI) test to distinguish pleiotropy from linkage from the observed associations identified through SMR analysis. Our integrative SMR analyses and HEIDI test revealed 45 candidate biomarker genes (FDR < 0.05; PHEIDI > 0.01) that were pleiotropically or potentially causally associated with stroke risk. Of those candidate biomarker genes, 10 genes (HTRA1, PMF1, FBN2, C9orf84, COL4A1, BAG4, NEK6, SH2B3, SH3PXD2A, ACAD10) were differentially expressed in genome-wide blood transcriptomics data from stroke and healthy individuals (FDR < 0.05). Functional enrichment analysis of the identified candidate biomarker genes revealed gene ontologies and pathways involved in stroke, including “cell aging”, “metal ion binding” and “oxidative damage”. Based on the evidence of genetically regulated expression of genes through SMR and directly measured expression of genes in blood, our integrative analysis suggests ten genes as blood biomarkers of stroke risk. Furthermore, our study provides a better understanding of the influence of DNA methylation on the expression of genes linked to stroke risk.

Identification of dynamic gene expression profiles during sequential vaccination with ChAdOx1/BNT162b2 using machine learning methods.

To date, COVID-19 remains a serious global public health problem. Vaccination against SARS-CoV-2 has been adopted by many countries as an effective coping strategy. The strength of the body's immune response in the face of viral infection correlates with the number of vaccinations and the duration of vaccination. In this study, we aimed to identify specific genes that may trigger and control the immune response to COVID-19 under different vaccination scenarios. A machine learning-based approach was designed to analyze the blood transcriptomes of 161 individuals who were classified into six groups according to the dose and timing of inoculations, including I-D0, I-D2-4, I-D7 (day 0, days 2-4, and day 7 after the first dose of ChAdOx1, respectively) and II-D0, II-D1-4, II-D7-10 (day 0, days 1-4, and days 7-10 after the second dose of BNT162b2, respectively). Each sample was represented by the expression levels of 26,364 genes. The first dose was ChAdOx1, whereas the second dose was mainly BNT162b2 (Only four individuals received a second dose of ChAdOx1). The groups were deemed as labels and genes were considered as features. Several machine learning algorithms were employed to analyze such classification problem. In detail, five feature ranking algorithms (Lasso, LightGBM, MCFS, mRMR, and PFI) were first applied to evaluate the importance of each gene feature, resulting in five feature lists. Then, the lists were put into incremental feature selection method with four classification algorithms to extract essential genes, classification rules and build optimal classifiers. The essential genes, namely, NRF2, RPRD1B, NEU3, SMC5, and TPX2, have been previously associated with immune response. This study also summarized expression rules that describe different vaccination scenarios to help determine the molecular mechanism of vaccine-induced antiviral immunity.

Single-cell Sequence Analysis Combined with Multiple Machine Learning to Identify Markers in Sepsis Patients: LILRA5.

Sepsis is a disease with a very high mortality rate, mainly involving an immune-dysregulated response due to bacterial infection. Most studies are currently limited to the whole blood transcriptome level; however, at the single cell level, there is still a great deal unknown about specific cell subsets and disease markers. We obtained 29 peripheral blood single-cell sequencing data, including 66,283 cells from 10 confirmed samples of sepsis infection and 19 healthy samples. Cells related to the sepsis phenotype were identified and characterized by the "scissor" method. The regulatory relationships of sepsis-related phenotype cells in the cellular communication network were clarified using the "cell chat" method. The least absolute shrinkage and selection operator (LASSO), support vector machine (SVM), and random forest (RF) were used to identify sepsis signature genes of diagnostic value. External validation was performed using multiple datasets from the GEO database (GSE28750, GSE185263, GSE57065) and 40 clinical samples. Bayesian algorithm was used to calculate the regulatory network of LILRA5 co-expressed genes. The stability of atenolol-targeting LILRA5 was determined by molecular docking techniques. Ultimately, action trajectory and survival analyses demonstrate the effectiveness of atenolol-targeted LILRA5 in treating patients with sepsis. We successfully identified 1215 healthy phenotypic cells and 462 sepsis phenotypic cells. We focused on 447 monocytes of the sepsis phenotype. Among the cellular communications, there were a large number of differences between these cells and other immune cells showing a significant inflammatory phenotype compared to the healthy phenotypic cells. Together, the three machine learning algorithms identified the LILRA5 marker gene in sepsis patients, and validation results from multiple external datasets as well as real-world clinical samples demonstrated the robust diagnostic performance of LILRA5. The AUC values of LILRA5 in the external datasets GSE28750, GSE185263, and GSE57065 could reach 0.875, 0.940, and 0.980, in that order. Bayesian networks identified a large number of unknown regulatory relationships for LILRA5 co-expression. Molecular docking results demonstrated the possibility of atenolol targeting LILRA5 for the treatment of sepsis. Behavioral trajectory analysis and survival analysis demonstrate that atenolol has a desirable therapeutic effect. LILRA5 is a marker gene in sepsis patients, and atenolol can stably target LILRA5.

Integrative Analysis of Blood Transcriptomics and Metabolomics Reveals Molecular Regulation of Backfat Thickness in Qinchuan Cattle

A crucial goal of reducing backfat thickness (BFT) is to indirectly improve feed conversion efficiency. This phenotype has been reported in certain papers; however, the molecular mechanism has yet to be fully revealed. Two extreme BFT groups, consisting of four Qinchuan cattle, were chosen for this study. We performed metabolite and transcriptome analyses of blood from cattle with a high BFT (H-BFT with average = 1.19) and from those with a low BFT (L-BFT with average = 0.39). In total, 1106 differentially expressed genes (DEGs) and 86 differentially expressed metabolites (DEMs) were identified in the extreme trait. In addition, serum ceramide was strongly correlated with BFT and could be used as a potential biomarker. Moreover, the most notable finding was that the functional genes (SMPD3 and CERS1) and metabolite (sphingosine 1-phosphate (S1P)) were filtered out and significantly enriched in the processes related to the sphingolipid metabolism. This investigation contributed to a better understanding of the subcutaneous fat depots in cattle. In general, our results indicated that the sphingolipid metabolism, involving major metabolites (serum ceramide and S1P) and key genes (SMPD3 and CERS1), could regulate BFT through blood circulation.

Systems prediction of chronic lung allograft dysfunction: Results and perspectives from the Cohort of Lung Transplantation and Systems prediction of Chronic Lung Allograft Dysfunction cohorts.

Chronic lung allograft dysfunction (CLAD) is the leading cause of poor long-term survival after lung transplantation (LT). Systems prediction of Chronic Lung Allograft Dysfunction (SysCLAD) aimed to predict CLAD. To predict CLAD, we investigated the clinicome of patients with LT; the exposome through assessment of airway microbiota in bronchoalveolar lavage cells and air pollution studies; the immunome with works on activation of dendritic cells, the role of T cells to promote the secretion of matrix metalloproteinase-9, and subpopulations of T and B cells; genome polymorphisms; blood transcriptome; plasma proteome studies and assessment of MSK1 expression. Clinicome: the best multivariate logistic regression analysis model for early-onset CLAD in 422 LT eligible patients generated a ROC curve with an area under the curve of 0.77. Exposome: chronic exposure to air pollutants appears deleterious on lung function levels in LT recipients (LTRs), might be modified by macrolides, and increases mortality. Our findings established a link between the lung microbial ecosystem, human lung function, and clinical stability post-transplant. Immunome: a decreased expression of CLEC1A in human lung transplants is predictive of the development of chronic rejection and associated with a higher level of interleukin 17A; Immune cells support airway remodeling through the production of plasma MMP-9 levels, a potential predictive biomarker of CLAD. Blood CD9-expressing B cells appear to favor the maintenance of long-term stable graft function and are a potential new predictive biomarker of BOS-free survival. An early increase of blood CD4 + CD57 + ILT2+ T cells after LT may be associated with CLAD onset. Genome: Donor Club cell secretory protein G38A polymorphism is associated with a decreased risk of severe primary graft dysfunction after LT. Transcriptome: blood POU class 2 associating factor 1, T-cell leukemia/lymphoma domain, and B cell lymphocytes, were validated as predictive biomarkers of CLAD phenotypes more than 6 months before diagnosis. Proteome: blood A2MG is an independent predictor of CLAD, and MSK1 kinase overexpression is either a marker or a potential therapeutic target in CLAD. Systems prediction of Chronic Lung Allograft Dysfunction generated multiple fingerprints that enabled the development of predictors of CLAD. These results open the way to the integration of these fingerprints into a predictive handprint.

Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis is a fatal and incurable neurodegenerative disease that mainly affects the neurons of the motor system. Despite the increasing understanding of its genetic components, their biological meanings are still poorly understood. Indeed, it is still not clear to which extent the pathological features associated with amyotrophic lateral sclerosis are commonly shared by the different genes causally linked to this disorder. To address this point, we combined multiomics analysis covering the transcriptional, epigenetic and mutational aspects of heterogenous human induced pluripotent stem cell-derived C9orf72-, TARDBP-, SOD1- and FUS-mutant motor neurons as well as datasets from patients' biopsies. We identified a common signature, converging towards increased stress and synaptic abnormalities, which reflects a unifying transcriptional program in amyotrophic lateral sclerosis despite the specific profiles due to the underlying pathogenic gene. In addition, whole genome bisulphite sequencing linked the altered gene expression observed in mutant cells to their methylation profile, highlighting deep epigenetic alterations as part of the abnormal transcriptional signatures linked to amyotrophic lateral sclerosis. We then applied multi-layer deep machine-learning to integrate publicly available blood and spinal cord transcriptomes and found a statistically significant correlation between their top predictor gene sets, which were significantly enriched in toll-like receptor signalling. Notably, the overrepresentation of this biological term also correlated with the transcriptional signature identified in mutant human induced pluripotent stem cell-derived motor neurons, highlighting novel insights into amyotrophic lateral sclerosis marker genes in a tissue-independent manner. Finally, using whole genome sequencing in combination with deep learning, we generated the first mutational signature for amyotrophic lateral sclerosis and defined a specific genomic profile for this disease, which is significantly correlated to ageing signatures, hinting at age as a major player in amyotrophic lateral sclerosis. This work describes innovative methodological approaches for the identification of disease signatures through the combination of multiomics analysis and provides novel knowledge on the pathological convergencies defining amyotrophic lateral sclerosis.

The turning point of COVID-19 severity is associated with a unique circulating neutrophil gene signature.

COVID-19 has a broad spectrum of clinical manifestations associated with the host immune response heterogeneity. Despite the advances in COVID-19 research, it is still crucial to seek a panel of molecular markers that enable accurate stratification of COVID-19 patients. Here, we performed a study that combined analysis of blood transcriptome, demographic data, clinical aspects and laboratory findings from 66 participants classified into different degrees of COVID-19 severity and healthy subjects. We identified a perturbation in blood-leukocyte transcriptional profile associated with COVID-19 aggravation, which was mainly related to processes that disfavoured lymphocyte activation and favoured neutrophil activation. This transcriptional profile stratified patients according to COVID-19 severity. Hence, it enabled identification of a turning point in transcriptional dynamics that distinguished disease outcomes and non-hospitalized from hospitalized moderate patients. Central genes of this unique neutrophil signature were S100A9, ANXA3, CEACAM6, VNN1, OLFM4, IL1R2, TCN1 and CD177. Our study indicates the molecular changes that are linked with the differing clinical aspects presented by humans when suffering from COVID-19, which involve neutrophil activation.