Predicting tissue-specific gene expression from whole blood transcriptome

Inferring Characteristics of the Tumor Immune Microenvironment of Patients with HNSCC from Single-Cell Transcriptomics of Peripheral Blood

In this study, we explore the possibility of inferring characteristics of the tumor immune microenvironment from the blood. Specifically, we investigate two datasets of patients with head and neck squamous cell carcinoma with matched single-cell RNA sequencing (scRNA-seq) from peripheral blood mononuclear cells (PBMCs) and tumor tissues. Our analysis shows that the immune cell fractions and gene expression profiles of various immune cells within the tumor microenvironment can be inferred from the matched PBMC scRNA-seq data. We find that the established exhausted T-cell signature can be predicted from the blood and serve as a valuable prognostic blood biomarker of immunotherapy response. Additionally, our study reveals that the inferred ratio between tumor memory B- and regulatory T-cell fractions is predictive of immunotherapy response and is superior to the well-established cytolytic and exhausted T-cell signatures. These results highlight the promising potential of PBMC scRNA-seq in cancer immunotherapy and warrant, and will hopefully facilitate, further investigations on a larger scale. The code for predicting tumor immune microenvironment from PBMC scRNA-seq, TIMEP, is provided, offering other researchers the opportunity to investigate its prospective applications in various other indications.

SIGNIFICANCE

Our work offers a new and promising paradigm in liquid biopsies to unlock the power of blood single-cell transcriptomics in cancer immunotherapy.

A multi-modal framework improves prediction of tissue-specific gene expression from a surrogate tissue

BACKGROUND

Tissue-specific analysis of the transcriptome is critical to elucidating the molecular basis of complex traits, but central tissues are often not accessible. We propose a methodology, Multi-mOdal-based framework to bridge the Transcriptome between PEripheral and Central tissues (MOTPEC).

METHODS

Multi-modal regulatory elements in peripheral blood are incorporated as features for gene expression prediction in 48 central tissues. To demonstrate the utility, we apply it to the identification of BMI-associated genes and compare the tissue-specific results with those derived directly from surrogate blood.

FINDINGS

MOTPEC models demonstrate superior performance compared with both baseline models in blood and existing models across the 48 central tissues. We identify a set of BMI-associated genes using the central tissue MOTPEC-predicted transcriptome data. The MOTPEC-based differential gene expression (DGE) analysis of BMI in the central tissues (including brain caudate basal ganglia and visceral omentum adipose tissue) identifies 378 genes overlapping the results from a TWAS of BMI, while only 162 overlapping genes are identified using gene expression in blood. Cellular perturbation analysis further supports the utility of MOTPEC for identifying trait-associated gene sets and narrowing the effect size divergence between peripheral blood and central tissues.

INTERPRETATION

The MOTPEC framework improves the gene expression prediction accuracy for central tissues and enhances the identification of tissue-specific trait-associated genes.

FUNDING

This research is supported by the National Natural Science Foundation of China 82204118 (D.Z.), the seed funding of the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province (2020E10004), the National Institutes of Health (NIH) Genomic Innovator Award R35HG010718 (E.R.G.), NIH/NHGRI R01HG011138 (E.R.G.), NIH/NIA R56AG068026 (E.R.G.), NIH Office of the Director U24OD035523 (E.R.G.), and NIH/NIGMS R01GM140287 (E.R.G.).

Integrated Blood Transcriptome and Multi-Tissue Trace Mineral Analyses of Healthy Stocker Cattle Fed Complexed or Inorganic Trace Mineral Supplement

Supplementing trace minerals is common in managing bovine respiratory disease (BRD) in post-weaned cattle; however, its influence on host immunity and metabolism in high-risk cattle remains unclear. We aimed to assess the impact of three supplementation programs on liver and serum trace element concentrations and blood gene expression. Fifty-six high-risk beef steers were randomly assigned to one of three groups over 60 days: (1) sulfate-sourced Cu, Co, Mn, and Zn (INR), (2) amino acid-complexed Cu, Mn, Co, and Zn (AAC), or (3) AAC plus trace mineral and vitamin drench (COMBO). Serum and liver biopsies for Cu, Co, Mn, and Zn at d0, d28, and d60 were analyzed from cattle free of BRD (n = 9 INR; n = 6 AAC; n = 10 COMBO). Differences and correlations of mineral concentrations were analyzed via generalized linear mixed models and Spearman's rank coefficients, respectively (p < 0.05). Whole blood RNA samples from healthy cattle (n = 4 INR; n = 4 AAC; n = 4 COMBO) at d0, d13, d28, d45, and d60 were sequenced and analyzed for differentially expressed genes (DEGs) via glmmSeq (FDR < 0.05), edgeR (FDR < 0.10), and Trendy (p < 0.10). Serum and liver Cu and Co concentrations increased over time in all groups, with higher liver Cu in COMBO (487.985 μg/g) versus AAC (392.043 μg/g) at d60 (p = 0.013). Serum and liver Cu concentrations (ρ = 0.579, p = 6.59 × 10-8) and serum and liver Co concentrations (ρ = 0.466, p = 2.80 × 10-5) were linearly correlated. Minimal gene expression differences were found between AAC versus COMBO (n = 2 DEGs) and INR versus COMBO (n = 0 DEGs) over time. AAC versus INR revealed 107 DEGs (d13-d60) with increased traits in AAC including metabolism of carbohydrates/fat-soluble vitamins, antigen presentation, ATPase activity, and B- and T-cell activation, while osteoclast differentiation and neutrophil degranulation decreased in AAC compared to INR. Our study identifies gene expression differences in high-risk cattle fed inorganic or amino acid-complexed mineral supplements, revealing adaptive immune and metabolic mechanisms that may be improved by organically sourced supplementation.

A systematic assessment of the impact of rare canonical splice site variants on splicing using functional and in silico methods

Canonical splice site variants (CSSVs) are often presumed to cause loss-of-function (LoF) and are assigned very strong evidence of pathogenicity (according to American College of Medical Genetics/Association for Molecular Pathology criterion PVS1). The exact nature and predictability of splicing effects of unselected rare CSSVs in blood-expressed genes are poorly understood. We identified 168 rare CSSVs in blood-expressed genes in 112 individuals using genome sequencing, and studied their impact on splicing using RNA sequencing (RNA-seq). There was no evidence of a frameshift, nor of reduced expression consistent with nonsense-mediated decay, for 25.6% of CSSVs: 17.9% had wildtype splicing only and normal junction depths, 3.6% resulted in cryptic splice site usage and in-frame insertions or deletions, 3.6% resulted in full exon skipping (in frame), and 0.6% resulted in full intron inclusion (in frame). Blind to these RNA-seq data, we attempted to predict the precise impact of CSSVs by applying in silico tools and the ClinGen Sequence Variant Interpretation Working Group 2018 guidelines for applying PVS1 criterion. The predicted impact on splicing using (1) SpliceAI, (2) MaxEntScan, and (3) AutoPVS1, an automatic classification tool for PVS1 interpretation of null variants that utilizes Ensembl Variant Effect Predictor and MaxEntScan, was concordant with RNA-seq analyses for 65%, 63%, and 61% of CSSVs, respectively. In summary, approximately one in four rare CSSVs did not show evidence for LoF based on analysis of RNA-seq data. Predictions from in silico methods were often discordant with findings from RNA-seq. More caution may be warranted in applying PVS1-level evidence to CSSVs in the absence of functional data.

Investigation of missense mutation-related type 1 diabetes mellitus through integrating genomic databases and bioinformatic approach

Though genes are already known to be responsible for type 1 diabetes mellitus (T1DM), the knowledge of missense mutation of that disease gene has still to be under covered. A genomic database and a bioinformatics-based approach are integrated in the present study in order to address this issue. Initially, nine variants associated with T1DM were retrieved from the GWAS catalogue. Different genomic algorithms such as PolyPhen2.0, SNPs and GTEx analyser programs were used to study the structural and functional effects of these mutations. Subsequently, SNPnexus was also employed to understand the effect of these mutations on the function of the expressed protein. Nine missense variants of T1DM were identified using the GWAS catalogue database. Among these nine SNPs, three were predicted to be related to the progression of T1DM disease by affecting the protein level. TYK2 gene variants with SNP rs34536443 were thought to have a probably damaging effect. Meanwhile, both COL4A3 and IFIH1 genes with SNPs rs55703767 and rs35667974, respectively, might alter protein function through a possibly damaging prediction. Among the variants of the three genes, the TYK2 gene with SNP rs34536443 had the strongest contribution in affecting the development of T1DM, with a score of 0.999. We sincerely hope that the results could be of immense importance in understanding the genetic basis of T1DM.

Identification and characterization of whole blood gene expression and splicing quantitative trait loci during early to mid-lactation of dairy cattle

BACKGROUND

Characterization of regulatory variants (e.g., gene expression quantitative trait loci, eQTL; gene splicing QTL, sQTL) is crucial for biologically interpreting molecular mechanisms underlying loci associated with complex traits. However, regulatory variants in dairy cattle, particularly in specific biological contexts (e.g., distinct lactation stages), remain largely unknown. In this study, we explored regulatory variants in whole blood samples collected during early to mid-lactation (22-150 days after calving) of 101 Holstein cows and analyzed them to decipher the regulatory mechanisms underlying complex traits in dairy cattle.

RESULTS

We identified 14,303 genes and 227,705 intron clusters expressed in the white blood cells of 101 cattle. The average heritability of gene expression and intron excision ratio explained by cis-SNPs is 0.28 ± 0.13 and 0.25 ± 0.13, respectively. We identified 23,485 SNP-gene expression pairs and 18,166 SNP-intron cluster pairs in dairy cattle during early to mid-lactation. Compared with the 2,380,457 cis-eQTLs reported to be present in blood in the Cattle Genotype-Tissue Expression atlas (CattleGTEx), only 6,114 cis-eQTLs (P < 0.05) were detected in the present study. By conducting colocalization analysis between cis-e/sQTL and the results of genome-wide association studies (GWAS) from four traits, we identified a cis-e/sQTL (rs109421300) of the DGAT1 gene that might be a key marker in early to mid-lactation for milk yield, fat yield, protein yield, and somatic cell score (PP4 > 0.6). Finally, transcriptome-wide association studies (TWAS) revealed certain genes (e.g., FAM83H and TBC1D17) whose expression in white blood cells was significantly (P < 0.05) associated with complex traits.

CONCLUSIONS

This study investigated the genetic regulation of gene expression and alternative splicing in dairy cows during early to mid-lactation and provided new insights into the regulatory mechanisms underlying complex traits of economic importance.

Integrated analysis of transcriptome and milk metagenome in subclinical mastitic and healthy cows

OBJECTIVE

Abnormally increased somatic cell counts (SCCs) in milk is usually a sign of bovine subclinical mastitis. Mutual interaction between the host and its associated microbiota plays an important role in developing such diseases. The main objective of this study was to explore the difference between cows with elevated SCCs and healthy cattle from the perspective of host-microbe interplay.

METHODS

A total of 31 milk samples and 23 bovine peripheral blood samples were collected from Holstein dairy cattle to conduct an integrated analysis of transcriptomic and metagenomics.

RESULTS

The results showed that Ralstonia and Sphingomonas were enriched in cows with subclinical mastitis. The relative abundance of the two bacteria was positively correlated with the expression level of bovine transcobalamin 1 and uridine phosphorylase 1 encoding gene. Moreover, functional analysis revealed a distinct alternation in some important microbial biological processes.

CONCLUSION

These results reveal the relative abundance of Ralstonia and Sphingomonas other than common mastitis-causing pathogens varied from healthy cows to those with subclinical mastitis and might be associated with elevated SCCs. Potential association was observed between bovine milk microbiota composition and the transcriptional pattern of some genes, thus providing new insights to understand homeostasis of bovine udder.

Community cohesion looseness in gene networks reveals individualized drug targets and resistance

Community cohesion plays a critical role in the determination of an individual's health in social science. Intriguingly, a community structure of gene networks indicates that the concept of community cohesion could be applied between the genes as well to overcome the limitations of single gene-based biomarkers for precision oncology. Here, we develop community cohesion scores which precisely quantify the community ability to retain the interactions between the genes and their cellular functions in each individualized gene network. Using breast cancer as a proof-of-concept study, we measure the community cohesion score profiles of 950 case samples and predict the individualized therapeutic targets in 2-fold. First, we prioritize them by finding druggable genes present in the community with the most and relatively decreased scores in each individual. Then, we pinpoint more individualized therapeutic targets by discovering the genes which greatly contribute to the community cohesion looseness in each individualized gene network. Compared with the previous approaches, the community cohesion scores show at least four times higher performance in predicting effective individualized chemotherapy targets based on drug sensitivity data. Furthermore, the community cohesion scores successfully discover the known breast cancer subtypes and we suggest new targeted therapy targets for triple negative breast cancer (e.g. KIT and GABRP). Lastly, we demonstrate that the community cohesion scores can predict tamoxifen responses in ER+ breast cancer and suggest potential combination therapies (e.g. NAMPT and RXRA inhibitors) to reduce endocrine therapy resistance based on individualized characteristics. Our method opens new perspectives for the biomarker development in precision oncology.

MiRNA-miRNA interaction network in peripheral blood of patients with myocardial infarction: a gene expression meta-analysis

In recent years, investigations have revealed that microRNAs (miRNAs) can bind together and form a miRNA-miRNA-mRNA regulatory network that alters the consequence of miRNA-mRNA interaction. If we consider the miRNA that binds to mRNA as the primary miRNA and the miRNA that binds to the primary miRNA as the secondary one, secondry miRNAs can act as master regulators upstream of primary miRNAs and their target mRNAs. One of the distinguishing characteristics of secondary miRNAs as master regulators within a diverse set of differentially expressed genes is the absence of direct target mRNA for them. Instead, these master regulators exclusively govern the regulation of miRNAs that target specific mRNAs. Through in silico analysis, we identified 18 miRNAs among 385 differentially expressed miRNAs (DEmiRNAs) with no direct target mRNAs among 58 differentially expressed mRNAs (DEmRNAs) in peripheral blood of patients with myocardial infarction (MI). Instead, these secondary miRNAs targeted 9 primary miRNAs that had 36 direct targets among 58 DEmRNAs. We found that one primary miRNA might be regulated by more than one secondary miRNAs and each secondary miRNA can target more than one primary miRNAs. Among identified miRNA-miRNA-mRNA networks miR-188-5p/miR-299-3p/natural killer cell granule protein (NKG7), miR-200a-3p/miR-199b-5p/granzyme B (GZMB), and miR-377-3p/miR-581/oviductal glycoprotein 1 (OVGP1) exhibited higher scors in terms of expression levels (>2-fold increase or decrease) and strengh of interactions (ΔG < -5). Given the extensive network of miRNA interactions, focusing on master regulators opens up avenues for identifying key regulatory nodes for more effective therapeutic strategies.

Effects of exercise on whole-blood transcriptome profile in children with overweight/obesity

BACKGROUND

The current knowledge about the molecular mechanisms underlying the health benefits of exercise is still limited, especially in childhood. We set out to investigate the effects of a 20-week exercise intervention on whole-blood transcriptome profile (RNA-seq) in children with overweight/obesity.

METHODS

Twenty-four children (10.21 ± 1.33 years, 46% girls) with overweight/obesity, were randomized to either a 20-week exercise program (intervention group; n = 10), or to a no-exercise control group (n = 14). Whole-blood transcriptome profile was analyzed using RNA-seq by STRT technique with GlobinLock technology.

RESULTS

Following the 20-week exercise intervention program, 161 genes were differentially expressed between the exercise and the control groups among boys, and 121 genes among girls (p-value <0.05), while after multiple correction, no significant difference between exercise and control groups persisted in gene expression profiles (FDR >0.05). Genes enriched in GO processes and molecular pathways showed different immune response in boys (antigen processing and presentation, infections, and T cell receptor complex) and in girls (Fc epsilon RI signaling pathway) (FDR <0.05).

CONCLUSION

These results suggest that 20-week exercise intervention program alters the molecular pathways involved in immune processes in children with overweight/obesity.

The declining insulinogenic index correlates with inflammation and metabolic dysregulation in non-obese individuals assessed by blood gene expression

AIMS

Diabetes onset is difficult to predict. Since decreased insulinogenic index (IGI) is observed in prediabetes, and blood gene expression correlates with insulin secretion, candidate biomarkers can be identified.

METHODS

We collected blood from 96 participants (54 males, 42 females) in 2008 (age: 52.5 years) and 2016 for clinical and gene expression analyses. IGI was derived from values of insulin and glucose at fasting and at 30 min post-OGTT. Two subgroups were identified based on IGI variation: "Minor change in IGI" group with absolute value variation between -0.05 and +0.05, and "Decrease in IGI" group with a variation between -20 and -0.05.

RESULTS

Following the comparison of "Minor change in IGI" and "Decrease in IGI" groups at time 0 (2008), we identified 77 genes correlating with declining IGI, related to response to lipid, carbohydrate, and hormone metabolism, response to stress and DNA metabolic processes. Over the eight years, genes correlating to declining IGI were related to inflammation, metabolic and hormonal dysregulation. Individuals with minor change in IGI, instead, featured homeostatic and regenerative responses.

CONCLUSIONS

By blood gene expression analysis of non-obese individuals, we identified potential gene biomarkers correlating to declining IGI, associated to a pathophysiology of inflammation and metabolic dysregulation.

Transcriptional risk scores in Alzheimer's disease: From pathology to cognition

INTRODUCTION

Our previously developed blood-based transcriptional risk scores (TRS) showed associations with diagnosis and neuroimaging biomarkers for Alzheimer's disease (AD). Here, we developed brain-based TRS.

METHODS

We integrated AD genome-wide association study summary and expression quantitative trait locus data to prioritize target genes using Mendelian randomization. We calculated TRS using brain transcriptome data of two independent cohorts (N = 878) and performed association analysis of TRS with diagnosis, amyloidopathy, tauopathy, and cognition. We compared AD classification performance of TRS with polygenic risk scores (PRS).

RESULTS

Higher TRS values were significantly associated with AD, amyloidopathy, tauopathy, worse cognition, and faster cognitive decline, which were replicated in an independent cohort. The AD classification performance of PRS was increased with the inclusion of TRS up to 16% with the area under the curve value of 0.850.

DISCUSSION

Our results suggest brain-based TRS improves the AD classification of PRS and may be a potential AD biomarker.

HIGHLIGHTS

Transcriptional risk score (TRS) is developed using brain RNA-Seq data. Higher TRS values are shown in Alzheimer's disease (AD). TRS improves the AD classification power of PRS up to 16%. TRS is associated with AD pathology presence. TRS is associated with worse cognitive performance and faster cognitive decline.

Whole Blood-based Transcriptional Risk Score for Nonobese Type 2 Diabetes Predicts Dynamic Changes in Glucose Metabolism

CONTEXT

The performance of peripheral blood transcriptional markers in evaluating risk of type 2 diabetes (T2D) with normal body mass index (BMI) is unknown.

OBJECTIVE

We developed a whole blood-based transcriptional risk score (wb-TRS) for nonobese T2D and assessed its contributions on disease risk and dynamic changes in glucose metabolism.

METHODS

Using a community-based cohort with blood transcriptome data, we developed the wb-TRS in 1105 participants aged ≥40 years who maintained a normal BMI for up to 10 years, and we validated the wb-TRS in an external dataset. Potential biological significance was explored.

RESULTS

The wb-TRS included 144 gene transcripts. Compared to the lowest tertile, wb-TRS in tertile 3 was associated with 8.91-fold (95% CI, 3.53-22.5) higher risk and each 1-unit increment was associated with 2.63-fold (95% CI, 1.87-3.68) higher risk of nonobese T2D. Furthermore, baseline wb-TRS significantly associated with dynamic changes in average, daytime, nighttime, and 24-hour glucose, HbA1c values, and area under the curve of glucose measured by continuous glucose monitoring over 6 months of intervention. The wb-TRS improved the prediction performance for nonobese T2D, combined with fasting glucose, triglycerides, and demographic and anthropometric parameters. Multi-contrast gene set enrichment (Mitch) analysis implicated oxidative phosphorylation, mTORC1 signaling, and cholesterol metabolism involved in nonobese T2D pathogenesis.

CONCLUSION

A whole blood-based nonobese T2D-associated transcriptional risk score was validated to predict dynamic changes in glucose metabolism. These findings suggested several biological pathways involved in the pathogenesis of nonobese T2D.

Increased expression of individual genes in whole blood is associated with late-stage lung cancer at and close to diagnosis

Lung cancer (LC) mortality rates are still increasing globally. As survival is linked to stage, there is a need to identify markers for earlier LC diagnosis and individualized treatment. The whole blood transcriptome of LC patients represents a source of potential LC biomarkers. We compared expression of > 60,000 genes in whole blood specimens taken from LC cases at diagnosis (n = 128) and controls (n = 62) using genome-wide RNA sequencing, and identified 14 candidate genes associated with LC. High expression of ANXA3, ARG1 and HP was strongly associated with lower survival in late-stage LC cases (hazard ratios (HRs) = 2.81, 2.16 and 2.54, respectively). We validated these markers in two independent population-based studies with pre-diagnostic whole blood specimens taken up to eight years prior to LC diagnosis (n = 163 cases, 184 matched controls). ANXA3 and ARG1 expression was strongly associated with LC in these specimens, especially with late-stage LC within two years of diagnosis (odds ratios (ORs) = 3.47 and 5.00, respectively). Additionally, blood CD4 T cells, NK cells and neutrophils were associated with LC at diagnosis and improved LC discriminative ability beyond candidate genes. Our results indicate that in whole blood, increased expression levels of ANXA3, ARG1 and HP are diagnostic and prognostic markers of late-stage LC.

Harnessing Genomics and Transcriptomics Approaches to Improve Female Fertility in Beef Cattle-A Review

Female fertility is the foundation of the cow-calf industry, impacting both efficiency and profitability. Reproductive failure is the primary reason why beef cows are sold in the U.S. and the cause of an estimated annual gross loss of USD 2.8 billion. In this review, we discuss the status of the genomics, transcriptomics, and systems genomics approaches currently applied to female fertility and the tools available to cow-calf producers to maximize genetic progress. We highlight the opportunities and limitations associated with using genomic and transcriptomic approaches to discover genes and regulatory mechanisms related to beef fertility. Considering the complex nature of fertility, significant advances in precision breeding will rely on holistic, multidisciplinary approaches to further advance our ability to understand, predict, and improve reproductive performance. While these technologies have advanced our knowledge, the next step is to translate research findings from bench to on-farm applications.

Building an optimal predictive model for imputing tissue-specific gene expression by combining genotype and whole-blood transcriptome data

Accurate imputation of tissue-specific gene expression can be a powerful tool for understanding the biological mechanisms underlying human complex traits. Existing imputation methods can be grouped into two categories according to the types of predictors used. The first category uses genotype data, while the second category uses whole-blood expression data. Both data types can be easily collected from blood, avoiding invasive tissue biopsies. In this study, we attempted to build an optimal predictive model for imputing tissue-specific gene expression by combining the genotype and whole-blood expression data. We first evaluated the imputation performance of each standalone model (using genotype data [GEN model] and using whole-blood expression data [WBE model]) using their respective data types across 47 human tissues. The WBE model outperformed the GEN model in most tissues by a large gain. Then, we developed several combined models that leverage both types of predictors to further improve imputation performance. We tried various strategies, including utilizing a merged dataset of the two data types (MERGED models) and integrating the imputation outcomes of the two standalone models (inverse variance-weighted [IVW] models). We found that one of the MERGED models noticeably outperformed the standalone models. This model involved a fixed ratio between the two regularization penalty factors for the two predictor types so that the contribution of the whole-blood transcriptome is upweighted compared with the genotype. Our study suggests that one can improve the imputation of tissue-specific gene expression by combining the genotype and whole-blood expression, but the improvement can be largely dependent on the combination strategy chosen.

Altered Peripheral Blood Gene Expression in Childhood Cancer Survivors With Anthracycline-Induced Cardiomyopathy - A COG-ALTE03N1 Report

Background Anthracycline-induced cardiomyopathy is a leading cause of premature death in childhood cancer survivors, presenting a need to understand the underlying pathogenesis. We sought to examine differential blood-based mRNA expression profiles in anthracycline-exposed childhood cancer survivors with and without cardiomyopathy. Methods and Results We designed a matched case-control study (Children's Oncology Group-ALTE03N1) with mRNA sequencing on total RNA from peripheral blood in 40 anthracycline-exposed survivors with cardiomyopathy (cases) and 64 matched survivors without (controls). DESeq2 identified differentially expressed genes. Ingenuity Pathway Analyses (IPA) and Gene Set Enrichment Analyses determined the potential roles of altered genes in biological pathways. Functional validation was performed by gene knockout in human-induced pluripotent stem cell-derived cardiomyocytes using CRISPR/Cas9 (clustered regularly interspaced short palindromic repeats/CRISPR-associated protein 9) technology. Median age at primary cancer diagnosis for cases and controls was 8.2 and 9.7 years, respectively. Thirty-six differentially expressed genes with fold change ≥±2 were identified; 35 were upregulated. IPA identified "hepatic fibrosis" and "iron homeostasis" pathways to be significantly modulated by differentially expressed genes, including toxicology functions of myocardial infarction, cardiac damage, and cardiac dilation. Leading edge analysis from Gene Set Enrichment Analyses identified lactate dehydrogenase A (LDHA) and cluster of differentiation 36 (CD36) genes to be significantly upregulated in cases. Interleukin 1 receptor type 1, 2 (IL1R1, IL1R2), and matrix metalloproteinase 8, 9 (MMP8, MMP9) appeared in multiple canonical pathways. LDHA-knockout human-induced pluripotent stem cell-derived cardiomyocytes showed increased sensitivity to doxorubicin. Conclusions We identified differential mRNA expression profiles in peripheral blood of anthracycline-exposed childhood cancer survivors with and without cardiomyopathy. Upregulation of LDHA and CD36 genes suggests metabolic perturbations in a failing heart. Dysregulation of proinflammatory cytokine receptors IL1R1 and IL1R2 and matrix metalloproteinases, MMP8 and MMP9 indicates structural remodeling that accompanies the clinical manifestation of symptomatic cardiotoxicity.

Connectome-based modelling of neurodegenerative diseases: towards precision medicine and mechanistic insight

Neurodegenerative diseases are the most common cause of dementia. Although their underlying molecular pathologies have been identified, there is substantial heterogeneity in the patterns of progressive brain alterations across and within these diseases. Recent advances in neuroimaging methods have revealed that pathological proteins accumulate along specific macroscale brain networks, implicating the network architecture of the brain in the system-level pathophysiology of neurodegenerative diseases. However, the extent to which 'network-based neurodegeneration' applies across the wide range of neurodegenerative disorders remains unclear. Here, we discuss the state-of-the-art of neuroimaging-based connectomics for the mapping and prediction of neurodegenerative processes. We review findings supporting brain networks as passive conduits through which pathological proteins spread. As an alternative view, we also discuss complementary work suggesting that network alterations actively modulate the spreading of pathological proteins between connected brain regions. We conclude this Perspective by proposing an integrative framework in which connectome-based models can be advanced along three dimensions of innovation: incorporating parameters that modulate propagation behaviour on the basis of measurable biological features; building patient-tailored models that use individual-level information and allowing model parameters to interact dynamically over time. We discuss promises and pitfalls of these strategies for improving disease insights and moving towards precision medicine.

Hypergraph factorization for multi-tissue gene expression imputation

Integrating gene expression across tissues and cell types is crucial for understanding the coordinated biological mechanisms that drive disease and characterise homeostasis. However, traditional multitissue integration methods cannot handle uncollected tissues or rely on genotype information, which is often unavailable and subject to privacy concerns. Here we present HYFA (Hypergraph Factorisation), a parameter-efficient graph representation learning approach for joint imputation of multi-tissue and cell-type gene expression. HYFA is genotype-agnostic, supports a variable number of collected tissues per individual, and imposes strong inductive biases to leverage the shared regulatory architecture of tissues and genes. In performance comparison on Genotype-Tissue Expression project data, HYFA achieves superior performance over existing methods, especially when multiple reference tissues are available. The HYFA-imputed dataset can be used to identify replicable regulatory genetic variations (eQTLs), with substantial gains over the original incomplete dataset. HYFA can accelerate the effective and scalable integration of tissue and cell-type transcriptome biorepositories.

CCND1 Overexpression in Idiopathic Dilated Cardiomyopathy: A Promising Biomarker?

Cardiomyopathy, a disorder of electrical or heart muscle function, represents a type of cardiac muscle failure and culminates in severe heart conditions. The prevalence of dilated cardiomyopathy (DCM) is higher than that of other types (hypertrophic cardiomyopathy and restrictive cardiomyopathy) and causes many deaths. Idiopathic dilated cardiomyopathy (IDCM) is a type of DCM with an unknown underlying cause. This study aims to analyze the gene network of IDCM patients to identify disease biomarkers. Data were first extracted from the Gene Expression Omnibus (GEO) dataset and normalized based on the RMA algorithm (Bioconductor package), and differentially expressed genes were identified. The gene network was mapped on the STRING website, and the data were transferred to Cytoscape software to determine the top 100 genes. In the following, several genes, including VEGFA, IGF1, APP, STAT1, CCND1, MYH10, and MYH11, were selected for clinical studies. Peripheral blood samples were taken from 14 identified IDCM patients and 14 controls. The RT-PCR results revealed no significant differences in the expression of the genes APP, MYH10, and MYH11 between the two groups. By contrast, the STAT1, IGF1, CCND1, and VEGFA genes were overexpressed in patients more than in controls. The highest expression was found for VEGFA, followed by CCND1 (p < 0.001). Overexpression of these genes may contribute to disease progression in patients with IDCM. However, more patients and genes need to be analyzed in order to achieve more robust results.

Gene expression in African Americans, Puerto Ricans and Mexican Americans reveals ancestry-specific patterns of genetic architecture

We explored ancestry-related differences in the genetic architecture of whole-blood gene expression using whole-genome and RNA sequencing data from 2,733 African Americans, Puerto Ricans and Mexican Americans. We found that heritability of gene expression significantly increased with greater proportions of African genetic ancestry and decreased with higher proportions of Indigenous American ancestry, reflecting the relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most anc-eQTLs (89%) were driven by population differences in allele frequency. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait associations using transcriptome prediction models trained in our admixed population than models trained using data from the Genotype-Tissue Expression project. Our study highlights the importance of measuring gene expression across large and ancestrally diverse populations for enabling new discoveries and reducing disparities.

Toward a Systematic Assessment of Sex Differences in Cystic Fibrosis

(1) Background: Cystic fibrosis (CF) is a disease with well-documented clinical differences between female and male patients. However, this gender gap is very poorly studied at the molecular level. (2) Methods: Expression differences in whole blood transcriptomics between female and male CF patients are analyzed in order to determine the pathways related to sex-biased genes and assess their potential influence on sex-specific effects in CF patients. (3) Results: We identify sex-biased genes in female and male CF patients and provide explanations for some sex-specific differences at the molecular level. (4) Conclusion: Genes in key pathways associated with CF are differentially expressed between sexes, and thus may account for the gender gap in morbidity and mortality in CF.

Towards a Synthesis of the Non-Genetic and Genetic Views of Cancer in Understanding Pancreatic Ductal Adenocarcinoma Initiation and Prevention

While much of the research in oncogenesis and cancer therapy has focused on mutations in key cancer driver genes, more recent work suggests a complementary non-genetic paradigm. This paradigm focuses on how transcriptional and phenotypic heterogeneity, even in clonally derived cells, can create sub-populations associated with oncogenesis, metastasis, and therapy resistance. We discuss this complementary paradigm in the context of pancreatic ductal adenocarcinoma. A better understanding of cellular transcriptional heterogeneity and its association with oncogenesis can lead to more effective therapies that prevent tumor initiation and slow progression.

BrainGENIE: The Brain Gene Expression and Network Imputation Engine

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

mRNA Signatures in Peripheral White Blood Cells Predict Reproductive Potential in Beef Heifers at Weaning

Reproductive failure is a major contributor to inefficiency within the cow-calf industry. Particularly problematic is the inability to diagnose heifer reproductive issues prior to pregnancy diagnosis following their first breeding season. Therefore, we hypothesized that gene expression from the peripheral white blood cells at weaning could predict the future reproductive potential of beef heifers. To investigate this, the gene expression was measured using RNA-Seq in Angus-Simmental crossbred heifers sampled at weaning and retrospectively classified as fertile (FH, n = 8) or subfertile (SFH, n = 7) after pregnancy diagnosis. We identified 92 differentially expressed genes between the groups. Network co-expression analysis identified 14 and 52 hub targets. ENSBTAG00000052659, OLR1, TFF2, and NAIP were exclusive hubs to the FH group, while 42 hubs were exclusive to the SFH group. The differential connectivity between the networks of each group revealed a gain in connectivity due to the rewiring of major regulators in the SFH group. The exclusive hub targets from FH were over-represented for the CXCR chemokine receptor pathway and inflammasome complex, while for the SFH, they were over-represented for immune response and cytokine production pathways. These multiple interactions revealed novel targets and pathways predicting reproductive potential at an early stage of heifer development.

Predicting tumor immune microenvironment and checkpoint therapy response of head & neck cancer patients from blood immune single-cell transcriptomics

The immune state of tumor microenvironment is crucial for determining immunotherapy response but is not readily accessible. Here we investigate if we can infer the tumor immune state from the blood and further predict immunotherapy response. First, we analyze a dataset of head and neck squamous cell carcinoma (HNSCC) patients with matched scRNA-Seq of peripheral blood mononuclear cells (PBMCs) and tumor tissues. We find that the tumor immune cell fractions of different immune cell types and many of the genes they express can be inferred from the matched PBMC scRNA-Seq. Second, analyzing another HNSCC dataset with PBMC scRNA-Seq and immunotherapy response, we find that the inferred ratio between tumor memory B and regulatory T cell fractions is predictive of immunotherapy response and is superior to the well-established cytolytic and exhausted T-cell signatures. Overall, these results showcase the potential of scRNA-Seq liquid biopsies in cancer immunotherapy, calling for their larger-scale testing.

SIGNIFICANCE

This head and neck cancer study demonstrates the potential of using blood single-cell transcriptomics to (1) infer the tumor immune status and (2) predict immunotherapy response from the tumor immune status inferred from blood. These results showcase the potential of single-cell transcriptomics liquid biopsies for further advancing personalized cancer immunotherapy.

Psychotherapies and digital interventions for OCD in adults: What do we know, what do we need still to explore?

BACKGROUND

Despite significant advances in the understanding and treatment of obsessive compulsive disorder (OCD), current treatment options are limited in terms of efficacy for symptom remission. Thus, assessing the potential role of iterative or alternate psychotherapies is important. Also, the potential role of digital technologies to enhance the accessibility of these therapies, should not be underestimated. We also need to embrace the idea of a more personalized treatment choice, being cognisant of clinical, genetic and neuroimaging predictors of treatment response.

PROCEDURES

Non-systematic review of current literature on emerging psychological and digital therapies for OCD, as well as of potential biomarkers of treatment response.

FINDINGS

A number of 'third wave' therapies (e.g., Acceptance and Commitment Therapy, Mindfulness-Based Cognitive Therapy) have an emerging and encouraging evidence base in OCD. Other approaches entail employment of elements of other psychotherapies such as Dialectical Behaviour Therapy; or trauma-focussed therapies such as Eye Movement Desensitisation and Reprocessing, and Imagery Rescripting and Narrative Therapy. Further strategies include Danger Ideation Reduction Therapy and Habit Reversal. For these latter approaches, large-scale randomised controlled trials are largely lacking, and the precise role of these therapies in treating people with OCD, remains to be clarified. A concentrated 4-day program (the Bergen program) has shown promising short- and long-term results. Exercise, music, and art therapy have not been adequately tested in people with OCD, but may have an adjunctive role. Digital technologies are being actively investigated for enhancing reach and efficacy of psychological therapies for OCD. Biomarkers, including genetic and neuroimaging, are starting to point to a future with more 'personalised medicine informed' treatment strategizing for OCD.

CONCLUSIONS

There are a number of potential psychological options for the treatment of people with OCD who do not respond adequately to exposure/response prevention or cognitive behaviour therapy. Adjunctive exercise, music, and art therapy might be useful, albeit the evidence base for these is very small. Consideration should be given to different ways of delivering such interventions, including group-based, concentrated, inpatient, or with outreach, where appropriate. Digital technologies are an emerging field with a number of potential applications for aiding the treatment of OCD. Biomarkers for treatment response determination have much potential capacity and deserve further empirical testing.

DDX43 mRNA expression and protein levels in relation to clinicopathological profile of breast cancer

BACKGROUND

Breast cancer (BC) is the most often diagnosed cancer in women globally. Cancer cells appear to rely heavily on RNA helicases. DDX43 is one of DEAD- box RNA helicase family members. But, the relationship between clinicopathological, prognostic significance in different BC subtypes and DDX43 expression remains unclear. Therefore, the purpose of this study was to assess the clinicopathological significance of DDX43 protein and mRNA expression in different BC subtypes.

MATERIALS AND METHODS

A total of 80 females newly diagnosed with BC and 20 control females that were age-matched were recruited for this study. DDX43 protein levels were measured by ELISA technique. We used a real-time polymerase chain reaction quantification (real-time PCR) to measure the levels of DDX43 mRNA expression. Levels of DDX43 protein and mRNA expression within BC patients had been compared to those of control subjects and correlated with clinicopathological data.

RESULTS

The mean normalized serum levels of DDX43 protein were slightly higher in control than in both benign and malignant groups, but this result was non-significant. The mean normalized level of DDX43 mRNA expression was higher in the control than in both benign and malignant cases, although the results were not statistically significant and marginally significant, respectively. Moreover, the mean normalized level of DDX43 mRNA expression was significantly higher in benign than in malignant cases. In malignant cases, low DDX43 protein expression was linked to higher nuclear grade and invasive duct carcinoma (IDC), whereas high mRNA expression was linked to the aggressive types of breast cancer such as TNBC, higher tumor and nuclear grades.

CONCLUSION

This study explored the potential of using blood DDX43 mRNA expression or protein levels, or both in clinical settings as a marker of disease progression in human breast cancer. DDX43 mRNA expression proposes a less invasive method for discriminating benign from malignant BC.

The effect of infection risk on female blood transcriptomics

Defenses against pathogens can take on many forms. For instance, behavioral avoidance of diseased conspecifics is widely documented. Interactions with these infectious conspecifics can also, however, lead to physiological changes in uninfected animals, an effect that is much less well understood. These changes in behavior and physiology are particularly important to study in a reproductive context, where they can impact reproductive decisions and offspring quality. Here, we studied how an acute (3 h) exposure to an immune-challenged male affected female blood transcriptomics and behavior. We predicted that females paired with immune-challenged males would reduce eating and drinking behaviors (as avoidance behaviors) and that their blood would show activation of immune and stress responses. We used female Japanese quail as a study system because they have been shown to respond to male traits, in terms of their own physiology and egg investment. Only two genes showed significant differential expression due to treatment, including an increase in the threonine dehydrogenase (TDH) transcript, an enzyme important for threonine breakdown. However, hundreds of genes in pathways related to activation of immune responses showed coordinated up-regulation in females exposed to immune-challenged males. Suppressed pathways revealed potential changes to metabolism and reduced responsiveness to glucocorticoids. Contrary to our prediction, we found that females paired with immune-challenged males increased food consumption. Water consumption was not changed by treatment. These findings suggest that even short exposure to diseased conspecifics can trigger both behavioral and physiological responses in healthy animals.

Whole blood transcriptome characterization of young female triathlon athletes following an endurance exercise: a pilot study

The vast majority of studies focusing on the effects of endurance exercise on hematological parameters and leukocyte gene expression were performed in adult men, so our aim was to investigate these changes in young females. Four young (age 15.3 ± 1.3 yr) elite female athletes completed an exercise session, in which they accomplished the cycling and running disciplines of a junior triathlon race. Blood samples were taken immediately before the exercise, right after the exercise, and then 1, 2, and 7 days later. Analysis of cell counts and routine biochemical parameters were complemented by RNA sequencing (RNA-seq) to whole blood samples. The applied exercise load did not trigger remarkable changes in either cardiovascular or biochemical parameters; however, it caused a significant increase in the percentage of neutrophils and a significant reduction in the ratio of lymphocytes immediately after exercise. Furthermore, endurance exercise induced a characteristic gene expression pattern change in the blood transcriptome. Gene set enrichment analysis (GSEA) using the Reactome database revealed that the expression of genes involved in immune processes and neutrophil granulocyte activation was upregulated, whereas the expression of genes important in translation and rRNA metabolism was downregulated. Comparison of a set of immune cell gene signatures (ImSig) and our transcriptomic data identified 15 overlapping genes related to T-cell functions and involved in podosome formation and adhesion to the vessel wall. Our results suggest that RNA-seq to whole blood together with ImSig analysis are useful tools for the investigation of systemic responses to endurance exercise.

Advances in liquid biopsy in neuroblastoma

Even with intensive treatment of high-risk neuroblastoma (NB) patients, half of high-risk NB patients still relapse. New therapies targeting the biological characteristics of NB have important clinical value for the personalized treatment of NB. However, the current biological markers for NB are mainly analyzed by tissue biopsy. In recent years, circulating biomarkers of NB based on liquid biopsy have attracted more and more attention. This review summarizes the analytes and methods for liquid biopsy of NB. We focus on the application of liquid biopsy in the diagnosis, prognosis assessment, and monitoring of NB. Finally, we discuss the prospects and challenges of liquid biopsy in NB.

Complement component C4 structural variation and quantitative traits contribute to sex-biased vulnerability in systemic sclerosis

Copy number (CN) polymorphisms of complement C4 play distinct roles in many conditions, including immune-mediated diseases. We investigated the association of C4 CN with systemic sclerosis (SSc) risk. Imputed total C4, C4A, C4B, and HERV-K CN were analyzed in 26,633 individuals and validated in an independent cohort. Our results showed that higher C4 CN confers protection to SSc, and deviations from CN parity of C4A and C4B augmented risk. The protection contributed per copy of C4A and C4B differed by sex. Stronger protection was afforded by C4A in men and by C4B in women. C4 CN correlated well with its gene expression and serum protein levels, and less C4 was detected for both in SSc patients. Conditioned analysis suggests that C4 genetics strongly contributes to the SSc association within the major histocompatibility complex locus and highlights classical alleles and amino acid variants of HLA-DRB1 and HLA-DPB1 as C4-independent signals.

Immune and inflammation: related factor alterations as biomarkers for predicting prognosis and responsiveness to PD-1 monoclonal antibodies in cervical cancer

PURPOSE

We aimed to elucidate the potential mechanisms of effective responsiveness to PD-1 monoclonal antibody and evaluate more reliable biomarkers to improve the ability to predict the populations of cervical cancer (CC) suitable for immunotherapy.

METHODS

Peripheral blood samples of CC patients undergoing anti-PD-1 therapy were collected before and after treatment. Differentially expressed genes (DEGs) were analyzed between partial response (PR) and progressive disease (PD) patients. A novel prognostic inflammation and immune-related response gene (IRRG) model was constructed and its prognostic role, correlation with tumor immunity and tumor mutation were evaluated.

RESULTS

DEGs in PR patient after treatment could predict the response to PD-1 monoclonal antibodies. Among PR-specific pathways, tumor immunity, leukocyte migration, and cytokine activities were prominently enriched. Additionally, an IRRG signature comprising CTLA4, AZU1, C5, LAT, CXCL2, GDF7, MPL, PPARG and CELA1 was established and validated to predict the prognosis of CC with great accuracy and specificity. This signature could reflect the tumor microenvironment (TME) and tumor mutational burden (TMB). We also found stimulated adaptive immunity and downregulated inflammation at baseline in patients with sensitive responses to PD-1 monoclonal antibody.

CONCLUSION

We developed an IRRG signature and verified that it was an independent prognostic factor for predicting survival and could reflect a sensitive response to PD-1 monoclonal antibody, which plays a nonnegligible role in the TME of CC. Further investigations are warranted to confirm that patients with stimulated adaptive immunity and downregulated inflammation at baseline could achieve a better survival benefit from PD-1 monoclonal antibody.

Identification of Cardiovascular Disease-Related Genes Based on the Co-Expression Network Analysis of Genome-Wide Blood Transcriptome

Inference of co-expression network and identification of disease-related modules and gene sets can help us understand disease-related molecular pathophysiology. We aimed to identify a cardiovascular disease (CVD)-related transcriptomic signature, specifically, in peripheral blood tissue, based on differential expression (DE) and differential co-expression (DcoE) analyses. Publicly available blood sample datasets for coronary artery disease (CAD) and acute coronary syndrome (ACS) statuses were integrated to establish a co-expression network. A weighted gene co-expression network analysis was used to construct modules that include genes with highly correlated expression values. The DE criterion is a linear regression with module eigengenes for module-specific genes calculated from principal component analysis and disease status as the dependent and independent variables, respectively. The DcoE criterion is a paired t-test for intramodular connectivity between disease and matched control statuses. A total of 21 and 23 modules were established from CAD status- and ACS-related datasets, respectively, of which six modules per disease status (i.e., obstructive CAD and ACS) were selected based on the DE and DcoE criteria. For each module, gene-gene interactions with extremely high correlation coefficients were individually selected under the two conditions. Genes displaying a significant change in the number of edges (gene-gene interaction) were selected. A total of 6, 10, and 7 genes in each of the three modules were identified as potential CAD status-related genes, and 14 and 8 genes in each of the two modules were selected as ACS-related genes. Our study identified gene sets and genes that were dysregulated in CVD blood samples. These findings may contribute to the understanding of CVD pathophysiology.

ReDisX, a machine learning approach, rationalizes rheumatoid arthritis and coronary artery disease patients uniquely upon identifying subpopulation differentiation markers from their genomic data

Diseases originate at the molecular-genetic layer, manifest through altered biochemical homeostasis, and develop symptoms later. Hence, symptomatic diagnosis is inadequate to explain the underlying molecular-genetic abnormality and individual genomic disparities. The current trends include molecular-genetic information relying on algorithms to recognize the disease subtypes through gene expressions. Despite their disposition toward disease-specific heterogeneity and cross-disease homogeneity, a gap still exists in describing the extent of homogeneity within the heterogeneous subpopulation of different diseases. They are limited to obtaining the holistic sense of the whole genome-based diagnosis resulting in inaccurate diagnosis and subsequent management. Addressing those ambiguities, our proposed framework, ReDisX, introduces a unique classification system for the patients based on their genomic signatures. In this study, it is a scalable machine learning algorithm deployed to re-categorize the patients with rheumatoid arthritis and coronary artery disease. It reveals heterogeneous subpopulations within a disease and homogenous subpopulations across different diseases. Besides, it identifies granzyme B (GZMB) as a subpopulation-differentiation marker that plausibly serves as a prominent indicator for GZMB-targeted drug repurposing. The ReDisX framework offers a novel strategy to redefine disease diagnosis through characterizing personalized genomic signatures. It may rejuvenate the landscape of precision and personalized diagnosis and a clue to drug repurposing.

Blood biomarkers representing maternal-fetal interface tissues used to predict early-and late-onset preeclampsia but not COVID-19 infection

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Endothelial dysfunction misleads blood marker discovery by differential expression.

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Blood-derived surrogate transcriptome of target-tissue avoids the false discovery.

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ITGA5 implies polymicrobial infection of maternal-fetal interface in preeclampsia.

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ITGA5 and IRF6 implies viral co-infection in early-onset preeclampsia.

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ITGA5, IRF6, and P2RX7 differ imminent preeclampsia from COVID-19 infection.

Background

A well-known blood biomarker (soluble fms-like tyrosinase-1 [sFLT-1]) for preeclampsia, i.e., a pregnancy disorder, was found to predict severe COVID-19, including in males. True biomarker may be masked by more-abrupt changes related to endothelial instead of placental dysfunction. This study aimed to identify blood biomarkers that represent maternal-fetal interface tissues for predicting preeclampsia but not COVID-19 infection.

Methods

The surrogate transcriptome of tissues was determined by that in maternal blood, utilizing four datasets (n = 1354) which were collected before the COVID-19 pandemic. Applying machine learning, a preeclampsia prediction model was chosen between those using blood transcriptome (differentially expressed genes [DEGs]) and the blood-derived surrogate for tissues. We selected the best predictive model by the area under the receiver operating characteristic (AUROC) using a dataset for developing the model, and well-replicated in datasets both with and without an intervention. To identify eligible blood biomarkers that predicted any-onset preeclampsia from the datasets but that were not positive in the COVID-19 dataset (n = 47), we compared several methods of predictor discovery: (1) the best prediction model; (2) gene sets of standard pipelines; and (3) a validated gene set for predicting any-onset preeclampsia during the pandemic (n = 404). We chose the most predictive biomarkers from the best method with the significantly largest number of discoveries by a permutation test. The biological relevance was justified by exploring and reanalyzing low- and high-level, multiomics information.

Results

A prediction model using the surrogates developed for predicting any-onset preeclampsia (AUROC of 0.85, 95 % confidence interval [CI] 0.77 to 0.93) was the only that was well-replicated in an independent dataset with no intervention. No model was well-replicated in datasets with a vitamin D intervention. None of the blood biomarkers with high weights in the best model overlapped with blood DEGs. Blood biomarkers were transcripts of integrin-α5 (ITGA5), interferon regulatory factor-6 (IRF6), and P2X purinoreceptor-7 (P2RX7) from the prediction model, which was the only method that significantly discovered eligible blood biomarkers (n = 3/100 combinations, 3.0 %; P =.036). Most of the predicted events (73.70 %) among any-onset preeclampsia were cluster A as defined by ITGA5 (Z-score ≥ 1.1), but were only a minority (6.34 %) among positives in the COVID-19 dataset. The remaining were predicted events (26.30 %) among any-onset preeclampsia or those among COVID-19 infection (93.66 %) if IRF6 Z-score was ≥-0.73 (clusters B and C), in which none was the predicted events among either late-onset preeclampsia (LOPE) or COVID-19 infection if P2RX7 Z-score was <0.13 (cluster C). Greater proportions of predicted events among LOPE were cluster A (82.85 % vs 70.53 %) compared to early-onset preeclampsia (EOPE). The biological relevance by multiomics information explained the biomarker mechanism, polymicrobial infection in any-onset preeclampsia by ITGA5, viral co-infection in EOPE by ITGA5-IRF6, a shared prediction with COVID-19 infection by ITGA5-IRF6-P2RX7, and non-replicability in datasets with a vitamin D intervention by ITGA5.

Conclusions

In a model that predicts preeclampsia but not COVID-19 infection, the important predictors were genes in maternal blood that were not extremely expressed, including the proposed blood biomarkers. The predictive performance and biological relevance should be validated in future experiments.

Co-Expression Network and Integrative Analysis of Metabolome and Transcriptome Uncovers Biological Pathways for Fertility in Beef Heifers

Reproductive failure remains a significant challenge to the beef industry. The omics technologies have provided opportunities to improve reproductive efficiency. We used a multistaged analysis from blood profiles to integrate metabolome (plasma) and transcriptome (peripheral white blood cells) in beef heifers. We used untargeted metabolomics and RNA-Seq paired data from six AI-pregnant (AI-P) and six nonpregnant (NP) Angus-Simmental crossbred heifers at artificial insemination (AI). Based on network co-expression analysis, we identified 17 and 37 hub genes in the AI-P and NP groups, respectively. Further, we identified TGM2, TMEM51, TAC3, NDRG4, and PDGFB as more connected in the NP heifers’ network. The NP gene network showed a connectivity gain due to the rewiring of major regulators. The metabolomic analysis identified 18 and 15 hub metabolites in the AI-P and NP networks. Tryptophan and allantoic acid exhibited a connectivity gain in the NP and AI-P networks, respectively. The gene–metabolite integration identified tocopherol-a as positively correlated with ENSBTAG00000009943 in the AI-P group. Conversely, tocopherol-a was negatively correlated in the NP group with EXOSC2, TRNAUIAP, and SNX12. In the NP group, α-ketoglutarate-SMG8 and putrescine-HSD17B13 were positively correlated, whereas a-ketoglutarate-ALAS2 and tryptophan-MTMR1 were negatively correlated. These multiple interactions identified novel targets and pathways underlying fertility in bovines.

ACLY and CKD: A Mendelian Randomization Analysis

Introduction

Adenosine triphosphate-citrate lyase (ACLY) inhibition is a therapeutic strategy under investigation for atherosclerotic cardiovascular disease, nonalcoholic steatohepatitis, and metabolic syndrome. Mouse models suggest that ACLY inhibition could reduce inflammation and kidney fibrosis. Genetic analysis of ACLY in chronic kidney disease (CKD) has not been performed.

Methods

We constructed a genetic instrument by selecting variants associated with ACLY expression in the expression quantitative trait loci genetics consortium (eQTLGen) from blood samples from 31,684 participants. In a 2-sample Mendelian randomization analysis, we evaluated the effect of genetically predicted ACLY expression on the risk of CKD, estimated glomerular filtration rate (eGFR), and albumin-to-creatinine ratio (ACR) using the CKD Genetics (CKDGen) consortium, UK Biobank, and the Finnish Genetics (FinnGen) consortium totaling 66,396 CKD cases and 958,517 controls.

Results

ACLY is constitutively expressed in all cell types including in whole blood. The genetic instrument included 13 variants and explained 1.5% of the variation in whole blood ACLY gene expression. A 34% reduction in ACLY expression score was associated with a 0.04 mmol/l reduced low-density lipoprotein (LDL) cholesterol (P = 3.4 × 10^-4) and a 9% reduced risk of CKD (stages 3, 4, 5, dialysis, or eGFR < 60 ml/min per 1.73 m²) (odds ratio [OR] = 0.91, 95% CI: 0.85-0.98, P = 0.008), but no association was observed with either eGFR or ACR.

Conclusion

Mendelian randomization analyses revealed that genetically reduced ACLY expression was associated with reduced risk of CKD but had no effect on either eGFR or ACR. Further evaluation of ACLY in kidney disease is warranted.

Impaired CD4+ T cell differentiation in HIV-1 infected patients receiving early anti-retroviral therapy

Differentiation of CD4+ T naïve (T_N) into central memory (T_CM) cells involves extensive molecular processes. We compared the transcriptomes of CD4+ T_N and T_CM cells from HIV-1 infected patients receiving early anti-retroviral therapy (ART; EA; n = 13) and controls (n = 15). Comparison of protein coding genes between T_CM and T_N revealed 533 and 82 differentially expressed genes (DEGs) in controls and EA, respectively. A high degree of transcriptional complexity was detected during transition of CD4+ T_N to T_CM cells in controls involving 70 TFs, 20 master regulators of T cell differentiation (TBX21, GATA3, RARA, FOXP3, RORC); in EA only 7 TFs were modulated with expression of several master regulators remaining unchanged during differentiation. Analysis of interactions between modulated TFs and target genes revealed important regulatory interactions missing in EA group. We conclude that T cell differentiation in EA patients is impaired due to reduced modulation of genes involved in transition from CD4+ T_N to T_CM cells.

Transcriptional and Epigenetic Response to Sedentary Behavior and Physical Activity in Children and Adolescents: A Systematic Review

BACKGROUND

The links of sedentary behavior and physical activity with health outcomes in children and adolescents is well known. However, the molecular mechanisms involved are poorly understood. We aimed to synthesize the current knowledge of the association of sedentary behavior and physical activity (acute and chronic effects) with gene expression and epigenetic modifications in children and adolescents.

METHODS

PubMed, Web of Science, and Scopus databases were systematically searched until April 2022. A total of 15 articles were eligible for this review. The risk of bias assessment was performed using the Joanna Briggs Institute Critical Appraisal Tool for Systematic Reviews and/or a modified version of the Downs and Black checklist.

RESULTS

Thirteen studies used candidate gene approach, while only 2 studies performed high-throughput analyses. The candidate genes significantly linked to sedentary behavior or physical activity were: FOXP3, HSD11B2, IL-10, TNF-α, ADRB2, VEGF, HSP70, SOX, and GPX. Non-coding Ribonucleic acids (RNAs) regulated by sedentary behavior or physical activity were: miRNA-222, miRNA-146^a, miRNA-16, miRNA-126, miR-320^a, and long non-coding RNA MALAT1. These molecules are involved in inflammation, immune function, angiogenic process, and cardiovascular disease. Transcriptomics analyses detected thousands of genes that were altered following an acute bout of physical activity and are linked to gene pathways related to immune function, apoptosis, and metabolic diseases.

CONCLUSION

The evidence found to date is rather limited. Multidisciplinary studies are essential to characterize the molecular mechanisms in response to sedentary behavior and physical activity in the pediatric population. Larger cohorts and randomized controlled trials, in combination with multi-omics analyses, may provide the necessary data to bring the field forward.

SYSTEMATIC REVIEW REGISTRATION

[www.ClinicalTrials.gov], identifier [CRD42021235431].

Genetically regulated expression in late-onset Alzheimer's disease implicates risk genes within known and novel loci

Late-onset Alzheimer disease (LOAD) is highly polygenic, with a heritability estimated between 40 and 80%, yet risk variants identified in genome-wide studies explain only ~8% of phenotypic variance. Due to its increased power and interpretability, genetically regulated expression (GReX) analysis is an emerging approach to investigate the genetic mechanisms of complex diseases. Here, we conducted GReX analysis within and across 51 tissues on 39 LOAD GWAS data sets comprising 58,713 cases and controls from the Alzheimer's Disease Genetics Consortium (ADGC) and the International Genomics of Alzheimer's Project (IGAP). Meta-analysis across studies identified 216 unique significant genes, including 72 with no previously reported LOAD GWAS associations. Cross-brain-tissue and cross-GTEx models revealed eight additional genes significantly associated with LOAD. Conditional analysis of previously reported loci using established LOAD-risk variants identified eight genes reaching genome-wide significance independent of known signals. Moreover, the proportion of SNP-based heritability is highly enriched in genes identified by GReX analysis. In summary, GReX-based meta-analysis in LOAD identifies 216 genes (including 72 novel genes), illuminating the role of gene regulatory models in LOAD.

Advances in Non-Coding RNA Sequencing

Non-coding RNAs (ncRNAs) comprise a set of abundant and functionally diverse RNA molecules. Since the discovery of the first ncRNA in the 1960s, ncRNAs have been shown to be involved in nearly all steps of the central dogma of molecular biology. In recent years, the pace of discovery of novel ncRNAs and their cellular roles has been greatly accelerated by high-throughput sequencing. Advances in sequencing technology, library preparation protocols as well as computational biology helped to greatly expand our knowledge of which ncRNAs exist throughout the kingdoms of life. Moreover, RNA sequencing revealed crucial roles of many ncRNAs in human health and disease. In this review, we discuss the most recent methodological advancements in the rapidly evolving field of high-throughput sequencing and how it has greatly expanded our understanding of ncRNA biology across a large number of different organisms.

Eight-year longitudinal study of whole blood gene expression profiles in individuals undergoing long-term medical follow-up

Blood circulates throughout the body via the peripheral tissues, contributes to host homeostasis and maintains normal physiological functions, in addition to responding to lesions. Previously, we revealed that gene expression analysis of peripheral blood cells is a useful approach for assessing diseases such as diabetes mellitus and cancer because the altered gene expression profiles of peripheral blood cells can reflect the presence and state of diseases. However, no chronological assessment of whole gene expression profiles has been conducted. In the present study, we collected whole blood RNA from 61 individuals (average age at registration, 50 years) every 4 years for 8 years and analyzed gene expression profiles using a complementary DNA microarray to examine whether these profiles were stable or changed over time. We found that the genes with very stable expression were related mostly to immune system pathways, including antigen cell presentation and interferon-related signaling. Genes whose expression was altered over the 8-year study period were principally involved in cellular machinery pathways, including development, signal transduction, cell cycle, apoptosis, and survival. Thus, this chronological examination study showed that the gene expression profiles of whole blood can reveal unmanifested physiological changes.

Exploration of Potential Genetic Biomarkers for Heart Failure: A Systematic Review

Patients with heart failure (HF) often present with signs and symptoms that are often nonspecific and with a wide differential diagnosis, making diagnosis and prognosis of HF by clinical presentation alone challenging. Our knowledge on genetic diversity is rapidly evolving with high-throughput DNA sequencing technology, which makes a great potential for genetic biomarker development. The present review attempts to provide a comprehensive review on the modification of major genetic components in HF patients and to explore the potential application of these components as clinical biomarkers in the diagnosis and in monitoring the progress of HF. The literature search was conducted using six databases, resulting in the inclusion of eighteen studies in the review. The findings of these studies were summarized narratively. An appraisal of the reporting quality of the included studies was conducted using a twelve-item checklist adapted from the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist. The findings showed that changes in genetic components in patients with HF compared to healthy controls could be noninvasive diagnostic or prognostic tools for HF with higher specificity and sensitivity in comparison with the traditional biomarkers. This review provided evidence for the potential of developing genetic biomarkers of HF.