Whole blood transcriptome analysis in bipolar disorder reveals strong lithium effect

Cell-type deconvolution of bulk-blood RNA-seq reveals biological insights into neuropsychiatric disorders

Genome-wide association studies (GWASs) have uncovered susceptibility loci associated with psychiatric disorders such as bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome, and the causal mechanisms of the link between genetic variation and disease risk is unknown. Expression quantitative trait locus (eQTL) analysis of bulk tissue is a common approach used for deciphering underlying mechanisms, although this can obscure cell-type-specific signals and thus mask trait-relevant mechanisms. Although single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell-type proportions and cell-type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-seq from 1,730 samples derived from whole blood in a cohort ascertained from individuals with BP and SCZ, this study estimated cell-type proportions and their relation with disease status and medication. For each cell type, we found between 2,875 and 4,629 eGenes (genes with an associated eQTL), including 1,211 that are not found on the basis of bulk expression alone. We performed a colocalization test between cell-type eQTLs and various traits and identified hundreds of associations that occur between cell-type eQTLs and GWASs but that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on the regulation of cell-type expression loci and found examples of genes that are differentially regulated according to lithium use. Our study suggests that applying computational methods to large bulk RNA-seq datasets of non-brain tissue can identify disease-relevant, cell-type-specific biology of psychiatric disorders and psychiatric medication.

Selecting mRNA markers in blood for age estimation of the donor of a biological stain

RNA has gained a substantial amount of attention within the forensic field over the last decade. There is evidence that RNAs are differentially expressed with biological age. Since RNA can be co-extracted with DNA from the same piece of evidence, RNA-based analysis appears as a promising molecular alternative for predicting the biological age and hence inferring the chronological age of a person. Using RNA-Seq data we searched for markers in blood potentially associated with age. We used our own RNA-Seq data from dried blood stains as well as publicly available RNA-Seq data from whole blood, and compared two different approaches to select candidate markers. The first approach focused on individual gene analysis with DESeq2 to select the genes most correlated with age, while the second approach employed lasso regression to select a set of genes for optimal prediction of age. We present two lists with 270 candidate markers, one for each approach.

Filter and Wrapper Stacking Ensemble (FWSE): a robust approach for reliable biomarker discovery in high-dimensional omics data

Selecting informative features, such as accurate biomarkers for disease diagnosis, prognosis and response to treatment, is an essential task in the field of bioinformatics. Medical data often contain thousands of features and identifying potential biomarkers is challenging due to small number of samples in the data, method dependence and non-reproducibility. This paper proposes a novel ensemble feature selection method, named Filter and Wrapper Stacking Ensemble (FWSE), to identify reproducible biomarkers from high-dimensional omics data. In FWSE, filter feature selection methods are run on numerous subsets of the data to eliminate irrelevant features, and then wrapper feature selection methods are applied to rank the top features. The method was validated on four high-dimensional medical datasets related to mental illnesses and cancer. The results indicate that the features selected by FWSE are stable and statistically more significant than the ones obtained by existing methods while also demonstrating biological relevance. Furthermore, FWSE is a generic method, applicable to various high-dimensional datasets in the fields of machine intelligence and bioinformatics.

Identification and prediction model of placenta-brain axis genes associated with neurodevelopmental delay in moderate and late preterm children

BACKGROUND

Moderate and late preterm (MLPT) birth accounts for the vast majority of preterm births, which is a global public health problem. The association between MLPT and neurobehavioral developmental delays in children and the underlying biological mechanisms need to be further revealed. The "placenta-brain axis" (PBA) provides a new perspective for gene regulation and risk prediction of neurodevelopmental delays in MLPT children.

METHODS

The authors performed multivariate logistic regression models between MLPT and children's neurodevelopmental outcomes, using data from 129 MLPT infants and 3136 full-term controls from the Ma'anshan Birth Cohort (MABC). Furthermore, the authors identified the abnormally regulated PBA-related genes in MLPT placenta by bioinformatics analysis of RNA-seq data and RT-qPCR verification on independent samples. Finally, the authors established the prediction model of neurodevelopmental delay in children with MLPT using multiple machine learning models.

RESULTS

The authors found an increased risk of neurodevelopmental delay in children with MLPT at 6 months, 18 months, and 48 months, especially in boys. Further verification showed that APOE and CST3 genes were significantly correlated with the developmental levels of gross-motor domain, fine-motor domain, and personal social domain in 6-month-old male MLPT children.

CONCLUSIONS

These findings suggested that there was a sex-specific association between MLPT and neurodevelopmental delays. Moreover, APOE and CST3 were identified as placental biomarkers. The results provided guidance for the etiology investigation, risk prediction, and early intervention of neurodevelopmental delays in children with MLPT.

Cell type deconvolution of bulk blood RNA-Seq to reveal biological insights of neuropsychiatric disorders

Genome-wide association studies (GWAS) have uncovered susceptibility loci associated with psychiatric disorders like bipolar disorder (BP) and schizophrenia (SCZ). However, most of these loci are in non-coding regions of the genome with unknown causal mechanisms of the link between genetic variation and disease risk. Expression quantitative trait loci (eQTL) analysis of bulk tissue is a common approach to decipher underlying mechanisms, though this can obscure cell-type specific signals thus masking trait-relevant mechanisms. While single-cell sequencing can be prohibitively expensive in large cohorts, computationally inferred cell type proportions and cell type gene expression estimates have the potential to overcome these problems and advance mechanistic studies. Using bulk RNA-Seq from 1,730 samples derived from whole blood in a cohort ascertained for individuals with BP and SCZ this study estimated cell type proportions and their relation with disease status and medication. We found between 2,875 and 4,629 eGenes for each cell type, including 1,211 eGenes that are not found using bulk expression alone. We performed a colocalization test between cell type eQTLs and various traits and identified hundreds of associations between cell type eQTLs and GWAS loci that are not detected in bulk eQTLs. Finally, we investigated the effects of lithium use on cell type expression regulation and found examples of genes that are differentially regulated dependent on lithium use. Our study suggests that computational methods can be applied to large bulk RNA-Seq datasets of non-brain tissue to identify disease-relevant, cell type specific biology of psychiatric disorders and psychiatric medication.

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.

Constrained neuro fuzzy inference methodology for explainable personalised modelling with applications on gene expression data

Interpretable machine learning models for gene expression datasets are important for understanding the decision-making process of a classifier and gaining insights on the underlying molecular processes of genetic conditions. Interpretable models can potentially support early diagnosis before full disease manifestation. This is particularly important yet, challenging for mental health. We hypothesise this is due to extreme heterogeneity issues which may be overcome and explained by personalised modelling techniques. Thus far, most machine learning methods applied to gene expression datasets, including deep neural networks, lack personalised interpretability. This paper proposes a new methodology named personalised constrained neuro fuzzy inference (PCNFI) for learning personalised rules from high dimensional datasets which are structurally and semantically interpretable. Case studies on two mental health related datasets (schizophrenia and bipolar disorders) have shown that the relatively short and simple personalised fuzzy rules provided enhanced interpretability as well as better classification performance compared to other commonly used machine learning methods. Performance test on a cancer dataset also showed that PCNFI matches previous benchmarks. Insights from our approach also indicated the importance of two genes (ATRX and TSPAN2) as possible biomarkers for early differentiation of ultra-high risk, bipolar and healthy individuals. These genes are linked to cognitive ability and impulsive behaviour. Our findings suggest a significant starting point for further research into the biological role of cognitive and impulsivity-related differences. With potential applications across bio-medical research, the proposed PCNFI method is promising for diagnosis, prognosis, and the design of personalised treatment plans for better outcomes in the future.

RNA biomarkers for alcohol use disorder

Alcohol use disorder (AUD) is highly prevalent and one of the leading causes of disability in the US and around the world. There are some molecular biomarkers of heavy alcohol use and liver damage which can suggest AUD, but these are lacking in sensitivity and specificity. AUD treatment involves psychosocial interventions and medications for managing alcohol withdrawal, assisting in abstinence and reduced drinking (naltrexone, acamprosate, disulfiram, and some off-label medications), and treating comorbid psychiatric conditions (e.g., depression and anxiety). It has been suggested that various patient groups within the heterogeneous AUD population would respond more favorably to specific treatment approaches. For example, there is some evidence that so-called reward-drinkers respond better to naltrexone than acamprosate. However, there are currently no objective molecular markers to separate patients into optimal treatment groups or any markers of treatment response. Objective molecular biomarkers could aid in AUD diagnosis and patient stratification, which could personalize treatment and improve outcomes through more targeted interventions. Biomarkers of treatment response could also improve AUD management and treatment development. Systems biology considers complex diseases and emergent behaviors as the outcome of interactions and crosstalk between biomolecular networks. A systems approach that uses transcriptomic (or other -omic data, e.g., methylome, proteome, metabolome) can capture genetic and environmental factors associated with AUD and potentially provide sensitive, specific, and objective biomarkers to guide patient stratification, prognosis of treatment response or relapse, and predict optimal treatments. This Review describes and highlights state-of-the-art research on employing transcriptomic data and artificial intelligence (AI) methods to serve as molecular biomarkers with the goal of improving the clinical management of AUD. Considerations about future directions are also discussed.

Inferring miRNA sponge modules across major neuropsychiatric disorders

The role of non-coding RNAs in neuropsychiatric disorders (NPDs) is an emerging field of study. The long non-coding RNAs (lncRNAs) are shown to sponge the microRNAs (miRNAs) from interacting with their target mRNAs. Investigating the sponge activity of lncRNAs in NPDs will provide further insights into biological mechanisms and help identify disease biomarkers. In this study, a large-scale inference of the lncRNA-related miRNA sponge network of pan-neuropsychiatric disorders, including autism spectrum disorder (ASD), schizophrenia (SCZ), and bipolar disorder (BD), was carried out using brain transcriptomic (RNA-Seq) data. The candidate miRNA sponge modules were identified based on the co-expression pattern of non-coding RNAs, sharing of miRNA binding sites, and sensitivity canonical correlation. miRNA sponge modules are associated with chemical synaptic transmission, nervous system development, metabolism, immune system response, ribosomes, and pathways in cancer. The identified modules showed similar and distinct gene expression patterns depending on the neuropsychiatric condition. The preservation of miRNA sponge modules was shown in the independent brain and blood-transcriptomic datasets of NPDs. We also identified miRNA sponging lncRNAs that may be potential diagnostic biomarkers for NPDs. Our study provides a comprehensive resource on miRNA sponging in NPDs.

In silico analysis of metabolic effects of bipolar disorder on prefrontal cortex identified altered GABA, glutamate-glutamine cycle, energy metabolism and amino acid synthesis pathways

Bipolar disorder (BP) is a lifelong psychiatric condition, which often disrupts the daily life of the patients. It is characterized by unstable and periodic mood changes, which cause patients to display unusual shifts in mood, energy, activity levels, concentration and the ability to carry out day-to-day tasks. BP is a major psychiatric condition, and it is still undertreated. The causes and neural mechanisms of bipolar disorder are unclear, and diagnosis is still mostly based on psychiatric examination, furthermore the unstable character of the disorder makes diagnosis challenging. Identification of the molecular mechanisms underlying the disease may improve the diagnosis and treatment rates. Single nucleotide polymorphisms (SNP) and transcriptome profiles of patients were studied along with signalling pathways that are thought to be associated with bipolar disorder. Here, we present a computational approach that uses publicly available transcriptome data from bipolar disorder patients and healthy controls. Along with statistical analyses, data are integrated with a genome-scale metabolic model and protein-protein interaction network. Healthy individuals and bipolar disorder patients are compared based on their metabolic profiles. We hypothesize that energy metabolism alterations in bipolar disorder relate to perturbations in amino-acid metabolism and neuron-astrocyte exchange reactions. Due to changes in amino acid metabolism, neurotransmitters and their secretion from neurons and metabolic exchange pathways between neurons and astrocytes such as the glutamine-glutamate cycle are also altered. Changes in negatively charged (-1) KIV and KMV molecules are also observed, and it indicates that charge balance in the brain is highly altered in bipolar disorder. Due to this fact, we also hypothesize that positively charged lithium ions may stabilize the disturbed charge balance in neurons in addition to its effects on neurotransmission. To the best of our knowledge, our approach is unique as it is the first study using genome-scale metabolic models in neuropsychiatric research.

Co-Expression Networks Unveiled Long Non-Coding RNAs as Molecular Targets of Drugs Used to Treat Bipolar Disorder

Long non-coding RNAs (lncRNAs) may play a role in psychiatric diseases including bipolar disorder (BD). We investigated mRNA-lncRNA co-expression patterns in neuronal-like cells treated with widely prescribed BD medications. The aim was to unveil insights into the complex mechanisms of BD medications and highlight potential targets for new drug development. Human neuronal-like (NT2-N) cells were treated with either lamotrigine, lithium, quetiapine, valproate or vehicle for 24 h. Genome-wide mRNA expression was quantified for weighted gene co-expression network analysis (WGCNA) to correlate the expression levels of mRNAs with lncRNAs. Functional enrichment analysis and hub lncRNA identification was conducted on key co-expressed modules associated with the drug response. We constructed lncRNA-mRNA co-expression networks and identified key modules underlying these treatments, as well as their enriched biological functions. Processes enriched in key modules included synaptic vesicle cycle, endoplasmic reticulum-related functions and neurodevelopment. Several lncRNAs such as GAS6-AS1 and MIR100HG were highlighted as driver genes of key modules. Our study demonstrates the key role of lncRNAs in the mechanism(s) of action of BD drugs. Several lncRNAs have been suggested as major regulators of medication effects and are worthy of further investigation as novel drug targets to treat BD.

Powerful eQTL mapping through low coverage RNA sequencing

Mapping genetic variants that regulate gene expression (eQTL mapping) in large-scale RNA sequencing (RNA-seq) studies is often employed to understand functional consequences of regulatory variants. However, the high cost of RNA-seq limits sample size, sequencing depth, and, therefore, discovery power in eQTL studies. In this work, we demonstrate that, given a fixed budget, eQTL discovery power can be increased by lowering the sequencing depth per sample and increasing the number of individuals sequenced in the assay. We perform RNA-seq of whole-blood tissue across 1,490 individuals at low coverage (5.9 million reads/sample) and show that the effective power is higher than that of an RNA-seq study of 570 individuals at moderate coverage (13.9 million reads/sample). Next, we leverage synthetic datasets derived from real RNA-seq data (50 million reads/sample) to explore the interplay of coverage and number individuals in eQTL studies, and show that a 10-fold reduction in coverage leads to only a 2.5-fold reduction in statistical power to identify eQTLs. Our work suggests that lowering coverage while increasing the number of individuals in RNA-seq is an effective approach to increase discovery power in eQTL studies.

A Comprehensive Evaluation of Cross-Omics Blood-Based Biomarkers for Neuropsychiatric Disorders

The identification of peripheral multi-omics biomarkers of brain disorders has long been hindered by insufficient sample size and confounder influence. This study aimed to compare biomarker potential for different molecules and diseases. We leveraged summary statistics of five blood quantitative trait loci studies (N = 1980 to 22,609) and genome-wide association studies (N = 9725 to 500,199) from 14 different brain disorders, such as Schizophrenia (SCZ) and Alzheimer's Disease (AD). We applied summary-based and two-sample Mendelian Randomization to estimate the associations between blood molecules and brain disorders. We identified 524 RNA, 807 methylation sites, 29 proteins, seven cytokines, and 22 metabolites having a significant association with at least one of 14 brain disorders. Simulation analyses indicated that a cross-omics combination of biomarkers had better performance for most disorders, and different disorders could associate with different omics. We identified an 11-methylation-site model for SCZ diagnosis (Area Under Curve, AUC = 0.74) by analyzing selected candidate markers in published datasets (total N = 6098). Moreover, we constructed an 18-methylation-sites model that could predict the prognosis of elders with mild cognitive impairment (hazard ratio = 2.32). We provided an association landscape between blood cross-omic biomarkers and 14 brain disorders as well as a suggestion guide for future clinical discovery and application.

A lithium-doped surface inspires immunomodulatory functions for enhanced osteointegration through PI3K/AKT signaling axis regulation

The response of immune systems is crucial to the success of biomedical implants in vivo and in particular, orthopedic implants must possess appropriate immunomodulatory functions to allow sufficient osteointegration. In this work, lithium (Li) is incorporated into titanium (Ti) implants by plasma electrolytic oxidation to realize slow and sustained release of Li ions. In vitro cellular behaviors of mice bone marrow derived macrophages (BMDMs), including gene expression, cytokine secretion, and surface marker analysis suggest that a low dose of Li incorporation could enhance the recruitment of BMDMs, restrict pro-inflammatory polarization (M1 phenotype), and promote anti-inflammatory polarization (M2 phenotype). The in vivo air pouch implantation model is constructed to simulate the microenvironment associated with aseptic loosening and the histology results confirm that a small dose of Li could relieve inflammatory reactions surrounding the implants. Moreover, compared to the Li-free group, the macrophage-conditioned culture medium (MCM) from Li-doped samples is more beneficial for the osteogenic differentiation of the mouse embryo cell line (C3H10T1/2) and angiogenesis of human umbilical vein endothelial cells (HUVECs), which is further confirmed by better osteointegration ability in the bone implantation model of Li-incorporating Ti implants. Furthermore, the molecular mechanism study discloses that osteoimmunomodulatory activity of Li-incorporating Ti implants is achieved by regulating the cascade molecules in the PI3K/AKT signalling pathway. This work reveals that favorable immune-modulated osteogenesis and osseointegration of bone implants can be realized by the incorporation of Li which broadens the strategy to develop the next generation of immunomodulatory biomaterials.

Mini review: Recent advances on epigenetic effects of lithium

Lithium (Li) remains the first line long-term treatment of bipolar disorders notwithstanding a high inter-individual variability of response. Significant research effort has been undertaken to understand the molecular mechanisms underlying Li cellular and clinical effects in order to identify predictive biomarkers of response. Li response has been shown to be partly heritable, however mechanisms that do not rely on DNA variants could also be involved. In recent years, modulation of epigenetic marks in relation with the level of Li response has appeared increasingly plausible. Recent results in this field of research have provided new insights into the molecular processes involved in Li effects. In this review, we examined the literature investigating the involvement of three epigenetic mechanisms (DNA methylation, noncoding RNAs and histone modifications) in Li clinical efficacy in bipolar disorder.

Differences in telomere length between patients with bipolar disorder and controls are influenced by lithium treatment

Aim: To assess the role of lithium treatment in the relationship between bipolar disorder (BD) and leukocyte telomere length (LTL). Materials & methods: We compared LTL between 131 patients with BD, with or without a history of lithium treatment, and 336 controls. We tested the association between genetically determined LTL and BD in two large genome-wide association datasets. Results: Patients with BD with a history lithium treatment showed longer LTL compared with never-treated patients (p = 0.015), and similar LTL compared with controls. Patients never treated with lithium showed shorter LTL compared with controls (p = 0.029). Mendelian randomization analysis showed no association between BD and genetically determined LTL. Conclusion: Our data support previous findings showing that long-term lithium treatment might protect against telomere shortening.