Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review.

Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.

Intraoperative opioids are associated with decreased recurrence rates in colon adenocarcinoma: a retrospective observational cohort study.

BACKGROUND: Opioid-induced immunomodulation may be important in colon adenocarcinoma, where tumour DNA mismatch repair (MMR) can determine the level of immune activation with consequences for therapeutic response and prognosis. We evaluated the relationship between intraoperative opioid exposure, MMR subtype, and oncological outcomes after surgery for colon adenocarcinoma. METHODS: Intraoperative opioid use (standardised by calculating morphine milligram equivalents) during stage I-III colon adenocarcinoma resection was reviewed retrospectively. Tumours were classified as DNA mismatch repair deficient (dMMR) or proficient (pMMR) by immunohistochemistry. The primary outcome was local tumour recurrence, distant tumour recurrence, or both (multivariable analysis). The exposures of interest were intraoperative analgesia and tumour subtype. Opioid-related gene expression was analysed using The Cancer Genome Atlas Colon Adenocarcinoma transcriptomic data. RESULTS: Clinical and pathological data were analysed from 1157 subjects (median age, 60 [51-70] yr; 49% female) who underwent curative resection for stage I-III colon adenocarcinoma. Higher intraoperative opioid doses were associated with reduced risk of tumour recurrence (hazard ratio=0.92 per 10 morphine milligram equivalents; 95% confidence interval [95% CI], 0.87-0.98; P=0.007), but not with overall survival. In tumours deficient in DNA MMR, tumour recurrence was less likely (HR=0.38; 95% CI, 0.21-0.68; P=0.001), with higher opioid dose associated with eightfold lower recurrence rates. Gene expression related to opioid signalling was different between dMMR and pMMR tumours. CONCLUSIONS: Higher intraoperative opioid dose was associated with a lower risk of tumour recurrence after surgery for stage I-III colon adenocarcinoma, but particularly so in tumours in which DNA MMR was deficient.

Intraoperative opioids are associated with improved recurrence-free survival in triple-negative breast cancer.

BACKGROUND: Opioid-induced immunomodulation may be of particular importance in triple-negative breast cancer (TNBC) where an immune response is associated with improved outcome and response to immunotherapy. We evaluated the association between intraoperative opioids and oncological outcomes and explored patterns of opioid receptor expression in TNBC. METHODS: Consecutive patients with stage I-III primary TNBC were identified from a prospectively maintained database. Opioid receptor expression patterns in the tumour microenvironment were analysed using publicly available bulk and single-cell RNA-seq data. RESULTS: A total of 1143 TNBC cases were retrospectively analysed. In multivariable analysis, higher intraoperative opioid dose was associated with favourable recurrence-free survival, hazard ratio 0.93 (95% confidence interval 0.88-0.99) per 10 oral morphine milligram equivalents increase (P=0.028), but was not significantly associated with overall survival, hazard ratio 0.96 (95% confidence interval 0.89-1.02) per 10 morphine milligram equivalents increase (P=0.2). Bulk RNA-seq analysis of opioid receptors showed that OPRM1 was nearly non-expressed. Compared with normal breast tissue OGFR, OPRK1, and OPRD1 were upregulated, while TLR4 was downregulated. At a single-cell level, OPRM1 and OPRD1 were not detectable; OPRK1 was expressed mainly on tumour cells, whereas OGFR and TLR4 were more highly expressed on immune cells. CONCLUSIONS: We found a protective effect of intraoperative opioids on recurrence-free survival in TNBC. Opioid receptor expression was consistent with a net protective effect of opioid agonism, with protumour receptors either not expressed or downregulated, and antitumour receptors upregulated. In this era of personalised medicine, efforts to differentiate the effects of opioids across breast cancer subtypes (and ultimately individual patients) should continue.

Intraoperative opioid exposure, tumour genomic alterations, and survival differences in people with lung adenocarcinoma.

BACKGROUND: Opioids have been linked to worse oncologic outcomes in surgical patients. Studies in certain cancer types have identified associations between survival and intra-tumoural opioid receptor gene alterations, but no study has investigated whether the tumour genome interacts with opioid exposure to affect survival. We sought to determine whether intraoperative opioid exposure is associated with recurrence-specific survival and overall survival in early-stage lung adenocarcinoma, and whether selected tumour genomics are associated with this relationship. Associations between ketamine and dexmedetomidine and outcomes were also studied. METHODS: Surgical patients (N=740) with pathological stage I-III lung adenocarcinoma and next-generation sequencing data were retrospectively reviewed from a prospectively maintained database. RESULTS: On multivariable analysis, ketamine administration was protective for recurrence-specific survival (hazard ratio = 0.44, 95% confidence interval 0.24-0.80; P=0.007), compared with no adjunct. Higher intraoperative oral morphine milligram equivalents were significantly associated with worse overall survival (hazard ratio=1.09/10 morphine milligram equivalents, 95% confidence interval 1.02-1.17; P=0.010). Significant interaction effects were found between morphine milligram equivalents and fraction genome altered and morphine milligram equivalents and CDKN2A, such that higher fraction genome altered or CDKN2A alterations were associated with worse overall survival at higher morphine milligram equivalents (P=0.044 and P=0.052, respectively). In contrast, alterations in the Wnt (P=0.029) and Hippo (P=0.040) oncogenic pathways were associated with improved recurrence-specific survival at higher morphine milligram equivalents, compared with unaltered pathways. CONCLUSIONS: Intraoperative opioid exposure is associated with worse overall survival, whereas ketamine exposure is associated with improved recurrence-specific survival in patients with early-stage lung adenocarcinoma. This is the first study to investigate tumour-specific genomic interactions with intraoperative opioid administration to modify survival associations.

Systems Genetic Analyses Highlight a TGFß-FOXO3 Dependent Striatal Astrocyte Network Conserved across Species and Associated with Stress, Sleep, and Huntington's Disease.

Recent systems-based analyses have demonstrated that sleep and stress traits emerge from shared genetic and transcriptional networks, and clinical work has elucidated the emergence of sleep dysfunction and stress susceptibility as early symptoms of Huntington's disease. Understanding the biological bases of these early non-motor symptoms may reveal therapeutic targets that prevent disease onset or slow disease progression, but the molecular mechanisms underlying this complex clinical presentation remain largely unknown. In the present work, we specifically examine the relationship between these psychiatric traits and Huntington's disease (HD) by identifying striatal transcriptional networks shared by HD, stress, and sleep phenotypes. First, we utilize a systems-based approach to examine a large publicly available human transcriptomic dataset for HD (GSE3790 from GEO) in a novel way. We use weighted gene coexpression network analysis and differential connectivity analyses to identify transcriptional networks dysregulated in HD, and we use an unbiased ranking scheme that leverages both gene- and network-level information to identify a novel astrocyte-specific network as most relevant to HD caudate. We validate this result in an independent HD cohort. Next, we computationally predict FOXO3 as a regulator of this network, and use multiple publicly available in vitro and in vivo experimental datasets to validate that this astrocyte HD network is downstream of a signaling pathway important in adult neurogenesis (TGFß-FOXO3). We also map this HD-relevant caudate subnetwork to striatal transcriptional networks in a large (n = 100) chronically stressed (B6xA/J)F2 mouse population that has been extensively phenotyped (328 stress- and sleep-related measurements), and we show that this striatal astrocyte network is correlated to sleep and stress traits, many of which are known to be altered in HD cohorts. We identify causal regulators of this network through Bayesian network analysis, and we highlight their relevance to motor, mood, and sleep traits through multiple in silico approaches, including an examination of their protein binding partners. Finally, we show that these causal regulators may be therapeutically viable for HD because their downstream network was partially modulated by deep brain stimulation of the subthalamic nucleus, a medical intervention thought to confer some therapeutic benefit to HD patients. In conclusion, we show that an astrocyte transcriptional network is primarily associated to HD in the caudate and provide evidence for its relationship to molecular mechanisms of neural stem cell homeostasis. Furthermore, we present a unified systems-based framework for identifying gene networks that are associated with complex non-motor traits that manifest in the earliest phases of HD. By analyzing and integrating multiple independent datasets, we identify a point of molecular convergence between sleep, stress, and HD that reflects their phenotypic comorbidity and reveals a molecular pathway involved in HD progression.

Opioids and immune checkpoint inhibitors differentially regulate a common immune network in triple-negative breast cancer.

Background: Opioids are the primary analgesics for cancer pain. Recent clinical evidence suggests opioids may counteract the effect of immune checkpoint inhibition (ICI) immunotherapy, but the mechanism for this interaction is unknown. The following experiments study how opioids and immunotherapy modulate a common RNA expression pathway in triple negative breast cancer (TNBC), a cancer subtype in which immunotherapy is increasingly used. This study identifies a mechanism by which opioids may decrease ICI efficacy, and compares ketamine, a non-opioid analgesic with emerging use in cancer pain, for potential ICI interaction. Methods: Tumor RNA expression and clinicopathologic data from a large cohort with TNBC (N=286) was used to identify RNA expression signatures of disease. Various drug-induced RNA expression profiles were extracted from multimodal RNA expression datasets and analyzed to estimate the RNA expression effects of ICI, opioids, and ketamine on TNBC. Results: We identified a RNA expression network in CD8+ T-cells that was relevant to TNBC pathogenesis and prognosis. Both opioids and anti-PD-L1 ICI regulated RNA expression in this network, suggesting a nexus for opioid-ICI interaction. Morphine and anti-PD-L1 therapy regulated RNA expression in opposing directions. By contrast, there was little overlap between the effect of ketamine and anti-PD-L1 therapy on RNA expression. Conclusions: Opioids and ICI may target a common immune network in TNBC and regulate gene expression in opposing fashion. No available evidence supports a similar interaction between ketamine and ICI.

NREM delta power and AD-relevant tauopathy are associated with shared cortical gene networks.

Reduced NREM sleep in humans is associated with AD neuropathology. Recent work has demonstrated a reduction in NREM sleep in preclinical AD, pointing to its potential utility as an early marker of dementia. We test the hypothesis that reduced NREM delta power and increased tauopathy are associated with shared underlying cortical molecular networks in preclinical AD. We integrate multi-omics data from two extensive public resources, a human Alzheimer's disease cohort from the Mount Sinai Brain Bank (N = 125) reflecting AD progression and a (C57BL/6J × 129S1/SvImJ) F2 mouse population in which NREM delta power was measured (N = 98). Two cortical gene networks, including a CLOCK-dependent circadian network, are associated with NREM delta power and AD tauopathy progression. These networks were validated in independent mouse and human cohorts. Identifying gene networks related to preclinical AD elucidate possible mechanisms associated with the early disease phase and potential targets to alter the disease course.

Identifying Clear Cell Renal Cell Carcinoma Coexpression Networks Associated with Opioid Signaling and Survival.

While opioids constitute the major component of perioperative analgesic regimens for surgery in general, a variety of evidence points to an association between perioperative opioid exposure and longer term oncologic outcomes. The mechanistic details underlying these effects are not well understood. In this study, we focused on clear cell renal cell carcinoma (ccRCC) and utilized RNA sequencing and outcome data from both The Cancer Genome Atlas, as well as a local patient cohort to identify survival-associated gene coexpression networks. We then projected drug-induced transcriptional profiles from in vitro cancer cells to predict drug effects on these networks and recurrence-free, cancer-specific, and overall survival. The opioid receptor agonist, leu-enkephalin, was predicted to have antisurvival effects in ccRCC, primarily through Th2 immune- and NRF2-dependent macrophage networks. Conversely, the antagonist, naloxone, was predicted to have prosurvival effects, primarily through angiogenesis, fatty acid metabolism, and hemopoesis pathways. Eight coexpression networks associated with survival endpoints in ccRCC were identified, and master regulators of the transition from the normal to disease state were inferred, a number of which are linked to opioid pathways. These results are the first to suggest a mechanism for opioid effects on cancer outcomes through modulation of survival-associated coexpression networks. While we focus on ccRCC, this methodology may be employed to predict opioid effects on other cancer types and to personalize analgesic regimens in patients with cancer for optimal outcomes. SIGNIFICANCE: This study suggests a possible molecular mechanism for opioid effects on cancer outcomes generally, with implications for personalization of analgesic regimens.

Shared Transcriptional Signatures in Major Depressive Disorder and Mouse Chronic Stress Models.

BACKGROUND: Most of our knowledge of the biological basis of major depressive disorder (MDD) is derived from studies of chronic stress models in rodents. While these models capture certain aspects of the behavioral and neuroendocrine features of MDD, the extent to which they reproduce the molecular pathology of the human syndrome remains unknown. METHODS: We systematically compared transcriptional signatures in two brain regions implicated in depression-medial prefrontal cortex and nucleus accumbens-of humans with MDD and of 3 chronic stress models in mice: chronic variable stress, adult social isolation, and chronic social defeat stress. We used differential expression analysis combined with weighted gene coexpression network analysis to create interspecies gene networks and assess the capacity of each stress paradigm to recapitulate the transcriptional organization of gene networks in human MDD. RESULTS: Our results show significant overlap between transcriptional alterations in medial prefrontal cortex and nucleus accumbens in human MDD and the 3 mouse chronic stress models, with each of the chronic stress paradigms capturing distinct aspects of MDD abnormalities. Chronic variable stress and adult social isolation better reproduce differentially expressed genes, while chronic social defeat stress and adult social isolation better reproduce gene networks characteristic of human MDD. We also identified several gene networks and their constituent genes that are most significantly associated with human MDD and mouse stress models. CONCLUSIONS: This study demonstrates the ability of 3 chronic stress models in mice to recapitulate distinct aspects of the broad molecular pathology of human MDD, with no one mouse model apparently better than another.

Parkinson's Disease is Associated with Dysregulations of a Dopamine-Modulated Gene Network Relevant to Sleep and Affective Neurobehaviors in the Striatum.

In addition to the characteristic motor symptoms, Parkinson's disease (PD) often involves a constellation of sleep and mood symptoms. However, the mechanisms underlying these comorbidities are largely unknown. We have previously reconstructed gene networks in the striatum of a population of (C57BL/6J x A/J) F2 mice and associated the networks to sleep and affective phenotypes, providing a resource for integrated analyses to investigate perturbed sleep and affective functions at the gene network level. Combining this resource with PD-relevant transcriptomic datasets from humans and mice, we identified four networks that showed elevated gene expression in PD patients, including a circadian clock and mitotic network that was altered similarly in mouse models of PD. We then utilized multiple types of omics data from public databases and linked this gene network to postsynaptic dopamine signaling in the striatum, CDK1-modulated transcriptional regulation, and the genetic susceptibility of PD. These findings suggest that dopamine deficiency, a key aspect of PD pathology, perturbs a circadian/mitotic gene network in striatal neurons. Since the normal functions of this network were relevant to sleep and affective behaviors, these findings implicate that dysregulation of functional gene networks may be involved in the emergence of non-motor symptoms in PD. Our analyses present a framework for integrating multi-omics data from diverse sources in mice and humans to reveal insights into comorbid symptoms of complex diseases.

Cross-species systems analysis identifies gene networks differentially altered by sleep loss and depression.

To understand the transcriptomic organization underlying sleep and affective function, we studied a population of (C57BL/6J × 129S1/SvImJ) F2 mice by measuring 283 affective and sleep phenotypes and profiling gene expression across four brain regions. We identified converging molecular bases for sleep and affective phenotypes at both the single-gene and gene-network levels. Using publicly available transcriptomic datasets collected from sleep-deprived mice and patients with major depressive disorder (MDD), we identified three cortical gene networks altered by the sleep/wake state and depression. The network-level actions of sleep loss and depression were opposite to each other, providing a mechanistic basis for the sleep disruptions commonly observed in depression, as well as the reported acute antidepressant effects of sleep deprivation. We highlight one particular network composed of circadian rhythm regulators and neuronal activity-dependent immediate-early genes. The key upstream driver of this network, Arc, may act as a nexus linking sleep and depression. Our data provide mechanistic insights into the role of sleep in affective function and MDD.

Epigenetic regulation of brain region-specific microglia clearance activity.

The rapid elimination of dying neurons and nonfunctional synapses in the brain is carried out by microglia, the resident myeloid cells of the brain. Here we show that microglia clearance activity in the adult brain is regionally regulated and depends on the rate of neuronal attrition. Cerebellar, but not striatal or cortical, microglia exhibited high levels of basal clearance activity, which correlated with an elevated degree of cerebellar neuronal attrition. Exposing forebrain microglia to apoptotic cells activated gene-expression programs supporting clearance activity. We provide evidence that the polycomb repressive complex 2 (PRC2) epigenetically restricts the expression of genes that support clearance activity in striatal and cortical microglia. Loss of PRC2 leads to aberrant activation of a microglia clearance phenotype, which triggers changes in neuronal morphology and behavior. Our data highlight a key role of epigenetic mechanisms in preventing microglia-induced neuronal alterations that are frequently associated with neurodegenerative and psychiatric diseases.

Corrigendum: Sex-specific transcriptional signatures in human depression.

This corrects the article DOI: 10.1038/nm.4386.

Transcriptional and physiological adaptations in nucleus accumbens somatostatin interneurons that regulate behavioral responses to cocaine.

The role of somatostatin interneurons in nucleus accumbens (NAc), a key brain reward region, remains poorly understood due to the fact that these cells account for < 1% of NAc neurons. Here, we use optogenetics, electrophysiology, and RNA-sequencing to characterize the transcriptome and functioning of NAc somatostatin interneurons after repeated exposure to cocaine. We find that the activity of somatostatin interneurons regulates behavioral responses to cocaine, with repeated cocaine reducing the excitability of these neurons. Repeated cocaine also induces transcriptome-wide changes in gene expression within NAc somatostatin interneurons. We identify the JUND transcription factor as a key regulator of cocaine action and confirmed, by use of viral-mediated gene transfer, that JUND activity in somatostatin interneurons influences behavioral responses to cocaine. Our results identify alterations in NAc induced by cocaine in a sparse population of somatostatin interneurons, and illustrate the value of studying brain diseases using cell type-specific whole transcriptome RNA-sequencing.

Gene Network Dysregulation in Dorsolateral Prefrontal Cortex Neurons of Humans with Cocaine Use Disorder.

Metabolic and functional alterations of neurons in the dorsolateral prefrontal cortex (dlPFC) are thought to contribute to impulsivity, which is a hallmark of addictive behaviors that underlie compulsive drug seeking and taking in humans. To determine if there is a transcriptional signature in dlPFC neurons of humans with cocaine use disorder, we performed total RNA-sequencing on neuronal nuclei isolated from post-mortem dlPFC of cocaine addicts and healthy controls. Our results point toward a transcriptional mechanism whereby cocaine alters specific gene networks in dlPFC neurons. In particular, we identified an AP-1 regulated transcriptional network in dlPFC neurons associated with cocaine use disorder that contains several differentially expressed hub genes. Several of these hub genes are GWAS hits for traits that might involve dysfunction of brain reward circuitry (Body-Mass Index, Obesity) or dlPFC (Bipolar disorder, Schizophrenia). Further study is warranted to determine their potential pathophysiological role in cocaine addiction.

EXPLORING THE REPRODUCIBILITY OF PROBABILISTIC CAUSAL MOLECULAR NETWORK MODELS.

Network reconstruction algorithms are increasingly being employed in biomedical and life sciences research to integrate large-scale, high-dimensional data informing on living systems. One particular class of probabilistic causal networks being applied to model the complexity and causal structure of biological data is Bayesian networks (BNs). BNs provide an elegant mathematical framework for not only inferring causal relationships among many different molecular and higher order phenotypes, but also for incorporating highly diverse priors that provide an efficient path for incorporating existing knowledge. While significant methodological developments have broadly enabled the application of BNs to generate and validate meaningful biological hypotheses, the reproducibility of BNs in this context has not been systematically explored. In this study, we aim to determine the criteria for generating reproducible BNs in the context of transcription-based regulatory networks. We utilize two unique tissues from independent datasets, whole blood from the GTEx Consortium and liver from the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Team (STARNET) study. We evaluated the reproducibility of the BNs by creating networks on data subsampled at different levels from each cohort and comparing these networks to the BNs constructed using the complete data. To help validate our results, we used simulated networks at varying sample sizes. Our study indicates that reproducibility of BNs in biological research is an issue worthy of further consideration, especially in light of the many publications that now employ findings from such constructs without appropriate attention paid to reproducibility. We find that while edge-to-edge reproducibility is strongly dependent on sample size, identification of more highly connected key driver nodes in BNs can be carried out with high confidence across a range of sample sizes.

Sex-specific transcriptional signatures in human depression.

Major depressive disorder (MDD) is a leading cause of disease burden worldwide. While the incidence, symptoms and treatment of MDD all point toward major sex differences, the molecular mechanisms underlying this sexual dimorphism remain largely unknown. Here, combining differential expression and gene coexpression network analyses, we provide a comprehensive characterization of male and female transcriptional profiles associated with MDD across six brain regions. We overlap our human profiles with those from a mouse model, chronic variable stress, and capitalize on converging pathways to define molecular and physiological mechanisms underlying the expression of stress susceptibility in males and females. Our results show a major rearrangement of transcriptional patterns in MDD, with limited overlap between males and females, an effect seen in both depressed humans and stressed mice. We identify key regulators of sex-specific gene networks underlying MDD and confirm their sex-specific impact as mediators of stress susceptibility. For example, downregulation of the female-specific hub gene Dusp6 in mouse prefrontal cortex mimicked stress susceptibility in females, but not males, by increasing ERK signaling and pyramidal neuron excitability. Such Dusp6 downregulation also recapitulated the transcriptional remodeling that occurs in prefrontal cortex of depressed females. Together our findings reveal marked sexual dimorphism at the transcriptional level in MDD and highlight the importance of studying sex-specific treatments for this disorder.

A systems approach identifies networks and genes linking sleep and stress: implications for neuropsychiatric disorders.

Sleep dysfunction and stress susceptibility are comorbid complex traits that often precede and predispose patients to a variety of neuropsychiatric diseases. Here, we demonstrate multilevel organizations of genetic landscape, candidate genes, and molecular networks associated with 328 stress and sleep traits in a chronically stressed population of 338 (C57BL/6J × A/J) F2 mice. We constructed striatal gene co-expression networks, revealing functionally and cell-type-specific gene co-regulations important for stress and sleep. Using a composite ranking system, we identified network modules most relevant for 15 independent phenotypic categories, highlighting a mitochondria/synaptic module that links sleep and stress. The key network regulators of this module are overrepresented with genes implicated in neuropsychiatric diseases. Our work suggests that the interplay among sleep, stress, and neuropathology emerges from genetic influences on gene expression and their collective organization through complex molecular networks, providing a framework for interrogating the mechanisms underlying sleep, stress susceptibility, and related neuropsychiatric disorders.