Transcriptional signatures of fentanyl use in the mouse ventral tegmental area.

Synthetic opioids such as fentanyl contribute to the vast majority of opioid-related overdose deaths, but fentanyl use remains broadly understudied. Like other substances with misuse potential, opioids cause lasting molecular adaptations to brain reward circuits, including neurons in the ventral tegmental area (VTA). The VTA contains numerous cell types that play diverse roles in opioid use and relapse; however, it is unknown how fentanyl experience alters the transcriptional landscape in specific subtypes. Here, we performed single nuclei RNA sequencing to study transcriptional programs in fentanyl-experienced mice. Male and female C57/BL6 mice self-administered intravenous fentanyl (1.5 µg/kg/infusion) or saline for 10 days. After 24 h abstinence, VTA nuclei were isolated and prepared for sequencing on the 10× platform. We identified different patterns of gene expression across cell types. In dopamine neurons, we found enrichment of genes involved in growth hormone signalling. In dopamine-glutamate-GABA combinatorial neurons, and some GABA neurons, we found enrichment of genes involved in Pi3k-Akt signalling. In glutamate neurons, we found enrichment of genes involved in cholinergic signalling. We identified transcriptional regulators for the differentially expressed genes in each neuron cluster, including downregulated transcriptional repressor Bcl6, and upregulated transcription factor Tcf4. We also compared the fentanyl-induced gene expression changes identified in mouse VTA with a published rat dataset in bulk VTA, and found overlap in genes related to GABAergic signalling and extracellular matrix interaction. Together, we provide a comprehensive picture of how fentanyl self-administration alters the transcriptional landscape of the mouse VTA that serves as the foundation for future mechanistic studies.

Multimodal Interrogation of Ventral Pallidum Projections Reveals Projection-Specific Signatures and Effects on Cocaine Reward.

The ventral pallidum (VP) is a central hub in the reward circuitry with diverse projections that have different behavioral roles attributed mostly to the connectivity with the downstream target. However, different VP projections may represent, as in the striatum, separate neuronal populations that differ in more than just connectivity. In this study, we performed in mice of both sexes a multimodal dissection of four major projections of the VP-to the lateral hypothalamus (VP→LH), ventral tegmental area (VP→VTA), lateral habenula (VP→LHb), and mediodorsal thalamus (VP→MDT)-with physiological, anatomical, genetic, and behavioral tools. We also tested for physiological differences between VP neurons receiving input from nucleus accumbens medium spiny neurons (MSNs) that express either the D1 (D1-MSNs) or the D2 (D2-MSNs) dopamine receptor. We show that each VP projection (1) when inhibited during a cocaine conditioned place preference (CPP) test affects performance differently, (2) receives a different pattern of inputs using rabies retrograde labeling, (3) shows differentially expressed genes using RNA sequencing, and (4) has projection-specific characteristics in excitability and synaptic input characteristics using whole-cell patch clamp. VP→LH and VP→VTA projections have different effects on CPP and show low overlap in circuit tracing experiments, as VP→VTA neurons receive more striatal input, while VP→LH neurons receive more olfactory input. Additionally, VP→VTA neurons are less excitable, while VP→LH neurons are more excitable than the average VP neuron, a difference driven mainly by D2-MSN-responding neurons. Thus, VP→VTA and VP→LH neurons may represent largely distinct populations of VP neurons.

Cochlear organoids reveal transcriptional programs of postnatal hair cell differentiation from supporting cells.

We explore the changes in chromatin accessibility and transcriptional programs for cochlear hair cell differentiation from postmitotic supporting cells using organoids from postnatal cochlea. The organoids contain cells with transcriptional signatures of differentiating vestibular and cochlear hair cells. Construction of trajectories identifies Lgr5+ cells as progenitors for hair cells, and the genomic data reveal gene regulatory networks leading to hair cells. We validate these networks, demonstrating dynamic changes both in expression and predicted binding sites of transcription factors (TFs) during organoid differentiation. We identify known regulators of hair cell development, Atoh1, Pou4f3, and Gfi1, and the analysis predicts the regulatory factors Tcf4, an E-protein and heterodimerization partner of Atoh1, and Ddit3, a CCAAT/enhancer-binding protein (C/EBP) that represses Hes1 and activates transcription of Wnt-signaling-related genes. Deciphering the signals for hair cell regeneration from mammalian cochlear supporting cells reveals candidates for hair cell (HC) regeneration, which is limited in the adult.

Transcriptomic profiling of reward and sensory brain areas in perinatal fentanyl exposed juvenile mice.

Use of the synthetic opioid fentanyl increased ~300% in the last decade, including among women of reproductive ages. Adverse neonatal outcomes and long-term behavioral disruptions are associated with perinatal opioid exposure. Our previous work demonstrated that perinatal fentanyl exposed mice displayed enhanced negative affect and somatosensory circuit and behavioral disruptions during adolescence. However, little is known about molecular adaptations across brain regions that underlie these outcomes. We performed RNA sequencing across three reward and two sensory brain areas to study transcriptional programs in perinatal fentanyl exposed juvenile mice. Pregnant dams received 10 µg/ml fentanyl in the drinking water from embryonic day 0 (E0) through gestational periods until weaning at postnatal day 21 (P21). RNA was extracted from nucleus accumbens (NAc), prelimbic cortex (PrL), ventral tegmental area (VTA), somatosensory cortex (S1) and ventrobasal thalamus (VBT) from perinatal fentanyl exposed mice of both sexes at P35. RNA sequencing was performed, followed by analysis of differentially expressed genes (DEGs) and gene co-expression networks. Transcriptome analysis revealed DEGs and gene modules significantly associated with exposure to perinatal fentanyl in a sex-wise manner. The VTA had the most DEGs, while robust gene enrichment occurred in NAc. Genes enriched in mitochondrial respiration were pronounced in NAc and VTA of perinatal fentanyl exposed males, extracellular matrix (ECM) and neuronal migration enrichment were pronounced in NAc and VTA of perinatal fentanyl exposed males, while genes associated with vesicular cycling and synaptic signaling were markedly altered in NAc of perinatal fentanyl exposed female mice. In sensory areas from perinatal fentanyl exposed females, we found alterations in mitochondrial respiration, synaptic and ciliary organization processes. Our findings demonstrate distinct transcriptomes across reward and sensory brain regions, with some showing discordance between sexes. These transcriptome adaptations may underlie structural, functional, and behavioral changes observed in perinatal fentanyl exposed mice.

Molecular, Circuit, and Stress Response Characterization of Ventral Pallidum Npas1-Neurons.

Altered activity of the ventral pallidum (VP) underlies disrupted motivation in stress and drug exposure. The VP is a very heterogeneous structure composed of many neuron types with distinct physiological properties and projections. Neuronal PAS 1-positive (Npas1+) VP neurons are thought to send projections to brain regions critical for motivational behavior. While Npas1+ neurons have been characterized in the globus pallidus external, there is limited information on these neurons in the VP. To address this limitation, we evaluated the projection targets of the VP Npas1+ neurons and performed RNA-sequencing on ribosome-associated mRNA from VP Npas1+ neurons to determine their molecular identity. Finally, we used a chemogenetic approach to manipulate VP Npas1+ neurons during social defeat stress (SDS) and behavioral tasks related to anxiety and motivation in Npas1-Cre mice. We used a similar approach in females using the chronic witness defeat stress (CWDS). We identified VP Npas1+ projections to the nucleus accumbens, ventral tegmental area, medial and lateral habenula, lateral hypothalamus, thalamus, medial and lateral septum, and periaqueductal gray area. VP Npas1+ neurons displayed distinct translatome representing distinct biological processes. Chemogenetic activation of hM3D(Gq) receptors in VP Npas1+ neurons increased susceptibility to a subthreshold SDS and anxiety-like behavior in the elevated plus maze and open field while the activation of hM4D(Gi) receptors in VP Npas1+ neurons enhanced resilience to chronic SDS and CWDS. Thus, the activity of VP Npas1+ neurons modulates susceptibility to social stressors and anxiety-like behavior. Our studies provide new information on VP Npas1+ neuron circuitry, molecular identity, and their role in stress response.SIGNIFICANCE STATEMENT The ventral pallidum (VP) is a structure connected to both reward-related and aversive brain centers. It is a key brain area that signals the hedonic value of natural rewards. Disruption in the VP underlies altered motivation in stress and substance use disorder. However, VP is a very heterogeneous area with multiple neuron subtypes. This study characterized the projection pattern and molecular signatures of VP Neuronal PAS 1-positive (Npas1+) neurons. We further used tools to alter receptor signaling in VP Npas1+ neurons in stress to demonstrate a role for these neurons in stress behavioral outcomes. Our studies have implications for understanding brain cell type identities and their role in brain disorders, such as depression, a serious disorder that is precipitated by stressful events.

Transcriptional signatures of fentanyl use in the mouse ventral tegmental area.

Synthetic opioids such as fentanyl contribute to the vast majority of opioid-related overdose deaths, but fentanyl use remains broadly understudied. Like other substances with misuse potential, opioids cause lasting molecular adaptations to brain reward circuits, including neurons in the ventral tegmental area (VTA). The VTA contains numerous cell types that play diverse roles in opioid use and relapse, however it is unknown how fentanyl experience alters the transcriptional landscape in specific subtypes. Here, we performed single nuclei RNA sequencing to study transcriptional programs in fentanyl experienced mice. Male and female C57/BL6 mice self-administered intravenous fentanyl (1.5 µg/kg/infusion) or saline for 10 days. After 24 hr abstinence, VTA nuclei were isolated and prepared for sequencing on the 10X platform. We identified different patterns of gene expression across cell types. In dopamine neurons, we found enrichment of genes involved in growth hormone signaling. In dopamine-glutamate-GABA combinatorial neurons, and some GABA neurons, we found enrichment of genes involved in Pi3k-Akt signaling. In glutamate neurons, we found enrichment of genes involved in cholinergic signaling. We identified transcriptional regulators for the differentially expressed genes in each neuron cluster, including downregulation of transcriptional repressor Bcl6, and upregulation of Wnt signaling partner Tcf4. We also compared the fentanyl-induced gene expression changes identified in mouse VTA with a published rat dataset in bulk VTA, and found overlap in genes related to GABAergic signaling and extracellular matrix interaction. Together, we provide a comprehensive picture of how fentanyl self-administration alters the transcriptional landscape of the mouse VTA, that serves for the foundation for future mechanistic studies.

Heterogeneous treatment effect analysis based on machine-learning methodology.

Heterogeneous treatment effect (HTE) analysis focuses on examining varying treatment effects for individuals or subgroups in a population. For example, an HTE-informed understanding can critically guide physicians to individualize the medical treatment for a certain disease. However, HTE analysis has not been widely recognized and used, even given the explosive increase of data availability attributed to the arrival of the Big Data era. Part of the reason behind its underuse is that data are often of high dimension and high complexity, which pose significant challenges for applying conventional HTE analysis methods. To meet these challenges, a newly developed causal forest HTE method has been derived from the random forest machine-learning algorithm. We conducted a systematic performance evaluation for the causal forest method against the conventional two-step method by simulating scenarios with different levels of complexity for the analysis. Our results show that causal forest outperforms the conventional HTE method in assessing treatment effect, especially when data are complex (e.g., nonlinear) and high dimensional, suggesting that causal forest is a promising tool for real-world applications of HTE analysis.

Predicting tissue-specific gene expression from whole blood transcriptome.

Complex diseases are mediated via transcriptional dysregulation in multiple tissues. Thus, knowing an individual's tissue-specific gene expression can provide critical information about her health. Unfortunately, for most tissues, the transcriptome cannot be obtained without invasive procedures. Could we, however, infer an individual's tissue-specific expression from her whole blood transcriptome? Here, we rigorously address this question. We find that an individual's whole blood transcriptome can significantly predict tissue-specific expression levels for ~60% of the genes on average across 32 tissues, with up to 81% of the genes in skeletal muscle. The tissue-specific expression inferred from the blood transcriptome is almost as good as the actual measured tissue expression in predicting disease state for six different complex disorders, including hypertension and type 2 diabetes, substantially surpassing the blood transcriptome. The code for tissue-specific gene expression prediction, TEEBoT, is provided, enabling others to study its potential translational value in other indications.

A transcription-centric model of SNP-age interaction.

Complex age-associated phenotypes are caused, in part, by an interaction between an individual's genotype and age. The mechanisms governing such interactions are however not entirely understood. Here, we provide a novel transcriptional mechanism-based framework-SNiPage, to investigate such interactions, whereby a transcription factor (TF) whose expression changes with age (age-associated TF), binds to a polymorphic regulatory element in an allele-dependent fashion, rendering the target gene's expression dependent on both, the age and the genotype. Applying SNiPage to GTEx, we detected ~637 significant TF-SNP-Gene triplets on average across 25 tissues, where the TF binds to a regulatory SNP in the gene's promoter or putative enhancer and potentially regulates its expression in an age- and allele-dependent fashion. The detected SNPs are enriched for epigenomic marks indicative of regulatory activity, exhibit allele-specific chromatin accessibility, and spatial proximity to their putative gene targets. Furthermore, the TF-SNP interaction-dependent target genes have established links to aging and to age-associated diseases. In six hypertension-implicated tissues, detected interactions significantly inform hypertension state of an individual. Lastly, the age-interacting SNPs exhibit a greater proximity to the reported phenotype/diseases-associated SNPs than eSNPs identified in an interaction-independent fashion. Overall, we present a novel mechanism-based model, and a novel framework SNiPage, to identify functionally relevant SNP-age interactions in transcriptional control and illustrate their potential utility in understanding complex age-associated phenotypes.

Comprehensive map of age-associated splicing changes across human tissues and their contributions to age-associated diseases.

Alternative splicing contributes to phenotypic diversity at multiple biological scales, and its dysregulation is implicated in both ageing and age-associated diseases in human. Cross-tissue variability in splicing further complicates its links to age-associated phenotypes and elucidating these links requires a comprehensive map of age-associated splicing changes across multiple tissues. Here, we generate such a map by analyzing ~8500 RNA-seq samples across 48 tissues in 544 individuals. Employing a stringent model controlling for multiple confounders, we identify 49,869 tissue-specific age-associated splicing events of 7 distinct types. We find that genome-wide splicing profile is a better predictor of biological age than the gene and transcript expression profiles, and furthermore, age-associated splicing provides additional independent contribution to age-associated complex diseases. We show that the age-associated splicing changes may be explained, in part, by concomitant age-associated changes of the upstream splicing factors. Finally, we show that our splicing-based model of age can successfully predict the relative ages of cells in 8 of the 10 paired longitudinal data as well as in 2 sets of cell passage data. Our study presents the first systematic investigation of age-associated splicing changes across tissues, and further strengthening the links between age-associated splicing and age-associated diseases.

Putative functional genes in idiopathic dilated cardiomyopathy.

Idiopathic dilated cardiomyopathy (DCM) is a complex disorder with a genetic and an environmental component involving multiple genes, many of which are yet to be discovered. We integrate genetic, epigenetic, transcriptomic, phenotypic, and evolutionary features into a method - Hridaya, to infer putative functional genes underlying DCM in a genome-wide fashion, using 213 human heart genomes and transcriptomes. Many genes identified by Hridaya are experimentally shown to cause cardiac complications. We validate the top predicted genes, via five different genome-wide analyses: First, the predicted genes are associated with cardiovascular functions. Second, their knockdowns in mice induce cardiac abnormalities. Third, their inhibition by drugs cause cardiac side effects in human. Fourth, they tend to have differential exon usage between DCM and normal samples. Fifth, analyzing 213 individual genotypes, we show that regulatory polymorphisms of the predicted genes are associated with elevated risk of cardiomyopathy. The stratification of DCM patients based on cardiac expression of the functional genes reveals two subgroups differing in key cardiac phenotypes. Integrating predicted functional genes with cardiomyocyte drug treatment experiments reveals novel potential drug targets. We provide a list of investigational drugs that target the newly identified functional genes that may lead to cardiac side effects.

Prediction and Subtyping of Hypertension from Pan-Tissue Transcriptomic and Genetic Analyses.

Hypertension (HT) is a complex systemic disease involving transcriptional changes in multiple organs. Here we systematically investigate the pan-tissue transcriptional and genetic landscape of HT spanning dozens of tissues in hundreds of individuals. We find that in several tissues, previously identified HT-linked genes are dysregulated and the gene expression profile is predictive of HT. Importantly, many expression quantitative trait loci (eQTL) SNPs associated with the population variance of the dysregulated genes are linked with blood pressure in an independent genome-wide association study, suggesting that the functional effect of HT-associated SNPs may be mediated through tissue-specific transcriptional dysregulation. Analyses of pan-tissue transcriptional dysregulation profile, as well as eQTL SNPs underlying the dysregulated genes, reveals substantial heterogeneity among the HT patients, revealing two broad groupings - a Diffused group where several tissues exhibit HT-associated molecular alterations and a Localized group where such alterations are localized to very few tissues. These two patient subgroups differ in several clinical phenotypes including respiratory, cerebrovascular, diabetes, and heart disease. These findings suggest that the Diffused and Localized subgroups may be driven by different molecular mechanisms and have different genetic underpinning.

CSF proteomics of secondary phase spinal cord injury in human subjects: perturbed molecular pathways post injury.

Recovery of sensory and motor functions following traumatic spinal cord injury (SCI) is dependent on injury severity. Here we identified 49 proteins from cerebrospinal fluid (CSF) of SCI patients, eight of which were differentially abundant among two severity groups of SCI. It was observed that the abundance profiles of these proteins change over a time period of days to months post SCI. Statistical analysis revealed that these proteins take part in several molecular pathways including DNA repair, protein phosphorylation, tRNA transcription, iron transport, mRNA metabolism, immune response and lipid and ATP catabolism. These pathways reflect a set of mechanisms that the system may adopt to cope up with the assault depending on the injury severity, thus leading to observed physiological responses. Apart from putting forward a picture of the molecular scenario at the injury site in a human study, this finding further delineates consequent pathways and molecules that may be altered by external intervention to restrict neural degeneration.

Universality splitting in distribution of number of miRNA co-targets.

In a recent work (Basu et al., in EPL 105:28007, 2014) it was pointed out that the link-weight distribution of microRNA co-target network of a wide class of species are universal up to scaling. The number cell types, widely accepted as a measure of complexity, turns out to be proportional to these scale-factor. In this article we discuss additional universal features of these networks and show that, this universality splits if one considers distribution of number of common targets of three or more number of microRNAs. These distributions for different species can be collapsed onto two distinct set of universal functions, revealing the fact that the species which appeared in early evolution have different complexity measure compared to those appeared late.

Comparison of modules of wild type and mutant Huntingtin and TP53 protein interaction networks: implications in biological processes and functions.

Disease-causing mutations usually change the interacting partners of mutant proteins. In this article, we propose that the biological consequences of mutation are directly related to the alteration of corresponding protein protein interaction networks (PPIN). Mutation of Huntingtin (HTT) which causes Huntington's disease (HD) and mutations to TP53 which is associated with different cancers are studied as two example cases. We construct the PPIN of wild type and mutant proteins separately and identify the structural modules of each of the networks. The functional role of these modules are then assessed by Gene Ontology (GO) enrichment analysis for biological processes (BPs). We find that a large number of significantly enriched ([Formula: see text]) GO terms in mutant PPIN were absent in the wild type PPIN indicating the gain of BPs due to mutation. Similarly some of the GO terms enriched in wild type PPIN cease to exist in the modules of mutant PPIN, representing the loss. GO terms common in modules of mutant and wild type networks indicate both loss and gain of BPs. We further assign relevant biological function(s) to each module by classifying the enriched GO terms associated with it. It turns out that most of these biological functions in HTT networks are already known to be altered in HD and those of TP53 networks are altered in cancers. We argue that gain of BPs, and the corresponding biological functions, are due to new interacting partners acquired by mutant proteins. The methodology we adopt here could be applied to genetic diseases where mutations alter the ability of the protein to interact with other proteins.

Fixed-energy sandpiles belong generically to directed percolation.

Fixed-energy sandpiles with stochastic update rules are known to exhibit a nonequilibrium phase transition from an active phase into infinitely many absorbing states. Examples include the conserved Manna model, the conserved lattice gas, and the conserved threshold transfer process. It is believed that the transitions in these models belong to an autonomous universality class of nonequilibrium phase transitions, the so-called Manna class. Contrarily, the present numerical study of selected (1+1)-dimensional models in this class suggests that their critical behavior converges to directed percolation after very long time, questioning the existence of an independent Manna class.