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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.

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 10μg/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.

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 STATEMENTThe 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.

The ventral pallidum (VP) is central to reward seeking and withdrawal from drugs of abuse. A characteristic of the VP is the diversity of its projection targets. Yet, it remains unknown whether different VP projections also differ in other aspects, such as their transcriptome, physiology and relevance to drug reward. In this study we perform 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 and show significant differences between projections in all aspects. Specifically, the VP→LHand VP→VTAprojections show minimal overlap and stand out as having opposite properties – VP→LHneurons show higher excitability compared to VP→VTAneurons, different pattern of inputs and differentially expressed genes. Moreover, inhibition of VP→LHprojections diminishes, while inhibition of VP→VTAenhances cocaine preference after cocaine withdrawal. This demonstrates that VP projections are heterogenous neuron populations with different roles in cocaine withdrawal.

Altered activity of the ventral pallidum (VP) underlies disrupted motivation in stress and drug exposure. The VP is a very heterogeneous structure comprised 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-seq 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 employed 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 transcriptomes representing distinct biological processes. Chemogenetic activation of VP Npas1+ neurons increased susceptibility to a subthreshold (S)SDS and anxiety-like behavior in the elevated plus maze and open field while the inhibition of VP Npas1+ neurons enhanced resilience to chronic (C)SDS and CWDS. Thus, the activity of VP Npas1+ neurons modulates susceptibility to social stressors and anxiety-like behavior. Our studies provide new information into VP Npas1+ neuron circuitry, molecular identity, and their role in stress response.

Complex diseases are systemic, largely mediated via transcriptional dysregulation in multiple tissues. Thus, knowledge of tissue-specific transcriptome in an individual can provide important information about an individual’s health. Unfortunately, with a few exceptions such as blood, skin, and muscle, an individual’s tissue-specific transcriptome is not accessible through non-invasive means. However, due to shared genetics and regulatory programs between tissues, the transcriptome in blood may be predictive of those in other tissues, at least to some extent. Here, based on GTEx data, we address this question in a rigorous, systematic manner, for the first time. We find that an individual’s whole blood gene expression and splicing profile can predict tissue-specific expression levels in a significant manner (beyond demographic variables) for many genes. On average, across 32 tissues, the expression of about 60% of the genes is predictable from blood expression in a significant manner, with a maximum of 81% of the genes for the musculoskeletal tissue. Remarkably, 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 predictors built directly from the blood transcriptome. The code for our pipeline for tissue-specific gene expression prediction – TEEBoT, is provided, enabling others to study its potential translational value in other indications.

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., Author summary Numerous traits, such as cardiovascular diseases and cancer, are associated with age. However, these associations vary across races and ethnicities, suggesting an interplay between age and the genetic background in determining the trait. Although previously studies have attempted to detect Age-Genotype interactions based on statistical models, they are mostly devoid of mechanism, thus limiting their efficacy and scope in informing therapeutic strategies. Here, we propose a novel framework to investigate such interactions, by incorporating a specific transcription-based mechanism in the model. More specifically, our model is based on the mechanistic scenario that an age-associated transcription factor (TF) binds to a regulatory polymorphism (SNP) in an allele-specific manner to regulate the transcription of the downstream gene in an Age- and Genotype-specific fashion. By analyzing 25 tissues in the GTEx consortium, we detected tissue specific SNP-TF-Gene interaction triplets and functionally validated the detected SNP based on epigenomic and functional data. What’s more, multiple lines of evidence link detected interactions to aging and to age-associated diseases. We expect our new methodological framework and the detected functionally relevant interactions will enhance understanding of the underlying mechanism of SNP-Age interaction and its contribution to age-associated diseases.

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.

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.

Complex networks are intrinsically modular. Resolving small modules is particularly difficult when the network is densely connected; wide variation of link-weights invites additional complexities. In this article we present an algorithm to detect community structure in densely connected weighted networks. First, modularity of the network is calculated by erasing the links having weights smaller than a cutoff q. Then one takes all the disjoint components obtained at q=qm, where the modularity is maximum, and modularize the components individually using Newman Girvan’s algorithm for weighted networks. The performance of the proposed algorithm is evaluated on four different types of network. Initially taking microRNA (miRNA) co-target network of Homo sapiens as an example, we show that this algorithm could reveal miRNA modules which are known to be relevant in biological context. We also demonstrate the algorithm for scientific collaboration network, character interaction network of the novel Les Miserables, neural network of C. elegans and email communication network among employees of a company. In all these cases this new algorithm could efficiently detect all the relevant modules, particularly the small ones which are very strongly connected.

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.

In a recent work [arXiv:1307.1382] it was pointed out that the link-weight distribution of microRNA (miRNA) 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 miRNAs. 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., 5 pages, 5 eps figures

A recent study of conserved Manna model, with both discrete and continuous variable, indicates that absorbing phase transitions therein belong to the directed percolation (DP) universality class. In this context we revisit critical behaviour in energy exchange models with a threshold. Contrary to the previous claims [PRE 83, 061130 (2011), arXiv:1102.1631], our results indicate that both the maximal and minimal versions of this model belong to the DP class.

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., Comment: article (4 pages, 9 eps figures) + Supplement (8 pages, 9 eps figures); Phys. Rev. Lett. 2012

Human micro RNAs (miRNAs) target about 90% of the coding genes and form a complex regulatory network. We study the community structure of the miRNA co-target network considering miRNAs as the nodes which are connected by weighted links. The weight of link that connects a pair of miRNAs denote the total number of common transcripts targeted by that pair. We argue that the network consists of about 74 modules, quite similar to the components (or clusters) obtained earlier [Online J Bioinformatics, 10,280 ], indicating that the components of the miRNA co-target network are self organized in a way to maximize the modularity., 7 Pages, 4 eps figures

We introduce a bond percolation procedure on a $D$-dimensional lattice where two neighbouring sites are connected by $N$ channels, each operated by valves at both ends. Out of a total of $N$, randomly chosen $n$ valves are open at every site. A bond is said to connect two sites if there is at least one channel between them, which has open valves at both ends. We show analytically that in all spatial dimensions, this system undergoes a discontinuous percolation transition in the $N\to \infty$ limit when $\gamma =\frac{\ln n}{\ln N}$ crosses a threshold. It must be emphasized that, in contrast to the ordinary percolation models, here the transition occurs even in one dimensional systems, albeit discontinuously. We also show that a special kind of discontinuous percolation occurs only in one dimension when $N$ depends on the system size., Comment: 6 pages, 6 eps figures

In a recent Letter Ciftci and Cakmak [EPL 87, 60003 (2009)] showed that the two dimensional random walk in a bounded domain, where walkers which cross the boundary return to a base curve near origin with deterministic rules, can produce regular patterns. Our numerical calculations suggest that the cumulative probability distribution function of the returning walkers along the base curve is a Devil's staircase, which can be explained from the mapping of these walks to a non-linear stochastic map. The non-trivial probability distribution function(PDF) is a universal feature of CCRW characterized by the fractal dimension d=1.75(0) of the PDF bounding curve., 4 pages, 7 eps figures, revtex4

We study the asymmetric exclusion process on a regular Cayley tree with arbitrary co-ordination number. In this model particles can enter the system only at the parent site and exit from one of the sites at the last level. In the bulk they move from the occupied sites to one of their unoccupied downward neighbours, chosen randomly. We show that the steady state current that flow from one level to the next is independent of the exit rate, and increase monotonically with the entry rate and the co-ordination number. Unlike TASEP, the model has only one phase and the density profile show no boundary layers. We argue that in blood, air or water circulations systems branching is essential to maintain a free flow within the system which is independent of exit rates., 8 pages, 3 eps figures