Antiviral activity of human OASL protein is mediated by enhancing signaling of the RIG-I RNA sensor.

Virus infection is sensed in the cytoplasm by retinoic acid-inducible gene I (RIG-I, also known as DDX58), which requires RNA and polyubiquitin binding to induce type I interferon (IFN) and activate cellular innate immunity. We show that the human IFN-inducible oligoadenylate synthetases-like (OASL) protein has antiviral activity and mediates RIG-I activation by mimicking polyubiquitin. Loss of OASL expression reduced RIG-I signaling and enhanced virus replication in human cells. Conversely, OASL expression suppressed replication of a number of viruses in a RIG-I-dependent manner and enhanced RIG-I-mediated IFN induction. OASL interacted and colocalized with RIG-I, and through its C-terminal ubiquitin-like domain specifically enhanced RIG-I signaling. Bone-marrow-derived macrophages from mice deficient for Oasl2 showed that among the two mouse orthologs of human OASL, Oasl2 is functionally similar to human OASL. Our findings show a mechanism by which human OASL contributes to host antiviral responses by enhancing RIG-I activation.

Circadian transcription factor NPAS2 and the NAD+ -dependent deacetylase SIRT1 interact in the mouse nucleus accumbens and regulate reward.

Substance use disorders are associated with disruptions to both circadian rhythms and cellular metabolic state. At the molecular level, the circadian molecular clock and cellular metabolic state may be interconnected through interactions with the nicotinamide adenine dinucleotide (NAD+ )-dependent deacetylase, sirtuin 1 (SIRT1). In the nucleus accumbens (NAc), a region important for reward, both SIRT1 and the circadian transcription factor neuronal PAS domain protein 2 (NPAS2) are highly enriched, and both are regulated by the metabolic cofactor NAD+ . Substances of abuse, like cocaine, greatly disrupt cellular metabolism and promote oxidative stress; however, their effects on NAD+ in the brain remain unclear. Interestingly, cocaine also induces NAc expression of both NPAS2 and SIRT1, and both have independently been shown to regulate cocaine reward in mice. However, whether NPAS2 and SIRT1 interact in the NAc and/or whether together they regulate reward is unknown. Here, we demonstrate diurnal expression of Npas2, Sirt1 and NAD+ in the NAc, which is altered by cocaine-induced upregulation. Additionally, co-immunoprecipitation reveals NPAS2 and SIRT1 interact in the NAc, and cross-analysis of NPAS2 and SIRT1 chromatin immunoprecipitation sequencing reveals several reward-relevant and metabolic-related pathways enriched among shared gene targets. Notably, NAc-specific Npas2 knock-down or a functional Npas2 mutation in mice attenuates SIRT1-mediated increases in cocaine preference. Together, our data reveal an interaction between NPAS2 and SIRT1 in the NAc, which may serve to integrate cocaine's effects on circadian and metabolic factors, leading to regulation of drug reward.

Flow blockage disrupts cilia-driven fluid transport in the epileptic brain.

A carpet of ependymal motile cilia lines the brain ventricular system, forming a network of flow channels and barriers that pattern cerebrospinal fluid (CSF) flow at the surface. This CSF transport system is evolutionary conserved, but its physiological function remains unknown. Here we investigated its potential role in epilepsy with studies focused on CDKL5 deficiency disorder (CDD), a neurodevelopmental disorder with early-onset epilepsy refractory to seizure medications and the most common cause of infant epilepsy. CDKL5 is a highly conserved X-linked gene suggesting its function in regulating cilia length and motion in the green alga Chlamydomonas might have implication in the etiology of CDD. Examination of the structure and function of airway motile cilia revealed both the CDD patients and the Cdkl5 knockout mice exhibit cilia lengthening and abnormal cilia motion. Similar defects were observed for brain ventricular cilia in the Cdkl5 knockout mice. Mapping ependymal cilia generated flow in the ventral third ventricle (v3V), a brain region with important physiological functions showed altered patterning of flow. Tracing of cilia-mediated inflow into v3V with fluorescent dye revealed the appearance of a flow barrier at the inlet of v3V in Cdkl5 knockout mice. Analysis of mice with a mutation in another epilepsy-associated kinase, Yes1, showed the same disturbance of cilia motion and flow patterning. The flow barrier was also observed in the Foxj1± and FOXJ1CreERT:Cdkl5y/fl mice, confirming the contribution of ventricular cilia to the flow disturbances. Importantly, mice exhibiting altered cilia-driven flow also showed increased susceptibility to anesthesia-induced seizure-like activity. The cilia-driven flow disturbance arises from altered cilia beating orientation with the disrupted polarity of the cilia anchoring rootlet meshwork. Together these findings indicate motile cilia disturbances have an essential role in CDD-associated seizures and beyond, suggesting cilia regulating kinases may be a therapeutic target for medication-resistant epilepsy.

Global genetic analysis in mice unveils central role for cilia in congenital heart disease.

Congenital heart disease (CHD) is the most prevalent birth defect, affecting nearly 1% of live births; the incidence of CHD is up to tenfold higher in human fetuses. A genetic contribution is strongly suggested by the association of CHD with chromosome abnormalities and high recurrence risk. Here we report findings from a recessive forward genetic screen in fetal mice, showing that cilia and cilia-transduced cell signalling have important roles in the pathogenesis of CHD. The cilium is an evolutionarily conserved organelle projecting from the cell surface with essential roles in diverse cellular processes. Using echocardiography, we ultrasound scanned 87,355 chemically mutagenized C57BL/6J fetal mice and recovered 218 CHD mouse models. Whole-exome sequencing identified 91 recessive CHD mutations in 61 genes. This included 34 cilia-related genes, 16 genes involved in cilia-transduced cell signalling, and 10 genes regulating vesicular trafficking, a pathway important for ciliogenesis and cell signalling. Surprisingly, many CHD genes encoded interacting proteins, suggesting that an interactome protein network may provide a larger genomic context for CHD pathogenesis. These findings provide novel insights into the potential Mendelian genetic contribution to CHD in the fetal population, a segment of the human population not well studied. We note that the pathways identified show overlap with CHD candidate genes recovered in CHD patients, suggesting that they may have relevance to the more complex genetics of CHD overall. These CHD mouse models and >8,000 incidental mutations have been sperm archived, creating a rich public resource for human disease modelling.

Benchmark Evaluation of Protein-Protein Interaction Prediction Algorithms.

Protein-protein interactions (PPIs) perform various functions and regulate processes throughout cells. Knowledge of the full network of PPIs is vital to biomedical research, but most of the PPIs are still unknown. As it is infeasible to discover all of them experimentally due to technical and resource limitations, computational prediction of PPIs is essential and accurately assessing the performance of algorithms is required before further application or translation. However, many published methods compose their evaluation datasets incorrectly, using a higher proportion of positive class data than occuring naturally, leading to exaggerated performance. We re-implemented various published algorithms and evaluated them on datasets with realistic data compositions and found that their performance is overstated in original publications; with several methods outperformed by our control models built on 'illogical' and random number features. We conclude that these methods are influenced by an over-characterization of some proteins in the literature and due to scale-free nature of PPI network and that they fail when tested on all possible protein pairs. Additionally, we found that sequence-only-based algorithms performed worse than those that employ functional and expression features. We present a benchmark evaluation of many published algorithms for PPI prediction. The source code of our implementations and the benchmark datasets created here are made available in open source.

Parkinson's disease and schizophrenia interactomes contain temporally distinct gene clusters underlying comorbid mechanisms and unique disease processes.

Genome-wide association studies suggest significant overlaps in Parkinson's disease (PD) and schizophrenia (SZ) risks, but the underlying mechanisms remain elusive. The protein-protein interaction network ('interactome') plays a crucial role in PD and SZ and can incorporate their spatiotemporal specificities. Therefore, to study the linked biology of PD and SZ, we compiled PD- and SZ-associated genes from the DisGeNET database, and constructed their interactomes using BioGRID and HPRD. We examined the interactomes using clustering and enrichment analyses, in conjunction with the transcriptomic data of 26 brain regions spanning foetal stages to adulthood available in the BrainSpan Atlas. PD and SZ interactomes formed four gene clusters with distinct temporal identities (Disease Gene Networks or 'DGNs'1-4). DGN1 had unique SZ interactome genes highly expressed across developmental stages, corresponding to a neurodevelopmental SZ subtype. DGN2, containing unique SZ interactome genes expressed from early infancy to adulthood, correlated with an inflammation-driven SZ subtype and adult SZ risk. DGN3 contained unique PD interactome genes expressed in late infancy, early and late childhood, and adulthood, and involved in mitochondrial pathways. DGN4, containing prenatally-expressed genes common to both the interactomes, involved in stem cell pluripotency and overlapping with the interactome of 22q11 deletion syndrome (comorbid psychosis and Parkinsonism), potentially regulates neurodevelopmental mechanisms in PD-SZ comorbidity. Our findings suggest that disrupted neurodevelopment (regulated by DGN4) could expose risk windows in PD and SZ, later elevating disease risk through inflammation (DGN2). Alternatively, variant clustering in DGNs may produce disease subtypes, e.g., PD-SZ comorbidity with DGN4, and early/late-onset SZ with DGN1/DGN2.

Schizophrenia interactome derived repurposable drugs and randomized control trials of two candidates.

There is a substantial unmet need for effective and patient-acceptable drugs to treat severe mental illnesses like schizophrenia. Computational analysis of genomic, transcriptomic, and pharmacologic data generated in the past two decades enables repurposing of drugs or compounds with acceptable safety profiles, namely those that are FDA-approved or reached late stages in clinical trials. We developed a rational approach to achieve this computationally for schizophrenia by studying drugs that target the proteins in its protein interaction network ('interactome'). This involved contrasting the transcriptomic modulations observed in the disorder and the drug; our analyses resulted in 12 candidate drugs, 9 of which had additional supportive evidence: their target networks were enriched for pathways relevant to schizophrenia etiology or for genes that had an association with diseases pathogenically similar to schizophrenia. To translate these computational results to the clinic, these shortlisted drugs must be tested empirically through randomized controlled trials (RCT), where their prior safety approvals obviate the need for time-consuming phase I and II studies. We selected two among the shortlisted candidates based on likely adherence and side effect profiles. We are testing them through adjunctive RCTs for patients with schizophrenia or schizoaffective disorder who experienced incomplete resolution of psychotic features with conventional treatment. The integrated computational analysis for identifying and ranking drugs for clinical trials can be iterated as additional data are obtained. Our approach could be expanded to enable disease subtype-specific drug discovery in future and should also be exploited for other psychiatric disorders.

Novel Protein-Protein Interactions Highlighting the Crosstalk between Hypoplastic Left Heart Syndrome, Ciliopathies and Neurodevelopmental Delays.

Hypoplastic left heart syndrome (HLHS) is a severe congenital heart disease (CHD) affecting 1 in 5000 newborns. We constructed the interactome of 74 HLHS-associated genes identified from a large-scale mouse mutagenesis screen, augmenting it with 408 novel protein-protein interactions (PPIs) using our High-Precision Protein-Protein Interaction Prediction (HiPPIP) model. The interactome is available on a webserver with advanced search capabilities. A total of 364 genes including 73 novel interactors were differentially regulated in tissue/iPSC-derived cardiomyocytes of HLHS patients. Novel PPIs facilitated the identification of TOR signaling and endoplasmic reticulum stress modules. We found that 60.5% of the interactome consisted of housekeeping genes that may harbor large-effect mutations and drive HLHS etiology but show limited transmission. Network proximity of diabetes, Alzheimer's disease, and liver carcinoma-associated genes to HLHS genes suggested a mechanistic basis for their comorbidity with HLHS. Interactome genes showed tissue-specificity for sites of extracardiac anomalies (placenta, liver and brain). The HLHS interactome shared significant overlaps with the interactomes of ciliopathy- and microcephaly-associated genes, with the shared genes enriched for genes involved in intellectual disability and/or developmental delay, and neuronal death pathways, respectively. This supported the increased burden of ciliopathy variants and prevalence of neurological abnormalities observed among HLHS patients with developmental delay and microcephaly, respectively.

Uncompensated mitochondrial oxidative stress underlies heart failure in an iPSC-derived model of congenital heart disease.

Hypoplastic left heart syndrome (HLHS) is a severe congenital heart disease with 30% mortality from heart failure (HF) in the first year of life, but the cause of early HF remains unknown. Induced pluripotent stem-cell-derived cardiomyocytes (iPSC-CM) from patients with HLHS showed that early HF is associated with increased apoptosis, mitochondrial respiration defects, and redox stress from abnormal mitochondrial permeability transition pore (mPTP) opening and failed antioxidant response. In contrast, iPSC-CM from patients without early HF showed normal respiration with elevated antioxidant response. Single-cell transcriptomics confirmed that early HF is associated with mitochondrial dysfunction accompanied with endoplasmic reticulum (ER) stress. These findings indicate that uncompensated oxidative stress underlies early HF in HLHS. Importantly, mitochondrial respiration defects, oxidative stress, and apoptosis were rescued by treatment with sildenafil to inhibit mPTP opening or TUDCA to suppress ER stress. Together these findings point to the potential use of patient iPSC-CM for modeling clinical heart failure and the development of therapeutics.

N-gram analysis of 970 microbial organisms reveals presence of biological language models.

BACKGROUND: It has been suggested previously that genome and proteome sequences show characteristics typical of natural-language texts such as "signature-style" word usage indicative of authors or topics, and that the algorithms originally developed for natural language processing may therefore be applied to genome sequences to draw biologically relevant conclusions. Following this approach of 'biological language modeling', statistical n-gram analysis has been applied for comparative analysis of whole proteome sequences of 44 organisms. It has been shown that a few particular amino acid n-grams are found in abundance in one organism but occurring very rarely in other organisms, thereby serving as genome signatures. At that time proteomes of only 44 organisms were available, thereby limiting the generalization of this hypothesis. Today nearly 1,000 genome sequences and corresponding translated sequences are available, making it feasible to test the existence of biological language models over the evolutionary tree. RESULTS: We studied whole proteome sequences of 970 microbial organisms using n-gram frequencies and cross-perplexity employing the Biological Language Modeling Toolkit and Patternix Revelio toolkit. Genus-specific signatures were observed even in a simple unigram distribution. By taking statistical n-gram model of one organism as reference and computing cross-perplexity of all other microbial proteomes with it, cross-perplexity was found to be predictive of branch distance of the phylogenetic tree. For example, a 4-gram model from proteome of Shigellae flexneri 2a, which belongs to the Gammaproteobacteria class showed a self-perplexity of 15.34 while the cross-perplexity of other organisms was in the range of 15.59 to 29.5 and was proportional to their branching distance in the evolutionary tree from S. flexneri. The organisms of this genus, which happen to be pathotypes of E.coli, also have the closest perplexity values with E. coli. CONCLUSION: Whole proteome sequences of microbial organisms have been shown to contain particular n-gram sequences in abundance in one organism but occurring very rarely in other organisms, thereby serving as proteome signatures. Further it has also been shown that perplexity, a statistical measure of similarity of n-gram composition, can be used to predict evolutionary distance within a genus in the phylogenetic tree.

Generalized and social anxiety disorder interactomes show distinctive overlaps with striosome and matrix interactomes.

Mechanisms underlying anxiety disorders remain elusive despite the discovery of several associated genes. We constructed the protein-protein interaction networks (interactomes) of six anxiety disorders and noted enrichment for striatal expression among common genes in the interactomes. Five of these interactomes shared distinctive overlaps with the interactomes of genes that were differentially expressed in two striatal compartments (striosomes and matrix). Generalized anxiety disorder and social anxiety disorder interactomes showed exclusive and statistically significant overlaps with the striosome and matrix interactomes, respectively. Systematic gene expression analysis with the anxiety disorder interactomes constrained to contain only those genes that were shared with striatal compartment interactomes revealed a bifurcation among the disorders, which was influenced by the anterior cingulate cortex, nucleus accumbens, amygdala and hippocampus, and the dopaminergic signaling pathway. Our results indicate that the functionally distinct striatal pathways constituted by the striosome and the matrix may influence the etiological differentiation of various anxiety disorders.

Malignant Pleural Mesothelioma Interactome with 364 Novel Protein-Protein Interactions.

Malignant pleural mesothelioma (MPM) is an aggressive cancer affecting the outer lining of the lung, with a median survival of less than one year. We constructed an 'MPM interactome' with over 300 computationally predicted protein-protein interactions (PPIs) and over 2400 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from Biological General Repository for Interaction Datasets (BioGRID) and Human Protein Reference Database (HPRD). Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, and gene expression in microarray experiments, and classifies the pairwise features as interacting or non-interacting based on a random forest model. We validated five novel predicted PPIs experimentally. The interactome is significantly enriched with genes differentially ex-pressed in MPM tumors compared with normal pleura and with other thoracic tumors, genes whose high expression has been correlated with unfavorable prognosis in lung cancer, genes differentially expressed on crocidolite exposure, and exosome-derived proteins identified from malignant mesothelioma cell lines. 28 of the interactors of MPM proteins are targets of 147 U.S. Food and Drug Administration (FDA)-approved drugs. By comparing disease-associated versus drug-induced differential expression profiles, we identified five potentially repurposable drugs, namely cabazitaxel, primaquine, pyrimethamine, trimethoprim and gliclazide. Preclinical studies may be con-ducted in vitro to validate these computational results. Interactome analysis of disease-associated genes is a powerful approach with high translational impact. It shows how MPM-associated genes identified by various high throughput studies are functionally linked, leading to clinically translatable results such as repurposed drugs. The PPIs are made available on a webserver with interactive user interface, visualization and advanced search capabilities.

Active learning for human protein-protein interaction prediction.

BACKGROUND: Biological processes in cells are carried out by means of protein-protein interactions. Determining whether a pair of proteins interacts by wet-lab experiments is resource-intensive; only about 38,000 interactions, out of a few hundred thousand expected interactions, are known today. Active machine learning can guide the selection of pairs of proteins for future experimental characterization in order to accelerate accurate prediction of the human protein interactome. RESULTS: Random forest (RF) has previously been shown to be effective for predicting protein-protein interactions. Here, four different active learning algorithms have been devised for selection of protein pairs to be used to train the RF. With labels of as few as 500 protein-pairs selected using any of the four active learning methods described here, the classifier achieved a higher F-score (harmonic mean of Precision and Recall) than with 3000 randomly chosen protein-pairs. F-score of predicted interactions is shown to increase by about 15% with active learning in comparison to that with random selection of data. CONCLUSION: Active learning algorithms enable learning more accurate classifiers with much lesser labelled data and prove to be useful in applications where manual annotation of data is formidable. Active learning techniques demonstrated here can also be applied to other proteomics applications such as protein structure prediction and classification.

Active machine learning for transmembrane helix prediction.

BACKGROUND: About 30% of genes code for membrane proteins, which are involved in a wide variety of crucial biological functions. Despite their importance, experimentally determined structures correspond to only about 1.7% of protein structures deposited in the Protein Data Bank due to the difficulty in crystallizing membrane proteins. Algorithms that can identify proteins whose high-resolution structure can aid in predicting the structure of many previously unresolved proteins are therefore of potentially high value. Active machine learning is a supervised machine learning approach which is suitable for this domain where there are a large number of sequences but only very few have known corresponding structures. In essence, active learning seeks to identify proteins whose structure, if revealed experimentally, is maximally predictive of others. RESULTS: An active learning approach is presented for selection of a minimal set of proteins whose structures can aid in the determination of transmembrane helices for the remaining proteins. TMpro, an algorithm for high accuracy TM helix prediction we previously developed, is coupled with active learning. We show that with a well-designed selection procedure, high accuracy can be achieved with only few proteins. TMpro, trained with a single protein achieved an F-score of 94% on benchmark evaluation and 91% on MPtopo dataset, which correspond to the state-of-the-art accuracies on TM helix prediction that are achieved usually by training with over 100 training proteins. CONCLUSION: Active learning is suitable for bioinformatics applications, where manually characterized data are not a comprehensive representation of all possible data, and in fact can be a very sparse subset thereof. It aids in selection of data instances which when characterized experimentally can improve the accuracy of computational characterization of remaining raw data. The results presented here also demonstrate that the feature extraction method of TMpro is well designed, achieving a very good separation between TM and non TM segments.

Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs.

World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://hagrid.dbmi.pitt.edu/corona that allows not only keyword search but also queries such as "PPIs where one protein is associated with 'virus' and the interactors with 'pulmonary'".

A reference catalog of DNA palindromes in the human genome and their variations in 1000 Genomes.

A palindrome in DNA is like a palindrome in language, but when read backwards, it is a complement of the forward sequence; effectively, the two halves of a sequence complement each other from its midpoint like in a double strand of DNA. Palindromes are distributed throughout the human genome and play significant roles in gene expression and regulation. Palindromic mutations are linked to many human diseases, such as neuronal disorders, mental retardation, and various cancers. In this work, we computed and analyzed the palindromic sequences in the human genome and studied their conservation in personal genomes using 1000 Genomes data. We found that ~30% of the palindromes exhibit variation, some of which are caused by rare variants. The analysis of disease/trait-associated single-nucleotide polymorphisms in palindromic regions showed that disease-associated risk variants are 14 times more likely to be present in palindromic regions than in other regions. The catalog of palindromes in the reference genome and 1000 Genomes is being made available here with details on their variations in each individual genome to serve as a resource for future and retrospective whole-genome studies identifying statistically significant palindrome variations associated with diseases or traits and their roles in disease mechanisms.

Cilia interactome with predicted protein-protein interactions reveals connections to Alzheimer's disease, aging and other neuropsychiatric processes.

Cilia are dynamic microtubule-based organelles present on the surface of many eukaryotic cell types and can be motile or non-motile primary cilia. Cilia defects underlie a growing list of human disorders, collectively called ciliopathies, with overlapping phenotypes such as developmental delays and cognitive and memory deficits. Consistent with this, cilia play an important role in brain development, particularly in neurogenesis and neuronal migration. These findings suggest that a deeper systems-level understanding of how ciliary proteins function together may provide new mechanistic insights into the molecular etiologies of nervous system defects. Towards this end, we performed a protein-protein interaction (PPI) network analysis of known intraflagellar transport, BBSome, transition zone, ciliary membrane and motile cilia proteins. Known PPIs of ciliary proteins were assembled from online databases. Novel PPIs were predicted for each ciliary protein using a computational method we developed, called High-precision PPI Prediction (HiPPIP) model. The resulting cilia "interactome" consists of 165 ciliary proteins, 1,011 known PPIs, and 765 novel PPIs. The cilia interactome revealed interconnections between ciliary proteins, and their relation to several pathways related to neuropsychiatric processes, and to drug targets. Approximately 184 genes in the cilia interactome are targeted by 548 currently approved drugs, of which 103 are used to treat various diseases of nervous system origin. Taken together, the cilia interactome presented here provides novel insights into the relationship between ciliary protein dysfunction and neuropsychiatric disorders, for e.g. interconnections of Alzheimer's disease, aging and cilia genes. These results provide the framework for the rational design of new therapeutic agents for treatment of ciliopathies and neuropsychiatric disorders.

Potentially repurposable drugs for schizophrenia identified from its interactome.

We previously presented the protein-protein interaction network of schizophrenia associated genes, and from it, the drug-protein interactome which showed the drugs that target any of the proteins in the interactome. Here, we studied these drugs further to identify whether any of them may potentially be repurposable for schizophrenia. In schizophrenia, gene expression has been described as a measurable aspect of the disease reflecting the action of risk genes. We studied each of the drugs from the interactome using the BaseSpace Correlation Engine, and shortlisted those that had a negative correlation with differential gene expression of schizophrenia. This analysis resulted in 12 drugs whose differential gene expression (drug versus normal) had an anti-correlation with differential expression for schizophrenia (disorder versus normal). Some of these drugs were already being tested for their clinical activity in schizophrenia and other neuropsychiatric disorders. Several proteins in the protein interactome of the targets of several of these drugs were associated with various neuropsychiatric disorders. The network of genes with opposite drug-induced versus schizophrenia-associated expression profiles were significantly enriched in pathways relevant to schizophrenia etiology and GWAS genes associated with traits or diseases that had a pathophysiological overlap with schizophrenia. Drugs that targeted the same genes as the shortlisted drugs, have also demonstrated clinical activity in schizophrenia and other related disorders. This integrated computational analysis will help translate insights from the schizophrenia drug-protein interactome to clinical research - an important step, especially in the field of psychiatric drug development which faces a high failure rate.