A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models

Idiopathic pulmonary fibrosis (IPF) is an aggressive interstitial lung disease with a high mortality rate. Putative drug targets in IPF have failed to translate into effective therapies at the clinical level. We identify TRAF2- and NCK-interacting kinase (TNIK) as an anti-fibrotic target using a predictive artificial intelligence (AI) approach. Using AI-driven methodology, we generated INS018_055, a small-molecule TNIK inhibitor, which exhibits desirable drug-like properties and anti-fibrotic activity across different organs in vivo through oral, inhaled or topical administration. INS018_055 possesses anti-inflammatory effects in addition to its anti-fibrotic profile, validated in multiple in vivo studies. Its safety and tolerability as well as pharmacokinetics were validated in a randomized, double-blinded, placebo-controlled phase I clinical trial (NCT05154240) involving 78 healthy participants. A separate phase I trial in China, CTR20221542, also demonstrated comparable safety and pharmacokinetic profiles. This work was completed in roughly 18 months from target discovery to preclinical candidate nomination and demonstrates the capabilities of our generative AI-driven drug-discovery pipeline.

PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery

PandaOmics is a cloud-based software platform that applies artificial intelligence and bioinformatics techniques to multimodal omics and biomedical text data for therapeutic target and biomarker discovery. PandaOmics generates novel and repurposed therapeutic target and biomarker hypotheses with the desired properties and is available through licensing or collaboration. Targets and biomarkers generated by the platform were previously validated in both in vitro and in vivo studies. PandaOmics is a core component of Insilico Medicine's Pharma.ai drug discovery suite, which also includes Chemistry42 for the de novo generation of novel small molecules, and inClinico─a data-driven multimodal platform that forecasts a clinical trial's probability of successful transition from phase 2 to phase 3. In this paper, we demonstrate how the PandaOmics platform can efficiently identify novel molecular targets and biomarkers for various diseases.

A comprehensive AI-driven analysis of large-scale omic datasets reveals novel dual-purpose targets for the treatment of cancer and aging

As aging and tumorigenesis are tightly interconnected biological processes, targeting their common underlying driving pathways may induce dual-purpose anti-aging and anti-cancer effects. Our transcriptomic analyses of 16,740 healthy samples demonstrated tissue-specific age-associated gene expression, with most tumor suppressor genes downregulated during aging. Furthermore, a large-scale pan-cancer analysis of 11 solid tumor types (11,303 cases and 4431 control samples) revealed that many cellular processes, such as protein localization, DNA replication, DNA repair, cell cycle, and RNA metabolism, were upregulated in cancer but downregulated in healthy aging tissues, whereas pathways regulating cellular senescence were upregulated in both aging and cancer. Common cancer targets were identified by the AI-driven target discovery platform-PandaOmics. Age-associated cancer targets were selected and further classified into four groups based on their reported roles in lifespan. Among the 51 identified age-associated cancer targets with anti-aging experimental evidence, 22 were proposed as dual-purpose targets for anti-aging and anti-cancer treatment with the same therapeutic direction. Among age-associated cancer targets without known lifespan-regulating activity, 23 genes were selected based on predicted dual-purpose properties. Knockdown of histone demethylase KDM1A, one of these unexplored candidates, significantly extended lifespan in Caenorhabditis elegans. Given KDM1A's anti-cancer activities reported in both preclinical and clinical studies, our findings propose KDM1A as a promising dual-purpose target. This is the first study utilizing an innovative AI-driven approach to identify dual-purpose target candidates for anti-aging and anti-cancer treatment, supporting the value of AI-assisted target identification for drug discovery.

Applying Artificial Intelligence to Identify Common Targets for Treatment of Asthma, Eczema, and Food Allergy

INTRODUCTION

Allergic disorders are common diseases marked by the abnormal immune response toward foreign antigens that are not pathogens. Often patients with food allergy also suffer from asthma and eczema. Given the similarities of these diseases and a shortage of effective treatments, developing novel therapeutics against common targets of multiple allergies would offer an efficient and cost-effective treatment for patients.

METHODS

We employed the artificial intelligence-driven target discovery platform, PandaOmics, to identify common targets for treating asthma, eczema, and food allergy. Thirty-two case-control comparisons were generated from 15, 11, and 6 transcriptomics datasets related to asthma (558 cases, 315 controls), eczema (441 cases, 371 controls), and food allergy (208 cases, 106 controls), respectively, and allocated into three meta-analyses for target identification. Top-100 high-confidence targets and Top-100 novel targets were prioritized by PandaOmics for each allergic disease.

RESULTS

Six common high-confidence targets (i.e., IL4R, IL5, JAK1, JAK2, JAK3, and NR3C1) across all three allergic diseases have approved drugs for treating asthma and eczema. Based on the targets' dysregulated expression profiles and their mechanism of action in allergic diseases, three potential therapeutic targets were proposed. IL5 was selected as a high-confidence target due to its strong involvement in allergies. PTAFR was identified for drug repurposing, while RNF19B was selected as a novel target for therapeutic innovation. Analysis of the dysregulated pathways commonly identified across asthma, eczema, and food allergy revealed the well-characterized disease signature and novel biological processes that may underlie the pathophysiology of allergies.

CONCLUSION

Altogether, our study dissects the shared pathophysiology of allergic disorders and reveals the power of artificial intelligence in the exploration of novel therapeutic targets.

Multiomic prediction of therapeutic targets for human diseases associated with protein phase separation

The phenomenon of protein phase separation (PPS) underlies a wide range of cellular functions. Correspondingly, the dysregulation of the PPS process has been associated with numerous human diseases. To enable therapeutic interventions based on the regulation of this association, possible targets should be identified. For this purpose, we present an approach that combines the multiomic PandaOmics platform with the FuzDrop method to identify PPS-prone disease-associated proteins. Using this approach, we prioritize candidates with high PandaOmics and FuzDrop scores using a profiling method that accounts for a wide range of parameters relevant for disease mechanism and pharmacological intervention. We validate the differential phase separation behaviors of three predicted Alzheimer's disease targets (MARCKS, CAMKK2, and p62) in two cell models of this disease. Overall, the approach that we present generates a list of possible therapeutic targets for human diseases associated with the dysregulation of the PPS process.

Biomedical generative pre-trained based transformer language model for age-related disease target discovery

Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.

AI-powered therapeutic target discovery

Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.

Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery

Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, we propose a novel approach to multimodal aging clock we call Precious1GPT utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. While the accuracy of the multimodal transformer is lower within each individual data type compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform.

Identification of dual-purpose therapeutic targets implicated in aging and glioblastoma multiforme using PandaOmics - an AI-enabled biological target discovery platform

Glioblastoma Multiforme (GBM) is the most aggressive and most common primary malignant brain tumor. The age of GBM patients is considered as one of the disease's negative prognostic factors and the mean age of diagnosis is 62 years. A promising approach to preventing both GBM and aging is to identify new potential therapeutic targets that are associated with both conditions as concurrent drivers. In this work, we present a multi-angled approach of identifying targets, which takes into account not only the disease-related genes but also the ones important in aging. For this purpose, we developed three strategies of target identification using the results of correlation analysis augmented with survival data, differences in expression levels and previously published information of aging-related genes. Several studies have recently validated the robustness and applicability of AI-driven computational methods for target identification in both cancer and aging-related diseases. Therefore, we leveraged the AI predictive power of the PandaOmics TargetID engine in order to rank the resulting target hypotheses and prioritize the most promising therapeutic gene targets. We propose cyclic nucleotide gated channel subunit alpha 3 (CNGA3), glutamate dehydrogenase 1 (GLUD1) and sirtuin 1 (SIRT1) as potential novel dual-purpose therapeutic targets to treat aging and GBM.

AlphaFold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor

The application of artificial intelligence (AI) has been considered a revolutionary change in drug discovery and development. In 2020, the AlphaFold computer program predicted protein structures for the whole human genome, which has been considered a remarkable breakthrough in both AI applications and structural biology. Despite the varying confidence levels, these predicted structures could still significantly contribute to structure-based drug design of novel targets, especially the ones with no or limited structural information. In this work, we successfully applied AlphaFold to our end-to-end AI-powered drug discovery engines, including a biocomputational platform PandaOmics and a generative chemistry platform Chemistry42. A novel hit molecule against a novel target without an experimental structure was identified, starting from target selection towards hit identification, in a cost- and time-efficient manner. PandaOmics provided the protein of interest for the treatment of hepatocellular carcinoma (HCC) and Chemistry42 generated the molecules based on the structure predicted by AlphaFold, and the selected molecules were synthesized and tested in biological assays. Through this approach, we identified a small molecule hit compound for cyclin-dependent kinase 20 (CDK20) with a binding constant Kd value of 9.2 ± 0.5 μM (n = 3) within 30 days from target selection and after only synthesizing 7 compounds. Based on the available data, a second round of AI-powered compound generation was conducted and through this, a more potent hit molecule, ISM042-2-048, was discovered with an average Kd value of 566.7 ± 256.2 nM (n = 3). Compound ISM042-2-048 also showed good CDK20 inhibitory activity with an IC50 value of 33.4 ± 22.6 nM (n = 3). In addition, ISM042-2-048 demonstrated selective anti-proliferation activity in an HCC cell line with CDK20 overexpression, Huh7, with an IC50 of 208.7 ± 3.3 nM, compared to a counter screen cell line HEK293 (IC50 = 1706.7 ± 670.0 nM). This work is the first demonstration of applying AlphaFold to the hit identification process in drug discovery.

High-confidence cancer patient stratification through multiomics investigation of DNA repair disorders

Multiple cancer types have limited targeted therapeutic options, in part due to incomplete understanding of the molecular processes underlying tumorigenesis and significant intra- and inter-tumor heterogeneity. Identification of novel molecular biomarkers stratifying cancer patients with different survival outcomes may provide new opportunities for target discovery and subsequent development of tailored therapies. Here, we applied the artificial intelligence-driven PandaOmics platform ( https://pandaomics.com/ ) to explore gene expression changes in rare DNA repair-deficient disorders and identify novel cancer targets. Our analysis revealed that CEP135, a scaffolding protein associated with early centriole biogenesis, is commonly downregulated in DNA repair diseases with high cancer predisposition. Further screening of survival data in 33 cancers available at TCGA database identified sarcoma as a cancer type where lower survival was significantly associated with high CEP135 expression. Stratification of cancer patients based on CEP135 expression enabled us to examine therapeutic targets that could be used for the improvement of existing therapies against sarcoma. The latter was based on application of the PandaOmics target-ID algorithm coupled with in vitro studies that revealed polo-like kinase 1 (PLK1) as a potential therapeutic candidate in sarcoma patients with high CEP135 levels and poor survival. While further target validation is required, this study demonstrated the potential of in silico-based studies for a rapid biomarker discovery and target characterization.

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with ill-defined pathogenesis, calling for urgent developments of new therapeutic regimens. Herein, we applied PandaOmics, an AI-driven target discovery platform, to analyze the expression profiles of central nervous system (CNS) samples (237 cases; 91 controls) from public datasets, and direct iPSC-derived motor neurons (diMNs) (135 cases; 31 controls) from Answer ALS. Seventeen high-confidence and eleven novel therapeutic targets were identified and will be released onto ALS.AI (http://als.ai/). Among the proposed targets screened in the c9ALS Drosophila model, we verified 8 unreported genes (KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA) whose suppression strongly rescues eye neurodegeneration. Dysregulated pathways identified from CNS and diMN data characterize different stages of disease development. Altogether, our study provides new insights into ALS pathophysiology and demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.

Hallmarks of aging-based dual-purpose disease and age-associated targets predicted using PandaOmics AI-powered discovery engine

Aging biology is a promising and burgeoning research area that can yield dual-purpose pathways and protein targets that may impact multiple diseases, while retarding or possibly even reversing age-associated processes. One widely used approach to classify a multiplicity of mechanisms driving the aging process is the hallmarks of aging. In addition to the classic nine hallmarks of aging, processes such as extracellular matrix stiffness, chronic inflammation and activation of retrotransposons are also often considered, given their strong association with aging. In this study, we used a variety of target identification and prioritization techniques offered by the AI-powered PandaOmics platform, to propose a list of promising novel aging-associated targets that may be used for drug discovery. We also propose a list of more classical targets that may be used for drug repurposing within each hallmark of aging. Most of the top targets generated by this comprehensive analysis play a role in inflammation and extracellular matrix stiffness, highlighting the relevance of these processes as therapeutic targets in aging and age-related diseases. Overall, our study reveals both high confidence and novel targets associated with multiple hallmarks of aging and demonstrates application of the PandaOmics platform to target discovery across multiple disease areas.

Massive interstitial copy-neutral loss-of-heterozygosity as evidence for cancer being a disease of the DNA-damage response

Background

The presence of loss-of-heterozygosity (LOH) mutations in cancer cell genomes is commonly encountered. Moreover, the occurrences of LOHs in tumor suppressor genes play important roles in oncogenesis. However, because the causative mechanisms underlying LOH mutations in cancer cells yet remain to be elucidated, enquiry into the nature of these mechanisms based on a comprehensive examination of the characteristics of LOHs in multiple types of cancers has become a necessity.

Methods

We performed next-generation sequencing on inter-Alu sequences of five different types of solid tumors and acute myeloid leukemias, employing the AluScan platform which entailed amplification of such sequences using multiple PCR primers based on the consensus sequences of Alu elements; as well as the whole genome sequences of a lung-to-liver metastatic cancer and a primary liver cancer. Paired-end sequencing reads were aligned to the reference human genome to identify major and minor alleles so that the partition of LOH products between homozygous-major vs. homozygous-minor alleles could be determined at single-base resolution. Strict filtering conditions were employed to avoid false positives. Measurements of LOH occurrences in copy number variation (CNV)-neutral regions were obtained through removal of CNV-associated LOHs.

Results

We found: (a) average occurrence of copy-neutral LOHs amounting to 6.9 % of heterologous loci in the various cancers; (b) the mainly interstitial nature of the LOHs; and (c) preference for formation of homozygous-major over homozygous-minor, and transitional over transversional, LOHs.

Conclusions

The characteristics of the cancer LOHs, observed in both AluScan and whole genome sequencings, point to the formation of LOHs through repair of double-strand breaks by interhomolog recombination, or gene conversion, as the consequence of a defective DNA-damage response, leading to a unified mechanism for generating the mutations required for oncogenesis as well as the progression of cancer cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-015-0104-2) contains supplementary material, which is available to authorized users.

Glioma Association and Balancing Selection of ZFPM2

ZFPM2, encoding a zinc finger protein and abundantly expressed in the brain, uterus and smooth muscles, plays important roles in cardiac and gonadal development. Abnormal expression of ZFPM2 in ovarian tumors and neuroblastoma has been reported but hitherto its genetic association with cancer and effects on gliomas have not been studied. In the present study, the hexamer insertion-deletion polymorphism rs71305152, located within a large haplotype block spanning intron 1 to intron 3 of ZFPM2, was genotyped in Chinese cohorts of glioma (n = 350), non-glioma cancer (n = 354) and healthy control (n = 463) by direct sequencing and length polymorphism in gel electrophoresis, and ZFPM2 expression in glioma tissues (n = 69) of different grades was quantified by real-time RT-PCR. Moreover, potential natural selection pressure acting on the gene was investigated. Disease-association analysis showed that the overall genotype of rs71305152 was significantly associated with gliomas (P = 0.016), and the heterozygous genotype compared to the combined homozygous genotypes was less frequent in gliomas than in controls (P = 0.005) or non-glioma cancers (P = 0.020). ZFPM2 mRNA expression was negatively correlated with the grades of gliomas (P = 0.002), with higher expression levels in the low-grade gliomas. In the astrocytoma subtype, higher ZFPM2 expression was also correlated with the rs71305152 heterozygous genotype (P = 0.028). In addition, summary statistics tests gave highly positive values, demonstrating that the gene is under the influence of balancing selection. These findings suggest that ZFPM2 is a glioma susceptibility gene, its genotype and expression showing associations with incidence and severity, respectively. Moreover, the balancing selection acting on ZFPM2 may be related to the important roles it has to play in multiple organ development or associated disease etiology.

Social cognitive role of schizophrenia candidate gene GABRB2

The occurrence of positive selection in schizophrenia-associated GABRB2 suggests a broader impact of the gene product on population fitness. The present study considered the possibility of cognition-related GABRB2 involvement by examining the association of GABRB2 with psychosis and altruism, respectively representing psychiatric and psychological facets of social cognition. Four single nucleotide polymorphisms (SNPs) were genotyped for quantitative trait analyses and population-based association studies. Psychosis was measured by either the Positive and Negative Syndrome Scale (PANSS) or antipsychotics dosage, and altruism was based on a self-report altruism scale. The minor alleles of SNPs rs6556547, rs1816071 and rs187269 in GABRB2 were correlated with high PANSS score for positive symptoms in a Han Chinese schizophrenic cohort, whereas those of rs1816071 and rs1816072 were associated with high antipsychotics dosage in a US Caucasian schizophrenic cohort. Moreover, strongly significant GABRB2-disease associations were found among schizophrenics with severe psychosis based on high PANSS positive score, but no significant association was observed for schizophrenics with only mild psychosis. Interestingly, in addition to association with psychosis in schizophrenics, rs187269 was also associated with altruism in healthy Han Chinese. Furthermore, parallel to correlation with severe psychosis, its minor allele was correlated with high altruism scores. These findings revealed that GABRB2 is associated with psychosis, the core symptom and an endophenotype of schizophrenia. Importantly, the association was found across the breadth of the psychiatric (psychosis) to psychological (altruism) spectrum of social cognition suggesting GABRB2 involvement in human cognition.

Epigenetic regulation on GABRB2 isoforms expression: developmental variations and disruptions in psychotic disorders

INTRODUCTION

To improve the understanding of psychotic abnormalities and their non-Mendelian inheritance patterns, the epigenetic regulation of the psychotic disorder-associated GABRB2, gene for the type A γ-aminobutyric acid receptor β(2)-subunit, was investigated.

METHODS

Expression of GABRB2, and the epigenetic regulatory enzymes histone deacetylases (HDACs) and DNA methyltransferases (DNMTs) in mouse and postmortem human brains was analyzed using real-time PCR.

RESULTS

Results showed that expression of GABRB2 isoforms significantly increased over time in both mouse and human, especially for the long splicing isoform. In the brains of non-psychiatric controls (CON), a significant positive correlation of GABRB2 expression with age was observed in individuals with MM genotypes of the single nucleotide polymorphisms (SNPs) rs187269 and rs1816072. This was reversed to a significant negative correlation in schizophrenics (SCZ). A similar reversal was also displayed by bipolar disorder (BPD) patients. In parallel, a significant co-variation of HDAC1 with GABRB2 expression observed in CON remained significant in BPD but not in SCZ; comparably, a significant co-variation of HDAC2 with GABRB2 expression observed in CON became non-significant in both SCZ and BPD. Moreover, co-variations of DNMT1 and DNMT3B with GABRB2, not observable in CON, became significant in BPD.

CONCLUSION

These findings demonstrated that GABRB2 expression was under epigenetic regulation that varied with development, genotype and disease status, and these regulatory mechanisms were observably disrupted in SCZ and BPD. This study provided insight into the complex inheritance patterns of psychiatric disorders, and pointed to the involvement of epigenetic dysregulation in the disease process of major psychotic disorders.

AluScan: a method for genome-wide scanning of sequence and structure variations in the human genome

BACKGROUND

To complement next-generation sequencing technologies, there is a pressing need for efficient pre-sequencing capture methods with reduced costs and DNA requirement. The Alu family of short interspersed nucleotide elements is the most abundant type of transposable elements in the human genome and a recognized source of genome instability. With over one million Alu elements distributed throughout the genome, they are well positioned to facilitate genome-wide sequence amplification and capture of regions likely to harbor genetic variation hotspots of biological relevance.

RESULTS

Here we report on the use of inter-Alu PCR with an enhanced range of amplicons in conjunction with next-generation sequencing to generate an Alu-anchored scan, or 'AluScan', of DNA sequences between Alu transposons, where Alu consensus sequence-based 'H-type' PCR primers that elongate outward from the head of an Alu element are combined with 'T-type' primers elongating from the poly-A containing tail to achieve huge amplicon range. To illustrate the method, glioma DNA was compared with white blood cell control DNA of the same patient by means of AluScan. The over 10 Mb sequences obtained, derived from more than 8,000 genes spread over all the chromosomes, revealed a highly reproducible capture of genomic sequences enriched in genic sequences and cancer candidate gene regions. Requiring only sub-micrograms of sample DNA, the power of AluScan as a discovery tool for genetic variations was demonstrated by the identification of 357 instances of loss of heterozygosity, 341 somatic indels, 274 somatic SNVs, and seven potential somatic SNV hotspots between control and glioma DNA.

CONCLUSIONS

AluScan, implemented with just a small number of H-type and T-type inter-Alu PCR primers, provides an effective capture of a diversity of genome-wide sequences for analysis. The method, by enabling an examination of gene-enriched regions containing exons, introns, and intergenic sequences with modest capture and sequencing costs, computation workload and DNA sample requirement is particularly well suited for accelerating the discovery of somatic mutations, as well as analysis of disease-predisposing germline polymorphisms, by making possible the comparative genome-wide scanning of DNA sequences from large human cohorts.

Imprinting in the schizophrenia candidate gene GABRB2 encoding GABA(A) receptor β(2) subunit

Schizophrenia is a complex genetic disorder, the inheritance pattern of which is likely complicated by epigenetic factors yet to be elucidated. In this study, transmission disequilibrium tests with family trios yielded significant differences between paternal and maternal transmissions of the disease-associated single-nucleotide polymorphism (SNP) rs6556547 and its haplotypes. The minor allele (T) of rs6556547 was paternally undertransmitted to male schizophrenic offsprings, and this parent-of-origin effect strongly suggested that GABRB2 is imprinted. 'Flipping' of allelic expression in heterozygotes of SNP rs2229944 (C/T) in GABRB2 or rs2290732 (G/A) in the neighboring GABRA1 was compatible with imprinting effects on gene expression. Clustering analysis of GABRB2 mRNA expressions suggested that imprinting brought about the observed two-tiered distribution of expression levels in controls with heterozygous genotype at the disease-associated SNP rs1816071 (A/G). The deficit of upper-tiered expressions accounted for the lowered expression levels in the schizophrenic heterozygotes. The occurrence of a two-tiered distribution furnished support for imprinting, and also pointed to the necessity of differentiating between two kinds of heterozygotes of different parental origins in disease association studies on GABRB2. Bisulfite sequencing revealed hypermethylation in the neighborhood of SNP rs1816071, and methylation differences between controls and schizophrenia patients. Notably, the two schizophrenia-associated SNPs rs6556547 and rs1816071 overlapped with a CpG dinucleotide, thereby opening the possibility that CpG methylation status of these sites could have an impact on the risk of schizophrenia. Thus multiple lines of evidence pointed to the occurrence of imprinting in the GABRB2 gene and its possible role in the development of schizophrenia.

Alternative-splicing in the exon-10 region of GABA(A) receptor beta(2) subunit gene: relationships between novel isoforms and psychotic disorders

Background

Non-coding single nucleotide polymorphisms (SNPs) in GABRB2, the gene for β₂-subunit of gamma-aminobutyric acid type A (GABA_A) receptor, have been associated with schizophrenia (SCZ) and quantitatively correlated to mRNA expression and alternative splicing.

Methods and Findings

Expression of the Exon 10 region of GABRB2 from minigene constructs revealed this region to be an “alternative splicing hotspot” that readily gave rise to differently spliced isoforms depending on intron sequences. This led to a search in human brain cDNA libraries, and the discovery of two novel isoforms, β_2S1 and β_2S2, bearing variations in the neighborhood of Exon-10. Quantitative real-time PCR analysis of postmortem brain samples showed increased β_2S1 expression and decreased β_2S2 expression in both SCZ and bipolar disorder (BPD) compared to controls. Disease-control differences were significantly correlated with SNP rs187269 in BPD males for both β_2S1 and β_2S2 expressions, and significantly correlated with SNPs rs2546620 and rs187269 in SCZ males for β_2S2 expression. Moreover, site-directed mutagenesis indicated that Thr³⁶⁵, a potential phosphorylation site in Exon-10, played a key role in determining the time profile of the ATP-dependent electrophysiological current run-down.

Conclusion

This study therefore provided experimental evidence for the importance of non-coding sequences in the Exon-10 region in GABRB2 with respect to β₂-subunit splicing diversity and the etiologies of SCZ and BPD.

Positive selection within the Schizophrenia-associated GABA(A) receptor beta(2) gene

The gamma-aminobutyric acid type-A (GABA_A) receptor plays a major role in inhibitory neurotransmissions. Intronic SNPs and haplotypes in GABRB2, the gene for GABA_A receptor β₂ subunit, are associated with schizophrenia and correlated with the expression of two alternatively spliced β₂ isoforms. In the present study, using chimpanzee as an ancestral reference, high frequencies were observed for the derived (D) alleles of the four SNPs rs6556547, rs187269, rs1816071 and rs1816072 in GABRB2, suggesting the occurrence of positive selection for these derived alleles. Coalescence-based simulation showed that the population frequency spectra and the frequencies of H56, the haplotype having all four D alleles, significantly deviated from neutral-evolution expectation in various demographic models. Haplotypes containing the derived allele of rs1816072 displayed significantly less diversity compared to haplotypes containing its ancestral allele, further supporting positive selection. The variations in DD-genotype frequencies in five human populations provided a snapshot of the evolutionary history, which suggested that the positive selections of the D alleles are recent and likely ongoing. The divergence between the DD-genotype profiles of schizophrenic and control samples pointed to the schizophrenia-relevance of positive selections, with the schizophrenic samples showing weakened selections compared to the controls. These DD-genotypes were previously found to increase the expression of β₂, especially its long isoform. Electrophysiological analysis showed that this long β₂ isoform favored by the positive selections is more sensitive than the short isoform to the inhibition of GABA_A receptor function by energy depletion. These findings represent the first demonstration of positive selection in a schizophrenia-associated gene.

GABRB2 association with schizophrenia: commonalities and differences between ethnic groups and clinical subtypes

BACKGROUND

Single nucleotide polymorphisms (SNPs) and haplotypes in intron 8 of type A gamma-aminobutyric acid (GABA(A)) receptor beta2 subunit gene (GABRB2) were initially found to be associated with schizophrenia in Chinese. This finding was subjected to cross-validation in this study with Japanese (JP) and German Caucasian (GE) subjects.

METHODS

Single nucleotide polymorphisms discovery and genotyping were carried out through resequencing of a 1839 base pair (bp) region in GABRB2. Tagging SNPs (tSNPs) were selected based on linkage disequilibrium (LD), combinations of which were analyzed with Bonferroni correction and permutation for disease association. Random resampling was applied to generate size- and gender-balanced cases and control subjects.

RESULTS

Out of the 17 SNPs (9.2/kilobase [kb]) revealed, 6 were population-specific. Population variations in LD were observable, and at least two low LD points were identified in both populations. Although disease association at single SNP level was only shown in GE, strong association was demonstrated in both JP (p = .0002 - .0191) and GE (p = .0033 - .0410) subjects, centering on haplotypes containing rs1816072 and rs1816071. Among different clinical subtypes, the most significant association was exhibited by systematic schizophrenia.

CONCLUSIONS

Cross-population validation of GABRB2 association with schizophrenia has been obtained with JP and GE subjects, with the genotype-disease correlations being strongest in systematic schizophrenia, the most severe subtype of the disease.

Two isoforms of GABA(A) receptor beta2 subunit with different electrophysiological properties: Differential expression and genotypical correlations in schizophrenia

Single nucleotide polymorphisms in type A gamma-aminobutyric acid (GABA(A)) receptor beta2 subunit gene (GABRB2) were found to be associated with schizophrenia in Chinese, German, Japanese and Portuguese. To explore potential functional consequences of these DNA sequence polymorphisms, this study examined the expression and electrophysiological properties of two alternatively spliced products of GABRB2 along with genotypical disease association analysis. Real-time quantitative polymerase chain reaction, performed with a cohort of 31 schizophrenics and 31 controls of US population, showed 21.7% reduction in the expression of the long isoform beta(2L), 13.4% in the short isoform beta(2S) and 15.8% in the sum of the two isoforms beta(2T) in postmortem schizophrenic brain. Furthermore, two independent mRNA quantitation methods showed that the relative expression of the long over the short isoforms was significantly decreased, suggesting the occurrence of altered splicing, in schizophrenia. In male schizophrenics, the heterozygous genotypes of rs1876071 (T/C) and rs1876072 (A/G) were correlated with reduced expression of beta(2L), beta(2S) and beta(2T), and the heterozygous of rs2546620 (A/G) and homozygous-minor of rs1876071 (C/C) and rs1876072 (G/G) were correlated with reduced expression of beta(2T). Significant correlations of expression levels with different alleles and haplotypes were also indicated by quantitative trait analysis. Recombinant GABA(A) receptors expressed in HEK293 human cells containing beta(2L) underwent a steeper current rundown upon repetitive GABA activation than receptors containing beta(2S). The results thus revealed genotype-dependent expression of the alternatively spliced isoforms of GABA(A) receptor beta2 subunit, giving rise to electrophysiological consequences that could play an important role in the pathogenesis mechanism of schizophrenia.