Gianluca C. Kikidis

Most genetic variants associated with complex heritability phenotypes lie in non-coding regions and are thought to influence disease risk by regulating gene expression. However, most transcriptome-wide association approaches primarily model local ( cis ) genetic effects, leaving much of gene regulation unexplained. Here, we show that incorporating distal ( trans ) regulatory effects improves the prediction of gene expression and the identification of disease-associated genes. Using RNA sequencing data from six human post-mortem brain regions, we developed INGENE and MODULE, two models capturing the combined influence of candidate trans- acting variants within gene coexpression networks. Integrating these models with conventional cis -based predictors improved gene expression imputation (maximum likelihood estimation, α = 0.05) for 18,744 genes across regions. Applying this framework to Psychiatric Genomics Consortium wave 3 genotypes identified 766 genes associated with schizophrenia ( P _FDR < 0.01), including 641 not previously reported by transcriptome-wide analyses. These findings highlight the contribution of distal regulatory mechanisms and gene network interactions to schizophrenia risk.

Schizophrenia genetic and environmental risk factors play out largely in early life, biasing development toward a pathogenic trajectory that becomes clinically apparent in early adulthood, when the disorder typically onsets. Here, we convert snapshots of gene expression in postmortem brain at a moment in time into a dynamic lifetime series employing the "liquidity" metric, a novel tool to track the evolution of networks across time from a multi-systemic perspective. The landscape of normal prefrontal cortical development becomes increasingly "liquid" during the first two decades of post-natal life, with sharp discontinuities in known critical periods such as birth and adolescence. Neurotypical individuals free of apparent neuropathology with relatively elevated polygenic risk scores for schizophrenia exhibit a generalized delay in the dynamics of liquidity across these trajectories compared to below-average-risk individuals. Impacted biological processes strongly converge on delayed GABA-A receptor functional maturation, involved in establishing Excitatory/Inhibitory balance in brain during early development. Similar to patients with schizophrenia, neurotypical high-risk individuals show an increased expression ratio between the genes SLC12A2 (protein NKCC1) and SLC12A5 (protein KCC2) relative to low-risk, involved in the control of the equilibrium potential of chloride ions that regulates GABA-A function. These results provide evidence that genetic risk for schizophrenia is associated with a delayed maturational profile and delayed maturation of GABAergic signaling without detectable neuropathology and well before the age of clinical onset. Interestingly, the same effect is not observed in the hippocampus and is not observed with genetic risk for other neuropsychiatric and immune conditions. The dynamics of maturation of GABA-A signaling in the dorsolateral prefrontal cortex emerge as an early contributor to translating genetic risk into an altered developmental trajectory associated with schizophrenia.

Genetic risk for schizophrenia (SCZ) has been linked to cognitive performance before the onset age. We examined how SCZ-related polygenic risk and resilience variants, and their co-expression patterns in the human brain, were associated with cognitive abilities across development in 16,520 non-psychiatric European and African ancestry children and adults. SCZ risk showed significant negative associations with spatial, verbal, and working memory across ancestries (all t<-2, pFDR4, pFDR5.75, p=0.02) or juvenile (ΔAIC>5.8, p=0.03) networks. Cross-ancestry correlations (R=0.52, p<0.01) highlight replicability. These findings support the neurodevelopmental basis of SCZ, suggesting that risk and resilience variants influence cognition from early life, independent of symptoms and elucidate biological pathways through which SCZ risk may influence early cognitive development.

Non-coding genetic variants statistically associated with complex heritability phenotypes are thought to act primarily through transcriptome regulatory mechanisms. Predictions of gene expression in tissue like the human brain traditionally rely primarily on cis -eQTLs. Here, we introduce INGENE and MODULE, trans -eQTLs models designed to enhance the prediction of gene expression by capturing the collective impact of candidate trans -eQTLs acting within co-expression networks. Exploiting RNA-seq data in six post-mortem brain regions (amygdala, caudate nucleus, dorsal/subgenual anterior cingulate cortex, dorsolateral prefrontal cortex, and hippocampus), we validate our models on two testing datasets, demonstrating increased gene predictability compared to both an original cis -based model and to EpiXcan, the leading benchmark in cis -model performance. Integration of cis - and trans -predictions significantly improves gene-level expression imputation (MLE α= 0.05) for 18,744 genes across the six brain regions considered. Applying cis and trans models to PGC wave 3 genotypes identifies 766 SCZ-associated genes across brain regions (pFDR < .01), emphasizing the complementary nature of cis and trans predictions in trait association discovery. Of these genes, 641 represent novel transcriptome-wide associations with schizophrenia, highlighting the role of trans -heritability and genetic interactions underlying risk for this disorder, in addition to further supporting 125 previous candidates.

Background: Elevated non-high-density lipoprotein cholesterol (non-HDL-C) is a significant risk factor for atherosclerotic cardiovascular diseases, particularly in individuals with metabolic syndrome (MetS) and major psychiatric disorders (MPDs), who may experience metabolic side effects of psychopharmacological treatments. We evaluated the cholesterol-lowering effects of an experimental pasta characterized by a high content of phytosterols, arabinoxylans, polyunsaturated and monounsaturated fatty acids, and vitamin E in individuals with MetS, with and without MPDs. Methods: In a double-blind, randomized trial, 298 participants with MetS were assigned to consume either experimental or conventional pasta for 3 months. Non-HDL-C levels were measured at baseline and follow-up. A polygenic risk score for hypercholesterolemia (TC-PRS) was calculated to assess any genetic influence on the intervention’s efficacy. The cholesterol-lowering effect of the experimental pasta was also tested in vitro by exposing human hepatocarcinoma cells, which developed lipid storage alterations due to olanzapine (OLZ) exposure, to an extract of the flour mixture used to prepare the experimental pasta. Results: The participants who consumed the experimental pasta exhibited a significantly greater reduction in serum non-HDL-C levels compared to the control group (p = 0.001). No significant interaction between pasta variety and the TC-PRS on non-HDL-C changes was found. The extract from the experimental flour mixture significantly reduced both the number and size of lipid droplets in HepG2 cells treated with OLZ. Conclusions: These results indicate that a low-impact lifestyle intervention may offer a practical strategy for improving the cholesterol profile and mitigating cardiovascular risk in patients with MetS, with and without an MPD.

Understanding the genetic architecture of schizophrenia (SCZ) is invaluable for the development of personalized treatment. In three independent cohorts, using an automated pipeline, we calculated 413 psychiatry-relevant polygenic scores. Using these scores, machine learning was applied to stratify SCZ patients into two biotypes in FinnGen (n_SCZ=7486), and to validate these results in the PsyCourse Study (n_SCZ=421) and Bari (n_SCZ=531). While the two biotypes showed comparable polygenic SCZ risk, they were primarily distinguished by a greater predisposition for neuroticism, depression-related traits, and lower cognitive performance in Biotype 1. The genetic prediction of Biotype 1 was phenotypically characterized by an increased prevalence of SCZ and a more severe and complex clinical manifestation. This illustrates that the penetrance of genetic SCZ risk might partially depend on the predisposition for the aforementioned traits through pleiotropic variants. Our results provide novel, replicable insights into the genetic architecture of SCZ and might inform future personalized treatments.

Transcriptome and proteome sequencing of brain tissue homogenate has helped unravel processes underlying schizophrenia (SCZ). However, most studies have lacked granularity at the cell type level and have focused on individual brain regions, rather than examining expression dynamics across multiple regions or illness-relevant circuitries. We used laser capture microdissection to collect excitatory neuron-enriched samples from hippocampal subregions CA1 and presubiculum (SUB), and from dorsolateral prefrontal cortex (DLPFC), a circuit prominently implicated in schizophrenia. Using RNA sequencing and quantitative proteomics, we show significantly superior discrimination of brain regional identity in the transcriptomic (>90% accuracy) and proteomic data (>97% accuracy) compared with gene-level expression data (<70% in bulk). Patients with SCZ show hippocampal-specific differential protein phosphorylation. SCZ risk co-expression gene-sets that replicate across transcript and protein networks are enriched for transmembrane transporters in the DLPFC and CA1 and postsynaptic processes in the SUB. We demonstrate a strong directional connectivity effect of SCZ risk in that excitatory synaptic genes in CA1 unidirectionally predict gene expression in SUB. Finally, parallel CA1 snRNA-seq results suggest that in SCZ excitatory efferents in CA1 are affected by interactions with glia and by downregulation of inhibitory neuropeptide inputs. Our study proposes molecular mechanisms by which hippocampal communication, previously associated with SCZ at the macroscopic level, may be altered at the inter-field and interregional circuit level.

Cognitive dysfunctions are core-enduring symptoms of schizophrenia, with important sex-related differences. Genetic variants of the DTBPN1 gene associated with reduced dysbindin-1 protein (Dys) expression negatively impact cognitive functions in schizophrenia through a functional epistatic interaction with Catechol-O-methyltransferase (COMT). Dys is involved in the trafficking of dopaminergic receptors, crucial for prefrontal cortex (PFC) signaling regulation. Moreover, dopamine signaling is modulated by estrogens via inhibition of COMT expression. We hypothesized a sex dimorphism in Dys-related cognitive functions dependent on COMT and estrogen levels. Our multidisciplinary approach combined behavioral-molecular findings on genetically modified mice, human postmortem Dys expression data, and in vivo fMRI during a working memory task performance. We found cognitive impairments in male mice related to genetic variants characterized by reduced Dys protein expression (p_Bonferroni = 0.0001), as well as in male humans through a COMT/Dys functional epistatic interaction involving PFC brain activity during working memory (t(23) = −3.21; p_FDR = 0.004). Dorsolateral PFC activity was associated with lower working memory performance in males only (p = 0.04). Also, male humans showed decreased Dys expression in dorsolateral PFC during adulthood (p_FDR = 0.05). Female Dys mice showed preserved cognitive performances with deficits only with a lack of estrogen tested in an ovariectomy model (p_Bonferroni = 0.0001), suggesting that genetic variants reducing Dys protein expression could probably become functional in females when the protective effect of estrogens is attenuated, i.e., during menopause. Overall, our results show the differential impact of functional variants of the DTBPN1 gene interacting with COMT on cognitive functions across sexes in mice and humans, underlying the importance of considering sex as a target for patient stratification and precision medicine in schizophrenia.

Alterations in fMRI-based brain functional network connectivity (FNC) are associated with schizophrenia (SCZ) and the genetic risk or subthreshold clinical symptoms preceding the onset of SCZ, which often occurs in early adulthood. Thus, age-sensitive FNC changes may be relevant to SCZ risk-related FNC. We used independent component analysis to estimate FNC from childhood to adulthood in 9,236 individuals. To capture individual brain features more accurately than single-session fMRI, we studied an average of three fMRI scans per individual. To identify potential familial risk–related FNC changes, we compared age-related FNC in first-degree relatives of SCZ patients mostly including unaffected siblings (SIB) with neurotypical controls (NC) at the same age stage. Then, we examined how polygenic risk scores for SCZ influenced risk-related FNC patterns. Finally, we investigated the same risk-related FNC patterns in adult SCZ patients (oSCZ) and young individuals with subclinical psychotic symptoms (PSY). Age-sensitive risk-related FNC patterns emerge during adolescence and early adulthood, but not before. Young SIB always followed older NC patterns, with decreased FNC in a cerebellar–occipitoparietal circuit and increased FNC in two prefrontal–sensorimotor circuits when compared to young NC. Two of these FNC alterations were also found in oSCZ, with one exhibiting reversed pattern. All were linked to polygenic risk for SCZ in unrelated individuals (R²varied from 0.02 to 0.05). Young PSY showed FNC alterations in the same direction as SIB when compared to NC. These results suggest that age-related neurotypical FNC correlates with genetic risk for SCZ and is detectable with MRI in young participants.