
==== Front
Schizophrenia (Heidelb)
Schizophrenia (Heidelb)
Schizophrenia
2754-6993
Nature Publishing Group UK London

488
10.1038/s41537-024-00488-8
Article
Gene expression changes in Brodmann’s Area 46 differentiate epidermal growth factor and immune system interactions in schizophrenia and mood disorders
Ketharanathan Tharini tharini.ketharanathan@unimelb.edu.au

123
Pereira Avril 12
http://orcid.org/0000-0002-9674-0227
Sundram Suresh 45
1 grid.1008.9 0000 0001 2179 088X The Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC 3052 Australia
2 https://ror.org/01ej9dk98 grid.1008.9 0000 0001 2179 088X Department of Psychiatry, University of Melbourne, Parkville, VIC 3052 Australia
3 https://ror.org/009k7c907 grid.410684.f 0000 0004 0456 4276 Northern Health, Epping, VIC 3076 Australia
4 https://ror.org/02bfwt286 grid.1002.3 0000 0004 1936 7857 Department of Psychiatry, School of Clinical Sciences, Monash University, Clayton, VIC 3168 Australia
5 https://ror.org/02t1bej08 grid.419789.a 0000 0000 9295 3933 Mental Health Program, Monash Health, Clayton, VIC 3168 Australia
6 9 2024
6 9 2024
2024
10 1 7625 11 2022
16 7 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
How early in life stress-immune related environmental factors increase risk predisposition to schizophrenia remains unknown. We examined if pro-inflammatory changes perturb the brain epidermal growth factor (EGF) system, a system critical for neurodevelopment and mature CNS functions including synaptic plasticity. We quantified genes from key EGF and immune system pathways for mRNA levels and eight immune proteins in post-mortem dorsolateral prefrontal (DLPFC; Brodmann’s Area (BA) 46) and orbitofrontal (OFC; BA11) cortices from people with schizophrenia, mood disorders and neurotypical controls. In BA46, 64 genes were differentially expressed, predominantly in schizophrenia, where attenuated expression of the MAPK-ERK, NRG1-PI3K-AKT and mTOR cascades indicated reduced EGF system signalling, and similarly diminished immune molecular expression, notably in TLR, TNF and complement pathways, along with low NF-κB1 and elevated IL12RB2 protein levels were noted. There was nominal evidence for altered convergence between ErbB-PI3K-AKT-mTOR and TLR pathways in BA46 in schizophrenia. Comparatively minimal changes were noted in BA11. Overall, distinct pathway gene expression changes may reflect variant pathological processes involving immune and EGF system signalling between schizophrenia and mood disorder, particularly in DLPFC. Further, the abnormal convergence between innate immune signalling and candidate EGF signalling pathways may indicate a pathologically important interaction in the developing brain in response to environmental stressors.

Subject terms

Schizophrenia
Molecular neuroscience
issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

Schizophrenia is a persistent, complex, heterogeneous neuropsychiatric disorder demonstrating inter- and intra-individual clinical variation. Genetic heritability of ~80% is a major risk factor for schizophrenia but with a polygenic architecture1. In addition, a concordance rate of ~50% in monozygotic twins2 highlights a multifactorial aetiology involving environmental factors in risk predisposition accounting for at least one-third of the variance in the liability for schizophrenia3. Here they act both as stand-alone risk factors and also interact with susceptibility genes to modify risk4. Such environmental factors range from those that operate early in life, such as maternal infections and nutritional deficiencies, and obstetric and neonatal complications, that likely impact the developing foetus or neonate; to those later in life such as early adversities and cannabis use5, but the mechanism/s that confer these risks remain unclear. One postulate is that these widely heterogenous early environmental risk factors may operate through early neurodevelopment by coalescing their effects through immune activation3,6–8. Activated immune mechanisms may then continue to operate postnatally into adulthood in conjunction with late environmental stressors leading to a chronically stimulated immune state7. In support of this, an immune active state has been detected in schizophrenia patients both peripherally and in brain through multiple lines of investigation9–11. Numerous putative mechanisms have been proposed to mediate chronic immune activity on mature CNS functions such as adult synaptogenesis and plasticity, neurogenesis and myelination7. These include C4-mediated synaptic pruning12, and dysfunctional microglia-mediated pathogenic synaptic formation, stripping and pruning11; or indirectly, through the reelin system13 or the regulation of DNA methylation14 which were shown to impact adult hippocampal neurogenesis13,14; the monoaminergic system15; activation of the HPA axis16 and release of glucocorticoids15,16; or growth factor systems17,18.

In this regard, the epidermal growth factor (EGF) system is of interest because it is involved in neuronal and glial cell differentiation, proliferation, survival and regulation of myelination, neurotransmitter function, and synaptic plasticity19–21. Converging evidence from genetic association, clinical and human post-mortem brain studies implicates EGF system disturbance in schizophrenia22–24. Specifically, two ligands, neuregulin124–27 and EGF23,28, their respective cognate receptors, ErbB424,25,29 and ErbB1 (EGFR)21,23 and downstream signalling molecules30,31 have been implicated. Moreover, we have shown that the antipsychotic drug, clozapine, signals through the EGF system32,33, while EGF and immune signalling markers have been identified among schizophrenia-associated loci in some GWAS34.

Considering this evidence, we speculated if pro-inflammatory changes triggered by environmental insults may plausibly influence the brain EGF system in early development and during post-birth CNS maturation, through to adulthood, affecting brain functions including synaptic plasticity. We previously tested this in a precocial rodent model (Acomys cahirinus) where poly I:C induced maternal immune activation (MIA) in utero resulted in elevated NF-κB1 in prepubescent offspring indicating delayed neuroinflammation, in concert with altered EGFR and PI3K-110δ protein levels in subcortical regions17. This occurred in tandem with schizophrenia-like behaviours such as decreased pre-pulse inhibition, and deficits in social interaction, memory and learning35. These findings were extended in Mus musculus to show that robust adult cognitive deficits paralleling those seen in schizophrenia were dependent on the timing of MIA and associated with Neuregulin1 and EGF signalling downregulation36. Together, these data suggest that the EGF system may be an implicated pathway between MIA and adult behavioural changes consonant with schizophrenia.

To test these rodent findings in schizophrenia, we examined post-mortem human brain tissue from people with schizophrenia, mood disorders as psychiatric controls, and neurotypical controls. We used a candidate pathway and gene approach to examine major EGF and immune signalling pathways and examined two frontal cortical regions implicated in these disorders, the dorsolateral prefrontal cortex (Brodmann’s Area 46, BA46) and the orbitofrontal cortex (BA11), to determine any specificity in changes between those two regions. We used real-time quantitative polymerase chain reaction (RT-qPCR) to determine whether representative genes in EGF and immune signalling pathways were differentially expressed and if there was aberrant convergence between these pathways in schizophrenia, focusing on hub molecules for the two systems. We then validated findings by protein estimation of selected molecules with western immunoblotting.

Results

Demographic, clinical and tissue quality data

The demographic, clinical and tissue quality data of the sample cohort are shown in Table 1 (see Supplementary Results—Table S2 for full data set). There were no significant differences in age at death, duration of illness, brain pH, post-mortem interval (PMI), freezer time or RNA integrity number (RIN) between the diagnostic groups. There were significantly more females in the mood disorder group compared to the other two groups and they also had a significantly older age of illness onset and shorter duration of illness than the schizophrenia group. Further, patients who died by suicide had a significantly shorter duration of illness compared to those who died by natural means.Table 1 Demographic, clinical and tissue quality markers in the sample cohort.

Variable	Healthy control	Schizophrenia	Mood disorder	Significance	
N	18	20	30		
Age at death	58.8 ± 10.4	50.9 ± 17.6	51.2 ± 17.6	p = 0.1655	
Sex	
 - Male	15 (83.3%)	16 (80.0%)	15 (50.0%)	p = 0.021	
 - Female	3 (16.7%)	4 (20.0%)	15 (50.0%)		
Age at onset		30.4 ± 13.31	38.4 ± 14.87	p = 0.0429	
Duration of illness		21.1 ± 14.79	15.0 ± 9.92	p = 0.1569	
Brain pH	6.4 ± 0.24	6.3 ± 0.26	6.5 ± 0.26	p = 0.1015	
PMI (hrs)	45.6 ± 13.03	46.9 ± 14.0	42.2 ± 16.05	p = 0.5100	
Freezer time (days)	2345 ± 609.2	2740 ± 641.2	2601 ± 793.9	p = 0.2269	
RIN (BA46)	7.3 ± 0.56	7.1 ± 1.12	7.4 ± 0.83	p = 0.4510	
RIN (BA11)	7.6 ± 0.78	7.1 ± 0.95	7.3 ± 0.82	p = 0.4979	
Mode of death - Suicide	0 (0%)	7 (35.0%)	30 (100%)		
  - Non-suicide	18 (100%)	13 (65.0%)	0 (0%)	p < 0.0005	
Data presented as mean ± SD.

PMI post mortem interval, RIN RNA integrity number.

mRNA expression of EGF and immune system genes in BA46

Of the 114 genes quantified for mRNA, six genes, C4B, EPGN, IFNG, IL12B, IL2 and IL4 were eliminated from analysis due to non-detects in a significant proportion of samples across all groups.

Among the rest, after preliminary analysis there were 68 (63%) significantly differentially expressed genes (DEG) across the diagnostic groups.

After gene expression were covaried with age, brain pH, PMI and RIN 64 genes remained differentially expressed (Table 2; Fig. 1). All except for 4 were significantly varied in schizophrenia in comparison to healthy controls, while about two-third of the total DEG (n = 42) were differentially expressed only in schizophrenia. Compared to this, only 30% of the DEG (n = 19) were changed in the mood disorder cohort, compared to healthy controls, all shared with schizophrenia except for ERBB4. All DEG were downregulated in the patient groups relative to healthy controls, except ERBB4 which was upregulated in the mood disorder group. Forty-three of the DEG (67%) were significantly altered between the patient groups, with all of them exhibiting the lowest expression in schizophrenia. More information on genes and pathways can be found in Supplementary Results (Figs. S1–S7), where DEGs are shown in schematic pathway diagrams, downloaded and modified with permission from KEGG37.Table 2 Summary of differentially expressed genes from BA46 RT-qPCR findings following covariation with age, brain pH, PMI and RIN.

Pathways	Gene	Mean expression [2−ΔCt]	Multiple comparisons	
HC	SCZ	MD	p value	SCZ–HC (p)	MD–HC (p)	SCZ–MD (p)	
EGF system	BTC	0.006	0.003	0.005	0.16	0.005			
	ERBB4	2.815	2.921	3.938	0.04		0.014		
	HBEGF	0.356	0.254	0.422	0.026	0.022		0.014	
	NRG1	0.022	0.009	0.017	0.032	0.011			
	NRG1 type III	0.284	0.13	0.223	<0.0001	<0.0001		0.001	
	NRG3	2.212	1.41	1.811	0.0051	0.0069		0.0354	
MAPK-RAS	BRAF	3.478	1.759	2.723	0.002	0.001		0.005	
	GRB2	2.685	2.81	3.31	0.0098			0.0211	
	RAPGEF2	7.442	3.507	4.819	0.0002	0.0001		0.0395	
	RASA1	1.056	0.579	0.810	0.001	0.001		0.001	
ErbB/EGFR	STAT5B	1.865	1.419	1.936	0.002	0.007		0.001	
	PAK2	1.497	1.104	1.280	0.014	0.004			
PI3K-AKT	BAD	0.345	0.203	0.262	0.0027	0.0018			
	CREB1	4.89	1.783	3.458	0.0164	0.0144			
	GSK3A	18.090	9.290	15.470	0.0013	0.0036		0.0051	
	PIK3CA	0.835	0.495	0.634	0.041	0.012			
	PIK3CD	0.124	0.100	0.135	0.049	0.047		0.023	
	PIK3R1	22.490	12.830	16.170	<0.0001	<0.0001	0.0019	0.0254	
	PPP2CA	27.810	8.736	16.980	<0.0001	<0.0001		0.001	
	PTEN	0.780	0.591	0.726	0.004	0.012		0.002	
mTOR	MLST8	1.227	0.605	0.840	<0.0001	<0.0001	0.042	<0.0001	
	MTOR	5.928	3.858	4.946	<0.0001	<0.0001		0.004	
	RICTOR	0.600	0.375	0.523	<0.0001	<0.0001		<0.0001	
	RPTOR	1.492	1.022	1.274	<0.0001	<0.0001	0.017	0.001	
	TSC1	6.151	3.113	4.255	<0.0001	<0.0001	0.021	0.001	
TNF signalling	BAG4	1.479	0.675	1.165	<0.0001	<0.0001	0.015	<0.0001	
	DUSP1	16.090	4.902	8.935	0.003	0.002			
	IKBKAP	1.339	0.580	0.924	<0.0001	<0.0001	0.012	<0.0001	
	MADD	5.619	4.066	4.990	0.0044	0.003			
	RAC1	9.679	3.123	5.841	<0.0001	<0.0001	0.0166	0.0047	
	RIPK1	0.251	0.084	0.120	<0.0001	<0.0001	<0.0001	0.017	
	TNFRSF1A	0.173	0.091	0.086	0.002	0.001	0.01		
TLR	ECSIT	23.690	3.255	7.564	<0.0001	<0.0001	0.012	0.0171	
	RAC1a								
	RIPK1a								
	TAB2	3.352	2.300	2.889	0.028	0.024		0.015	
	TICAM1	0.091	0.056	0.078	0.0029	0.003			
	TIRAP	0.053	0.018	0.040	0.004	0.001		0.04	
	TLR3	0.034	0.017	0.022	0.0004	0.0009	0.0016		
	TOLLIP	19.990	10.180	13.940	<0.0001	<0.0001	0.0124	0.048	
	TRAF6	4.702	1.687	2.641	0.0002	<0.0001	0.0334		
JAK-STAT	JAK1	8.902	6.047	7.321	0.0025	0.0017			
	SOCS1	0.057	0.054	0.050	0.0062	0.0123		0.0205	
	STAT1	2.558	1.970	2.500	0.009	0.004		0.019	
	STAT2	1.975	1.551	1.795	0.005	0.002		0.008	
	STAT5Ba								
NF-κB signalling	BCL2	1.210	0.507	0.676	<0.0001	<0.0001	0.01	0.004	
	CHUK	0.078	0.067	0.083	0.013			0.003	
	IKBKB	0.093	0.059	0.080	0.001	<0.0001		0.008	
	IKBKG	0.606	0.493	0.620	0.007			0.002	
	NFKB1	0.476	0.250	0.287	0.001	<0.0001		0.015	
	RIPK1a								
	TRAF6a								
Complement system	C1QB	0.220	0.105	0.164	0.015	0.008		0.016	
	CIQC	0.853	0.390	0.633	0.029	0.0312			
	C3	0.950	0.414	0.702	0.002	<0.0001	0.049	0.035	
	C4A	0.156	0.070	0.076	0.016	0.007	0.019		
Cytokine signalling	CXCL8	0.106	0.172	0.049	0.0124	0.0122			
	IFNA1	0.009	0.005	0.007	0.0155	0.0114			
	IFNAR1	1.047	0.828	1.044	0.003	0.003		0.002	
	IFNAR2	0.583	0.426	0.612	<0.0001	<0.0001		<0.0001	
	IFNGR1	0.496	0.387	0.458	0.041	0.028		0.026	
	IFNGR2	1.475	1.135	1.323	0.0004	0.0017		0.0017	
	IL10RA	0.062	0.023	0.047	<0.0001	<0.0001		0.0014	
	IL12A	0.117	0.050	0.083	0.0047	0.0096		0.0163	
	IL12RB2	0.437	0.299	0.425	0.004	0.002		0.006	
	IL2RB	0.019	0.006	0.012	0.0012	0.0009	0.0235		
p38 MAPK	DAXX	0.339	0.155	0.244	0.0001	<0.0001	0.0444		
MAPK-JNK	MAP3K1	0.432	0.271	0.407	0.0277	0.0226			
SCZ schizophrenia, HC healthy controls, MD mood disorder, TLR toll like receptor, TNF tumour necrosis factor.

aGenes represented in multiple pathways; for statistical data see first iteration.

Fig. 1 Venn diagram—distribution of the differentially expressed genes in the BA46 in the patient groups relative to controls.

HC healthy control, SCZ schizophrenia, MD mood disorder (Please note that the diagram shows only 61 genes as 3 genes were differentially expressed only between SCZ and MD).

mRNA expression of EGF and immune system genes in BA11

In OFC, among the 105 genes quantified for mRNA utilising 192.24 Dynamic Array IFCs (after eliminating those undetected or with very low expression in BA46, namely CASP3, CRADD, EPGN, IFNG, IL12B, IL12RB1, IL2, IL4, MDM2), ten genes were significantly differentially expressed across the diagnostic groups and among them three (3%), IKBKG, TICAM1 and PIK3CD, remained significant, following Benjamini–Hochberg correction for multiple testing (Table 3). These genes were unaffected by age, brain pH, PMI and RIN.Table 3 Summary of differentially expressed genes from BA11 RT-qPCR findings.

Pathways	Gene	Mean expression [2−ΔCt]	p value	Multiple comparisons	
HC	SCZ	MD	SCZ–HC (p)	MD–HC (p)	SCZ–MD (p)	
NF-κB	IKBKG	0.208	0.129	0.166	<0.0001	<0.0001	0.0091	0.0181	
PI3K-AKT	PIK3CD	0.018	0.014	0.019	0.0005	0.0045		0.0006	
TLR	TICAM1	0.113	0.075	0.104	<0.0001	<0.0001		0.0016	
SCZ schizophrenia, HC healthy controls, MD mood disorder, TLR toll like receptor.

In comparison to both healthy controls and mood disorder patients, all three DEG were significantly downregulated in schizophrenia and IKBKG was also significantly decreased in mood disorder compared to healthy controls (Table 3).

Determining EGF and immune system interactions

Interaction analysis was undertaken only with the BA46 expression data to identify the influence of immune marker (covariate) × diagnosis (factor) on the EGF system signalling markers and to determine any diagnosis specific altered associations between molecules of the EGF and immune systems.

PIK3CA and TLR3

TLR3 was a significant predictor of PIK3CA expression (b = 5.91, t = 2.31; p = 0.025) independent of diagnoses (F = 0.01, df = 2, p = 0.989) (Supplementary Results—Table S3). In addition, a positive correlation was noted between TLR3 and PIK3CA mRNA expression in mood disorder and healthy control groups (Fig. 2A), while in the schizophrenia group, contrastingly there was a flattened regression slope.Fig. 2 Scatterplots of correlation between expression of selected immune and EGF system gene pairs by diagnosis from general linear modelling analysis.

The interactions depicted are: TLR3 - PIK3CA (A), TLR3 - PIK3CD (B), NFKB1 - NRG1 (C), NFKB1 - MTOR (D), NFKB1 - RICTOR (E), IFNA1 - PIK3CA (F), IFNA1 - BRAF (G), IFNAR1 - PIK3CA (H), and IFNAR1 - BRAF (I).

PIK3CD and TLR3

Diagnosis was a significant predictor of the expression of PIK3CD, a suppressor of PI3K signalling (F = 4.70, df = 2, p = 0.013), (Supplementary Results—Table S3) but not TLR3 expression (F = 0.01, df = 1, p = 0.928). In comparison to a flattened response in the mood disorder and healthy control groups there was a positive correlation between TLR3 and PIK3CD in schizophrenia (Fig. 2B).

NRG1 and NFKB1

NFKB1 expression was found to be a significant predictor of NRG1 expression (Supplementary Results—Table S3) when controlled for diagnosis (F = 25.22, df = 1, p < 0.001). Also, a significant interaction between NRG1 and diagnosis covaried by NFKB1 expression was noted (F = 6.73, df = 2, p = 0.002), principally contributed by the schizophrenia group (b = −0.112, t = −2.46; p = 0.017) (Supplementary Results—Table S3). The regression slopes highlighted a significant flattening of NRG1 in response to NFKB1 in the schizophrenia group compared to a marked positive correlation between these variables in the mood disorder and healthy control groups (Fig. 2-C).

mTOR pathway molecules and NFKB1

There was significant predictive power of NFKB1 on expression of MTOR (b = 6.08, t = 6.06; p < 0.001) (Fig. 2-D) and RICTOR (b = 0.877, t = 5.04; p < 0.001) (Fig. 2-E), when controlled for diagnosis (Supplementary Results—Table S3). Further, NFKB1 significantly interacted with diagnosis in predicting MTOR expression (F = 5.55, df = 2, p = 0.006), and in schizophrenia there was a strong association between NFKB1 and MTOR expression (b = −5.54, t = −3.32; p = 0.001) compared to healthy controls which was not observed in mood disorder (Supplementary Results—Table S3).

PI3K and MAPK pathways and IFNA1 signalling

Activation of the TLR pathway gives rise to release of IFNs and their data were therefore incorporated into the GLM analysis. IFNG expression was non-detectable, therefore IFNA1 was plotted against PI3K-AKT and MAPK pathway molecules (Fig. 2F, G). The findings revealed a significant relationship of IFNα1 and its receptor in downregulating key genes of the MAPK and PI3K pathways in schizophrenia.

There was attenuated PI3K and MAPK signalling molecule expression in association with increasing IFNA1 expression levels in the schizophrenia cohort. IFNA1 significantly influenced PIK3CA (b = 50.4, t = 2.75, p = 0.008) and BRAF expression (b = 85.0, t = 4.41, p < 0.001) (Supplementary Results—Table S3) when controlled for diagnosis. Further, IFNA1 and diagnosis interacted significantly in predicting BRAF expression (F = 5.15, df = 2, p = 0.009), where BRAF had a significantly reduced response to IFNA1 in the schizophrenia group (b = −86.7, t = −3.21, p = 0.002).

Both IFNAR1, the high-affinity receptor for IFNα1 (Fig. 2H, I), and diagnosis were found to be significant predictors of PIK3CA (F = 36.94, df = 1, p < 0.0001 and F = 9.10, df = 2, p < 0.001, respectively) and BRAF expression when controlled for each other (F = 55.61, df = 1, p < 0.0001 and F = 5.44, df = 2, p = 0.007, respectively) (Supplementary Results—Table S3). There was also a significant diagnosis-specific interaction between IFNAR1 and these PI3K (F = 12.52, df = 2, p < 0.001) and MAPK (F = 8.92, df = 2, p < 0.001) pathway molecules in both patient cohorts in comparison to healthy controls (Supplementary Results—Table S3).

Immune proteins in human post-mortem brain BA46 and BA11

Among the seven proteins quantified, NF-κB1/p50 was significantly decreased in BA46 (p = 0.029, ES = −0.855) (Fig. 3A) and IL12RB2 elevated (p = 0.0232, ES = 0.77) in schizophrenia, compared to the control group (Fig. 3B). C3, C4A, IKKα, IKBKAP (ELP1), and IL12A were unchanged across the groups (p > 0.05, in all cases) (Supplementary Results—Fig. S1).Fig. 3 Altered protein expression.

NF-κB1 (A) and IL12RB2 (B) in BA46; IL12A (C) in BA11. Samples in duplicate. IC – internal control. HC - healthy control; SCZ - schizophrenia; MD - mood disorder.

In BA11, IL12A was significantly altered across the groups (F(2,65) = 3.505, p = 0.0358, ES = 0.642) (Fig. 3C) with increased protein levels in schizophrenia compared to controls (p = 0.0289, ES = −0.76), while C3, C4A, IKKα, IKBKAP (ELP1), NF-κB1/p50 and IL12RB2 were unaltered (p > 0.05, in all cases) (Supplementary Results—Fig. S2).

The mRNA and protein changes in BA46 and BA11 are summarised in Table 4.Table 4 Summary of mRNA and protein changes in DLPFC and OFC.

	DLPFC (BA46)	OFC (BA11)	
DEG	64	3	
DEG in patient groups vs HC	60—SCZ; 19—MD	3—SCZ; 1—MD	
Difference between the SCZ and MD groups	67% of the DEG altered between the patient groups.

All downregulated in SCZ

	All 3 genes altered between patient groups.

All downregulated in SCZ

	
Direction of gene expression change in patient groups	All downregulated except for ERBB4	All downregulated	
Significantly altered proteins	NF-κB1—decreased in SCZ

IL12RB2—increased in SCZ

	IL12A—increased in SCZ	
SCZ schizophrenia, MD mood disorder, HC healthy controls, DEG differentially expressed genes.

Discussion

In this study, we demonstrated differential mRNA and protein expression in EGF and immune system markers in BA46 and BA11 from patients with schizophrenia compared to a healthy sample and a psychiatric comparison group. Moreover, we showed there were plausible pivotal points of interaction between the two systems deduced from the expression patterns of selected markers.

In DLPFC, a major proportion of the EGF and immune system genes chosen for the study (n = 64; 56.0%) significantly varied in expression across the diagnostic cohorts, with most of the changes accounted for by the schizophrenia group. Although there was some overlap in the expression profiles of the schizophrenia and mood disorder cohorts, they differed considerably in the expression EGF system markers and their signalling pathways namely MAPK-RAS and PI3K-AKT, along with JAK-STAT, complement and cytokine signalling. A significant proportion of the EGF system and its signalling pathway genes showed differential expression in BA46, confirming it as a key area involved in EGF system changes in schizophrenia. Altered expression of hub genes such as TNFRSF1A, and IKK and mTOR complex genes pointed to possible downstream signalling convergence between the immune and EGF systems. Further to these mRNA changes, protein levels of immune markers NF-κB1 and IL12RB2 were significantly altered in DLPFC in schizophrenia.

There was considerable variation between regions with far fewer DEG (3%) in the OFC. These genes, IKBKG, TICAM1 and PIK3CD, belong to the TLR and PI3K pathways, and were downregulated in schizophrenia. TLR pathway involvement is also consistent with the observed increase in IL12A protein levels in OFC in patients with schizophrenia supporting a possible IL12-mediated pathology in the disorder.

Conventionally, EGF and immune signalling pathway interactions presume immune activation could alter growth factor system signalling, however, there is ample evidence for bi-directional interactions38–41.

In this study, we performed GLM analysis to identify immune marker × diagnosis interaction on mRNA expression of EGF system signalling markers where we showed correlations between ErbB signalling via PI3K-AKT and MAPK pathways, and TLR3 pathway genes in schizophrenia. Here, increased TLR3 expression correlated with an equally robust increase in PIK3CA and an attenuated response in PIK3CD in healthy controls and mood disorder patients which did not reach significance. Given that the PI3K-AKT pathway inhibits pro-inflammatory pathways such as IFN and TNF39,42,43, TLR activation could be eliciting PI3K-AKT activation through increases in PIK3CA and an attenuation in the inhibitor PIK3CD as an inflammation containing measure. In contrast, PIK3CD and TLR3 showed a trend positive correlation in schizophrenia, indicating possible suppression of PI3K-AKT signalling and potentially an enhanced inflammatory response in this group, in line with earlier PI3KCD findings31. Although this association may be a type II error due to the small sample size, it warrants examination in a larger sample because of its support for a schizophrenia-specific convergence between these pathways. Moreover, inhibition of the PI3K pathway enhances activation of MAPK, p38, JNK pathways and NFKB nuclear translocation that augments pro-inflammatory cytokine release43. Overall, it maybe that TLR-mediated activation of the PI3K-AKT pathway may act as a brake on excessive inflammation. Thus, the PI3K-AKT and immune pathways may finely co-regulate each other to contain risk of dysregulated inflammation.

To further support EGF system involvement, NRG1 expression in schizophrenia subjects was significantly attenuated in association with NFKB1 upregulation. The NRG1 receptor, ERBB4, has also been shown to downregulate in response to TLR4/5 upregulation in drug naïve schizophrenia patients38. Uncharacteristically, in these patients, TLR4 stimulation resulted in low cytokine production and ErbB activation by NRG1 in a pro-inflammatory response, in contrast to an anti-inflammatory IL10 response to similar ErbB stimulation in healthy subjects38.

In schizophrenia subjects, MTOR and RICTOR had a flattened relationship to NFKB1. These changes were distinctly different to expected increased mTOR activity in response to the IKK complex molecules with resulting NF-κB1 activation42, again indicative of abnormal interactions between the growth factor and immune systems in schizophrenia. Multiple points of crosstalk were identified between PI3K-AKT-mTOR signalling and immune signalling. For instance, GSK3B, which is inhibited by PI3K, upregulates bacterial lipopolysaccharide induced TNF expression43, and GSK3 is also involved in the activity of IFNγ (a product of TLR3/4-NFκB signalling)42. Reciprocally, the PI3K-AKT-mTOR and the MAPK-ERK pathways are activated by IFNs39,41. Our results, however, illustrated attenuated PI3K and MAPK signalling in response to increasing IFNA1 and IFNAR1 expression only in the schizophrenia cohort.

Similar changes have been noted in animal models of schizophrenia-like behaviour where low brain phosphorylated ERK (pERK) levels, following prenatal cytokine exposure, were accompanied by elevated HSP9044. As HSP90 restrains NRG1-ErbB activation, these changes not only indicate a generally disrupted growth factor signalling in a schizophrenia model but also suggest that these pathways are targeted by environmental factors relevant to neurodevelopmental disorders44.

In our study, the complement cascade was strongly changed in schizophrenia, with a majority of the complement components examined differentially downregulated in the schizophrenia cohort, namely C1Q isoforms C1QB and C1QC, C3 and C4A. This contrasted to mood disorder where changes were minimal. All components participate in synapse elimination, particularly C1Q and C3, tagging inappropriate synaptic connections between neurons for removal during the synaptic pruning process45,46. C1Q initiates the classical pathway by binding to immune complexes which leads to cleavage and activation of products of C2 and C4 that drive the amplification and cleavage of C3, the spontaneous hydrolysis of which then triggers the alternative pathway46. The C4A gene has been identified as a major contributor to the substantial risk of schizophrenia associated with the MHC locus12. This is through the association of increased copy number of the C4A long form allele (C4AL) to elevated C4A expression in multiple brain regions in schizophrenia. Further, C4A has been postulated to be associated with increased synaptic engulfment and C3 activation in schizophrenia12,47,48; and was found to be elevated in neonatal dried blood spots from individuals who later developed schizophrenia, indicating a potential link to early environmental insults49. Moreover, C4 expression was elevated in the cingulate cortex of adult offspring of mid-late gestation MIA models50. Overall, our study findings point to decreased complement activity in long-standing schizophrenia.

Using major depression and bipolar disorder as psychiatric comparators with schizophrenia in our study allowed examination of their distinct profiles as well as any shared pathology. We found that although there were common changes in the mTOR, TLR and the TNF signalling pathways between the schizophrenia and mood disorder cohorts, expression profiles between them were highly different. Also, the mood disorder group had less than a third of the number of genes significantly altered compared to the schizophrenia group in the DLPFC. This may signify some important pathological differences in EGF and immune system signalling between the two disorders.

TLR signalling attenuation in mood disorder in our study is in line with the pathological relevance of this system reported previously51–53. However, past findings predominantly of activation of this system in major depression were mostly based on peripheral marker expression51–54. Further downstream, NFKB1, and IL6 have been found elevated in newly diagnosed depressed patients55 and the TLR adaptor proteins, TRIF and MYD88 upregulated in expression in established depression54. This suggests TLR pathway involvement in depressive disorders but with varying effects across phase of illness and between central and peripheral systems.

Of relevance are previous findings in suicide victims with either a BPAD or major depression diagnosis. Here, TNF, TLR and pro-inflammatory cytokine levels were elevated in PFC (BA9)56,57 in contrast to our study where none of the cytokine signalling markers such as interleukins, IFNs and their receptors were differentially expressed, and the TNF, TLR markers were rather downregulated in the mood disorder subjects, in BA46 or BA11. This may suggest regional differences between BA9, BA46 and BA11 raising the possibility of differential regulation in immune system responses in these disorders. Alternatively, although suicide was ubiquitous among the mood disorder subjects in our study, and hence a possible factor in our outcomes, previous studies suggest that brain and peripheral blood immune alterations were not influenced specifically by suicide58.

Psychotic disorders, however, overlap considerably, not only through genetics, but also in terms of symptoms, outcome and treatment response59. Attempts to diagnostically parse them using neurobiological measurements, but excluding molecular biomarkers, as in the BSNIP study60, gave rise to the concept of biotypes that transcended diagnostic boundaries and contained a varying combination of psychosis diagnoses, highlighting the commonality in pathology in a proportion of patients59,60. It is possible that some shared molecules highlighted in our study may emerge as candidate molecular biomarkers for these proposed biotypes in future studies. Moreover, a biotype identified as being more prone to environmental stressors, spontaneous mutations and epigenetic changes60 may be where molecular markers of this study could be more precisely informative.

We show significant changes in EGF and immune system expression in adult frontal cortex in schizophrenia but how these findings relate to early environmental risk factors and subsequent neurodevelopmental changes is to be resolved. One argument is that early environmental risk factors shape neurodevelopment via immune activation and this immune active state becomes chronic, persisting into adulthood in conjunction with late environmental stressors to influence mature brain functions such as synaptogenesis and plasticity, neurogenesis and myelination3,6–8. In animal models, a hit in early life to the immune system triggered a lifelong increase in immune reactivity61. The changes noted in adolescence and early adulthood around illness onset can be influenced by many factors including antipsychotic medications, smoking, illicit drug use and medical co-morbidities. However, cytokines and transcriptional regulators in schizophrenia patients do not appear to be influenced by medication (antipsychotics, antidepressants, benzodiazepines, sodium valproate or non-steroidal anti-inflammatory drugs (NSAID)), smoking, inflammation-related illness at death or suicide62–64. Particularly the changes in innate immune components such as TLR and complement systems, as in our findings, carry additional significance in supporting the notion that early immune activation mechanisms can persist into adulthood and contribute to the risk and pathology of schizophrenia.

Moreover, a recent microarray study showed strong correlation between schizophrenia and cortical immune system changes where most immune gene expression were downregulated, similar to our findings, and a considerable proportion of those genes belonged to cytokine/chemokine signalling including Il-8, IL-10, and TNFα regulatory pathways65. Also, increased transcriptional changes of immune cell markers were noted in prefrontal cortex in schizophrenia66, further supporting persistent dysfunction of these systems in the brains of those affected.

Our RT-qPCR findings of regional immune system changes in schizophrenia using brain homogenate corroborate with microarray data from the anterior cingulate cortex using laser capture microdissection. Here, lamina-specific downregulation in immune pathways including IL-8, IL-10 was shown consonant with our data65. In addition, prefrontal cortical expression of immune markers in schizophrenia have been proposed to differ based on inflammatory subtypes66. Hence, overall there is strong evidence for immune system changes in the prefrontal cortex in schizophrenia but variation between studies may be driven by methodological and/or regional, diagnostic group and sub-group differences.

We observed a lack of association between mRNA and protein levels, for example in C3, C4A and IKBKAP which were highly differentially expressed but protein levels were unchanged. Generally, suggested explanations for these discordances include post-transcriptional, -translational modifications, abnormalities in the efficiency of these processes, mRNA stability, protein half-life or turnover or even different methods of quantification67–69.

The limitations of all post-mortem human brain studies are that at best they provide only cross-sectional associational evidence. Another disadvantage is the inability to control for potential confounding ante-mortem factors such as antipsychotic medications, smoking, substance use or BMI since those data were not available or available for only a part of the sample cohort. The use of a psychiatric comparator group was an attempt to contextualise these factors. Suicidal death and sex distribution were differentiating factors between the cohorts, however, this would be a real-world reflection of patient cohort differences, particularly in regard to mood disorder patients. Our modest sample size could have affected the power of the study.

A further limitation is the use of bulk tissue homogenate in assessing mRNA expression. This does not permit cell-specific identification of RNA changes and hence it is not possible to determine which cell types were responsible for the observed changes. A future single nuclei RNA sequencing study in the same tissue cohort could address this limitation.

Ante-mortem medication is an important potential confound in post-mortem psychiatric studies. Incomplete and inconsistent clinical and toxicological data prevented us from correlating expression values with drug dose or covarying in analyses. By including the mood disorder cohort who were exposed to similar medications it was possible to at least determine changes observed only in schizophrenia were unlikely to be wholly attributable to medication effects. Moreover, by interrogating a database that collated multiple gene expression studies measuring the effects of psychotropic medications in different in vivo and in vitro models (https://cdrl.shinyapps.io/Kaleidoscope/) all n = 64 DEG in our study were unchanged, up- and down-regulated depending on the tissue model, drug dose and type and duration of treatment. Thus, our findings should be interpreted with the caution of possible medication effects, although these are inconsistent in various models.

Conclusions

In conclusion, a large number of EGF and immune system genes were differentially downregulated in DLPFC in schizophrenia, but not in OFC. The pattern of changes in sequential components strongly suggests that the nominated pathways may indeed be altered in schizophrenia, and particularly the ErbB-PI3K-AKT and TLR cascades with some specific interactions noted between them. The prominent complement system changes noted were in agreement with evidence previously supporting their involvement in the pathology of schizophrenia12. Dysregulation of both the innate and adaptive immune systems was indicated in the schizophrenia disease state, compatible with earlier observations4. Aberrant convergence noted between innate immune signalling and candidate EGF signalling pathways provided some indication about their potential interactions in the developing brain in response to environmental stressors and is worthy of further investigation. Distinct pathway gene expression changes between schizophrenia and mood disorders may reflect variant pathological processes involving immune and EGF system signalling in DLPFC, between these sets of disorders. The findings confirm that BA46 could be a key region in the pathology of schizophrenia in relation to EGF and immune system dysfunction.

Materials and methods

Human post-mortem brain cohort

Human post-mortem brains were collected by the Victorian Institute of Forensic Medicine with approval from the institutional Ethics Committee and with written consent from the next of kin. BA46 and BA11 tissues were obtained from the Victorian Brain Bank Network (VBBN). The study was approved by the Human Research Ethics Committee of Melbourne Health.

The study cohort consisted of 18 healthy control, 20 schizophrenia, 21 major depression and 4 bipolar affective disorder (BPAD) subjects and 5 suicide completers without a known diagnosis. Given suicide frequently occurs in the context of mental illness especially that of major depression70, those without a diagnosis were presumptively ascribed an undiagnosed mood disorder and grouped in the mood disorder category along with major depression and BPAD patients for the purposes of analysis.

Clinical diagnoses

Clinical and demographic factors, tissue quality markers and medication status of subjects were collected by the VBBN through case history reviews using the DIBS (Diagnostic Instrument for Brain Studies)71. The data gathered included duration of illness - the time from first hospital admission to death; final recorded dose of antipsychotic medications expressed as chlorpromazine equivalents; if other medications such as anticholinergics and benzodiazepines were used in treatment; and alcohol and non-prescribed drug levels in blood or urine by toxicology investigations. Post-mortem interval (PMI) was estimated as the time from death to autopsy (see Supplementary Materials and Methods). The pH of the brain tissue was measured as an indicator of overall tissue preservation.

Real-time quantitative polymerase chain reaction (RT-qPCR)

RNA extraction

Total RNA was extracted from 50 to 100 mg of frozen grey matter using 1 ml TRIzol reagent (Invitrogen, CA, USA) and a QIAGEN RNeasy kit (QIAGEN) according to the manufacturer’s instructions. RNA was quantified using a Nanodrop Spectrophotometer (Thermofisher Scientific, MA, USA). RNA quality was measured in an Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA) as RNA integrity number (RIN).

First-strand cDNA synthesis

To 5 µg of total RNA, 50 ng/µl random hexamers, and 10 mM dDNTP mix were added. Following denaturation, cDNA was synthesised with the reaction components specified (see Supplementary Materials and Methods), including 200 U/µl Superscript III RT (Thermo Fisher Scientific). 10 µl cDNA synthesis mix was added to each RNA/primer mixture and reverse transcribed.

Quantitative PCR

PCR and mRNA quantification were performed utilizing a Fluidigm Biomark HD system with sixty-eight human cDNA samples (5 µl), 2 RT (reverse transcriptase) negative samples and 1 no-template control (NTC). For quality control check, qPCR using SYBR HsGAPDH was performed (Applied Biosystems PN4333764F). The RT negative sample showed more than 10 Ct (cycle threshold) value difference with the corresponding cDNA sample implying very low or absence of genomic DNA contamination.

117 human TaqMan assays were selected including the housekeeping genes GAPDH, ACTB and HPRT1 (Supplementary Materials and Methods—Table S1). FAM-MGB TaqMan gene expression assays were provided as 20X forward and reverse primer and probe mixes. Each primer was at a concentration of 18 μM and probe at 4 μM. For preamplification and subsequent multiplex amplification protocols for TaqMan assays, see Supplementary Materials and Methods.

A 96.96 Dynamic Array IFC (integrated fluidic circuits) which enabled 9216 reactions, and/or a 192.24 Dynamic Array IFC, enabling 4608 reactions, were used. Assays and samples were combined in Dynamic Array IFCs according to Fluidigm® 96.96 Real-Time PCR Workflow Quick Reference PN 6800088. Output data were analysed with Fluidigm Real-Time PCR analysis software (V4.1.1). For each sample the Ct values for each gene were averaged across the technical repeats as were the housekeeping genes. Given the variation observed in GAPDH levels between the study groups it was omitted as a reference gene. For data computation, the geometric mean of ACTB and HPRT1 was subtracted from the average Ct value of the gene of interest, giving a value of ΔCt, which was used to compute the final 2−ΔCt value, utilized for statistical analysis.

Determination of brain protein levels using Western immunoassay

Brain tissue was homogenised in a (tissue weight × 10) volume (μl) of homogenisation buffer comprising 0.05 M Tris pH 7.5, 50% glycerol, protease inhibitor cocktail (Sigma-Aldrich, Missouri, USA) 1:100 and 0.015 mM aprotinin. 10 μg of protein homogenate was denatured and loaded on to 4–15% Mini-PROTEANR TGXTM Precast Gels (Bio-Rad Laboratories, CA, USA). For electrophoresis, protein transfer and labelling using primary and secondary antibodies and membrane stripping and re-probing steps refer to Supplementary Materials and Methods. Imaging was then carried out using enhanced chemiluminescence (ECL) reagents (GE Healthcare, UK). Images were analysed with Multi Gauge imaging software (Fujifilm, Tokyo, Japan). The optical densities of proteins of interest were normalised first against GAPDH, and then against an internal control of pooled samples. Duplicates were averaged for each sample per protein measured and statistically analysed.

Data analysis

One-way ANOVA with post hoc Tukey’s correction for multiple comparisons was used to compare diagnostic group mean values for each gene or protein. For RT-qPCR data, multiple testing was corrected using the Benjamini–Hochberg procedure (false discovery rate set at 0.05) and the findings were further adjusted for confounding variables such as age, post mortem interval (PMI), brain pH and RIN.

General linear modeling (GLM) was employed using RT-qPCR data to test for interactions between EGF and immune pathway molecules. This analysis was to explore potentially altered convergence between EGF and immune system pathways, assigning a number of key EGF system signalling markers, particularly those that could be considered hub molecules, as dependent variables, with diagnosis and selected key immune markers as independent variables. This was to identify the influence of immune marker (covariate) × diagnosis (factor) on the EGF system signalling markers utilizing the mRNA expression values, in order to determine if there are any diagnosis-specific altered associations between the molecules of these two systems.

All analyses were performed using IBM SPSS™ Version 22, 26 and 29, GraphPad Prism™ Version 6-8 and Minitab 18 (Minitab Inc., State College, PA, USA) software as appropriate.

Supplementary information

Supplementary Results

Supplementary Materials and Methods

Supplementary information

The online version contains supplementary material available at 10.1038/s41537-024-00488-8.

Acknowledgements

Research reported in this publication was supported in part by Northwestern Mental Health Seed Grant and RANZCP Research and Education Fund received by T.K.

Author contributions

All authors were involved in conceptualization and designing of the study. T.K. collected and analysed the data, and wrote the original and subsequent drafts. A.P. provided input into data collection and analysis. A.P. and S.S. critically reviewed and edited the manuscript drafts.

Data availability

The data supporting the findings of this study are available upon reasonable request.

Competing interests

The authors declare no competing interests.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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