Multiple genes encoding mitochondrial ribosomes are downregulated in brain and blood samples of individuals with schizophrenia

Figures & data

Table 1. Characteristics of individual studies included in the meta-analysis.

Brain samples datasets
Accession	Publication	Brain region	# SZ	# CNT	Platform	Mean age (standard dev.)	Mean PMI (standard dev.)	Mean pH (standard dev.)
GDS3345	(Iwamoto et al. 2004)	BA10	12 7M:5F	15 9M:6F	HG U95 Av.2	SZ: 45 (14) CNT:48 (11) p = 0.47	SZ: 33 (16) CNT:24(10) p = 0.07	SZ:6.2(0.2) CNT:6.3(0.2) p = 0.37
GDS4523	(Maycox et al. 2009)	BA10	27 19M:8F	23 12M:11F	HG U133 Plus 2.0	SZ: 73 (15) CNT:69 (22) p = 0.45	SZ: 8.2 (7) CNT:10 (4) p = 0.3	SZ:6.1 (0.2) CNT:6.5(0.3) P = 8 × 10^-6
		BA10	Total: 39 26 M:13F	Total: 38 21 M:17F
GSE21935	(Barnes et al. 2011)	BA22	23 13M:10F	17 9M:8F	U133 Plus 2.0 Array	SZ: 72 (17) CNT:65 (22) p = 0.25	SZ: 7 (6) CNT: 9 (4) p = 0.36	SZ:6.2 (0.2) CNT:6.5 (0.3) P = 2.3 × 10^-6
GSE37981	(Pietersen et al. 2014))	STG Pyramid.	9 4M:5F	8 4M:4F	U133 X3P Array	SZ: 67 (20) CNT:67 (21) p = 0.99	SZ: 17 (5) CNT: 18 (3) p = 0.71	Not provided
GSE46509	(Pietersen et al. 2014)	STG Pravalb.	7 3M:4F	8 4M:4F	U133 X3P Array	SZ: 69 (22) CNT:67 (21) p = 0.87	SZ:15.8 (6) CNT: 18 (3) p = 0.38	Not provided
		BA22/ STG	Total: 39 20 M:19F	Total: 33 17 M: 16F
GDS1917	(Paz et al. 2006)	CRBLM	13 13M:0F	14 14M:0F	U133 Plus 2.0 Array	SZ: 46 (12) CNT:43 (10) p = 0.5	SZ:12.8 (5) CNT: 15.6 (6) p = 0.18	Not provided
GSE35978	(Chen et al. 2013)	CRBLM	44 32M:12F	50 31M: 19F	Gene 1.0 ST Array	SZ:43 (9). CNT:46 (9) p = 0.18	SZ: 33 (15) CNT:28(11) p = 0.042	SZ:6.4 (0.2) CNT:6.5 (0.3) p = 0.44
Stanley#6		CRBLM	10 7M:3F	14 9M:5F	U95 Av2 Array	SZ:46 (14). CNT:47 (9) p = 0.83	SZ: 34 (14) CNT:24(10) p = 0.071	SZ:6.2 (0.2) CNT:6.3 (0.2) p = 0.82
		CRBLM	Total: 67 52 M:15F	Total: 78 54 M: 24F
GSE35978	(Chen et al. 2013))	Parietal cortex	51 37M:14F	45 31M:14F	Gene 1.0 ST Array	SZ:43(10). CNT:46(9) p = 0.14	SZ: 31(16) CNT:27(12) p = 0.17	SZ: 6.4(0.3) CNT:6.5(0.3) p = 0.015
GSE53987	(Lanz et al. 2019)	AssStr	15 8M:7F	18 10M:8F	HG U133 Plus 2.0	SZ:48(6). CNT:48(11) p = 0.81	SZ: 19(7) CNT:20(5) p = 0.57	SZ: 6.5(0.4) CNT:6.6(0.2) p = 0.25
			Overall: 211 143 M:68F	Overall: 212 133 M:79F
Blood samples datasets
Accession		Publication	Blood samples type	# SZ	# CNT	Platform	Mean age (standard dev.)
GSE18312		data (Bousman et al. 2010)	Whole blood	12 9M:3F	8 5M:3F	HuEx-1_0-st	SZ:43(9); CNT:45(7) p = 0.57
GSE27383		(van Beveren et al. 2012)	Peripheral Blood Mononuclear Cells (PBMCs)	41 41M:0F	29 29M:0F	HG-U133_Plus_2	Not provided
				53	37

Abbreviations. Schizophrenia: schizophrenia; CNT: controls; BA: Brodmann Area; STG: Superior Temporal Gyrus; CRBLM: Cerebellum; AssStr: Associative striatum; PMI: post-mortem interval; M: males; F: females. Two-sided t-test associated p-values are listed for each dataset, for having different (SZ vs. CNT) mean age, PMI and pH.

Figure 1. The flow of information through the different stages of the selection of gene expression datasets for meta-analysis.

Table 2. Brain samples statistically significant MRPL and MRPS gene expression meta-analysis results.

#	Gene symbol	Random effects Hedges	Lower	Upper
MRPL genes – brain samples meta-analysis results
1	MRPL24	−0.21	−0.42	−0.01
2	MRPL3	−0.26	−0.45	−0.06
3	MRPL4	−0.26	−0.47	−0.05
4	MRPL40	−0.24	−0.43	−0.05
5	MRPL45	−0.23	−0.44	−0.03
6	MRPL47	−0.29	−0.53	−0.05
7	MRPL48	−0.24	−0.47	−0.02
8	MRPL52	−0.23	−0.44	−0.03
9	MRPL53	−0.31	−0.58	−0.03
10	MRPL54	−0.27	−0.48	−0.06
11	MRPL55	−0.23	−0.43	−0.02
12	MRPL57	−0.24	−0.44	−0.03
13	MRPL9	−0.2	−0.4	−0.01
MRPS genes – brain samples meta-analysis results
1	MRPS12	−0.37	−0.56	−0.18
2	MRPS15	−0.31	−0.52	−0.11
3	MRPS25	−0.24	−0.45	−0.04
4	MRPS36	−0.22	−0.43	−0.02
5	MRPS7	−0.31	−0.51	−0.1

The standardised difference (Random effects Hedges [Hedges 1981]) is positive if the expression is higher in schizophrenia vs. the control group and negative (blue) if the expression is higher in the control group vs. schizophrenia. The colour strength is proportional to the magnitude of the differential expression measure. A gene is defined as significantly down- or upregulated if its confidence interval does not cross zero. The lower and upper limits of the 95% confidence interval are given in the fourth and fifth columns.

Table 3. Blood samples statistically significant MRPL and MRPS gene expression meta-analysis results.

	Gene symbol	Random effects Hedges	Lower	Upper
MRPL genes – blood samples meta-analysis results
1	MRPL11	−0.45	−0.88	−0.02
2	MRPL17	−0.74	−1.17	−0.3
3	MRPL34	−0.58	−1.01	−0.15
4	MRPL4	−0.76	−1.19	−0.32
5	MRPL41	−0.49	−0.91	−0.06
6	MRPL49	−0.75	−1.18	−0.31
7	MRPL51	−0.68	−1.11	−0.25
MRPS genes – blood samples meta-analysis results
1	MRPS17	−0.56	−0.98	−0.13
2	MRPS2	−0.5	−0.93	−0.07
3	MRPS26	−0.77	−1.21	−0.34
4	MRPS5	−0.64	−1.16	−0.13
5	MRPS6	−0.57	−1	−0.14
6	MRPS7	−0.45	−0.88	−0.02

The standardised difference (Random effects Hedges [Hedges 1981]) is positive if the expression is higher in schizophrenia vs. the control group and negative (blue) if the expression is higher in the control group vs. schizophrenia. The colour strength is proportional to the magnitude of the differential expression measure. A gene is defined as significantly down- or upregulated if its confidence interval does not cross zero. The lower and upper limits of the 95% confidence interval are given in the fourth and fifth columns.

Figure 2. Meta-analysis of MRPL4 and MRPS7 differential expression in ten brain samples and two blood samples of individuals with schizophrenia vs. healthy controls. (A) Brain samples MRPL4 meta-analysis. A forest plot was generated using the function “forest” from the “meta” package in R, version 4.9–2 (General Package for Meta-Analysis) (Schwarzer 2007). Each square represents the standardised difference (Hedges’ g [Hedges 1981]) between schizophrenia and control for a specific dataset, with the area of the square reflecting the weight (determined by the sample size) given to that dataset in the meta-analysis. Each horizontal line represents the 95% confidence interval for the mean difference in that study. The vertical line shows the point of zero difference. The standardised difference is positive (negative) if the expression is higher (lower) in schizophrenia vs. the control group. The vertical dashed red line points at the centre of the diamond, which represents the overall difference across both studies, and its width represents a 95% confidence interval. (B) Blood samples MRPL4 meta-analysis. (C) Brain samples MRPS7 meta-analysis. (D) Blood samples MRPS7 meta-analysis.

Iwamoto K, Kakiuchi C, Bundo M, Ikeda K, Kato T. 2004. Molecular characterization of bipolar disorder by comparing gene expression profiles of postmortem brains of major mental disorders. Mol Psychiatry. 9(4):406–416.

 PubMed Web of Science ®Google Scholar

Maycox PR, Kelly F, Taylor A, Bates S, Reid J, Logendra R, Barnes MR, Larminie C, Jones N, Lennon M, et al. 2009. Analysis of gene expression in two large schizophrenia cohorts identifies multiple changes associated with nerve terminal function. Mol Psychiatry. 14(12):1083–1094.

 PubMed Web of Science ®Google Scholar

Barnes MR, Huxley-Jones J, Maycox PR, Lennon M, Thornber A, Kelly F, Bates S, Taylor A, Reid J, Jones N, et al. 2011. Transcription and pathway analysis of the superior temporal cortex and anterior prefrontal cortex in schizophrenia. J Neurosci Res. 89(8):1218–1227.

 PubMed Web of Science ®Google Scholar

Pietersen CY, Mauney SA, Kim SS, Lim MP, Rooney RJ, Goldstein JM, Petryshen TL, Seidman LJ, Shenton ME, McCarley RW, et al. 2014. Molecular profiles of pyramidal neurons in the superior temporal cortex in schizophrenia. J Neurogenet. 28(1-2):53–69.

 PubMed Web of Science ®Google Scholar

Pietersen CY, Mauney SA, Kim SS, Passeri E, Lim MP, Rooney RJ, Goldstein JM, Petreyshen TL, Seidman LJ, Shenton ME, et al. 2014. Molecular profiles of parvalbumin-immunoreactive neurons in the superior temporal cortex in schizophrenia. J Neurogenet. 28(1–2):70–85.

 PubMed Web of Science ®Google Scholar

Paz RD, Andreasen NC, Daoud SZ, Conley R, Roberts R, Bustillo J, Perrone-Bizzozero NI. 2006. Increased expression of activity-dependent genes in cerebellar glutamatergic neurons of patients with schizophrenia. Am J Psychiatry. 163(10):1829–1831.

 PubMed Web of Science ®Google Scholar

Chen C, Cheng L, Grennan K, Pibiri F, Zhang C, Badner JA, Kelsoe JR, Greenwood TA, Nievergelt CM, Barrett TB, et al. 2013. Two gene co-expression modules differentiate psychotics and controls. Mol Psychiatry. 18(12):1308–1314.

 PubMed Web of Science ®Google Scholar

Lanz TA, Reinhart V, Sheehan MJ, Rizzo SJS, Bove SE, James LC, Volfson D, Lewis DA, Kleiman RJ. 2019. Postmortem transcriptional profiling reveals widespread increase in inflammation in schizophrenia: a comparison of prefrontal cortex, striatum, and hippocampus among matched tetrads of controls with subjects diagnosed with schizophrenia, bipolar or major depressive disorder. Transl Psychiatry. 9(1):1–13.

 PubMedGoogle Scholar

Bousman CA, Chana G, Glatt SJ, Chandler SD, Lucero GR, Tatro E, May T, Lohr JB, Kremen WS, Tsuang MT, et al. 2010. Preliminary evidence of ubiquitin proteasome system dysregulation in schizophrenia and bipolar disorder: convergent pathway analysis findings from two independent samples. Am J Med Genet. 153B(2):494–502.

 PubMed Web of Science ®Google Scholar

van Beveren NJM, Buitendijk GHS, Swagemakers S, Krab LC, Röder C, de Haan L, van der Spek P, Elgersma Y. 2012. Marked reduction of AKT1 expression and deregulation of AKT1-associated pathways in peripheral blood mononuclear cells of schizophrenia patients. PLoS One. 7(2):e32618.

 PubMed Web of Science ®Google Scholar

Hedges LV. 1981. Distribution theory for glass’s estimator of effect size and related estimators. J Educ Stat. 6(2):107–128.

 Google Scholar

Schwarzer G. 2007. Meta: An R package for meta-analysis. R News. 7(3):40–45.

 Google Scholar