2019-05-23

014_ Transcriptional profiling of SCZ and BD patients and the association of innate immunity markers with clinical treatment

Research Question and Aims

Our aim is to determine if a set of specified innate immune markers can predict clinical outcome of psychosis. In a Norwegian cohort of SCZ and BD patients, we have identified elevated expression level of granulocyte-associated genes in peripheral whole blood compared to healthy controls. Our focus now is to understand if this expression pattern is caused by an ongoing inflammation or a medication effect and to determine if the gene expression correlates with clinical status or outcome. If there is a medication effect, we aim to clarify if certain antipsychotics correlate better with the inflammatory transcription profile than others, and if there is a difference in BD patients on antipsychotics compared to BD not on antipsychotics.

Analytic Plan

We hypothesize that a cluster of innate immunity genes correlates with medication response in a subset of patients with SCZ and BD, and that these genes can be used as biomarkers to stratify patients that may benefit from anti-inflammatory augmentation treatment. We will address this by doing RNAseq on patient samples and healthy controls and compare the gene expression with clinical data and medication type. Neurocognitive data will be used as a measure of the cognitive functioning of the patients. We will correlate gene expression and cognitive functioning to evaluate if certain genes should be further evaluated as potential biomarkers.
Patient samples should include SCZ on antipsychotics, BD on antipsychotics and BD not on antipsychotics. The diagnosis should meet the diagnostic criteria for broad schizophrenia or bipolar spectrum disorders according to DSM-IV.
RNAseq will be done on an Illumina HISeq 4000 platform. Data processing of the sequencing reads will be done in the following programs: FastQC (technical QC), Hisat2 (alignment of reads), FeatureCounts (to count reads to genomic features, e.g. genes, exons, promoters), DESeq2 (normalization, differential gene expression analysis and statistical analyses). Standard statistical association tools will be used to control for confounders.

Resources needed

RNA from 100 SCZ on antipsychotics (preferable on the same antipsychotic), 100 BD on antipsychotics, 100 BD not on antipsychotics and 100 healthy controls. Preferably, samples should be 50/50 males and females.
Regarding the SCZ samples: These should not be the same as already RNA sequenced in Bonn. If possible, it would be interesting to get access to the RNAseq data from Bonn. Required information would be the expression level of our specified innate immune markers and clinical and demographic data as specified below.

Recruitment data:
v1_id
v1_stat

Demographic data:
v1_sex
v1_ageBL

Ethnicity:
v1_cntr_brth
v1_cntr_brth_m
v1_cntr_brth_f
v1_cntr_brth_gmm
v1_cntr_brth_gfm
v1_cntr_brth_gmf
v1_cntr_brth_gff

Psychiatric history:
v1_cur_psy_trm
v1_oupat_psy_trm
v1_age_1st_out_trm
v1_daypat_inpat_trm
v1_age_1st_inpat_trm
v1_dur_illness

Medication:
v1_clin_medication_variables_1
v1_con_medication_variables_1
v1_adv
v1_medchange
v1_lith
v1_lith_prd
v1_med_pst_wk
v1_med_pst_sx_mths

Physical measures and somatic diseases:
v1_height
v1_weight
v1_waist
v1_bmi
v1_chol_trig
v1_diabetes
v1_copd
v1_autoimm
v1_inf