2020-06-30

035_ Circulating smallRNA as biomarker for neuropsychiatric diseases: investigating differences in the miRNAome of schizophrenia patients

Research Question and Aims

Recent research indicates that circulating small non-coding RNAs might serve as diagnostic biomarker for CNS diseases. The best studied small non-coding RNAs are microRNAs that are 19-22 nucleotide long RNA molecules regulating protein homeostasis via binding to a target mRNA thereby causing its degradation or inhibition of translation. MicroRNAs are particularly interesting as potential biomarker since microRNAome alterations reflect complex changes in cellular homeostasis and could therefore indicate the presence of multiple pathologies. Moreover, microRNAs are extremely stable in cell free environments, are resistant to thaw-freeze cycles and have been implicated with cognition, neuropsychiatric and neurodegenerative diseases.

Within the KFO241/PsyCourse the Fischer laboratory is performing smallRNA-sequencing of all individuals of the PsyCourse cohort at baseline and is currently continuing the analysis of samples collected longitudinally.

The aim of this project is to correlate circulating smallRNA signatures with Schizophrenia phenotypes obtained in the context of the PsyCourse cohort. The ultimate aim is to develop biomarker that inform about relevant patho-mechanisms and allow for stratified therapies, the prediction of disease course and therapeutic efficacy.

In this particular project we plan to study schizophrenia. We like to point out that this project has been initiated as part of the KFO241 and most of the phenotypic data had already been made available. We hypothesize that changes in the microRNAome reflect - at least in part- long-term adaptation to maladaptive environmental and genetic risk factors.

Analytic Plan

Our lab has by now generated smallRNA sequencing data from PAX-Gene blood samples from the majority of PsyCourse participants at visit 1 and will continue to generate corresponding data. The overall aim is to link smallRNA expression to relevant disease phenotypes and thereby develop smallRNA signatures that are suitable for patient stratification, the prediction of the course of the disease and therapeutic efficacy.

While typical biomarker studies usually stop after the identification of potential features that may serve as biomarker, in our approach this is only the first step. Rather we develop multiple subsequent filtering steps that include cutting edge experimental approaches in animal models, murine and human cell cultures systems to refine such initial biomarker signatures.

In this project we already used differential expression and unbiased co-expression analysis that was correlated with phenotypes of interest. In this case simply "schizophrenia diagnosis" or "control" after correcting of confounding factors. This data revealed a number of differentially expressed microRNAs.

As a first filtering step we generated small RNAome in postmortem human brain samples from schizophrenia patients and controls and cross-correlated these data with the analysis of circulating smallRNA comparing healthy controls and schizophrenia patients from the PsyCourse cohort. Since microRNA expression is believed to reflect not only genetic but also environmental risk, we further analyzed which microRNAs would be more severely de-regulated in schizophrenia patients that had encountered a relevant environmental risk factor, in this case early life stress (ELS). Thus, we asked if any of the microRNAs we had identified so far would be further de-regulated in schizophrenia patients with ELS compared to patients without ELS. By this we were able to finally select microRNA99b as a target for further analysis. Our current data (unpublished) suggest that microRNA99b regulates synaptic plasticity - at least in part - by the activation of microglia that initiate engulfment of synapses. Consequently, manipulating microRNA99b in mice leads to schizophrenia-like phenotypes and we currently conduct experiments in human iPSC-derived brain organoids.

We expect to finish the experimental work by the end of the summer and then write the corresponding manuscript.

For schizophrenia patients and controls we plan, to investigate the general changes and correlations of microRNA expression to phenotypic data, specifically to the available data from neuropsychological testing: TMT-A and TMT-B time, Digit span test, Digit Symbol test, VLMT, Psychopathology (PANSS total score), severity of the disease (CGI), global assessment of functioning (GAF). We also request information about medication and other parameters relevant to schizophrenia. At present we have discussed with the PsyCourse team that data is available (Visit 1) for 262 controls and 439 schizophrenia patients. Any additional data that will become available from the PsyCourse cohort should be added to this cohort.

In a first approach, we plan to study differences amongst controls and patients at baseline, hence at Visit 1 since smallRNA sequencing data is available for this time point. Using an established bioinformatics pipeline that is constantly developed further we aim to detect microRNA expression clusters that are linked to the presence to specific phenotypic traits linked to schizophrenia. To accomplish this, we aim to use a co-expression-based approach. The advantage of this approach would be that the gene expression matrix can be used to construct clusters based on the expression patterns and later can be decomposed into singular value to correlate with phenotype traits available in Schizophrenic patients and healthy subjects.

After adjusting for known and latent covariates, we will use this method for the following objectives:
1. Construct a microRNA co-expression network
2. Identify clusters and link those with phenotypic traits related to schizophrenia
3. Find hub microRNAs in interesting clusters

Resources needed

ELS variables:
ELS total: v3_cts_els_dic
Item 1: feeling of not to be loved: v3_cts_1
Item 2: physical violence: v3_cts_2
Item 3: feeling of being hated: v3_cts_3
Item 2: sexual abuse: v3_cts_4
Item 2: no one cared: v3_cts_5

Demography:
Visit V1
Identification code: v1_id
Clinical/conrol status: v1_stat
Gender: v1_sex
Age (at first interview, years): v1_age
Marital status: v1_marital_stat
Partnership status: v1_partner
Children: v1_no_bio_chld
Siblings: V1_brothers
V1_sisters
v1_hlf_brthrs
v1_hlf_sstrs
Living alone: v1_liv_aln
School education: v1_school
Professional education: v1_prof_dgr
Currently paid employment: v1_curr_paid_empl
Months of work absence (last 5 years): v1_wrk_abs_pst_5_yrs
Country of birth: v1_cntr_brth

Neuropsychology: (cognitive tests)
Visit V1
Motivation for neuropsychological tests: v1_nrpsy_mtv
TMT-A (time): v1_nrpsy_tmt_A_rt
TMT-A (errors): v1_nrpsy_tmt_A_err
TMT-B (time): v1_nrpsy_tmt_B_rt
TMT-B (errors): v1_nrpsy_tmt_B_err
Digit Span Test (forward): v1_nrpsy_dgt_sp_frw
Digit Span Test (backward): v1_nrpsy_dgt_sp_bck
Digit Symbol Test: v1_nrpsy_dg_sym
Intelligence: v1_nrpsy_mwtb

Psychopathology and functioning:
Visit V1
PANSS positive sum score: v1_panss_sum_pos
PANSS negative sum score: v1_panss_sum_neg
PANSS general Psychopathology sum score: v1_panss_sum_gen
PANSS total score: v1_panss_sum_tot
Illness severity (CGI): V1_CGI_S
Global Assessment of Functioning: V1_GAF

Clinical variables, intervening variables:
Visit V1
Diagnosis: v1_scid_dsm_dx
v1_scid_dsm_dx_cat
Affective Syndromes: v1_scid_age_MDE
v1_scid_no_MDE
Mania and hypomania: v1_scid_age_mania
v1_scid_no_mania
v1_scid_age_hypomania
v1_scid_no_hypomania
Disease duration (years): v1_dur_illness
Treatment (inpatients vs. outpatients): v1_cur_psy_trm
Number hospitalizations (as daypatient or inpatient): v1_tms_daypat_outpat_trm
Family history of psychiatric illness: v1_fam_hist
Alcohol: alcohol consume last 12 months: v1_alc_pst12_mths
strong alcohol consume last 6 months: v1_alc_5orm
alcohol dependence lifetime: v1_lftm_alc_dep
Ever used illicit drugs: v1_evr_ill_drg
Depressive symptoms: v1_bdi2_sum
v1_idsc_sum
Suicide ideation: v1_scid_evr_suic_ide
v1_scid_suic_ide

Physical measures and somatic diseases
Body mass index (BMI): v1_bmi
Elevated cholesterol or triglyceride levels: v1_chol_trig
Hypertension: v1_hyperten
Angina pectoris: v1_ang_pec
Heart attack: v1_heart_att
Stroke: v1_stroke
Diabetes: v1_diabetes
Hyperthyroidism: v1_hyperthy
Hypothyroidism: v1_hypothy
Osteoporosis: v1_osteopor
Asthma: v1_asthma
COPD/chronic Bronchitis: v1_copd
Allergies: v1_allerg
Autoimmune diseases: v1_autoimm
Epilepsy: v1_epilepsy
Migraine: v1_migraine

Medication data
Visit V1
v1_Antidepressants (Antidepressiva (ja/nein)
v1_Antipsychotics (Antipsychotika (ja/nein)
v1_Mood_stabilizers (Phasenprophylaktika (ja/nein)
v1_Other_psychiatric (Psychopharmaka nicht zuordenbar (ja/nein)