033_ Circulating smallRNA as biomarker for neuropsychiatric diseases: investigating cross-sectional differences in the miRNAome of depression patients
Research Question and Aims
Recent research indicates that circulating small non-coding RNAs might serve as diagnostic biomarker for various. 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 translation1. 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.
Our laboratory has performed 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 therefore to correlate circulating smallRNA signatures with phenotypes obtained in the context of the PsyCourse cohort. The ultimate aim is to develop biomarker for stratified therapies of affective and non-affective psychosis. Examples are approaches to identify circulating smallRNAs indicative of patients that had encountered early-life stress2, have a rather unfavorable course of the disease and are resistant to therapy or allow us to predict response to specific treatments.
Thus, our objective is to explore the entire spectrum of phenotypes collected in the the PsyCourse data with respect to circulating smallRNAs.
In this particular project we plan to study depression as a common and serious mood disorder. There is evidence that the pathogenesis of mood disorders including depression involves differential expression of microRNAs in relevant brain areas3. We hypothesize that changes in the microRNAome reflect - at least in part - long-term adaptation to maladaptive environmental and genetic risk factors. There is also evidence that pathological changes observed in relevant brain regions are reflected in blood and can thus be used as biomarker to detect disease onset and progression, hence, to stratify patients.
Our lab has by now generated smallRNA sequencing data from PAX-Gene blood samples of 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.
A typical analytical plan will be the identification of smallRNA co-expression modules using for example weighted co-expression analysis which will be correlated with phenotypes of interest. In addition, we will use supervised and unsupervised machine learning approaches to identify smallRNA features that can explain phenotypes. These data will be cross correlated with other available data, for example from GWAS studies, and guide us the generate novel hypothesis of biomarker signatures that ideally also inform about pathomechansims. On this basis we furthermore plan to perform mechanistic studies in animal and/or cellular models. A bona fide example is a current project in which we compared the circulating smallRNA amongst healthy controls, schizophrenia patients and schizophriena patients that had encounted early life stress (ELS). We identify microRNA99b as one key factor linked to the more severe phenotypes observed in ELS-schizophrenia patients and were able to confirm this observation in a mouse model. We, moreover, find that microRNA99b is linked to inflammatory processes and could be a target for stratified therapy (Fig 1).
In this particular project we plan to study the small non coding RNAome linked to depression.
We have generated smallRNA-sequencing data from Psycourse probands. We will analyze these data for microRNA expression comparing healthy controls to patients with depressive phenotypes. Thus, we will take into account age, gender, level of education and family history etc. as covariate and will correct the molecular data using for example linear regression if necessary.
For depression patients and controls we plan to consider data available 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 depression. At present we have discussed with the PsyCourse team that data is available for 262 controls and 80 depression 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. However, we also plan to ask if potential microRNA signatures at Visit 1 may predict the course of the disease with response to treatment.
Using an established bioinformatics pipeline that is constantly developed further we aim to detect conserved microRNA expression cluster that are linked to the presence to specific phenotypic traits linked to depression.
We furthermore aim to study identified candidate microRNAs at the mechanistic level. To this end we will employ rodents as well as murine and human neuronal cell culture as model systems and use established tools to manipulate microRNAs in vivo (e.g via viral approaches or lipid nanoparticles) followed by molecular and behavioral analysis.
Identification code v1_id
Clinical/conrol status v1_stat
Age (at first interview, years): v1_age
Marital status: v1_marital_stat
Partnership status: v1_partner
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)
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
Psychopathology and functioning:
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:
Affective Syndromes: v1_scid_age_MDE
Disease duration (years): v1_dur_illness
Treatment (inpatients vs. outpatients): v1_cur_psy_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
Suicide ideation: v1_scid_evr_suic_ide
Physical measures and somatic diseases
Body mass index (BMI): v1_bmi
Elevated cholesterol or triglyceride levels: v1_chol_trig
Angina pectoris: v1_ang_pec
Heart attack: v1_heart_att
COPD/chronic Bronchitis: v1_copd
Autoimmune diseases: v1_autoimm
v1_Antidepressants (Antidepressiva (ja/nein)
v1_Antipsychotics (Antipsychotika (ja/nein)
v1_Mood_stabilizers (Phasenprophylaktika (ja/nein)
v1_Other_psychiatric (Psychopharmaka nicht zuordenbar (ja/nein)