bioRxiv Science⌕ Search

bioRxiv · 10.1101/2024.04.30.591973

A brain-enriched circRNA blood biomarker can predict response to SSRI antidepressants

Abstract

Major Depressive Disorder (MDD) is a debilitating psychiatric disorder that currently affects more than 20% of the adult US population and is a leading cause of disability worldwide. Although treatment with antidepressants, such as Selective Serotonin Reuptake Inhibitors (SSRIs), has demonstrated clinical efficacy, the inherent complexity and heterogeneity of the disease and the "trial and error" approach in choosing the most effective antidepressant treatment for each patient, allows for only a subset of patients to achieve response to the first line of treatment. Circular RNAs (circRNAs), are highly stable and brain-enriched non-coding RNAs that are mainly derived from the backsplicing and covalent joining of exons and introns of protein-coding genes. They are known to be important for brain development and function, to cross the blood-brain-barrier, and to be highly sensitive to changes in neuronal activity or activation of various neuronal receptors. Here we present evidence of a brain-enriched circRNA that is regulated by Serotonin 5-HT2A and Brain-Derived Neurotrophic Factor (BDNF) receptor activity and whose expression in the blood can predict response to SSRI treatment. We present data using circRNA-specific PCR in baseline whole blood samples from the Establishing moderators and biosignatures of antidepressant response in clinical care (EMBARC) study, showing that before treatment this circRNA is differentially expressed between future responders and non-responders to sertraline. We further show that the expression of this circRNA is upregulated following sertraline treatment and that its trajectory of change post-treatment is associated with long-term remission. Furthermore, we show that the biomarker potential of this circRNA is specific to SSRIs, and not associated with prediction of response or remission after Placebo or Bupropion treatment. Lastly, we provide evidence in animal mechanistic and neuronal culture studies, suggesting that the same circRNA is enriched in the brain and is regulated by 5-HT2A and BDNF receptor signaling. Taken together, our data identify a brain-enriched circRNA associated with known mechanisms of antidepressant response that can serve as a blood biomarker for predicting response and remission with SSRI treatment.

Source connections

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Papageorgiou, G., Ibrahim, E.-C., Maxson, G., Gorgievski, V., Lozano, E., Belzeaux, R., Carmody, T., Tzavara, E. T., Trivedi, M. T., Mellios, N.. 2024-05-01. A brain-enriched circRNA blood biomarker can predict response to SSRI antidepressants. https://doi.org/10.1101/2024.04.30.591973

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Same Movements, Different Skills: How Context Shapes Learning in Humans and Neural Networks

What determines whether experiences are integrated into shared representations or split? Motor skill learning offers a revealing test: when two tasks require an identical sequence of movements, both intuition and theory suggest that practice in one should generalize to the other. Yet we find that changing context can make identical movements different skills. In simple recurrent networks trained on the same sequence across contexts, splitting was greatest when strong contextual inputs coincided with a high initial weight scale; neither alone sufficed. Greater representational separation coincided with separate context-specific learning curves. Humans practicing the same finger sequence while alternating between two performance-irrelevant contexts likewise exhibited context-specific curves, rather than the single continuous trajectory observed when practicing in a single context. State-space modeling favored split learning states in humans and very weakly coupled states in the most context-dependent networks. These findings have broader implications for understanding how learning generalizes across contexts.

neuroscience↗

Postpartum resource scarcity reshapes maternal care, offspring prioritization, and the steroid-sensitive medial preoptic area

Parental care requires balancing offspring needs with environmental constraints, yet how resource scarcity alters the organization of maternal responses remains poorly understood. Here, we examined maternal behavior, offspring prioritization, and steroid receptor-associated extracellular matrix organization in postpartum rats exposed to limited bedding and nesting (LBN) or control conditions with ample nesting materials. LBN increased pup-directed and reduced self-directed behaviors but also increased behavioral entropy, a measure of behavioral fragmentation, indicating less predictable caregiving sequences. LBN dams preferentially retrieved male pups first, whereas control dams preferentially retrieved females first. LBN also increased the proportion of pup-investigation time directed toward males compared with controls. In offspring on postnatal day 10, corticosterone was elevated in female but not male LBN pups, a pattern that paralleled the maternal male bias. To identify associated neurobiological changes, we assessed steroid receptors and extracellular matrix organization in the medial preoptic area (mPOA), a key region for maternal behavior. LBN increased numbers of estrogen receptor (ER)- and androgen receptor (AR)-immunoreactive cells, reduced perineuronal net (PNN) abundance, and decreased receptor-positive cell enwrapment. Because PNNs constrain structural plasticity, these findings suggest that the postpartum environment alters the organization of steroid receptor-associated extracellular matrix, potentially modulating experience-dependent plasticity in neurosteroid-sensitive circuits. In sum, LBN increases the levels of maternal engagement while reshaping its temporal organization and allocation, accompanied by selective remodeling of steroid-sensitive preoptic architecture. Therefore, maternal adaptation to adversity may be better understood as changes in caregiving allocation strategies rather than a simple gain or loss of maternal care.

neuroscience↗

The Computational Grammar of Human Behaviour in a Biological Environment

Human behaviour unfolds as an extended stream of spontaneous actions shaped by environmental demands and the agent's goals. The organisation of these actions, situated between setting abstract goals and concrete movement execution, remains poorly characterised. Here, we introduce DynaClade, an unsupervised temporal action segmentation algorithm for human full-body motion capture data, and apply it to freely moving participants navigating ecologically realistic risky foraging environments in virtual reality. DynaClade identifies discrete actions whose kinematic characteristics and spatial distributions correspond to environmental affordances, supporting the validity of the approach. Among these actions, we discovered a hitherto undescribed movement pattern defined by increased body sway without locomotion. Action sequences were highly non-random and showed clear history dependence beyond first-order Markov structure, most of which was explained by a second-order prediction. This higher-order structure was well captured in a hidden Markov model, in which some of the observed actions were associated with more than one latent state. Each of these latent states corresponded to the same action in a distinct action context. These findings suggest that spontaneous human actions are predominantly selected based on the current action and context, rather than following pre-planned or habitually assembled sequences.

neuroscience↗