Study links major depression symptoms to endocytosis, hypersomnia, immune and motor pathways

A new genetic analysis of major depressive disorder has identified a striking link between one of depression’s most overlooked symptoms—hypersomnia, or excessive sleep—and the biological process cells use to internalize and recycle material. The study, published in BMC Psychiatry, reports that genes involved in endocytosis showed a strong negative association with hypersomnia across six brain tissues. The finding does not mean that impaired endocytosis causes people to sleep too much, nor does it demonstrate that the pathway is active or disrupted in the brains of patients. Instead, it points to a molecular signal that may help researchers understand why depression can look radically different from one person to another. The analysis also highlights immune, metabolic and motor-related gene patterns associated with other symptoms, while warning that pathway-level results can be misleading when a small number of genes dominate the statistics.

Depression is often treated as a single disorder in genetic studies, but its diagnostic criteria encompass a broad collection of experiences: low mood, loss of interest, problems with concentration, appetite and weight changes, altered sleep, fatigue, agitation and slowed movement. These symptoms do not necessarily share identical biological origins. In particular, hypersomnia and weight gain may represent a different neurobiological profile from insomnia and weight loss, a pattern sometimes associated with immunometabolic or “reversed-neurovegetative” depression. The new work therefore examined symptoms individually rather than assuming that every feature reflects one common depression factor. Its central question was whether biological pathways previously nominated in a mouse experiment involving nicotinamide mononucleotide, or NMN, would show symptom-specific associations with genetically predicted gene expression in the human brain.

To investigate that question, Ngo Cheung reconstructed pathway-level statistics from publicly available Summary-level PrediXcan, or S-PrediXcan, results. S-PrediXcan is a transcriptome-wide association method that uses genetic variants to predict how strongly genes are likely to be expressed in a tissue, then tests whether those genetically predicted expression levels are associated with a trait. In this case, the available gene-level Z scores covered 12 depression symptoms across multiple brain tissues. A Z score expresses the strength and direction of an association in standardized units: positive values indicate that higher genetically predicted expression tends to track with the symptom, while negative values indicate the opposite pattern. The researcher combined evidence across tissues and aggregated genes into Kyoto Encyclopedia of Genes and Genomes, or KEGG, pathways.

The most robust signal involved endocytosis and hypersomnia. The pathway produced a Stouffer Z score of approximately −5.52, a statistic generated by combining standardized evidence from many genes. Its permutation probability was 0.0025, and 194 genes contributed to the result. The association was consistent across six brain tissues and ranked first among 240 tested pathway comparisons. A size-matched union-null analysis, designed to compare the pathway with randomly assembled gene sets of similar size, produced a probability of 0.005. These safeguards are important because large pathways can appear significant simply by containing many genes. The result also survived checks designed to determine whether the association was distributed across the pathway rather than being driven by a narrow selection of genes with unusually large effects. According to the study, the signal aligned with the core direction of hypersomnia rather than with a small, contradictory subset of genes.

Endocytosis is a fundamental cellular transport process. It begins when a cell membrane folds inward and encloses molecules, receptors or membrane fragments in a small vesicle. The vesicle can then merge with compartments such as endosomes, where its contents are sorted, recycled or sent for degradation. In neurons, endocytosis is essential for recycling synaptic vesicles after neurotransmitter release, maintaining the balance of receptors at the cell surface and regulating communication between nerve cells. Disturbances in this machinery could, in principle, affect neuronal signaling, energy use or responses to external signals. But the study did not measure endocytosis directly in people with depression, and it did not establish whether the pathway is overactive or underactive in patients. The negative statistical association with hypersomnia means only that the direction of genetically predicted expression across the implicated genes was inversely related to the symptom in the analyzed data.

Other findings were more limited but added to the study’s picture of symptom diversity. Antigen processing and presentation, a biological process involved in displaying protein fragments to immune cells, showed a positive association with hypersomnia. However, the effect was small and heavily weighted toward TAPBP, a gene involved in loading peptides onto major histocompatibility complex class I molecules. This concentration means the result should not be interpreted as evidence that the entire immune pathway has a uniform relationship with excessive sleep. Rather, it identifies a specific gene and process for further investigation. The study’s authors treated the immune result as less definitive than the endocytosis signal, emphasizing the importance of distinguishing a pathway that is broadly supported from one that appears significant because of one influential component.

The analysis also found positive associations between psychomotor agitation and two gene sets: peroxisome and a vasopressin-labelled set. Peroxisomes are intracellular organelles that help break down fatty acids, manage reactive oxygen species and carry out other aspects of lipid and energy metabolism. In the agitation analysis, the signal was supported mainly by IDH2 and HSD17B4 rather than by HMGCL. IDH2 participates in mitochondrial metabolism and redox balance, while HSD17B4 has roles in peroxisomal fatty-acid processing. The vasopressin-labelled set, meanwhile, was driven more strongly by dynein and genes in the CREB3 family than by AVP itself. Dynein is a motor protein complex that transports cargo along microtubules, and CREB3-family proteins regulate gene expression in response to cellular stress and secretory demands. These results illustrate why pathway labels alone can be deceptive: a set named after a hormone or organelle may owe its statistical association to genes with quite different cellular functions.

One especially dramatic gene-level contrast involved HMGCL, which showed an extreme difference between weight gain and weight loss. HMGCL encodes an enzyme involved in ketone-body production, linking it to energy metabolism during periods when carbohydrate availability is low. Such a sharp contrast might appear to connect HMGCL directly to the peroxisome–agitation association, but the study found that it did not. This distinction is central to interpreting modern genetic pathway analyses. A gene can show a compelling association with one symptom while contributing little or nothing to a separate pathway signal involving another symptom. Conversely, a pathway can appear important because several modestly associated genes point in the same direction, even when no single gene dominates. The researcher therefore used leave-one-gene-out tests, bootstrap confidence intervals and gene-class decomposition to examine how stable each result remained when individual contributors were removed.

The work is best viewed as a prioritization study rather than a discovery of a depression mechanism or a treatment target. Its data were derived from summary statistics and genetically predicted expression, not from direct measurements of RNA or endocytosis in the brains of people experiencing depression. Genetic prediction also does not capture every factor that controls gene activity, including medication, stress, sleep history, metabolic state, cell type and environmental exposures. The analysis cannot show that NMN improves depression, that NMN changes human brain pathways, or that patients with a particular symptom profile would respond to NMN or any other intervention. Nor does it establish biological subtypes of depression. The NMN connection comes from the origin of the nominated KEGG gene sets in a mouse aging experiment; it is not evidence of NMN responsiveness in humans.

Even with those limitations, the findings could influence how future depression research is designed. Instead of asking whether a gene is associated with “depression” in the broadest possible sense, investigators may test whether it is linked specifically to hypersomnia, insomnia, weight gain, weight loss, agitation or psychomotor slowing. The endocytosis–hypersomnia association could be examined in larger cohorts, directly measured in relevant neuronal and glial cell types, and tested using colocalization analyses to determine whether the same genetic variants influence both gene expression and the symptom. Researchers could also investigate whether the signal varies by brain region, ancestry, age, sex or metabolic status. For now, the study’s most consequential message is methodological as much as biological: depression’s symptoms may carry distinct genetic signatures, and a pathway label is only the beginning of the explanation—not the explanation itself.