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Major depressive disorder (MDD) is associated with high heterogeneity in clinical presentation. In addition, response to treatment with selective serotonin reuptake inhibitors (SSRIs) varies considerably among patients. Therefore, identifying genetic variants that may contribute to SSRI treatment responses in MDD is essential. In this study, we analyzed the syndromal factor structures of the Hamilton Depression Rating Scale in 479 patients with MDD by using exploratory factor analysis. All patients were followed up biweekly for 8 weeks. Treatment response was defined for all syndromal factors and total scores. In addition, a genome-wide association study was performed to investigate the treatment outcomes at week 4 and repeatedly assess all visits during follow-up by using mixed models adjusted for age, gender, and population substructure. Moreover, the role of genetic variants in suicidal and sexual side effects was explored, and five syndromal factors for depression were derived: core, insomnia, somatic anxiety, psychomotor-insight, and anorexia. Subsequently, several known genes were mapped to suggestive signals for treatment outcomes, including single-nucleotide polymorphisms (SNPs) in PRF1, UTP20, MGAM, and ENSG00000286536 for psychomotor-insight and in C4orf51 for anorexia. In total, 33 independent SNPs for treatment responses were tested in a mixed model, 12 of which demonstrated a p value <0.05. The most significant SNP was rs2182717 in the ENSR00000803469 gene located on chromosome 6 for the core syndromal factor (β = -0.638, p = 1.8 × 10 -4 ) in terms of symptom improvement over time. Patients with a GG or GA genotype with the rs2182717 SNP also exhibited a treatment response (β = 0.089, p = 2.0 × 10 -6 ) at week 4. Moreover, rs1836075352 was associated with sexual side effects (p = 3.2 × 10 -8 ). Pathway and network analyses using the identified SNPs revealed potential biological functions involved in treatment response, such as neurodevelopment-related functions and immune processes. In conclusion, we identified loci that may affect the clinical response to treatment with antidepressants in the context of empirically defined depressive syndromal factors and side effects among the Taiwanese Han population, thus providing novel biological targets for further investigation., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Although many studies on brain-age prediction in patients with schizophrenia have been reported recently, none has predicted brain age based on different neuroimaging modalities and different brain regions in these patients. Here, we constructed brain-age prediction models with multimodal MRI and examined the deviations of aging trajectories in different brain regions of participants with schizophrenia recruited from multiple centers. The data of 230 healthy controls (HCs) were used for model training. Next, we investigated the differences in brain age gaps between participants with schizophrenia and HCs from two independent cohorts. A Gaussian process regression algorithm with fivefold cross-validation was used to train 90, 90, and 48 models for gray matter (GM), functional connectivity (FC), and fractional anisotropy (FA) maps in the training dataset, respectively. The brain age gaps in different brain regions for all participants were calculated, and the differences in brain age gaps between the two groups were examined. Our results showed that most GM regions in participants with schizophrenia in both cohorts exhibited accelerated aging, particularly in the frontal lobe, temporal lobe, and insula. The parts of the white matter tracts, including the cerebrum and cerebellum, indicated deviations in aging trajectories in participants with schizophrenia. However, no accelerated brain aging was noted in the FC maps. The accelerated aging in 22 GM regions and 10 white matter tracts in schizophrenia potentially exacerbates with disease progression. In individuals with schizophrenia, different brain regions demonstrate dynamic deviations of brain aging trajectories. Our findings provided more insights into schizophrenia neuropathology., (© 2023. The Author(s).)

Stable catalysts are essential to address energy and environmental challenges, especially for applications in harsh environments (for example, high temperature, oxidizing atmosphere and steam). In such conditions, supported metal catalysts deactivate due to sintering-a process where initially small nanoparticles grow into larger ones with reduced active surface area-but strategies to stabilize them can lead to decreased performance. Here we report stable catalysts prepared through the encapsulation of platinum nanoparticles inside an alumina framework, which was formed by depositing an alumina precursor within a separately prepared porous organic framework impregnated with platinum nanoparticles. These catalysts do not sinter at 800 °C in the presence of oxygen and steam, conditions in which conventional catalysts sinter to a large extent, while showing similar reaction rates. Extending this approach to Pd-Pt bimetallic catalysts led to the small particle size being maintained at temperatures as high as 1,100 °C in air and 10% steam. This strategy can be broadly applied to other metal and metal oxides for applications where sintering is a major cause of material deactivation., (© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)

Secreted polypeptides are a fundamental axis of intercellular and endocrine communication. However, a global understanding of the composition and dynamics of cellular secretomes in intact mammalian organisms has been lacking. Here, we introduce a proximity biotinylation strategy that enables labeling, detection and enrichment of secreted polypeptides in a cell type-selective manner in mice. We generate a proteomic atlas of hepatocyte, myocyte, pericyte and myeloid cell secretomes by direct purification of biotinylated secreted proteins from blood plasma. Our secretome dataset validates known cell type-protein pairs, reveals secreted polypeptides that distinguish between cell types and identifies new cellular sources for classical plasma proteins. Lastly, we uncover a dynamic and previously undescribed nutrient-dependent reprogramming of the hepatocyte secretome characterized by the increased unconventional secretion of the cytosolic enzyme betaine-homocysteine S-methyltransferase (BHMT). This secretome profiling strategy enables dynamic and cell type-specific dissection of the plasma proteome and the secreted polypeptides that mediate intercellular signaling.

Inference of causality in time series has been principally based on the prediction paradigm. Nonetheless, the predictive causality approach may underestimate the simultaneous and reciprocal nature of causal interactions observed in real-world phenomena. Here, we present a causal-decomposition approach that is not based on prediction, but based on the covariation of cause and effect: cause is that which put, the effect follows; and removed, the effect is removed. Using empirical mode decomposition, we show that causal interaction is encoded in instantaneous phase dependency at a specific time scale, and this phase dependency is diminished when the causal-related intrinsic component is removed from the effect. Furthermore, we demonstrate the generic applicability of our method to both stochastic and deterministic systems, and show the consistency of causal-decomposition method compared to existing methods, and finally uncover the key mode of causal interactions in both modelled and actual predator-prey systems.

To analyze the functioning of the posterior cingulate cortex (PCC) in depression, we performed the first fully voxel-level resting state functional-connectivity neuroimaging analysis of depression of the PCC, with 336 patients with major depressive disorder and 350 controls. Voxels in the PCC had significantly increased functional connectivity with the lateral orbitofrontal cortex, a region implicated in non-reward and which is thereby implicated in depression. In patients receiving medication, the functional connectivity between the lateral orbitofrontal cortex and PCC was decreased back towards that in the controls. In the 350 controls, it was shown that the PCC has high functional connectivity with the parahippocampal regions which are involved in memory. The findings support the theory that the non-reward system in the lateral orbitofrontal cortex has increased effects on memory systems, which contribute to the rumination about sad memories and events in depression. These new findings provide evidence that a key target to ameliorate depression is the lateral orbitofrontal cortex.