Protein kinase C-theta (PKC-theta) is essential for mature T cell activation; however, the mechanism by which it is recruited to the TCR signaling machinery is unknown. Here we show that T cell stimulation by antibodies or peptide-major histocompatibility complex (MHC) induces translocation of PKC-theta to membrane lipid rafts, which localize to the immunological synapse. Raft translocation was mediated by the PKC-theta regulatory domain and required Lck but not ZAP-70. In addition, PKC-theta was associated with Lck in the rafts. An isolated PKC-straight theta catalytic fragment did not partition into rafts or activate the transcription factor NF-kappa B, although addition of a Lck-derived raft-localization sequence restored these functions. Thus, physiological T cell activation translocates PKC-theta to rafts, which localize to the T cell synapse; this PKC-theta translocation is important for its function.

In Hubei, China, where the COVID-19 epidemic first emerged, the government has enforced strict quarantine and lockdown measures. Longitudinal studies suggest that the impact of adverse events on psychological adjustment is highly heterogenous. To better understand protective and risk factors that predict longitudinal psychopathology and resilience following strict COVID-19 lockdowns, this study used unsupervised machine learning to identify half-year longitudinal trajectories (April, June, August, and October, 2020) of three mental health outcomes (depression, anxiety, and PTSD) among a sample of Hubei residents (N = 326), assessed a broad range of person-and context-level predictors, and applied LASSO logistic regression, a supervised machine learning approach, to select best predictors for trajectory memberships of resilience and chronic psychopathology. Across outcomes, most individuals remained resilient.Models with both person-and context-level predictors showed excellent predictive accuracy, except for models predicting chronic anxiety. The person-level models showed either good or excellent predictive accuracy. The context-level models showed good predictive accuracy for depression trajectories but were only fair in predicting trajectories of anxiety and PTSD. Overall, the most critical person-level predictors were worry, optimism, fear of COVID, and coping flexibility, whereas important context-level predictors included features of stressful life events, community satisfaction, and family support. This study identified clinical patterns of response to COVID-19 lockdowns and used a combination of risk and protective factors to accurately differentiate these patterns. These findings have implications for clinical risk identifications and interventions in the context of potential trauma.

Background: The Coronavirus Disease 2019 (COVID-19) pandemic has led to overwhelming levels of distress as it spread rapidly from Wuhan, Hubei province to other regions in China. To contain the transmission of COVID-19, China has executed strict lockdown and quarantine policies, particularly in provinces with the highest severity (i.e., Hubei). Although the challenges faced by individuals across provinces may share some similarities, it remains unknown as to whether and how the severity of COVID-19 is related to elevation in depression.Methods: The present study compared depression among individuals who lived in mildly, moderately, and severely impacted provinces in China following the lockdown (N = 1,200) to norm data obtained from a representative sample within the same provinces in 2016 (N = 950), and examined demographic correlates of depression in 2020.Results: Residents in 2020, particularly those living in more heavily impacted provinces, reported increased levels of depression than the 2016 sample. Subsequent analyses of sub-dimensions of depression replicated the findings for depressed mood but not for positive affect, as the latter only declined among residents in the most severely impacted area. Increased depressed mood was associated with female, younger age, fewer years of education, and being furloughed from work, whereas reduced positive affect was associated with younger age and fewer years of education only.Conclusions: This study underscored the impact of COVID-19 on depression and suggested individual characteristics that may warrant attention.

The coronavirus disease 2019 (COVID-19) has disrupted multiple domains of life including sleep. The present study used a longitudinal dataset ( N = 671) and a person-centered analytic approach – latent profile analysis (LPA) – to elucidate the relationship between sleep and depression. We used LPA to identify profiles of sleep patterns assessed by Pittsburg Sleep Quality Index (PSQI) at the beginning of the study. The profiles were then used as a predictor of depression magnitude and variability over time. Three latent profiles were identified (medicated insomnia sleepers [MIS], inefficient sleepers [IS], and healthy sleepers [HS]). MIS exhibited the highest level of depression magnitude over time, followed by IS, followed by HS. A slightly different pattern emerged for the variability of depression: While MIS demonstrated significantly greater depression variability than both IS and HS, IS and HS did not differ in their variability of depression over time. Medicated insomnia sleepers exhibited both the greatest depression magnitude and variability than inefficient sleepers and healthy sleepers, while the latter two showed no difference in depression variability despite inefficient sleepers’ greater depression magnitude than healthy sleepers. Clinical implications and limitations are discussed.