A neuroprotective role of Ufmylation through Atg9 in the aging brain of Drosophila.

Ufmylation is a recently identified small ubiquitin-like modification, whose biological function and relevant cellular targets are poorly understood. Here we present evidence of a neuroprotective role for Ufmylation involving Autophagy-related gene 9 (Atg9) during Drosophila aging. The Ufm1 system ensures the health of aged neurons via Atg9 by coordinating autophagy and mTORC1, and maintaining mitochondrial homeostasis and JNK (c-Jun N-terminal kinase) activity. Neuron-specific expression of Atg9 suppresses the age-associated movement defect and lethality caused by loss of Ufmylation. Furthermore, Atg9 is identified as a conserved target of Ufm1 conjugation mediated by Ddrgk1, a critical regulator of Ufmylation. Mammalian Ddrgk1 was shown to be indispensable for the stability of endogenous Atg9A protein in mouse embryonic fibroblast (MEF) cells. Taken together, our findings might have important implications for neurodegenerative diseases in mammals.

Resting frontal EEG asymmetry and schizotypal traits: a test-retest study.

Introduction: Increase in right relative to left frontal electroencephalography (EEG) activity has been observed in patients with schizophrenia, both in cognitive tasks and during rest; and this lateralisation may be related to the severity of schizotypal traits. Methods: We used the Schizotypal Personality Questionnaire (SPQ) to assess schizotypal traits, and examined the correlation between these traits and resting EEG frontal asymmetry (left-right) in 52 college students, as well as the reliability of this correlation over a three-month interval. Results: A higher total score on the SPQ was correlated with reduced asymmetry in different frequency bands: gamma and beta2 frequency bands at baseline, and delta and alpha frequency bands three months later. Additionally, the reduced left relative to right frontal gamma and beta2 asymmetry was correlated with the participants' verbal fluency ability. However, this correlation was no longer statistically significant after the total SPQ score was controlled. Conclusions: These findings suggest that resting frontal EEG asymmetry is correlated with powers in different frequency bands, and may be an endophenotype for schizophrenia spectrum disorders.

Early Consciousness Disorder in Acute Large Hemispheric Infarction: An Analysis Based on Quantitative EEG and Brain Network Characteristics.

BACKGROUND: Large hemispheric infarction (LHI) is an ischemic stroke affecting at least two-thirds of the middle cerebral artery territory, with or without involvement of the anterior cerebral artery or posterior cerebral artery, and approximately 77% of LHI patients have early consciousness disorder (ECD). We constructed a functional brain network for LHI patients with an acute consciousness disorder to identify new diagnostic markers related to ECDs by analyzing brain network characteristics and mechanisms. METHODS: Between August 1, 2017, and September 30, 2018, patients with acute (< 1 month) LHI were admitted to the neurocritical care unit at Xuanwu Hospital of Capital Medical University. Electroencephalography (EEG) data were recorded, and the MATLAB platform (2017b) was used to calculate spectral power, entropy, coherence and phase synchronization. The quantitative EEG and brain network characteristics of different consciousness states and different frequency bands were analyzed (α = 0.05). EEG data were recorded 38 times in 30 patients, 25 of whom were in the ECD group, while 13 patients were in the conscious group. RESULTS: (1) Spectral power analysis: The conscious group had higher beta relative spectral power across the whole brain, higher alpha spectral power in the frontal-parietal lobe on the infarction contralateral side, and lower theta and delta spectral power in the central-occipital lobe on the infarction contralateral side than the ECD group. (2) Entropy analysis: The conscious group had higher approximate entropy (ApEn) and permutation entropy (PeEn) across the whole brain than the ECD group. (3) Coherence: The conscious group had higher alpha coherence in nearly the whole brain and higher beta coherence in the bilateral frontal-parietal and parietal-occipital lobes than the ECD group. (4) Phase synchronization: The conscious group had higher alpha and beta synchronization in nearly the whole brain, particularly in the frontal-parietal and parietal-occipital lobes, than the ECD group. (5) Graph theory: The conscious group had higher small-worldness in each frequency band than the ECD group. CONCLUSION: In patients with LHI, higher levels of consciousness were associated with more alpha and beta oscillations and fewer delta and theta oscillations. Higher ApEn, PeEn, total brain connectivity, and small-worldness and a wider signal distribution range corresponded to a higher consciousness level.

The effect of EEG microstate on the characteristics of TMS-EEG.

OBJECTIVE: Differences in neural states at the time of transcranial magnetic stimulation (TMS) can lead to variations in the effectiveness of TMS stimulation. Strategies that aim to lock neural activity states and improve the precision of stimulation timing in TMS optimization should gradually receive attention. One feasible approach is to utilize microstate locking for TMS stimulation, and understanding the impact of microstates at the time of stimulation on TMS response forms the foundation of this approach. APPROACH: TMS-EEG data were extracted from 21 healthy subjects through experiments. Based on the different microstates at the time of stimulation, the trials were classified into four datasets. TMS-evoked potential (TEP), topographical distribution, and natural frequency, were computed for each dataset to explore the differences in TMS-EEG characteristics across different microstates. MAIN RESULTS: The N100 component of microstate C group (-2.376 µV) was significantly higher (p = 0.003) than of microstate D group (-1.739 µV), and the P180 component of microstate D group (2.482 µV) was significantly higher (p = 0.024) than of microstate B group (1.766 µV) and slightly higher (p = 0.058) than of microstate C group (1.863 µV) by calculating the ROI. The topographical distribution of TEP components during microstate C and microstate D still retained the template characteristics of the microstate at the time of stimulation, and the natural frequencies did not differ among the four classical microstates. SIGNIFICANCE: This study showed the potential for future closed-loop TMS based on microstates and would guiding the development of microstate-based closed-loop TMS techniques.

Preclinical Efficacy of Cap-Dependent and Independent mRNA Vaccines against Bovine Viral Diarrhea Virus-1.

Bovine viral diarrhea virus (BVDV) is an RNA virus associated with severe economic losses in animal production. Effective vaccination and viral surveillance are urgent for the prevention and control of BVDV infection. However, the application of traditional modified live vaccines and inactivated vaccines is faced with tremendous challenges. In the present study, we describe the preclinical efficacy of two BVDV mRNA vaccines tested in mice and guinea pigs, followed by a field trial in goats, where they were compared to a commercial vaccine (formaldehyde inactivated). The two mRNAs were engineered to express the envelope protein E2 of BVDV-1, the most prevalent subtype across the world, through a 5' cap-dependent or independent fashion. Better titers of neutralizing antibodies against BVDV-1 were achieved using the capped RNA in the sera of mice and guinea pigs, with maximum values reaching 9.4 and 13.7 (by -log2), respectively, on the 35th day post-vaccination. At the same time point, the antibody levels in goats were 9.1 and 10.2 for the capped and capless RNAs, respectively, and there were no significant differences compared to the commercial vaccine. The animals remained healthy throughout the experiment, as reflected by their normal leukogram profiles. Collectively, our findings demonstrate that mRNA vaccines have good safety and immunogenicity, and we laid a strong foundation for the further exploitation of efficient and safe BVDV vaccines.

Quantitative EEG and brain network analysis: predicting awakening from early coma after cardiopulmonary resuscitation.

OBJECTIVE: For patients in early coma after cardiopulmonary resuscitation (CPR), quantitative electroencephalogram (EEG) and brain network analysis was performed to identify relevant indicators of awakening. METHODS: A prospective cohort study was conducted on comatose patients after CPR in the neuro-critical care unit. The included patients received clinical evaluation. The bedside high-density (64-lead) EEG monitoring was performed for visual grading and calculation of power spectrum and brain network parameters. A 3-month prognostic assessment was performed and the patients were dichotomized into the awakening group and the unawakening group. RESULTS: A total of 25 patients were included. The awakening group had higher GCS score, more slow wave pattern and reactive EEG than the unawakening group (P = 0.003, P < 0.001, P < 0.001, respectively). Compared with the unawakening group, (1) the awakening group had significantly higher absolute and relative θ power and slow/fast band ratio of the whole brain (P < 0.05), (2) the awakening group had stronger connection based on coherence, phase synchronization, phase lag index and cross-correlation (P < 0.05), (3) the awakening group had higher small-worldness, clustering coefficient and average path length based on graph theory (P < 0.05). CONCLUSIONS: The power spectrum and brain network characteristics in patients in early coma after CPR have predictive value for recovery.

Transcranial Direct Current Stimulation Modulates EEG Microstates in Low-Functioning Autism: A Pilot Study.

Autism spectrum disorder (ASD) is a heterogeneous disorder that affects several behavioral domains of neurodevelopment. Transcranial direct current stimulation (tDCS) is a new method that modulates motor and cognitive function and may have potential applications in ASD treatment. To identify its potential effects on ASD, differences in electroencephalogram (EEG) microstates were compared between children with typical development (n = 26) and those with ASD (n = 26). Furthermore, children with ASD were divided into a tDCS (experimental) and sham stimulation (control) group, and EEG microstates and Autism Behavior Checklist (ABC) scores before and after tDCS were compared. Microstates A, B, and D differed significantly between children with TD and those with ASD. In the experimental group, the scores of microstates A and C and ABC before tDCS differed from those after tDCS. Conversely, in the control group, neither the EEG microstates nor the ABC scores before the treatment period (sham stimulation) differed from those after the treatment period. This study indicates that tDCS may become a viable treatment for ASD.

Emerging role of UFMylation in secretory cells involved in the endocrine system by maintaining ER proteostasis.

Ubiquitin-fold modifier 1 (UFM1) is a ubiquitin-like molecule (UBL) discovered almost two decades ago, but our knowledge about the cellular and molecular mechanisms of this novel protein post-translational modification is still very fragmentary. In this review, we first summarize the core enzymes and factors involved in the UFMylation cascade, which, similar to ubiquitin, is consecutively catalyzed by UFM1-activating enzyme 5 (UBA5), UFM1-conjugating enzyme 1 (UFC1) and UFM1-specific ligase 1 (UFL1). Inspired by the substantial implications of UFM1 machinery in the secretory pathway, we next concentrate on the puzzling role of UFMylation in maintaining ER protein homeostasis, intending to illustrate the underlying mechanisms and future perspectives. At last, given a robust ER network is a hallmark of healthy endocrine secretory cells, we emphasize the function of UFM1 modification in physiology and pathology in the context of endocrine glands pancreas and female ovaries, aiming to provide precise insight into other internal glands of the endocrine system.

Comatose Patients After Cardiopulmonary Resuscitation: An Analysis Based on Quantitative Methods of EEG Reactivity.

Objective: Every year, approximately 50-110/1,00,000 people worldwide suffer from cardiac arrest, followed by hypoxic-ischemic encephalopathy after cardiopulmonary resuscitation (CPR), and approximately 40-66% of patients do not recover. The purpose of this study was to identify the brain network parameters and key brain regions associated with awakening by comparing the reactivity characteristics of the brain networks between the awakening and unawakening groups of CPR patients after coma, thereby providing a basis for further awakening interventions. Method: This study involved a prospective cohort study. Using a 64-electrode electroencephalography (EEG) wireless 64A system, EEG signals were recorded from 16 comatose patients after CPR in the acute phase (<1 month) from 2019 to 2020. MATLAB (2017b) was used to quantitatively analyze the reactivity (power spectrum and entropy) and brain network characteristics (coherence and phase lag index) after pain stimulation. The patients were divided into an awakening group and an unawakening group based on their ability to execute commands or engage in repeated and continuous purposeful behavior after 3 months. The above parameters were compared to determine whether there were differences between the two groups. Results: (1) Power spectrum: the awakening group had higher gamma, beta and alpha spectral power after pain stimulation in the frontal and parietal lobes, and lower delta and theta spectral power in the bilateral temporal and occipital lobes than the unawakening group. (2) Entropy: after pain stimulation, the awakening group had higher entropy in the frontal and parietal lobes and lower entropy in the temporal occipital lobes than the unawakening group. (3) Connectivity: after pain stimulation, the awakening group had stronger gamma and beta connectivity in nearly the whole brain, but weaker theta and delta connectivity in some brain regions (e.g., the frontal-occipital lobe and parietal-occipital lobe) than the unawakening group. Conclusion: After CPR, comatose patients were more likely to awaken if there was a higher stimulation of fast-frequency band spectral power, higher entropy, stronger whole-brain connectivity and better retention of frontal-parietal lobe function after pain stimulation.

Potential of Drosophila melanogaster (fruit fly) as a dietary protein source for broilers.

This study was conducted to systematically assess and compare the fluctuations in crude protein (CP), crude fat (CF), and mineral content of staged (larva to adult) Drosophila (fruit fly) to that of a market-purchased black soldier fly larvae (BSFL) product. Results suggested that the relative CP content by dry matter ranged from 40.11% to 53.73% during Drosophila development, significantly higher (P < 0.001) than the 36.90% in BSFL. The relative CF was higher in BSFL (39.14%) compared to that of Drosophila (27.03-30.10%, P < 0.001). Although both insects contained sufficient levels of minerals to meet the dietary requirements of most animals, Drosophila overall possessed a lower content of iron, sodium, and calcium (P < 0.001) with a higher gross energy than the BSFL (P < 0.01). Comparative studies of amino acid (AA) and fatty acid (FA) profiles were further carried out among Drosophila larva (DL), pupa, and BSFL for their economic effectiveness. The AA spectra of insect larvae generally were similar except that the DL was higher in certain AA such as lysine (P < 0.01), which is an essential AA often critical for chicken growth. In contrast, the BSFL included more essential FA such as linoleic (C18:2, ω-6) and linolenic (C18:3, ω-3) acids (P < 0.01). To follow up, a husbandry trial was performed by allotting 120, 1-d-old, weight-matched, Arbor Acres broilers at random into treatment groups consisting of a low-protein diet background that contained ~20% CP supplemented with 4% BSFL and 4% or 8% DL. The average daily growth (ADG) and average daily feed intake (ADFI) of broilers, compared to the control low-protein diet, were significantly improved by feeding DL diets (P < 0.01), with better live and carcass weight and higher muscle pH (P < 0.001), which were positively correlated with the inclusion level of DL (P < 0.001). However, no differences between the control and 4% BSFL diet were observed for the performance parameters mentioned above. Moreover, all birds under our experimental setting exhibited a comparable feed conversion ratio (FCR) and were in a healthy status as indicated by the meat traits and hematological indexes within normal physiological ranges. Collectively, the findings in this study provide a theoretical basis for the further exploitation of Drosophila as potential dietary ingredients for feed production in order to meet the food challenge in the future.

Insects are regarded as one of the most promising protein sources for feed production due to its high nutritional value and low environmental cost. The objectives of this study were to analyze the dynamic nutritional composition of Drosophila (fruit fly) at various developmental phases in parallel with a commercial black soldier fly larvae (BSFL) meal, as well as to determine the effect of diets with their inclusion on broilers. Results showed that Drosophila larvae possessed a higher crude protein and a lower crude fat content when compared to the BSFL product. In the feeding trial, the performance of broilers receiving Drosophila diets was remarkably improved, with no significant influence on bird metabolic status and meat quality, except the pH of breast and thigh muscles in Drosophila diet groups being higher than that of the control group, but still in the normal range. To sum up, Drosophila meal evaluated herein has a good nutritional composition and thereby elicits a beneficial impact on the growth performance and meat production of broilers, making it a potential dietary protein source for poultry.

Scale-free dynamics of microstate sequence in negative schizophrenia and depressive disorder.

OBJECTIVE: Negative schizophrenia (NSZ) and depressive disorder (DE) have many clinical similarities (e.g., lack of energy, social withdrawal). The purpose of this study was to explore microstate (MS) and scale-free dynamics of microstate sequence (SFML) in NSZ patients, DE patients and healthy controls (HC). METHODS: The subjects included 30 NSZ patients, 32 DE patients and 34 age-matched healthy controls. A resting-state electroencephalogram (rsEEG) was recorded under two conditions: (1) resting state with eyes opened (EO) and (2) resting state with eyes closed (EC). First, rsEEG signals were filtered into 1-45 Hz. Then, MS analysis was performed using the Microstate EEGLAB toolbox. Finally, the SFML feature of the sequence, which was transformed from the MS label sequence, was extracted by the Hurst exponent (HE). RESULTS: The rsEEG data of all subjects were clustered into six topographies. We could conclude that DE and NSZ patients show similar abnormalities in EO state. However, in the EC state, MS A, and B values were unique to NSZ patients, while DE patients had different values for MS C D and F. We also found a large correlation between these features and clinical information. In SFML, the Hurst exponent of the EO state might be more useful in assessing the characteristics of NSZ, while that of EC state can be used to understand these disorders with different random walk classifications. SIGNIFICANCE: The methods are associated with the ability to dynamically change of brain and information processing system. The MS and SFML of the EO state can be used to reflect the similar abnormalities of NSZ and DE patients. We recommend the EC state as the appropriate state to study the difference between the disorders. By combing the two states and these method, we can learn and study more similarities and differences between NSZ and DE.

Application of Fast Perturbational Complexity Index to the Diagnosis and Prognosis for Disorders of Consciousness.

OBJECTIVE: Diagnosis and prognosis of patients with disorders of consciousness (DOC) is a challenge for neuroscience and clinical practice. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is an effective tool to measure the level of consciousness. However, a scientific and accurate method to quantify TMS-evoked activity is still lacking. This study applied fast perturbational complexity index (PCIst) to the diagnosis and prognosis of DOC patients. METHODS: TMS-EEG data of 30 normal healthy participants (NOR) and 181 DOC patients were collected. The PCIst was used to assess the time-space complexity of TMS-evoked potentials (TEP). We selected parameters of PCIst in terms of data length, data delay, sampling rate and frequency band. In addition, we collected Coma Recovery Scale-Revised (CRS-R) values for 114 DOC patients after one year. Finally, we trained the classification and regression model. RESULTS: 1) PCIst shows the differences among NOR, minimally consciousness state (MCS) and unresponsive wakefulness syndrome (UWS) and has low computational cost. 2) Optimal parameters of data length and delay after TMS are 300 ms and 101-300 ms. Significant differences of PCIst at 5-8 Hz and 9-12 Hz bands are found among NOR, MCS and UWS groups. PCIst still works when TEP is down-sampled to 250 Hz. 3) PCIst at 9-12 Hz shows the highest performance in diagnosis and prognosis of DOC. CONCLUSIONS: This study confirms that PCIst can quantify the level of consciousness. PCIst is a potential measure for the diagnosis and prognosis of DOC patients.

The identification of children with autism spectrum disorder by SVM approach on EEG and eye-tracking data.

OBJECTIVE: To identify autistic children, we used features extracted from two modalities (EEG and eye-tracking) as input to a machine learning approach (SVM). METHODS: A total of 97 children aged from 3 to 6 were enrolled in the present study. After resting-state EEG data recording, the children performed eye-tracking tests individually on own-race and other-race stranger faces stimuli. Power spectrum analysis was used for EEG analysis and areas of interest (AOI) were selected for face gaze analysis of eye-tracking data. The minimum redundancy maximum relevance (MRMR) feature selection method combined with SVM classifiers were used for classification of autistic versus typically developing children. RESULTS: Results showed that classification accuracy from combining two types of data reached a maximum of 85.44%, with AUC = 0.93, when 32 features were selected. LIMITATIONS: The sample consisted of children aged from 3 to 6, and no younger patients were included. CONCLUSIONS: Our machine learning approach, combining EEG and eye-tracking data, may be a useful tool for the identification of children with ASD, and may help for diagnostic processes.