De novo missense variants in HDAC3 leading to epigenetic machinery dysfunction are associated with a variable neurodevelopmental disorder.

Histone deacetylase 3 (HDAC3) is a crucial epigenetic modulator essential for various developmental and physiological functions. Although its dysfunction is increasingly recognized in abnormal phenotypes, to our knowledge, there have been no established reports of human diseases directly linked to HDAC3 dysfunction. Using trio exome sequencing and extensive phenotypic analysis, we correlated heterozygous de novo variants in HDAC3 with a neurodevelopmental disorder having variable clinical presentations, frequently associated with intellectual disability, developmental delay, epilepsy, and musculoskeletal abnormalities. In a cohort of six individuals, we identified missense variants in HDAC3 (c.277G>A [p.Asp93Asn], c.328G>A [p.Ala110Thr], c.601C>T [p.Pro201Ser], c. 797T>C [p.Leu266Ser], c.799G>A [p.Gly267Ser], and c.1075C>T [p.Arg359Cys]), all located in evolutionarily conserved sites and confirmed as de novo. Experimental studies identified defective deacetylation activity in the p.Asp93Asn, p.Pro201Ser, p.Leu266Ser, and p.Gly267Ser variants, positioned near the enzymatic pocket. In addition, proteomic analysis employing co-immunoprecipitation revealed that the disrupted interactions with molecules involved in the CoREST and NCoR complexes, particularly in the p.Ala110Thr variant, consist of a central pathogenic mechanism. Moreover, immunofluorescence analysis showed diminished nuclear to cytoplasmic fluorescence ratio in the p.Ala110Thr, p.Gly267Ser, and p.Arg359Cys variants, indicating impaired nuclear localization. Taken together, our study highlights that de novo missense variants in HDAC3 are associated with a broad spectrum of neurodevelopmental disorders, which emphasizes the complex role of HDAC3 in histone deacetylase activity, multi-protein complex interactions, and nuclear localization for proper physiological functions. These insights open new avenues for understanding the molecular mechanisms of HDAC3-related disorders and may inform future therapeutic strategies.

Genome-wide CRISPR screening identifies tyrosylprotein sulfotransferase-2 as a target for augmenting anti-PD1 efficacy.

BACKGROUND: Immune checkpoint therapy (ICT) provides durable responses in select cancer patients, yet resistance remains a significant challenge, prompting the exploration of underlying molecular mechanisms. Tyrosylprotein sulfotransferase-2 (TPST2), known for its role in protein tyrosine O-sulfation, has been suggested to modulate the extracellular protein-protein interactions, but its specific role in cancer immunity remains largely unexplored. METHODS: To explore tumor cell-intrinsic factors influencing anti-PD1 responsiveness, we conducted a pooled loss-of-function genetic screen in humanized mice engrafted with human immune cells. The responsiveness of cancer cells to interferon-γ (IFNγ) was estimated by evaluating IFNγ-mediated induction of target genes, STAT1 phosphorylation, HLA expression, and cell growth suppression. The sulfotyrosine-modified target gene of TPST2 was identified by co-immunoprecipitation and mass spectrometry. The in vivo effects of TPST2 inhibition were evaluated using mouse syngeneic tumor models and corroborated by bulk and single-cell RNA sequencing analyses. RESULTS: Through in vivo genome-wide CRISPR screening, TPST2 loss-of-function emerged as a potential enhancer of anti-PD1 treatment efficacy. TPST2 suppressed IFNγ signaling by sulfating IFNγ receptor 1 at Y397 residue, while its downregulation boosted IFNγ-mediated signaling and antigen presentation. Depletion of TPST2 in cancer cells augmented anti-PD1 antibody efficacy in syngeneic mouse tumor models by enhancing tumor-infiltrating lymphocytes. RNA sequencing data revealed TPST2's inverse correlation with antigen presentation, and increased TPST2 expression is associated with poor prognosis and altered cancer immunity across cancer types. CONCLUSIONS: We propose TPST2's novel role as a suppressor of cancer immunity and advocate for its consideration as a therapeutic target in ICT-based treatments.

Indocyanine Green (ICG) in Robotic Gastrectomy: A Retrospective Review of Lymphadenectomy Outcomes for Gastric Cancer.

Radical gastrectomy is essential for gastric cancer treatment. While guidelines advise dissecting at least 16 lymph nodes, some research suggests over 30 nodes might be beneficial. This study assessed ICG-guided robotic gastrectomy's effectiveness in thorough lymph node dissection. We analyzed data from 393 stage II or III gastric cancer patients treated at Seoul St. Mary's Hospital from 2016-2022. Patients were categorized into conventional laparoscopy (G1, n = 288), ICG-guided laparoscopy (G2, n = 61), and ICG-guided robotic surgery (G3, n = 44). Among 391 patients, 308 (78.4%) achieved proper lymphadenectomy. The ICG-robotic group (G3) showed the highest success rate at 90.9%. ICG-guided robotic surgery was a significant predictor for achieving proper lymphadenectomy, with an odds ratio of 3.151. In conclusion, ICG-robotic gastrectomy improves lymphadenectomy outcomes in selected gastric cancer cases, indicating a promising surgical approach for the future.

Purification and characterization of different proteasome species from mammalian cells.

Proteasomes are heterogeneous in forms and functions, but how the equilibrium among the 20S, 26S, and 30S proteasomes is achieved and altered is elusive. Here, we present a protocol for purifying and characterizing proteasome species. We describe steps for generating stable cell lines; affinity purifying the proteasome species; and characterizing them through native PAGE, activity assay, size-exclusion chromatography, and mass spectrometry. These standardized methods may contribute to biochemical studies of cellular proteasomes under both physiological and pathological conditions. For complete details on the use and execution of this protocol, please refer to Choi et al. (2023).1.

In-depth proteome analysis of brain tissue from Ewsr1 knockout mouse by multiplexed isobaric tandem mass tag labeling.

EWS RNA binding protein 1 (EWSR1) is a multifunctional protein whose epigenetic signatures contribute to the pathogenesis of various human diseases, such as neurodegenerative disorders, skin development, and tumorigenic processes. However, the specific cellular functions and physiological characteristics of EWSR1 remain unclear. In this study, we used quantitative mass spectrometry-based proteomics with tandem mass tag labeling to investigate the global proteome changes in brain tissue in Ewsr1 knockout and wild-type mice. From 9115 identified proteins, we selected 118 differentially expressed proteins, which is common to three quantitative data processing strategies including only protein level normalizations and spectrum-protein level normalization. Bioinformatics analysis of these common differentially expressed proteins revealed that proteins up-regulated in Ewsr1 knockout mouse are mostly related to the positive regulation of bone remodeling and inflammatory response. The down-regulated proteins were associated with the regulation of neurotransmitter levels or amino acid metabolic processes. Collectively, these findings provide insight into the physiological function and pathogenesis of EWSR1 on protein level. Better understanding of EWSR1 and its protein interactions will advance the field of clinical research into neuronal disorders. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD026994.

Association between Vascular Calcification and Esophagojejunal Anastomotic Complications after Total Gastrectomy for Gastric Cancer: A Propensity-Matched Study.

Esophagojejunal anastomosis (EJA) complications after total gastrectomy are related to significant morbidity and mortality. The aim of this study was to evaluate the association between arterial calcifications and EJA complications such as leak and stricture for gastric cancer. Between January 2014 and October 2019, 30 patients with EJA complications after total gastrectomy were enrolled and matched to 30 patients without complications through retrospective data review. Arterial calcification grade on preoperative computed tomography (CT) was reported in the abdominal aorta and superior mesenteric artery (SMA) as "absent", "minor", or "major", and in the jejunal vascular arcade (JVA) and left inferior phrenic artery (LIPA) as "absent" or "present". A Chi-square test was used to compare the variables between the two groups. p-Value < 0.050 was considered statistically significant. Among 30 patients, the numbers of patients with leak and stricture were 23 and seven, respectively. Aortic calcifications were not associated with EJA complications regardless of their grade (p = 0.440). Only major SMA calcifications were associated with EJA complications, as they were present in five patients (16.7%) in the complication group and absent in the non-complication group (p = 0.020). Major SMA calcifications were more related to anastomotic stricture than leak. Three (13.0%) out of 23 patients with leak and two (28.6%) out of seven with stricture had major SMA calcifications (p = 0.028). No calcifications were detected in the JVA or LIPA in any of the 60 patients. Major SMA calcifications were found to be associated with EJA complications, especially in stricture.

Codeless Deep Learning of COVID-19 Chest X-Ray Image Dataset with KNIME Analytics Platform.

OBJECTIVES: This paper proposes a method for computer-assisted diagnosis of coronavirus disease 2019 (COVID-19) through chest X-ray imaging using a deep learning model without writing a single line of code using the Konstanz Information Miner (KNIME) analytics platform. METHODS: We obtained 155 samples of posteroanterior chest X-ray images from COVID-19 open dataset repositories to develop a classification model using a simple convolutional neural network (CNN). All of the images contained diagnostic information for COVID-19 and other diseases. The model would classify whether a patient was infected with COVID-19 or not. Eighty percent of the images were used for model training, and the rest were used for testing. The graphic user interface-based programming in the KNIME enabled class label annotation, data preprocessing, CNN model training and testing, performance evaluation, and so on. RESULTS: 1,000 epochs training were performed to test the simple CNN model. The lower and upper bounds of positive predictive value (precision), sensitivity (recall), specificity, and f-measure are 92.3% and 94.4%. Both bounds of the model's accuracies were equal to 93.5% and 96.6% of the area under the receiver operating characteristic curve for the test set. CONCLUSIONS: In this study, a researcher who does not have basic knowledge of python programming successfully performed deep learning analysis of chest x-ray image dataset using the KNIME independently. The KNIME will reduce the time spent and lower the threshold for deep learning research applied to healthcare.