Interleukin-3 coordinates glial-peripheral immune crosstalk to incite multiple sclerosis.

Glial cells and central nervous system (CNS)-infiltrating leukocytes contribute to multiple sclerosis (MS). However, the networks that govern crosstalk among these ontologically distinct populations remain unclear. Here, we show that, in mice and humans, CNS-resident astrocytes and infiltrating CD44hiCD4+ T cells generated interleukin-3 (IL-3), while microglia and recruited myeloid cells expressed interleukin-3 receptor-ɑ (IL-3Rɑ). Astrocytic and T cell IL-3 elicited an immune migratory and chemotactic program by IL-3Rɑ+ myeloid cells that enhanced CNS immune cell infiltration, exacerbating MS and its preclinical model. Multiregional snRNA-seq of human CNS tissue revealed the appearance of IL3RA-expressing myeloid cells with chemotactic programming in MS plaques. IL3RA expression by plaque myeloid cells and IL-3 amount in the cerebrospinal fluid predicted myeloid and T cell abundance in the CNS and correlated with MS severity. Our findings establish IL-3:IL-3RA as a glial-peripheral immune network that prompts immune cell recruitment to the CNS and worsens MS.

Meet the authors: Ying Lu, Donghoon Lee, and Yanan Zhu.

We talk to co-first authors Donghoon Lee and Yanan Zhu and co-corresponding author Ying Lu about their paper "Molecular mechanism for activation of the 26S proteasome by ZFAND5" (this issue of Molecular Cell), the challenges of the project, their scientific pathways, and the late Dr. Alfred Goldberg.

Molecular mechanism for activation of the 26S proteasome by ZFAND5.

Various hormones, kinases, and stressors (fasting, heat shock) stimulate 26S proteasome activity. To understand how its capacity to degrade ubiquitylated proteins can increase, we studied mouse ZFAND5, which promotes protein degradation during muscle atrophy. Cryo-electron microscopy showed that ZFAND5 induces large conformational changes in the 19S regulatory particle. ZFAND5's AN1 Zn-finger domain interacts with the Rpt5 ATPase and its C terminus with Rpt1 ATPase and Rpn1, a ubiquitin-binding subunit. Upon proteasome binding, ZFAND5 widens the entrance of the substrate translocation channel, yet it associates only transiently with the proteasome. Dissociation of ZFAND5 then stimulates opening of the 20S proteasome gate. Using single-molecule microscopy, we showed that ZFAND5 binds ubiquitylated substrates, prolongs their association with proteasomes, and increases the likelihood that bound substrates undergo degradation, even though ZFAND5 dissociates before substrate deubiquitylation. These changes in proteasome conformation and reaction cycle can explain the accelerated degradation and suggest how other proteasome activators may stimulate proteolysis.

Drosophila Myoblast Fusion: Invasion and Resistance for the Ultimate Union.

Cell-cell fusion is indispensable for creating life and building syncytial tissues and organs. Ever since the discovery of cell-cell fusion, how cells join together to form zygotes and multinucleated syncytia has remained a fundamental question in cell and developmental biology. In the past two decades, Drosophila myoblast fusion has been used as a powerful genetic model to unravel mechanisms underlying cell-cell fusion in vivo. Many evolutionarily conserved fusion-promoting factors have been identified and so has a surprising and conserved cellular mechanism. In this review, we revisit key findings in Drosophila myoblast fusion and highlight the critical roles of cellular invasion and resistance in driving cell membrane fusion.

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes.

The discovery of drivers of cancer has traditionally focused on protein-coding genes1-4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5' region of TP53, in the 3' untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.

26S proteasomes become stably activated upon heat shock when ubiquitination and protein degradation increase.

Heat shock (HS) promotes protein unfolding, and cells respond by stimulating HS gene expression, ubiquitination of cell proteins, and proteolysis by the proteasome. Exposing HeLa and other cells to 43 °C for 2 h caused a twofold increase in the 26S proteasomes' peptidase activity assayed at 37 °C. This increase in activity occurred without any change in proteasome amount and did not require new protein synthesis. After affinity-purification from HS cells, 26S proteasomes still hydrolyzed peptides, adenosine 5'-triphosphate, and ubiquitinated substrates more rapidly without any evident change in subunit composition, postsynthetic modification, or association with reported proteasome-activating proteins. After returning HS cells to 37 °C, ubiquitin conjugates and proteolysis fell rapidly, but proteasome activity remained high for at least 16 h. Exposure to arsenite, which also causes proteotoxic stress in the cytosol, but not tunicamycin, which causes endoplasmic reticulum stress, also increased ubiquitin conjugate levels and 26S proteasome activity. Although the molecular basis for the enhanced proteasomal activity remains elusive, we studied possible signaling mechanisms. Proteasome activation upon proteotoxic stress required the accumulation of ubiquitinated proteins since blocking ubiquitination by E1 inhibition during HS or arsenite exposure prevented the stimulation of 26S activity. Furthermore, increasing cellular content of ubiquitin conjugates at 37 °C by inhibiting deubiquitinating enzymes with RA190 or b-AP15 also caused proteasome activation. Thus, cells respond to proteotoxic stresses, apparently in response to the accumulation of ubiquitinated proteins, by activating 26S proteasomes, which should help promote the clearance of damaged cell proteins.

Androgen action in cell fate and communication during prostate development at single-cell resolution.

Androgens/androgen receptor (AR)-mediated signaling pathways are essential for prostate development, morphogenesis and regeneration. Specifically, stromal AR signaling has been shown to be essential for prostatic initiation. However, the molecular mechanisms underlying AR-initiated mesenchymal-epithelial interactions in prostate development remain unclear. Here, using a newly generated mouse model, we have directly addressed the fate and role of genetically marked AR-expressing cells during embryonic prostate development. Androgen signaling-initiated signaling pathways were identified in mesenchymal niche populations at single-cell transcriptomic resolution. The dynamic cell-signaling networks regulated by stromal AR were additionally characterized in relation to prostatic epithelial bud formation. Pseudotime analyses further revealed the differentiation trajectory and fate of AR-expressing cells in both prostatic mesenchymal and epithelial cell populations. Specifically, the cellular properties of Zeb1-expressing progenitors were assessed. Selective deletion of AR signaling in a subpopulation of mesenchymal rather than epithelial cells dysregulated the expression of the master regulators and significantly impaired prostatic bud formation. These data provide novel, high-resolution evidence demonstrating the important role of mesenchymal androgen signaling in the cellular niche controlling prostate early development by initiating dynamic mesenchyme-epithelia cell interactions.

Stromal androgen and hedgehog signaling regulates stem cell niches in pubertal prostate development.

Stromal androgen-receptor (AR) action is essential for prostate development, morphogenesis and regeneration. However, mechanisms underlying how stromal AR maintains the cell niche in support of pubertal prostatic epithelial growth are unknown. Here, using advanced mouse genetic tools, we demonstrate that selective deletion of stromal AR expression in prepubescent Shh-responsive Gli1-expressing cells significantly impedes pubertal prostate epithelial growth and development. Single-cell transcriptomic analyses showed that AR loss in these prepubescent Gli1-expressing cells dysregulates androgen signaling-initiated stromal-epithelial paracrine interactions, leading to growth retardation of pubertal prostate epithelia and significant development defects. Specifically, AR loss elevates Shh-signaling activation in both prostatic stromal and adjacent epithelial cells, directly inhibiting prostatic epithelial growth. Single-cell trajectory analyses further identified aberrant differentiation fates of prostatic epithelial cells directly altered by stromal AR deletion. In vivo recombination of AR-deficient stromal Gli1-lineage cells with wild-type prostatic epithelial cells failed to develop normal prostatic epithelia. These data demonstrate previously unidentified mechanisms underlying how stromal AR-signaling facilitates Shh-mediated cell niches in pubertal prostatic epithelial growth and development.

Applications of artificial intelligence-machine learning for detection of stress: a critical overview.

Psychological distress is a major contributor to human physiology and pathophysiology, and it has been linked to several conditions, such as auto-immune diseases, metabolic syndrome, sleep disorders, and suicidal thoughts and inclination. Therefore, early detection and management of chronic stress is crucial for the prevention of several diseases. Artificial intelligence (AI) and Machine Learning (ML) have promoted a paradigm shift in several areas of biomedicine including diagnosis, monitoring, and prognosis of disease. Here, our review aims to present some of the AI and ML applications for solving biomedical issues related to psychological stress. We provide several lines of evidence from previous studies highlighting that AI and ML have been able to predict stress and detect the brain normal states vs. abnormal states (notably, in post-traumatic stress disorder (PTSD)) with accuracy around 90%. Of note, AI/ML-driven technology applied to identify ubiquitously present stress exposure may not reach its full potential, unless future analytics focus on detecting prolonged distress through such technology instead of merely assessing stress exposure. Moving forward, we propose that a new subcategory of AI methods called Swarm Intelligence (SI) can be used towards detecting stress and PTSD. SI involves ensemble learning techniques to efficiently solve a complex problem, such as stress detection, and it offers particular strength in clinical settings, such as privacy preservation. We posit that AI and ML approaches will be beneficial for the medical and patient community when applied to predict and assess stress levels. Last, we encourage additional research to bring AI and ML into the standard clinical practice for diagnostics in the not-too-distant future.

Genome-based taxonomic identification and safety assessment of an Enterococcus strain isolated from a homemade dairy product.

The aim of this study was to explore the taxonomic identification and evaluate the safety of a bacterium, Enterococcus lactis IDCC 2105, isolated from homemade cheese in Korea, using whole genome sequence (WGS) analysis. It sought to identify the species level of this Enterococcus spp., assess its antibiotic resistance, and evaluate its virulence potential. WGS analysis confirmed the bacterial strain IDCC 2105 as E. lactis and identified genes responsible for resistance to erythromycin and clindamycin, specifically msrC, and eatAv, which are chromosomally located, indicating a minimal risk for horizontal gene transfer. The absence of plasmids in E. lactis IDCC 2105 further diminishes the likelihood of resistance gene dissemination. Additionally, our investigation into seven virulence factors, including hemolysis, platelet aggregation, biofilm formation, hyaluronidase, gelatinase, ammonia production, and ß-glucuronidase activity, revealed no detectable virulence traits. Although bioinformatic analysis suggested the presence of collagen adhesion genes acm and scm, these were not corroborated by phenotypic virulence assays. Based on these findings, E. lactis IDCC 2105 presents as a safe strain for potential applications, contributing valuable information on its taxonomy, antibiotic resistance profile, and lack of virulence factors, supporting its use in food products.

The effect of prescription drug insurance on the incidence of potentially inappropriate prescribing: Evidence from Medicare Part D.

The Medicare Part D program has been documented to increase the affordability and accessibility of drugs and improve the quality of prescription drug use; however, less is known about the equity impact of the Part D program on potentially inappropriate prescribing-specifically, incidences of polypharmacy and potentially inappropriate medication (PIM) use based on different racial/ethnic groups. Using a difference in the regression discontinuity design, we found that among Whites, Part D was associated with increases in polypharmacy and "broadly defined" PIM use, while the use of "always avoid" PIM remained unchanged. Conversely, Blacks and Hispanics reported no changes in such drug utilization patterns.

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study.

BACKGROUND: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. METHODS: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO2/FIO2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine). The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley's additive explanations (SHAP). RESULTS: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756-0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626-0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. CONCLUSION: Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

HDPose: Post-Hierarchical Diffusion with Conditioning for 3D Human Pose Estimation.

Recently, monocular 3D human pose estimation (HPE) methods were used to accurately predict 3D pose by solving the ill-pose problem caused by 3D-2D projection. However, monocular 3D HPE still remains challenging owing to the inherent depth ambiguity and occlusions. To address this issue, previous studies have proposed diffusion model-based approaches (DDPM) that learn to reconstruct a correct 3D pose from a noisy initial 3D pose. In addition, these approaches use 2D keypoints or context encoders that encode spatial and temporal information to inform the model. However, they often fall short of achieving peak performance, or require an extended period to converge to the target pose. In this paper, we proposed HDPose, which can converge rapidly and predict 3D poses accurately. Our approach aggregated spatial and temporal information from the condition into a denoising model in a hierarchical structure. We observed that the post-hierarchical structure achieved the best performance among various condition structures. Further, we evaluated our model on the widely used Human3.6M and MPI-INF-3DHP datasets. The proposed model demonstrated competitive performance with state-of-the-art models, achieving high accuracy with faster convergence while being considerably more lightweight.

Engineering of Solvation Entropy by Poly(4-styrenesulfonic acid) Additive in an Aqueous Electrochemical System for Enhanced Low-Grade Heat Harvesting.

The thermally regenerative electrochemical cycle (TREC) is a reliable and efficient approach to converting low-grade heat into electricity. A high temperature coefficient (α) is the key to maximize the energy conversion efficiency of the TREC system. In this study, we present significant improvement of α of a Prussian blue analogue (PBA)-based electrochemical cell by adding poly(4-styrenesulfonic acid) (PSS) to the electrolyte. Raman spectra showed that water-soluble charged polymers strongly affect the ion hydration structure and increase the entropy change (ΔS) during ion intercalation in PBA. A large α of -2.01 mV K-1 and high absolute heat-to-electricity conversion efficiency up to 1.83% was achieved with a TREC cell in the temperature range 10-40 °C. This study provides a fundamental understanding of the origin of α and a facile method to boosting the temperature coefficient for building a highly efficient low-grade heat harvesting system.

Determination of Sulfites in Dried Fruits by Paper Spray Ionization Tandem Mass Spectrometry.

Sulfite, a widely used food additive, is subject to regulated labeling. The extraction of sulfite as the stable hydroxymethylsulfonate (HMS) form and its quantitative analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been recognized for their good sensitivity, selectivity, and versatility across various food materials. This study aimed to develop a cost-effective and simpler method for sulfite quantitation, while maintaining the superior sensitivity and selectivity of mass spectrometry (MS). To achieve this, we introduced paper spray ionization (PSI), an ambient desorption ionization technique that could achieve the direct measurement of analytes without employing separation. We also employed a novel internal standard (IS) structurally similar to the analyte, replacing the more expensive isotopically labeled IS. Although the PSI-MS/MS method developed in this study exhibited slightly lower analytical performance compared to the conventional LC-MS/MS, it remained effective for sulfite analysis in dried fruits.

Medicaid Expansions and Access to Substance Abuse Treatment Services Among Limited English Proficiency Populations.

BACKGROUND: Although the myriad of provisions under the Affordable Care Act (ACA) have generally increased coverage and financial access to the health systems, language barriers represent a serious challenge to access to care among Limited English Proficiency (LEP) populations. OBJECTIVE: The aim of this study was to examine the effect of Medicaid expansions under the ACA on the availability of language services and Medicaid acceptance in substance abuse treatment (SAT) facilities. RESEARCH DESIGN: A quasi-experimental difference-in-differences design with multiple time periods was used to compare changes in the availability of language services and Medicaid as a payment source between Medicaid expansion and nonexpansion states. Facility-level observational data in the National Survey of Substance Abuse Treatment Services 2010-2019 was included. MEASURES: Availability of LEP services and Medicaid acceptance in the SAT facilities. RESULTS: The proportion of SAT facilities that provide LEP services increased from 40% in 2013 to 53% in 2019. The proportions by state are heterogeneous, ranging from approximately 20% to 70%. The ACA Medicaid expansions are not associated with changes in the availability of LEP services in the facilities. Moreover, Medicaid acceptance in the expansion states increased gradually following the expansion; however, the estimates are not statistically significant. CONCLUSION: The ACA Medicaid expansion had no impact on the availability of LEP services and the acceptance of Medicaid as a payment source in the SAT facilities.

Unexpected Reactivity of Cationic-to-Cationic Thiolate Ligand-Exchange Reaction on Au25 Clusters.

Thiolate-protected molecular noble metal clusters have attracted significant attention due to their unique physicochemical properties, which make them applicable in diverse fields such as catalysis, sensing, and bioimaging. Ligand-exchange reactions are a crucial technique for synthesizing and functionalizing these clusters, as they allow for the introduction of new ligands onto the cluster surface, which can alter their properties. While numerous studies have investigated neutral-to-neutral, neutral-to-anionic, and neutral-to-cationic ligand-exchange reactions, the cationic-to-cationic ligand-exchange reaction has never been reported, making the study of such reactions intriguing. In this study, the cationic ligand-exchange reaction on Au25(4-PyET-CH3+)x(4-PyET)18-x (x ≈ 9) clusters, which contain both neutral and cationic ligands in nearly equivalent amounts, was investigated. Contrary to our expectation that the cationic-to-cationic ligand-exchange reaction would be suppressed due to Coulombic repulsion between the surface cationic ligands and incoming cationic ligands, the originally existing cationic ligand was selectively exchanged. The choice of counterions for cationic ligands played a crucial role in controlling the selectivity of ligand exchange. For instance, bulky and hydrophobic counterions such as PF6- can cause steric hindrance and reduce Coulombic repulsion, which promotes cationic-to-cationic ligand exchange. Conversely, counterions like Cl- can lead to neutral-to-cationic ligand exchange due to reduced steric hindrance and increased Coulombic repulsion between cationic ligands. These findings provide a novel method for tailoring the properties of molecular gold clusters through controlled ligand exchange without requiring the design of thiolate ligands with varying geometrical structures.

Loss of androgen signaling in mesenchymal sonic hedgehog responsive cells diminishes prostate development, growth, and regeneration.

Prostate embryonic development, pubertal and adult growth, maintenance, and regeneration are regulated through androgen signaling-mediated mesenchymal-epithelial interactions. Specifically, the essential role of mesenchymal androgen signaling in the development of prostate epithelium has been observed for over 30 years. However, the identity of the mesenchymal cells responsible for this paracrine regulation and related mechanisms are still unknown. Here, we provide the first demonstration of an indispensable role of the androgen receptor (AR) in sonic hedgehog (SHH) responsive Gli1-expressing cells, in regulating prostate development, growth, and regeneration. Selective deletion of AR expression in Gli1-expressing cells during embryogenesis disrupts prostatic budding and impairs prostate development and formation. Tissue recombination assays showed that urogenital mesenchyme (UGM) containing AR-deficient mesenchymal Gli1-expressing cells combined with wildtype urogenital epithelium (UGE) failed to develop normal prostate tissue in the presence of androgens, revealing the decisive role of AR in mesenchymal SHH responsive cells in prostate development. Prepubescent deletion of AR expression in Gli1-expressing cells resulted in severe impairment of androgen-induced prostate growth and regeneration. RNA-sequencing analysis showed significant alterations in signaling pathways related to prostate development, stem cells, and organ morphogenesis in AR-deficient Gli1-expressing cells. Among these altered pathways, the transforming growth factor ß1 (TGFß1) pathway was up-regulated in AR-deficient Gli1-expressing cells. We further demonstrated the activation of TGFß1 signaling in AR-deleted prostatic Gli1-expressing cells, which inhibits prostate epithelium growth through paracrine regulation. These data demonstrate a novel role of the AR in the Gli1-expressing cellular niche for regulating prostatic cell fate, morphogenesis, and renewal, and elucidate the mechanism by which mesenchymal androgen-signaling through SHH-responsive cells elicits the growth and regeneration of prostate epithelium.

Deep learning-based computed tomographic image super-resolution via wavelet embedding.

Effort to realize high-resolution medical images have been made steadily. In particular, super resolution technology based on deep learning is making excellent achievement in computer vision recently. In this study, we developed a model that can dramatically increase the spatial resolution of medical images using deep learning technology, and we try to demonstrate the superiority of proposed model by analyzing it quantitatively. We simulated the computed tomography images with various detector pixel size and tried to restore the low-resolution image to high resolution image. We set the pixel size to 0.5, 0.8 and 1 mm2 for low resolution image and the high-resolution image, which were used for ground truth, was simulated with 0.25 mm2 pixel size. The deep learning model that we used was a fully convolution neural network based on residual structure. The result image demonstrated that proposed super resolution convolution neural network improve image resolution significantly. We also confirmed that PSNR and MTF was improved up to 38 % and 65% respectively. The quality of the prediction image is not significantly different depending on the quality of the input image. In addition, the proposed technique not only increases image resolution but also has some effect on noise reduction. In conclusion, we developed deep learning architectures for improving image resolution of computed tomography images. We quantitatively confirmed that the proposed technique effectively improves image resolution without distorting the anatomical structures.