The Nuclear Matrix Protein SAFB Cooperates with Major Satellite RNAs to Stabilize Heterochromatin Architecture Partially through Phase Separation.

Interphase chromatin is hierarchically organized into higher-order architectures that are essential for gene functions, yet the biomolecules that regulate these 3D architectures remain poorly understood. Here, we show that scaffold attachment factor B (SAFB), a nuclear matrix (NM)-associated protein with RNA-binding functions, modulates chromatin condensation and stabilizes heterochromatin foci in mouse cells. SAFB interacts via its R/G-rich region with heterochromatin-associated repeat transcripts such as major satellite RNAs, which promote the phase separation driven by SAFB. Depletion of SAFB leads to changes in 3D genome organization, including an increase in interchromosomal interactions adjacent to pericentromeric heterochromatin and a decrease in genomic compartmentalization, which could result from the decondensation of pericentromeric heterochromatin. Collectively, we reveal the integrated roles of NM-associated proteins and repeat RNAs in the 3D organization of heterochromatin, which may shed light on the molecular mechanisms of nuclear architecture organization.

A Novel Anti-CD38 Monoclonal Antibody for Treating Immune Thrombocytopenia.

BACKGROUND: Immune thrombocytopenia (ITP) is an autoimmune disease characterized by autoantibody-mediated platelet destruction. Treatment with CM313, a novel anti-CD38 monoclonal antibody, can result in targeted clearance of CD38-positive cells, including plasma cells. METHODS: We conducted a phase 1-2, open-label study to evaluate the safety and efficacy of CM313 in adult patients with ITP. CM313 was administered intravenously at a dose of 16 mg per kilogram of body weight every week for 8 weeks, followed by a 16-week follow-up period. The primary outcomes were adverse events and documentation of two or more consecutive platelet counts of at least 50×109 per liter within 8 weeks after the first dose of CM313. The status of peripheral-blood immune cells in patients and changes in the mononuclear phagocytic system in passive mouse models of ITP receiving anti-CD38 therapy were monitored. RESULTS: Of the 22 patients included in the study, 21 (95%) had two consecutive platelet counts of at least 50×109 per liter during the treatment period, with a median cumulative response duration of 23 weeks (interquartile range, 17 to 24). The median time to the first platelet count of at least 50×109 per liter was 1 week (range, 1 to 3). The most common adverse events that occurred during the study were infusion-related reaction (in 32% of the patients) and upper respiratory tract infection (in 32%). After CD38-targeted therapy, the percentage of CD56dimCD16+ natural killer cells, the expression of CD32b on monocytes in peripheral blood, and the number of macrophages in the spleen of the passive mouse models of ITP all decreased. CONCLUSIONS: In this study, anti-CD38 targeted therapy rapidly boosted platelet levels by inhibiting antibody-dependent cell-mediated cytotoxicity on platelets, maintained long-term efficacy by clearing plasma cells, and was associated with mainly low-grade toxic effects. (Funded by the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences and others; ClinicalTrials.gov number, NCT05694767).

Targeting of the CD161 inhibitory receptor enhances T-cell-mediated immunity against hematological malignancies.

ABSTRACT: The CD161 inhibitory receptor is highly upregulated by tumor-infiltrating T cells in multiple human solid tumor types, and its ligand, CLEC2D, is expressed by both tumor cells and infiltrating myeloid cells. Here, we assessed the role of the CD161 receptor in hematological malignancies. Systematic analysis of CLEC2D expression using the Cancer Cell Line Encyclopedia revealed that CLEC2D messenger RNA was most abundant in hematological malignancies, including B-cell and T-cell lymphomas as well as lymphocytic and myelogenous leukemias. CLEC2D protein was detected by flow cytometry on a panel of cell lines representing a diverse set of hematological malignancies. We, therefore, used yeast display to generate a panel of high-affinity, fully human CD161 monoclonal antibodies (mAbs) that blocked CLEC2D binding. These mAbs were specific for CD161 and had a similar affinity for human and nonhuman primate CD161, a property relevant for clinical translation. A high-affinity CD161 mAb enhanced key aspects of T-cell function, including cytotoxicity, cytokine production, and proliferation, against B-cell lines originating from patients with acute lymphoblastic leukemia, diffuse large B-cell lymphoma, and Burkitt lymphoma. In humanized mouse models, this CD161 mAb enhanced T-cell-mediated immunity, resulting in a significant survival benefit. Single cell RNA-seq data demonstrated that CD161 mAb treatment enhanced expression of cytotoxicity genes by CD4 T cells as well as a tissue-residency program by CD4 and CD8 T cells that is associated with favorable survival outcomes in multiple human cancer types. These fully human mAbs, thus, represent potential immunotherapy agents for hematological malignancies.

The ASH1-PEX16 regulatory pathway controls peroxisome biogenesis for appressorium-mediated insect infection by a fungal pathogen.

Entomopathogenic fungi infect insects by penetrating through the cuticle into the host body. To breach the host cuticle, some fungal pathogens produce specialized infection cells called appressoria, which develop enormous turgor pressure to allow cuticle penetration. However, regulatory mechanisms underlying appressorium turgor generation are poorly understood. Here, we show that the histone lysine methyltransferase ASH1 in the insecticidal fungus Metarhizium robertsii, which is strongly induced during infection of the mosquito cuticle, regulates appressorium turgor generation and cuticle penetration by activating the peroxin gene Mrpex16 via H3K36 dimethylation. MrPEX16 is required for the biogenesis of peroxisomes that participate in lipid catabolism and further promotes the hydrolysis of triacylglycerols stored in lipid droplets to produce glycerol for turgor generation, facilitating appressorium-mediated insect infection. Together, the ASH1-PEX16 pathway plays a pivotal role in regulating peroxisome biogenesis to promote lipolysis for appressorium turgor generation, providing insights into the molecular mechanisms underlying fungal pathogenesis.

MATR3-antisense LINE1 RNA meshwork scaffolds higher-order chromatin organization.

Long interspersed nuclear elements (LINEs) play essential roles in shaping chromatin states, while the factors that cooperate with LINEs and their roles in higher-order chromatin organization remain poorly understood. Here, we show that MATR3, a nuclear matrix protein, interplays with antisense LINE1 (AS L1) RNAs to form a meshwork via phase separation, providing a dynamic platform for chromatin spatial organization. MATR3 and AS L1 RNAs affect the nuclear localization of each other. After MATR3 depletion, the chromatin, particularly H3K27me3-modified chromatin, redistributes in the cell nuclei. Topologically associating domains (TADs) that highly transcribe MATR3-associated AS L1 RNAs show decreased intra-TAD interactions in both AML12 and ES cells. MATR3 depletion increases the accessibility of H3K27me3 domains adjacent to MATR3-associated AS L1, without affecting H3K27me3 modifications. Furthermore, amyotrophic lateral sclerosis (ALS)-associated MATR3 mutants alter biophysical features of the MATR3-AS L1 RNA meshwork and cause an abnormal H3K27me3 staining. Collectively, we reveal a role of the meshwork formed by MATR3 and AS L1 RNAs in gathering chromatin in the nucleus.

Characteristics and outcomes of COVID-19 in Chinese immune thrombocytopenia patients: A prospective cohort study.

The coronavirus disease 2019 (COVID-19) pandemic has a significant impact on the immune system. This is the first and largest study on pre-existing immune thrombocytopenia (ITP) patients infected with COVID-19 in China. We prospectively collected ITP patients infected with COVID-19 enrolled in the National Longitudinal Cohort of Hematological Diseases (NICHE, NCT04645199) and followed up for at least 1 month after infection. One thousand and one hundred forty-eight pre-existing ITP patients were included. Two hundred and twelve (18.5%) patients showed a decrease in the platelet (PLT) count after infection. Forty-seven (4.1%) patients were diagnosed with pneumonia. Risk factors for a decrease in the PLT count included baseline PLT count <50 × 109/L (OR, 1.76; 95% CI, 1.25-2.46; p = 0.001), maintenance therapy including thrombopoietin receptor agonists (TPO-RAs) (OR, 2.27; 95% CI, 1.60-3.21; p < 0.001) and previous splenectomy (OR, 1.98; 95% CI, 1.09-3.61; p = 0.03). Risk factors for pneumonia included age ≥40 years (OR, 2.45; 95% CI, 1.12-5.33; p = 0.02), ≥2 comorbidities (OR, 3.47; 95% CI, 1.63-7.64; p = 0.001), maintenance therapy including TPO-RAs (OR, 2.14; 95% CI, 1.17-3.91; p = 0.01) and immunosuppressants (OR, 3.05; 95% CI, 1.17-7.91; p = 0.02). In this cohort study, we described the characteristics of pre-existing ITP patients infected with COVID-19 and identified several factors associated with poor outcomes.

ggpicrust2: an R package for PICRUSt2 predicted functional profile analysis and visualization.

SUMMARY: Microbiome research is now moving beyond the compositional analysis of microbial taxa in a sample. Increasing evidence from large human microbiome studies suggests that functional consequences of changes in the intestinal microbiome may provide more power for studying their impact on inflammation and immune responses. Although 16S rRNA analysis is one of the most popular and a cost-effective method to profile the microbial compositions, marker-gene sequencing cannot provide direct information about the functional genes that are present in the genomes of community members. Bioinformatic tools have been developed to predict microbiome function with 16S rRNA gene data. Among them, PICRUSt2 (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) has become one of the most popular functional profile prediction tools, which generates community-wide pathway abundances. However, no state-of-art inference tools are available to test the differences in pathway abundances between comparison groups. We have developed ggpicrust2, an R package, for analyzing functional profiles derived from 16S rRNA sequencing. This powerful tool enables researchers to conduct extensive differential abundance analyses and generate visually appealing visualizations that effectively highlight functional signals. With ggpicrust2, users can obtain publishable results and gain deeper insights into the functional composition of their microbial communities. AVAILABILITY AND IMPLEMENTATION: The package is open-source under the MIT and file license and is available at CRAN and https://github.com/cafferychen777/ggpicrust2. Its shiny web is available at https://a95dps-caffery-chen.shinyapps.io/ggpicrust2_shiny/.

Radiomic analysis reveals diverse prognostic and molecular insights into the response of breast cancer to neoadjuvant chemotherapy: a multicohort study.

BACKGROUND: Breast cancer patients exhibit various response patterns to neoadjuvant chemotherapy (NAC). However, it is uncertain whether diverse tumor response patterns to NAC in breast cancer patients can predict survival outcomes. We aimed to develop and validate radiomic signatures indicative of tumor shrinkage and therapeutic response for improved survival analysis. METHODS: This retrospective, multicohort study included three datasets. The development dataset, consisting of preoperative and early NAC DCE-MRI data from 255 patients, was used to create an imaging signature-based multitask model for predicting tumor shrinkage patterns and pathological complete response (pCR). Patients were categorized as pCR, nonpCR with concentric shrinkage (CS), or nonpCR with non-CS, with prediction performance measured by the area under the curve (AUC). The prognostic validation dataset (n = 174) was used to assess the prognostic value of the imaging signatures for overall survival (OS) and recurrence-free survival (RFS) using a multivariate Cox model. The gene expression data (genomic validation dataset, n = 112) were analyzed to determine the biological basis of the response patterns. RESULTS: The multitask learning model, utilizing 17 radiomic signatures, achieved AUCs of 0.886 for predicting tumor shrinkage and 0.760 for predicting pCR. Patients who achieved pCR had the best survival outcomes, while nonpCR patients with a CS pattern had better survival than non-CS patients did, with significant differences in OS and RFS (p = 0.00012 and p = 0.00063, respectively). Gene expression analysis highlighted the involvement of the IL-17 and estrogen signaling pathways in response variability. CONCLUSIONS: Radiomic signatures effectively predict NAC response patterns in breast cancer patients and are associated with specific survival outcomes. The CS pattern in nonpCR patients indicates better survival.

Hydrophobically Associated Hydrogel for High Sensitivity and Resolution of an Interdigital Electrode Pressure Sensor.

Hydrogel-based flexible strain sensors have been known for their excellent ability to convert different motions of humans into electrical signals, thus enabling real-time monitoring of various human health parameters. In this work, a composite hydrogel with hydrophobic association and hybrid cross-linking was fabricated by using polyacrylamide (PAm), surfactant sodium dodecyl sulfate (SDS), lauryl methacrylate (LMA), and polypyrrole (PPy). The dynamic dissociation-conjugation among LMA, SDS, and PPy could dissipate energy to improve the toughness of hydrogels. The SDS/PPy/LMPAm composite hydrogel with a toughness of 1.44 MJ/m3, tensile fracture stress of 345 kPa, tensile strain of 1021%, and electrical conductivity of 0.57 S/m was obtained. Furthermore, an interdigital electrode flexible pressure sensor was designed to replace the bipolar electrode flexible pressure sensor, which greatly improved the sensitivity and resolution of the pressure sensor. The SDS/PPy/LMPAm composite hydrogel-based interdigital electrode flexible pressure sensor showed extraordinary stability and identified different hand gestures as well as monitored the pulse signal of humans. Moreover, the characteristic systolic and diastolic peaks were clearly observed. The pulse frequency (65 times/min) and the radial artery augmentation index (0.57) were calculated, which are very important in evaluating the arterial vessel wall and function of human arteries.

HMDB 5.0: the Human Metabolome Database for 2022.

The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB's search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB's ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.

NP-MRD: the Natural Products Magnetic Resonance Database.

The Natural Products Magnetic Resonance Database (NP-MRD) is a comprehensive, freely available electronic resource for the deposition, distribution, searching and retrieval of nuclear magnetic resonance (NMR) data on natural products, metabolites and other biologically derived chemicals. NMR spectroscopy has long been viewed as the 'gold standard' for the structure determination of novel natural products and novel metabolites. NMR is also widely used in natural product dereplication and the characterization of biofluid mixtures (metabolomics). All of these NMR applications require large collections of high quality, well-annotated, referential NMR spectra of pure compounds. Unfortunately, referential NMR spectral collections for natural products are quite limited. It is because of the critical need for dedicated, open access natural product NMR resources that the NP-MRD was funded by the National Institute of Health (NIH). Since its launch in 2020, the NP-MRD has grown quickly to become the world's largest repository for NMR data on natural products and other biological substances. It currently contains both structural and NMR data for nearly 41,000 natural product compounds from >7400 different living species. All structural, spectroscopic and descriptive data in the NP-MRD is interactively viewable, searchable and fully downloadable in multiple formats. Extensive hyperlinks to other databases of relevance are also provided. The NP-MRD also supports community deposition of NMR assignments and NMR spectra (1D and 2D) of natural products and related meta-data. The deposition system performs extensive data enrichment, automated data format conversion and spectral/assignment evaluation. Details of these database features, how they are implemented and plans for future upgrades are also provided. The NP-MRD is available at https://np-mrd.org.

Early phonetic learning without phonetic categories: Insights from large-scale simulations on realistic input.

Before they even speak, infants become attuned to the sounds of the language(s) they hear, processing native phonetic contrasts more easily than nonnative ones. For example, between 6 to 8 mo and 10 to 12 mo, infants learning American English get better at distinguishing English and [l], as in "rock" vs. "lock," relative to infants learning Japanese. Influential accounts of this early phonetic learning phenomenon initially proposed that infants group sounds into native vowel- and consonant-like phonetic categories-like and [l] in English-through a statistical clustering mechanism dubbed "distributional learning." The feasibility of this mechanism for learning phonetic categories has been challenged, however. Here, we demonstrate that a distributional learning algorithm operating on naturalistic speech can predict early phonetic learning, as observed in Japanese and American English infants, suggesting that infants might learn through distributional learning after all. We further show, however, that, contrary to the original distributional learning proposal, our model learns units too brief and too fine-grained acoustically to correspond to phonetic categories. This challenges the influential idea that what infants learn are phonetic categories. More broadly, our work introduces a mechanism-driven approach to the study of early phonetic learning, together with a quantitative modeling framework that can handle realistic input. This allows accounts of early phonetic learning to be linked to concrete, systematic predictions regarding infants' attunement.

Automated Speech Analysis for Risk Detection of Depression, Anxiety, Insomnia, and Fatigue: Algorithm Development and Validation Study.

BACKGROUND: While speech analysis holds promise for mental health assessment, research often focuses on single symptoms, despite symptom co-occurrences and interactions. In addition, predictive models in mental health do not properly assess the limitations of speech-based systems, such as uncertainty, or fairness for a safe clinical deployment. OBJECTIVE: We investigated the predictive potential of mobile-collected speech data for detecting and estimating depression, anxiety, fatigue, and insomnia, focusing on other factors than mere accuracy, in the general population. METHODS: We included 865 healthy adults and recorded their answers regarding their perceived mental and sleep states. We asked how they felt and if they had slept well lately. Clinically validated questionnaires measuring depression, anxiety, insomnia, and fatigue severity were also used. We developed a novel speech and machine learning pipeline involving voice activity detection, feature extraction, and model training. We automatically modeled speech with pretrained deep learning models that were pretrained on a large, open, and free database, and we selected the best one on the validation set. Based on the best speech modeling approach, clinical threshold detection, individual score prediction, model uncertainty estimation, and performance fairness across demographics (age, sex, and education) were evaluated. We used a train-validation-test split for all evaluations: to develop our models, select the best ones, and assess the generalizability of held-out data. RESULTS: The best model was Whisper M with a max pooling and oversampling method. Our methods achieved good detection performance for all symptoms, depression (Patient Health Questionnaire-9: area under the curve [AUC]=0.76; F1-score=0.49 and Beck Depression Inventory: AUC=0.78; F1-score=0.65), anxiety (Generalized Anxiety Disorder 7-item scale: AUC=0.77; F1-score=0.50), insomnia (Athens Insomnia Scale: AUC=0.73; F1-score=0.62), and fatigue (Multidimensional Fatigue Inventory total score: AUC=0.68; F1-score=0.88). The system performed well when it needed to abstain from making predictions, as demonstrated by low abstention rates in depression detection with the Beck Depression Inventory and fatigue, with risk-coverage AUCs below 0.4. Individual symptom scores were accurately predicted (correlations were all significant with Pearson strengths between 0.31 and 0.49). Fairness analysis revealed that models were consistent for sex (average disparity ratio [DR] 0.86, SD 0.13), to a lesser extent for education level (average DR 0.47, SD 0.30), and worse for age groups (average DR 0.33, SD 0.30). CONCLUSIONS: This study demonstrates the potential of speech-based systems for multifaceted mental health assessment in the general population, not only for detecting clinical thresholds but also for estimating their severity. Addressing fairness and incorporating uncertainty estimation with selective classification are key contributions that can enhance the clinical utility and responsible implementation of such systems.

The synthesis and properties of Ca3 Sc2 Si3 O12 :Ce3+ (CSSG) phosphor are utilized for the development of human-centric lighting light-emitting diodes (HCL-LEDs).

In this study, cerium ion (Ce3+ )-doped calcium scandium silicate garnet (Ca3 Sc2 Si3 O12 , abbreviated CSSG) phosphors were successfully synthesized using the sol-gel method. The crystal phase, morphology, and photoluminescence properties of the synthesized phosphors were thoroughly investigated. Under excitation by a blue light-emitting diode (LED) chip (450 nm), the CSSG phosphor displayed a wide emission spectrum spanning from green to yellow. Remarkably, the material exhibited exceptional thermal stability, with an emissivity ratio at 150°C to that at 25°C reaching approximately 85%. Additionally, the material showcased impressive optical performance when tested with a blue LED chip, including a color rendering index (CRI) exceeding 90, an R9 value surpassing 50, and a biological impact ratio (M/P) above 0.6. These noteworthy findings underscore the potential applications of CSSG as a white light-converting phosphor, particularly in the realm of human-centered lighting.

[Scientific connotation of temperature control in processing of Chinese medicinal materials based on theory of quality evaluation through morphological identification].

Processing of Chinese medicinal materials is an important part in the Chinese medicine heritage, and the temperature control in the processing has a direct impact on the quality and efficacy of traditional Chinese medicines. However, the processing of Chinese medicinal materials has the problems of subjective temperature judgement, determination of the end point based on experience, unclear processing mechanism, unstable quality of products, and inconsistent processing standards. The temperature control in the processing is reflected in the appearance and internal quality of Chinese medicinal materials. The theory of quality evaluation through morphological identification is developed based on the comprehensive evaluation of the shape, color, taste, and components, which is associated with the temperature control in the processing. To solve the problems above, this paper puts forward the following solutions. The first is literature mining. By review of the ancient medical works and pharmaceutical experience, the temperature control in processing and the evolution of processing methods can be revealed. Second, according to the ancient method, the processing principle can be explored, on the basis of which the processing technology can be innovated. Third, the standard operating procedure(SOP) should be established to quantify the fire temperature, providing a theoretical basis for the formulation of Chinese medicinal material processing standards. Moreover, it provides a basis for improving the quality of processed products and increasing the safety and effectiveness of traditional Chinese medicines.

Accelerated Reversible Conversion of Li2S2 to Li2S by Spidroin Regulated Li+ Flux for High-performance Li-Sulfur Batteries.

Regulating the transformation of sulfur species is the key to improving the electrochemical performance of lithium-sulfur (Li-S) batteries, in particular, to accelerate the reversible conversion between solid phase Li2S2 and Li2S. Herein, we introduced Spidroin, which is a main protein in spider silk, as a dual functional separator coating in Li-S batteries to effectively adsorb polysulfides via the sequence of amino acids in its primary structure and regulate Li+ flux through the ß-sheet of its secondary structure, thus accelerating the reversible transformation between Li2S2 and Li2S. Spidroin-based Li-S cells exhibited an exceptional electrochemical performance with a high specific capacity of  744.1 mAh g-1 at 5C and a high areal capacity of 7.5 mAh cm-2 at a low electrolyte-to-sulfur (E/S) ratio of 6 µL mgs-1 and a sulfur loading of 8.6 mgs cm-2.

Publisher Correction: Analysis of the Human Protein Atlas Image Classification competition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Author Correction: Analysis of the Human Protein Atlas Image Classification competition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Publisher Correction: Analysis of the Human Protein Atlas Image Classification competition.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Engineering yeast for high-level production of diterpenoid sclareol.

The diterpenoid sclareol is an industrially important precursor for alternative sustainable supply of ambergris. However, its current production from plant extraction is neither economical nor environmental-friendly, since it requires laborious and cost-intensive purification procedures and plants cultivation is susceptible to environmental factors. Engineering cell factories for bio-manufacturing can enable sustainable production of natural products. However, stringent metabolic regulation poses challenges to rewire cellular metabolism for overproduction of compounds of interest. Here we used a modular approach to globally rewire the cellular metabolism for improving sclareol production to 11.4 g/L in budding yeast Saccharomyces cerevisiae, the highest reported diterpenoid titer in microbes. Metabolic flux analysis showed that modular balanced metabolism drove the metabolic flux toward the biosynthesis of targeted molecules, and transcriptomic analysis revealed that the expression of central metabolism genes was shaped for a new balanced metabolism, which laid a foundation in extensive metabolic engineering of other microbial species for sustainable bio-production.