Decoding temporal heterogeneity in NSCLC through machine learning and prognostic model construction.

BACKGROUND: Non-small cell lung cancer (NSCLC) is a prevalent and heterogeneous disease with significant genomic variations between the early and advanced stages. The identification of key genes and pathways driving NSCLC tumor progression is critical for improving the diagnosis and treatment outcomes of this disease. METHODS: In this study, we conducted single-cell transcriptome analysis on 93,406 cells from 22 NSCLC patients to characterize malignant NSCLC cancer cells. Utilizing cNMF, we classified these cells into distinct modules, thus identifying the diverse molecular profiles within NSCLC. Through pseudotime analysis, we delineated temporal gene expression changes during NSCLC evolution, thus demonstrating genes associated with disease progression. Using the XGBoost model, we assessed the significance of these genes in the pseudotime trajectory. Our findings were validated by using transcriptome sequencing data from The Cancer Genome Atlas (TCGA), supplemented via LASSO regression to refine the selection of characteristic genes. Subsequently, we established a risk score model based on these genes, thus providing a potential tool for cancer risk assessment and personalized treatment strategies. RESULTS: We used cNMF to classify malignant NSCLC cells into three functional modules, including the metabolic reprogramming module, cell cycle module, and cell stemness module, which can be used for the functional classification of malignant tumor cells in NSCLC. These findings also indicate that metabolism, the cell cycle, and tumor stemness play important driving roles in the malignant evolution of NSCLC. We integrated cNMF and XGBoost to select marker genes that are indicative of both early and advanced NSCLC stages. The expression of genes such as CHCHD2, GAPDH, and CD24 was strongly correlated with the malignant evolution of NSCLC at the single-cell data level. These genes have been validated via histological data. The risk score model that we established (represented by eight genes) was ultimately validated with GEO data. CONCLUSION: In summary, our study contributes to the identification of temporal heterogeneous biomarkers in NSCLC, thus offering insights into disease progression mechanisms and potential therapeutic targets. The developed workflow demonstrates promise for future applications in clinical practice.

Tinnitus pitch does not always fall within the frequency range of hearing loss - a cross-sectional study on the mechanism of tinnitus production.

BACKGROUND: Clinically, we find that tinnitus patients often have hearing loss. According to the most accepted mechanism of tinnitus, that is, the spontaneous discharge and abnormal synchronization of neurons after afferent reduction, tinnitus frequency is closely related to the frequency of hearing loss. OBJECTIVE: The purpose of this study was to investigate the correlation of tinnitus pitch with the frequency of hearing loss. MATERIALS AND METHODS: A total of 500 patients with unilateral or bilateral chronic tinnitus were enrolled in this study. All patients underwent pure tone audiometry (PTA) and tinnitus acoustic examination. Hearing loss levels and frequencies were recorded. The relationship between tinnitus pitch and hearing loss level and frequency was statistically analyzed. RESULTS: Our results showed that 96.6% of the 500 tinnitus patients had hearing loss. Statistical analysis showed that low frequency (LF) tinnitus was correlated with LF hearing loss, but moderate frequency & high frequency (MF&HF) tinnitus was not significantly associated with MF&HF hearing loss. The coincidence of tinnitus pitch with the highest hearing threshold correlated with the degree of hearing loss. CONCLUSION AND SIGNIFICANCE: The vast majority of patients with chronic subjective tinnitus had hearing loss, and the frequency of tinnitus correlated with the degree and frequency of hearing loss but not exactly fall within the frequency range of hearing loss.

Inflammatory cytokine profiles in erectile dysfunction: a bidirectional Mendelian randomization.

Objectives: Inflammatory cytokines (ICs) play an important role in erectile dysfunction (ED). Previous studies have demonstrated that most ED patients have high levels of tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and interleukin-8 (IL-8). The causality between 41 ICs and ED is investigated using the Mendelian randomization (MR) approach. Methods: Single nucleotide polymorphisms (SNPs) exposure data of 41 ICs came from a genome-wide association study (GWAS) of 8293 subjects. At the same time, the FINNGEN R9 database provided the ED outcome data containing 2205 ED patients and 164104 controls. MR-Egger (ME), inverse variance weighting (IVW), and weighted median (WM) were applied to conduct the MR study and IVW was taken as the main criterion. Results: From a genetic perspective, the increase of interferon-inducible protein-10 (IP-10) level significantly increased the risk of ED (P=0.043, odds ratio (OR)=1.269, 95% confidence interval (95%CI): 1.007-1.600), while the increase of interleukin-1 receptor antagonist (IL-1RA) markedly decreased the risk of ED (P=0.037, OR=0.768, 95%CI: 0.600-0.984). Meanwhile, IP-10 (p=0.099) and IL-1RA (p=0.135) failed to demonstrate causality in reverse MR analysis. Conclusions: Changes in ICs levels will significantly affect the risk of ED, especially IP-10 as a risk component for ED and IL-1RA as a protective component for ED. In the future, we can achieve targeted treatment and prevention of ED by intervening with specific inflammatory factors.

Predicting drug synergy using a network propagation inspired machine learning framework.

Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability. Here, we proposed a novel network propagation-based machine learning framework to predict synergistic drug combinations. Based on the topological information of a comprehensive drug-drug association network, we innovatively introduced an affinity score between drug pairs as one of the features to train machine learning models. We applied network-based strategy to evaluate their therapeutic potential to different cancer types. Finally, we identified 17 specific-, 21 general- and 40 broad-spectrum antitumor drug combinations, in which 69% drug combinations were validated by vitro cellular experiments, 83% drug combinations were validated by literature reports and 100% drug combinations were validated by biological function analyses. By quantifying the network relationships between drug targets and cancer-related driver genes in the human protein-protein interactome, we show the existence of four distinct patterns of drug-drug-disease relationships. We also revealed that 32 biological pathways were correlated with the synergistic mechanism of broad-spectrum antitumor drug combinations. Overall, our model offers a powerful scalable screening framework for cancer treatments.

DAPredict: a database for drug action phenotype prediction.

The phenotypes of drug action, including therapeutic actions and adverse drug reactions (ADRs), are important indicators for evaluating the druggability of new drugs and repositioning the approved drugs. Here, we provide a user-friendly database, DAPredict (http://bio-bigdata.hrbmu.edu.cn/DAPredict), in which our novel original drug action phenotypes prediction algorithm (Yang,J., Zhang,D., Liu,L. et al. (2021) Computational drug repositioning based on the relationships between substructure-indication. Brief. Bioinformatics, 22, bbaa348) was embedded. Our algorithm integrates characteristics of chemical genomics and pharmacogenomics, breaking through the limitations that traditional drug development process based on phenotype cannot analyze the mechanism of drug action. Predicting phenotypes of drug action based on the local active structures of drugs and proteins can achieve more innovative drug discovery across drug categories and simultaneously evaluate drug efficacy and safety, rather than traditional one-by-one evaluation. DAPredict contains 305 981 predicted relationships between 1748 approved drugs and 454 ADRs, 83 117 predicted relationships between 1478 approved drugs and 178 Anatomical Therapeutic Chemicals (ATC). More importantly, DAPredict provides an online prediction tool, which researchers can use to predict the action phenotypic spectrum of more than 110 000 000 compounds (including about 168 000 natural products) and corresponding proteins to analyze their potential effect mechanisms. DAPredict can also help researchers obtain the phenotype-corresponding active structures for structural optimization of new drug candidates, making it easier to evaluate the druggability of new drug candidates and develop more innovative drugs across drug categories. Database URL:  http://bio-bigdata.hrbmu.edu.cn/DAPredict/.

FLT3 tyrosine kinase inhibition modulates PRC2 and promotes differentiation in acute myeloid leukemia.

Internal tandem duplication mutations in fms-like tyrosine kinase 3 (FLT3-ITD) are recurrent in acute myeloid leukemia (AML) and increase the risk of relapse. Clinical responses to FLT3 inhibitors (FLT3i) include myeloid differentiation of the FLT3-ITD clone in nearly half of patients through an unknown mechanism. We identified enhancer of zeste homolog 2 (EZH2), a component of polycomb repressive complex 2 (PRC2), as a mediator of this effect using a proteomic-based screen. FLT3i downregulated EZH2 protein expression and PRC2 activity on H3K27me3. FLT3-ITD and loss-of-function mutations in EZH2 are mutually exclusive in human AML. We demonstrated that FLT3i increase myeloid maturation with reduced stem/progenitor cell populations in murine Flt3-ITD AML. Combining EZH1/2 inhibitors with FLT3i increased terminal maturation of leukemic cells and reduced leukemic burden. Our data suggest that reduced EZH2 activity following FLT3 inhibition promotes myeloid differentiation of FLT3-ITD leukemic cells, providing a mechanistic explanation for the clinical observations. These results demonstrate that in addition to its known cell survival and proliferation signaling, FLT3-ITD has a second, previously undefined function to maintain a myeloid stem/progenitor cell state through modulation of PRC2 activity. Our findings support exploring EZH1/2 inhibitors as therapy for FLT3-ITD AML.

Experimental validation and pan-cancer analysis identified COL10A1 as a novel oncogene and potential therapeutic target in prostate cancer.

BACKGROUND: Type X collagen (COL10) is a homologous trimeric non-fibrillar collagen found in the extracellular matrix of human tissues, and it exhibits a distinctive white appearance. Type X collagen α1 chain (COL10A1) is a specific cleaved fragment of type X collagen. However, the expression, prognostic significance, clinicopathological attributes and immune-related associations of COL10A1 in prostate cancer as well as in pan-cancer contexts remain poorly understood. METHODS: Using bioinformatic analysis of data from the most recent databases (TCGA, GTEx and GEO databases), we have extensively elucidated the role played by COL10A1 in terms of its expression patterns, prognostic implications, and immune efficacy across a pan-cancer spectrum. Subsequently, the biological functions of COL10A1 in prostate cancer were elucidated by experimental validation. RESULTS: Our findings have confirmed that COL10A1 was highly expressed in most cancers and was associated with poorer prognosis in cancer patients. Immune correlation analysis of COL10A1 in various cancers showed its significant correlation with Tumor mutational burden (TMB), microsatellite instability (MSI) and immune cell infiltration. In addition, knockdown of COL10A1 in prostate cancer resulted in a substantial reduction in the proliferation, migration, and invasive potential of prostate cancer cells. CONCLUSION: Our pan-cancer analysis of COL10A1 gene provided novel insights into its pivotal role in cancer initiation, progression, and therapeutic implications, underscoring its potential significance in prognosis and immunotherapeutic interventions for cancer, particularly prostate cancer.

Telephoto achromatic camera based on optical-digital co-design.

Due to the difficulty of correcting chromatic aberration (CA) in telephoto cameras, recent studies have combined image algorithms with simple optical structures, such as single-spherical lenses, for high-quality photography, moving away from complex optics. However, this approach often struggles to comprehensively address compounded issues arising from optical aberrations of simple optical systems, including defocus blur and multi-channel misalignment. To tackle this challenge, this manuscript presents an approach for developing a telephoto imaging system by leveraging the distinct characteristics of axial and lateral chromatic aberrations (ACA, LCA) over the visible spectrum. The optical design is limited to a specific wavelength range to preserve high-frequency information of the green channel. A cross-channel fitting method is presented to suppress the LCA. Subsequently, the powerful capabilities of deep learning are utilized to correct ACA, defocus blur, and other residual optical aberrations. Simulation experiments demonstrate the effectiveness of the proposed approach in mitigating the CA inherent in telephoto systems, thereby delivering high-quality imaging results over the whole visible waveband.

Off-axis four-mirror telescope with a wide field of view and a long focal length using double integrated mirrors.

An off-axis four-mirror optical system has the advantages of a wide field of view (FOV) and a small telephoto ratio. However, it will bring difficulties in assembly and detection. Here we report an off-axis four-mirror free-form telescope with a long focal length and a wide field of view based on two integration mirrors. The initial structure of a coaxial four-mirror optical system is established based on the Seidel aberration theory. A Zernike Fringe free-form surface is introduced to correct aberrations. By gradually increasing the entrance pupil diameter and the FOV, we finally obtain an off-axis four-mirror telescope with a FOV of 0.4∘×20∘, an F-number of 11.5, a long focal length of 2000 mm, and a volume of 360×400×600m m 3. In addition, four mirrors remain coaxially aligned, remarkably facilitating detection and adjustment. The proposed off-axis four-mirror telescope, with double integration mirrors, holds great potential for application in aerospace remote sensing observations.

Patient-derived Colonoids From Disease-spared Tissue Retain Inflammatory Bowel Disease-specific Transcriptomic Signatures.

BACKGROUND AND AIMS: A key histopathological feature of inflammatory bowel disease is damage to the mucosa, including breakdown of the epithelial barrier. Human enteroids and colonoids are a critical bench-to-bedside tool for studying the epithelium in inflammatory bowel disease. The goal of the current study was to define transcriptional differences in healthy versus diseased subjects that are sustained in enteroids and colonoids, including from disease-spared tissue. METHODS: Biopsies and matching enteroid or colonoid cultures from pediatric patients with ileal Crohn disease (N = 6) and control subjects (N = 17) were subjected to RNA sequencing followed by bioinformatic and machine learning analyses. Late passage enteroids were exposed to cytokines to assess durable transcriptional differences. RESULTS: We observed substantial overlap of pathways upregulated in Crohn disease in enteroids and ileal biopsies, as well as colonoids and rectal biopsies. KEGG pathways for cytokine-cytokine receptor interaction, chemokine signaling, protein export, and Toll-like receptor signaling were upregulated in both ileal and rectal biopsies, as well as enteroids and colonoids. In vitro cytokine exposure reactivated genes previously increased in biopsies. Machine learning predicted biopsy location (100% accuracy) and donor disease status (83% accuracy). A random forest classifier generated using ileal enteroids identified rectal colonoids from ileal Crohn disease subjects with 80% accuracy. CONCLUSION: We confirmed transcriptional profiles of Crohn disease biopsies are expressed in enteroids and colonoids. Furthermore, transcriptomic data from disease-spared rectal tissue can identify patients with ileal Crohn disease. Our data support the use of patient enteroids and colonoids as critical translational tools for the study of inflammatory bowel disease.

clusterBMA: Bayesian model averaging for clustering.

Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one 'best' model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic interpretation of the combined cluster structure and quantification of model-based uncertainty. In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms. We use clustering internal validation criteria to develop an approximation of the posterior model probability, used for weighting the results from each model. From a combined posterior similarity matrix representing a weighted average of the clustering solutions across models, we apply symmetric simplex matrix factorisation to calculate final probabilistic cluster allocations. In addition to outperforming other ensemble clustering methods on simulated data, clusterBMA offers unique features including probabilistic allocation to averaged clusters, combining allocation probabilities from 'hard' and 'soft' clustering algorithms, and measuring model-based uncertainty in averaged cluster allocation. This method is implemented in an accompanying R package of the same name. We use simulated datasets to explore the ability of the proposed technique to identify robust integrated clusters with varying levels of separation between subgroups, and with varying numbers of clusters between models. Benchmarking accuracy against four other ensemble methods previously demonstrated to be highly effective in the literature, clusterBMA matches or exceeds the performance of competing approaches under various conditions of dimensionality and cluster separation. clusterBMA substantially outperformed other ensemble methods for high dimensional simulated data with low cluster separation, with 1.16 to 7.12 times better performance as measured by the Adjusted Rand Index. We also explore the performance of this approach through a case study that aims to identify probabilistic clusters of individuals based on electroencephalography (EEG) data. In applied settings for clustering individuals based on health data, the features of probabilistic allocation and measurement of model-based uncertainty in averaged clusters are useful for clinical relevance and statistical communication.

OpenPBTA: The Open Pediatric Brain Tumor Atlas.

Pediatric brain and spinal cancers are collectively the leading disease-related cause of death in children; thus, we urgently need curative therapeutic strategies for these tumors. To accelerate such discoveries, the Children's Brain Tumor Network (CBTN) and Pacific Pediatric Neuro-Oncology Consortium (PNOC) created a systematic process for tumor biobanking, model generation, and sequencing with immediate access to harmonized data. We leverage these data to establish OpenPBTA, an open collaborative project with over 40 scalable analysis modules that genomically characterize 1,074 pediatric brain tumors. Transcriptomic classification reveals universal TP53 dysregulation in mismatch repair-deficient hypermutant high-grade gliomas and TP53 loss as a significant marker for poor overall survival in ependymomas and H3 K28-mutant diffuse midline gliomas. Already being actively applied to other pediatric cancers and PNOC molecular tumor board decision-making, OpenPBTA is an invaluable resource to the pediatric oncology community.

N6-methyladenosine participates in mouse hippocampus neurodegeneration via PD-1/PD-L1 pathway.

Developmental abnormalities and hippocampal aging leads to alteration in cognition. In the brain, N6-methyladenosine (m6A) is a common and reversible mRNA alteration that is essential for both neurodevelopment and neurodegeneration. However, its function in the postnatal hippocampus and the specific mechanisms regulating hippocampus-related neurodegeneration still awaits elucidate. We identified dynamic m6A modifications in postnatal hippocampus at different stages (at 10 days postnatally, and at 11 and 64 weeks of age). m6A shows a definite cell-specific methylation profile and m6A modification displays temporal dynamic during neurodevelopment and aging. Differentially methylated transcripts in the aged (64-week-old) hippocampus were enriched in microglia. The PD-1/PD-L1 pathways was identified that may participate in the cognitive dysfunction associated with an aged hippocampus. Furthermore, Mettl3 was spatiotemporally expressed in the postnatal hippocampus, which was highly expressed at the age of 11 weeks compared with the other two timepoints. Ectopic expression of METTL3 in mice hippocampus mediated by lentiviral infection resulted in high expression of genes related to PD-1/PD-L1 pathway and significant spatial cognitive deficit. Together, our data show that m6A dysregulation, which is mediated by METTL3, most likely contributes to cognitive deficits linked to the hippocampus via the PD-1/PD-L1 pathway.

Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs.

Background: Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study. Methods: In this study, the R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) project, and univariate Cox regression analysis was performed to find prognostic immune-related lncRNAs. A total of 117 combinations based on 10 algorithms were integrated to determine the immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERSORT algorithm, the R package maftools, pRRophetic, and clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, drug sensitivity analysis, and pathway enrichment score. Results: A total of 321 immune-related lncRNAs were found, and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the risk score (RS) developed by the 18-lncRNA signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high- and low-risk groups according to the median RS, and the low-risk group had a better prognosis, tumor immune microenvironment, and tumor signature enrichment score and a higher metabolism, frequency of genomic mutations, proportion of immune cell infiltration, and antitumor drug resistance. Furthermore, 86 differentially expressed genes (DEGs) between high- and low-risk groups were mainly enriched in immune-related pathways. Conclusion: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high- and low-risk groups according to the RS.

Efficacy and factors influencing outcomes of customized music therapy combined with a follow-up system in chronic tinnitus patients.

BACKGROUNDS: Tinnitus is a meaningless sound signal perceived by the patients in the absence of auditory stimuli. Due to the complex etiology and unclear mechanism, specific therapies for tinnitus are still in the exploratory stage. In recent years, personalized and customized music therapy has been proposed as an effective method for tinnitus treatment. The aim of this study was to explore the efficacy of customized therapy with a well-designed follow-up system in the treatment of tinnitus through a large sample one arm study and to identify the relevant factors affecting the treatment outcome. METHODS: The study investigated a total of 615 patients with unilateral or bilateral chronic tinnitus who received personalized and customized music therapy for 3 months. A complete follow-up system was designed by the professionals. Questionnaires of Tinnitus Handicap Inventory (THI), Hospital Anxiety and Depression Scale (HADS) and Visual Analogue Scale (VAS) were used to evaluate the therapeutic effects and relevant factors affecting the efficacy of therapy. RESULTS: The results showed a decreasing trend in THI and VAS scores after 3 months of therapy, with statistically significant differences between pre- and post-therapy time points (P < 0.001). All patients were divided into 5 groups according to THI scores, and the mean reduction score in catastrophic, severe, moderate, mild and slight group was 28, 19, 11, 5, 0 respectively. The proportion of tinnitus patients with anxiety was higher than that with depression (70.57% and 40.65%, respectively), and there were statistically significant differences between HADS-A/D scores pre- and post-therapy. Binary logistic regression showed that the baseline of THI, VAS scores, the duration of tinnitus and the state of anxiety prior to therapy were significant influencing factors of therapeutic efficacy. CONCLUSIONS: The magnitude of reduction in THI scores after music therapy depended on the severity of the patients' tinnitus, the higher the initial THI scores, the greater the potential for improvement in tinnitus disorders. Music therapy also reduced the anxiety and depression levels of tinnitus patients. Therefore, personalized and customized music therapy with a comprehensive follow-up system may be an effective treatment option for chronic tinnitus patients.

WSe2-loaded co-catalysts Cu3P and CNTs: Improving photocatalytic hydrogen precipitation and photocatalytic memory performance.

Photocatalytic decomposition of water for hydrogen production using semiconductor photocatalysts in visible light is considered one of the most promising environmentally friendly ways to produce hydrogen. In this work, the calcination method was adopted to prepare an efficient Cu3P/WSe2/CNTs composite photocatalysts. Cu3P and carbon nanotubes (CNTs) were used as co-catalysts to reduce the composite rate of the photogenerated supports of the photocatalyst. The unique metallic properties of Cu3P as a transition metal phosphide makes it a cost-effective alternative to noble metal co-catalysts. CNTs can serve both as co-catalysts and as a suitable carrier to accelerate the transfer rate of photogenerated electrons. The experimental results showed that the Cu3P/WSe2/CNTs composite photocatalyst exhibited stronger activities in photocatalytic hydrogen production than pure WSe2. In particular, a higher quantum yield of 30.27% at the range 400-700 nm was achieved with a loading of 4% CNTs, a calcination temperature of 300 °C and a calcination time of 2.0 h. In contrast, the quantum yield of pure WSe2 was only 14.01%. The highest hydrogen production rate was 6.987 mL in 4.0 h, and the average hydrogen production rate was 712.985 µmol·h-1g-1, which was 2.39 times higher than that of pure WSe2.The catalytic memory performance of the composite samples was also examined. The results indicated that the best catalytic memory performance was achieved under the pre-illumination condition of 5.0 h. The amount of hydrogen produced under darkness for 4.0 h was up to 4.934 mL and the average hydrogen production rate was 503.454 µmol·h-1g-1. The average hydrogen production rate was 1.69 times higher than the average hydrogen production rate of pure WSe2 under light conditions.

Phylogenomic analyses based on the plastid genome and concatenated nrDNA sequence data reveal cytonuclear discordance in genus Atractylodes (Asteraceae: Carduoideae).

Atractylodes species are widely distributed across East Asia and are cultivated as medicinal herbs in China, Japan, and Korea. Their unclear morphological characteristics and low levels of genetic divergence obscure the taxonomic relationships among these species. In this study, 24 plant samples were collected representing five species of Atractylodes located in China; of these, 23 belonged to members of the A. lancea complex. High-throughput sequencing was used to obtain the concatenated nrDNA sequences (18S-ITS1-5.8S-ITS2-28S) and plastid genomes. The concatenated nrDNA sequence lengths for all the Atractylodes species were 5,849 bp, and the GC content was 55%. The lengths of the whole plastid genome sequences ranged from 152,138 bp (A. chinensis) to 153,268 bp (A. lancea), while their insertion/deletion sites were mainly distributed in the intergenic regions. Furthermore, 33, 34, 36, 31, and 32 tandem repeat sequences, as well as 30, 30, 29, 30, and 30 SSR loci, were detected in A. chinensis, A. koreana, A. lancea, A. japonica, and A. macrocephala, respectively. In addition to these findings, a considerable number of heteroplasmic variations were detected in the plastid genomes, implying a complicated phylogenetic history for Atractylodes. The results of the phylogenetic analysis involving concatenated nrDNA sequences showed that A. lancea and A. japonica formed two separate clades, with A. chinensis and A. koreana constituting their sister clade, while A. lancea, A. koreana, A. chinensis, and A. japonica were found based on plastid datasets to represent a mixed clade on the phylogenetic tree. Phylogenetic network analysis suggested that A. lancea may have hybridized with the common ancestor of A. chinensis and A. japonica, while ABBA-BABA tests of SNPs in the plastid genomes showed that A. chinensis was more closely related to A. japonica than to A. lancea. This study reveals the extensive discordance and complexity of the relationships across the members of the A. lancea complex (A. lancea, A. chinensis, A. koreana, and A. japonica) according to cytonuclear genomic data; this may be caused by interspecific hybridization or gene introgression.

A recurrent de novo splice site variant involving DNM1 exon 10a causes developmental and epileptic encephalopathy through a dominant-negative mechanism.

Heterozygous pathogenic variants in DNM1 cause developmental and epileptic encephalopathy (DEE) as a result of a dominant-negative mechanism impeding vesicular fission. Thus far, pathogenic variants in DNM1 have been studied with a canonical transcript that includes the alternatively spliced exon 10b. However, after performing RNA sequencing in 39 pediatric brain samples, we find the primary transcript expressed in the brain includes the downstream exon 10a instead. Using this information, we evaluated genotype-phenotype correlations of variants affecting exon 10a and identified a cohort of eleven previously unreported individuals. Eight individuals harbor a recurrent de novo splice site variant, c.1197-8G>A (GenBank: NM_001288739.1), which affects exon 10a and leads to DEE consistent with the classical DNM1 phenotype. We find this splice site variant leads to disease through an unexpected dominant-negative mechanism. Functional testing reveals an in-frame upstream splice acceptor causing insertion of two amino acids predicted to impair oligomerization-dependent activity. This is supported by neuropathological samples showing accumulation of enlarged synaptic vesicles adherent to the plasma membrane consistent with impaired vesicular fission. Two additional individuals with missense variants affecting exon 10a, p.Arg399Trp and p.Gly401Asp, had a similar DEE phenotype. In contrast, one individual with a missense variant affecting exon 10b, p.Pro405Leu, which is less expressed in the brain, had a correspondingly less severe presentation. Thus, we implicate variants affecting exon 10a as causing the severe DEE typically associated with DNM1-related disorders. We highlight the importance of considering relevant isoforms for disease-causing variants as well as the possibility of splice site variants acting through a dominant-negative mechanism.

Exploring Precise Medication Strategies for OSCC Based on Single-Cell Transcriptome Analysis from a Dynamic Perspective.

At present, most patients with oral squamous cell carcinoma (OSCC) are in the middle or advanced stages at the time of diagnosis. Advanced OSCC patients have a poor prognosis after traditional therapy, and the complex heterogeneity of OSCC has been proven to be one of the main reasons. Single-cell sequencing technology provides a powerful tool for dissecting the heterogeneity of cancer. However, most of the current studies at the single-cell level are static, while the development of cancer is a dynamic process. Thus, understanding the development of cancer from a dynamic perspective and formulating corresponding therapeutic measures for achieving precise treatment are highly necessary, and this is also one of the main study directions in the field of oncology. In this study, we combined the static and dynamic analysis methods based on single-cell RNA-Seq data to comprehensively dissect the complex heterogeneity and evolutionary process of OSCC. Subsequently, for clinical practice, we revealed the association between cancer heterogeneity and the prognosis of patients. More importantly, we pioneered the concept of pseudo-time score of patients, and we quantified the levels of heterogeneity based on the dynamic development process to evaluate the relationship between the score and the survival status at the same stage, finding that it is closely related to the prognostic status. The pseudo-time score of patients could not only reflect the tumor status of patients but also be used as an indicator of the effects of drugs on the patients so that the medication strategy can be adjusted on time. Finally, we identified candidate drugs and proposed precision medication strategies to control the condition of OSCC in two respects: treatment and blocking.

Pathogenicity and impact of HLA class I alleles in aplastic anemia patients of different ethnicities.

Acquired aplastic anemia (AA) is caused by autoreactive T cell-mediated destruction of early hematopoietic cells. Somatic loss of human leukocyte antigen (HLA) class I alleles was identified as a mechanism of immune escape in surviving hematopoietic cells of some patients with AA. However, pathogenicity, structural characteristics, and clinical impact of specific HLA alleles in AA remain poorly understood. Here, we evaluated somatic HLA loss in 505 patients with AA from 2 multi-institutional cohorts. Using a combination of HLA mutation frequencies, peptide-binding structures, and association with AA in an independent cohort of 6,323 patients from the National Marrow Donor Program, we identified 19 AA risk alleles and 12 non-risk alleles and established a potentially novel AA HLA pathogenicity stratification. Our results define pathogenicity for the majority of common HLA-A/B alleles across diverse populations. Our study demonstrates that HLA alleles confer different risks of developing AA, but once AA develops, specific alleles are not associated with response to immunosuppression or transplant outcomes. However, higher pathogenicity alleles, particularly HLA-B*14:02, are associated with higher rates of clonal evolution in adult patients with AA. Our study provides insights into the immune pathogenesis of AA, opening the door to future autoantigen identification and improved understanding of clonal evolution in AA.