GraphCpG: imputation of single-cell methylomes based on locus-aware neighboring subgraphs.

MOTIVATION: Single-cell DNA methylation sequencing can assay DNA methylation at single-cell resolution. However, incomplete coverage compromises related downstream analyses, outlining the importance of imputation techniques. With a rising number of cell samples in recent large datasets, scalable and efficient imputation models are critical to addressing the sparsity for genome-wide analyses. RESULTS: We proposed a novel graph-based deep learning approach to impute methylation matrices based on locus-aware neighboring subgraphs with locus-aware encoding orienting on one cell type. Merely using the CpGs methylation matrix, the obtained GraphCpG outperforms previous methods on datasets containing more than hundreds of cells and achieves competitive performance on smaller datasets, with subgraphs of predicted sites visualized by retrievable bipartite graphs. Besides better imputation performance with increasing cell number, it significantly reduces computation time and demonstrates improvement in downstream analysis. AVAILABILITY AND IMPLEMENTATION: The source code is freely available at https://github.com/yuzhong-deng/graphcpg.git.

Multiregion WES of metastatic pancreatic neuroendocrine tumors revealed heterogeneity in genomic alterations, immune microenvironment and evolutionary patterns.

Pancreatic neuroendocrine tumors (PanNETs), though uncommon, have a high likelihood of spreading to other body parts. Previously, the genetic diversity and evolutionary patterns in metastatic PanNETs were not well understood. To investigate this, we performed multiregion sampling whole-exome sequencing (MRS-WES) on samples from 10 patients who had not received prior treatment for metastatic PanNETs. This included 29 primary tumor samples, 31 lymph node metastases, and 15 liver metastases. We used the MSK-MET dataset for survival analysis and validation of our findings. Our research indicates that mutations in the MEN1/DAXX genes might trigger the early stages of PanNET development. We categorized the patients based on the presence (MEN1/DAXXmut, n = 7) or absence (MEN1/DAXXwild, n = 3) of these mutations. Notable differences were observed between the two groups in terms of genetic alterations and clinically relevant mutations, confirmed using the MSK-MET dataset. Notably, patients with mutations in MEN1/DAXX/ATRX genes had a significantly longer median overall survival compared to those without these mutations (median not reached vs. 43.63 months, p = 0.047). Multiplex immunohistochemistry (mIHC) analysis showed a more prominent immunosuppressive environment in metastatic tumors, especially in patients with MEN1/DAXX mutations. These findings imply that MEN1/DAXX mutations lead PanNETs through a unique evolutionary path. The disease's progression pattern indicates that PanNETs can spread early, even before clinical detection, highlighting the importance of identifying biomarkers related to metastasis to guide personalized treatment strategies.

Investigating the brain functional abnormalities underlying pain hypervigilance in chronic neck and shoulder pain: a resting-state fMRI study.

PURPOSE: To investigate pain hypervigilance in individuals suffering from chronic neck and shoulder pain (CNSP) and its underlying brain mechanism. METHODS: The evaluation of pain vigilance was conducted through the utilization of pain vigilance and awareness questionnaires. Voxel-wise regional homogeneity (ReHo) from 60 CNSP patients and 60 healthy controls (HCs) using resting-state fMRI data. Voxel-wise two-sample T-test was conducted to reveal the ReHo variations between CNSP and HC. Correlation analyses were utilized to reveal the connection between brain abnormalities and medical measurements. Furthermore, a mediation analysis was conducted to elucidate the pathway-linking changes in brain function with medical measurements. RESULTS: Our present study revealed three main findings. Firstly, patients with CSNP demonstrated a heightened vigilance of pain in comparison to healthy adults, a common occurrence among individuals with chronic pain conditions. Secondly, we observed brain abnormalities in various brain regions in CSNP patients, and these alterations were associated with the extent of pain vigilance. Lastly, the pain hypervigilance impact on the severity of pain was found to be controlled by regional neural activity in the anterior cingulate cortex (ACC) in subjects with CSNP. CONCLUSION: Our findings suggested that long-term repetitive nociceptive input caused by chronic pain further aggravates the pain intensity by impairing the vigilance-related pain processing within the anterior cingulate cortex in CNSP patients.

Nonlinear error analysis of fast optical delay lines.

Optical delay lines have wide applications in terahertz time-domain spectroscopy and optical coherence tomography. In this study, a fast-rotating optical delay line (FRODL) with 24 turntable reflection surfaces was designed. By analyzing the working principle of the FRODL, a mathematical model was established for the nonlinear parameter error of the FRODL delay time. By constructing the polarization Michelson interference system and testing the FRODL structure, the error of actual assembly parameters of the FRODL was approximately 0.015 mm, the actual delay time of the FRODL was greater than 43.5 ps, and the linearity was 99.785%.

A platform utilizing Drosophila ovulation for nonhormonal contraceptive screening.

A significant unmet need for new contraceptive options for both women and men remains due to side-effect profiles, medical concerns, and the inconvenience of many currently available contraceptive products. Unfortunately, the development of novel nonsteroidal female contraceptive medicine has been stalled in the last couple of decades due to the lack of effective screening platforms. Drosophila utilizes conserved signaling pathways for follicle rupture, a final step in ovulation that is essential for female reproduction. Therefore, we explored the potential to use Drosophila as a model to screen compounds that could inhibit follicle rupture and be nonsteroidal contraceptive candidates. Using our ex vivo follicle rupture assay, we screened 1,172 Food and Drug Administration (FDA)-approved drugs and identified six drugs that could inhibit Drosophila follicle rupture in a dose-dependent manner. In addition, we characterized the molecular actions of these drugs in the inhibition of adrenergic signaling and follicle rupture. Furthermore, we validated that three of the four drugs consistently inhibited mouse follicle rupture in vitro and that two of them did not affect progesterone production. Finally, we showed that chlorpromazine, one of the candidate drugs, can significantly inhibit mouse follicle rupture in vivo. Our work suggests that Drosophila ovulation is a valuable platform for identifying lead compounds for nonsteroidal contraceptive development and highlights the potential of these FDA-approved drugs as novel nonsteroidal contraceptive agents.

Early Pulmonary Fibrosis-like Changes in the Setting of Heat Exposure: DNA Damage and Cell Senescence.

It is well known that extreme heat events happen frequently due to climate change. However, studies examining the direct health impacts of increased temperature and heat waves are lacking. Previous reports revealed that heatstroke induced acute lung injury and pulmonary dysfunction. This study aimed to investigate whether heat exposure induced lung fibrosis and to explore the underlying mechanisms. Male C57BL/6 mice were exposed to an ambient temperature of 39.5 ± 0.5 °C until their core temperature reached the maximum or heat exhaustion state. Lung fibrosis was observed in the lungs of heat-exposed mice, with extensive collagen deposition and the elevated expression of fibrosis molecules, including transforming growth factor-ß1 (TGF-ß1) and Fibronectin (Fn1) (p < 0.05). Moreover, epithelial-mesenchymal transition (EMT) occurred in response to heat exposure, evidenced by E-cadherin, an epithelial marker, which was downregulated, whereas markers of EMT, such as connective tissue growth factor (CTGF) and the zinc finger transcriptional repressor protein Slug, were upregulated in the heat-exposed lung tissues of mice (p < 0.05). Subsequently, cell senescence examination revealed that the levels of both senescence-associated ß-galactosidase (SA-ß-gal) staining and the cell cycle protein kinase inhibitor p21 were significantly elevated (p < 0.05). Mechanistically, the cGAS-STING signaling pathway evoked by DNA damage was activated in response to heat exposure (p < 0.05). In summary, we reported a new finding that heat exposure contributed to the development of early pulmonary fibrosis-like changes through the DNA damage-activated cGAS-STING pathway followed by cellular senescence.

An ex vivo ovulation system enables the discovery of novel ovulatory pathways and nonhormonal contraceptive candidates†.

Ovulation is an integral part of women's menstrual cycle and fertility. Understanding the mechanisms of ovulation has broad implications for the treatment of anovulatory diseases and the development of novel contraceptives. Now, few studies have developed effective models that both faithfully recapitulate the hallmarks of ovulation and possess scalability. We established a three-dimensional encapsulated in vitro follicle growth (eIVFG) system that recapitulates folliculogenesis and produces follicles that undergo ovulation in a controlled manner. Here, we determined whether ex vivo ovulation preserves molecular signatures of ovulation and demonstrated its use in discovering novel ovulatory pathways and nonhormonal contraceptive candidates through a high-throughput ovulation screening. Mature murine follicles from eIVFG were induced to ovulate ex vivo using human chorionic gonadotropin and collected at 0, 1, 4, and 8 hours post-induction. Phenotypic analyses confirmed key ovulatory events, including cumulus expansion, oocyte maturation, follicle rupture, and luteinization. Single-follicle RNA-sequencing analysis revealed the preservation of ovulatory genes and dynamic transcriptomic profiles and signaling. Soft clustering identified distinct gene expression patterns and new pathways that may critically regulate ovulation. We further used this ex vivo ovulation system to screen 21 compounds targeting established and newly identified ovulatory pathways. We discovered that proprotein convertases activate gelatinases to sustain follicle rupture and do not regulate luteinization and progesterone secretion. Together, our ex vivo ovulation system preserves molecular signatures of ovulation, presenting a new powerful tool for studying ovulation and anovulatory diseases as well as for establishing a high-throughput ovulation screening to identify novel nonhormonal contraceptives for women.

Interpreting Infrared Thermography with Deep Learning to Assess the Mortality Risk of Critically Ill Patients at Risk of Hypoperfusion.

Background: Hypoperfusion, a common manifestation of many critical illnesses, could lead to abnormalities in body surface thermal distribution. However, the interpretation of thermal images is difficult. Our aim was to assess the mortality risk of critically ill patients at risk of hypoperfusion in a prospective cohort by infrared thermography combined with deep learning methods. Methods: This post-hoc study was based on a cohort at high-risk of hypoperfusion. Patients' legs were selected as the region of interest. Thermal images and conventional hypoperfusion parameters were collected. Six deep learning models were attempted to derive the risk of mortality (range: 0 to 100%) for each patient. The area under the receiver operating characteristic curve (AUROC) was used to evaluate predictive accuracy. Results: Fifty-five hospital deaths occurred in a cohort consisting of 373 patients. The conventional hypoperfusion (capillary refill time and diastolic blood pressure) and thermal (low temperature area rate and standard deviation) parameters demonstrated similar predictive accuracies for hospital mortality (AUROC 0.73 and 0.77). The deep learning methods, especially the ResNet (18), could further improve the accuracy. The AUROC of ResNet (18) was 0.94 with a sensitivity of 84% and a specificity of 91% when using a cutoff of 36%. ResNet (18) presented a significantly increasing trend in the risk of mortality in patients with normotension (13 [7 to 26]), hypotension (18 [8 to 32]) and shock (28 [14 to 62]). Conclusions: Interpreting infrared thermography with deep learning enables accurate and non-invasive assessment of the severity of patients at risk of hypoperfusion.

CaMelia: imputation in single-cell methylomes based on local similarities between cells.

MOTIVATION: Single-cell DNA methylation sequencing detects methylation levels with single-cell resolution, while this technology is upgrading our understanding of the regulation of gene expression through epigenetic modifications. Meanwhile, almost all current technologies suffer from the inherent problem of detecting low coverage of the number of CpGs. Therefore, addressing the inherent sparsity of raw data is essential for quantitative analysis of the whole genome. RESULTS: Here, we reported CaMelia, a CatBoost gradient boosting method for predicting the missing methylation states based on the locally paired similarity of intercellular methylation patterns. On real single-cell methylation datasets, CaMelia yielded significant imputation performance gains over previous methods. Furthermore, applying the imputed data to the downstream analysis of cell-type identification, we found that CaMelia helped to discover more intercellular differentially methylated loci that were masked by the sparsity in raw data, and the clustering results demonstrated that CaMelia could preserve cell-cell relationships and improve the identification of cell types and cell subpopulations. AVAILABILITY AND IMPLEMENTATION: Python code is available at https://github.com/JxTang-bioinformatics/CaMelia. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Identification and evaluation of circulating small extracellular vesicle microRNAs as diagnostic biomarkers for patients with indeterminate pulmonary nodules.

BACKGROUND: The identification of indeterminate pulmonary nodules (IPNs) following a low-dose computed tomography (LDCT) is a major challenge for early diagnosis of lung cancer. The inadequate assessment of IPNs' malignancy risk results in a large number of unnecessary surgeries or an increased risk of cancer metastases. However, limited studies on non-invasive diagnosis of IPNs have been reported. METHODS: In this study, we identified and evaluated the diagnostic value of circulating small extracellular vesicle (sEV) microRNAs (miRNAs) in patients with IPNs that had been newly detected using LDCT scanning and were scheduled for surgery. Out of 459 recruited patients, 109 eligible patients with IPNs were enrolled in the training cohort (n = 47) and the test cohort (n = 62). An external cohort (n = 99) was used for validation. MiRNAs were extracted from plasma sEVs, and assessed using Small RNA sequencing. 490 lung adenocarcinoma samples and follow-up data were used to investigate the role of miRNAs in overall survival. RESULTS: A circulating sEV miRNA (CirsEV-miR) model was constructed from five differentially expressed miRNAs (DEMs), showing 0.920 AUC in the training cohort (n = 47), and further identified in the test cohort (n = 62) and in an external validation cohort (n = 99). Among five DEMs of the CirsEV-miR model, miR-101-3p and miR-150-5p were significantly associated with better overall survival (p = 0.0001 and p = 0.0069). The CirsEV-miR scores were calculated, which significantly correlated with IPNs diameters (p < 0.05), and were able to discriminate between benign and malignant PNs (diameter ≤ 1 cm). The expression patterns of sEV miRNAs in the benign, adenocarcinoma in situ/minimally invasive adenocarcinoma, and invasive adenocarcinoma subgroups were found to gradually change with the increase in aggressiveness for the first time. Among all DEMs of the three subgroups, five miRNAs (miR-30c-5p, miR-30e-5p, miR-500a-3p, miR-125a-5p, and miR-99a-5p) were also significantly associated with overall survival of lung adenocarcinoma patients. CONCLUSIONS: Our results indicate that the CirsEV-miR model could help distinguish between benign and malignant PNs, providing insights into the feasibility of circulating sEV miRNAs in diagnostic biomarker development. TRIAL REGISTRATION: Chinese Clinical Trials: ChiCTR1800019877. Registered 05 December 2018, https://www.chictr.org.cn/showproj.aspx?proj=31346 .

THz imaging technique for nondestructive analysis of debonding defects in ceramic matrix composites based on multiple echoes and feature fusion.

We propose a THz nondestructive analysis method based on multiple echoes and feature fusion. Conventionally, it is difficult to identify the debonding defects of the glue layer (II) due to the thin adhesive layer. To this end, a THz propagation model is established, and a quantitative method for determining the thickness of debonding defects based on multiple echoes is presented. The measurement error for preset defect thickness of 500 µm was 4%. Further, for determining the area of debonding defects, a feature fusion imaging algorithm is proposed to realize the lateral recognition of defects and quantitative analysis is used to improve the recognition ability of defects.

Chaotic Ensemble of Online Recurrent Extreme Learning Machine for Temperature Prediction of Control Moment Gyroscopes.

Control moment gyroscopes (CMG) are crucial components in spacecrafts. Since the anomaly of bearing temperature of the CMG shows apparent correlation with nearly all critical fault modes, temperature prediction is of great importance for health management of CMGs. However, due to the complicity of thermal environment on orbit, the temperature signal of the CMG has strong intrinsic nonlinearity and chaotic characteristics. Therefore, it is crucial to study temperature prediction under the framework of chaos time series theory. There are also several other challenges including poor data quality, large individual differences and difficulty in processing streaming data. To overcome these issues, we propose a new method named Chaotic Ensemble of Online Recurrent Extreme Learning Machine (CE-ORELM) for temperature prediction of control moment gyroscopes. By means of the CE-ORELM model, this proposed method is capable of dynamic prediction of temperature. The performance of the method was tested by real temperature data acquired from actual CMGs. Experimental results show that this method has high prediction accuracy and strong adaptability to the on-orbital temperature data with sudden variations. These superiorities indicate that the proposed method can be used for temperature prediction of control moment gyroscopes.

Quantitative proteomic study of arsenic treated mouse liver sinusoidal endothelial cells using a reverse super-SILAC method.

Liver sinusoidal endothelial cells are the border patrol in the liver. Their open transcellular fenestrations allow the transfer of small and dissolved substances from the blood into the liver parenchymal cells. Fenestrations are dynamic structures, and many drugs and diseases alter their size and number, thus making them an important target for modulation. There is an urgent need to understand how various diseases, toxic substances, and physiological conditions influence liver endothelial cell fenestrations, and how these changes affects liver function. This work represents a straightforward quantitative proteomics study of the in vivo arsenic-stressed liver sinusoidal endothelial cells using a reverse super-SILAC based method. The aim of this study was to identify proteins, which are up- or down-regulated in response to arsenic. This knowledge will aid in identification of potential targets and mechanisms of arsenic toxicity and novel ways to reverse these changes.

Immunogenic HLA-DR-Presented Self-Peptides Identified Directly from Clinical Samples of Synovial Tissue, Synovial Fluid, or Peripheral Blood in Patients with Rheumatoid Arthritis or Lyme Arthritis.

Human leukocyte antigen-antigen D related (HLA-DR) molecules are highly expressed in synovial tissue (ST), the target of the immune response in chronic inflammatory forms of arthritis. Here, we used LC-MS/MS to identify HLA-DR-presented self-peptides in cells taken directly from clinical samples: ST, synovial fluid mononuclear cells (SFMC), or peripheral blood mononuclear cells (PBMC) from five patients with rheumatoid arthritis (RA) and eight with Lyme arthritis (LA). We identified 1593 non-redundant HLA-DR-presented peptides, derived from 870 source proteins. A total of 67% of the peptides identified in SFMC and 55% of those found in PBMC were found in ST, but analysis of SFMC/PBMC also revealed new antigen-presented peptides. Peptides were synthesized and examined for reactivity with the patients' PBMC. To date, three autoantigens in RA and four novel autoantigens in LA, presented in ST and/or PBMC, were shown to be targets of T- and B-cell responses in these diseases; ongoing analyses may add to this list. Thus, immunoprecipitation and LC-MS/MS can now identify hundreds of HLA-DR-presented self-peptides from individual patients' tissues or fluids with mixed cell populations. Importantly, identification of HLA-DR-presented peptides from SFMC or PBMC allows testing of more patients, including those early in the disease. Direct analysis of clinical samples facilitates identification of novel immunogenic T-cell epitopes.

Quantitative and In-Depth Survey of the Isotopic Abundance Distribution Errors in Shotgun Proteomics.

Accuracy is an important metric when mass spectrometry (MS) is used in large-scale quantitative proteomics research. For MS-based quantification by extracting ion chromatogram (XIC), both the mass and intensity dimensions must be accurate. Although much research has focused on mass accuracy in recent years, less attention has been paid to intensity errors. Here, we investigated signal intensity measurement errors systematically and quantitatively using the natural properties of isotopic distributions. First, we defined a normalized isotopic abundance error model and presented its merits and demerits. Second, a comprehensive survey of the isotopic abundance errors using data sets with increasing sample complexities and concentrations was performed. We examined parameters such as error distribution, relationships between signal intensities within one isotopic cluster, and correlations between different peak errors in isotopic profiles. Our data demonstrated that the high resolution MS platforms might also generate large isotopic intensity measurement errors (approximately 20%). Meanwhile, this error can be reduced to less than 5% using a novel correction algorithm, which is based on the theoretical isotopic abundance distribution. Finally, a nonlinear relationship was observed as the abundance error decreased in isotopic profiles with higher intensity. Our findings are expected to provide insight into isotopic abundance recalibration in quantitative proteomics.

SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods.

SUMMARY: With the advance of experimental technologies, different stable isotope labeling methods have been widely applied to quantitative proteomics. Here, we present an efficient tool named SILVER for processing the stable isotope labeling mass spectrometry data. SILVER implements novel methods for quality control of quantification at spectrum, peptide and protein levels, respectively. Several new quantification confidence filters and indices are used to improve the accuracy of quantification results. The performance of SILVER was verified and compared with MaxQuant and Proteome Discoverer using a large-scale dataset and two standard datasets. The results suggest that SILVER shows high accuracy and robustness while consuming much less processing time. Additionally, SILVER provides user-friendly interfaces for parameter setting, result visualization, manual validation and some useful statistics analyses. AVAILABILITY AND IMPLEMENTATION: SILVER and its source codes are freely available under the GNU General Public License v3.0 at http://bioinfo.hupo.org.cn/silver.

Proteome-wide prediction of self-interacting proteins based on multiple properties.

Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of self-interacting proteins and provide clues for elucidating their functions.

Identification and characterization of transcript variants of chicken peroxisome proliferator-activated receptor gamma.

Peroxisome proliferator-activated receptor gamma regulates adipogenesis. The genomic structure of the chicken peroxisome proliferator-activated receptor gamma (cPPARγ) gene has not been fully characterized, and only one cPPARγ gene mRNA sequence has been reported in genetic databases. Using 5' rapid amplification of cDNA ends, we identified five different cPPARγ mRNAs that are transcribed from three transcription initiation sites. The open reading frame analysis showed that these five cPPARγ transcript variants (cPPARγ1 to 5) could encode two cPPARγ protein isoforms (cPPARγ1 and cPPARγ2), which differ only in their N-terminal region. Quantitative real-time RT-PCR analysis showed that, of these five cPPARγ transcript variants, cPPARγ1 was ubiquitously highly expressed in various chicken tissues, including adipose tissue, liver, kidney, spleen and duodenal; cPPARγ2 was exclusively highly expressed in adipose tissue; cPPARγ3 was highly expressed in adipose tissue, kidney, spleen and liver; cPPARγ4 and cPPARγ5 were ubiquitously weakly expressed in all the tested tissues, and comparatively, cPPARγ5 was highly expressed in adipose tissue, heart, liver and kidney. The comparison of the expression of the five cPPARγ transcript variants showed that adipose tissue cPPARγ1 expression was significantly higher in the fat line than in the lean line from 2 to 7 wk of age (P < 0.05 or P < 0.01). Adipose tissue cPPARγ3 expression was significantly higher in the fat line than in the lean line at 3, 5 and 6 wk of age (P < 0.01, P < 0.05), but lower at 4 wk of age (P < 0.05). Adipose tissue cPPARγ5 expression was significantly higher in the fat line than in the lean line at 3, 4, and 6 wk of age (P < 0.01) and at 2 and 7 wk of age (P < 0.05). This is the first report of transcript variants and protein isoforms of cPPARγ gene. Our findings provided a foundation for future investigations of the function and regulation of cPPARγ gene in adipose tissue development.