Effective multi-modal clustering method via skip aggregation network for parallel scRNA-seq and scATAC-seq data.

In recent years, there has been a growing trend in the realm of parallel clustering analysis for single-cell RNA-seq (scRNA) and single-cell Assay of Transposase Accessible Chromatin (scATAC) data. However, prevailing methods often treat these two data modalities as equals, neglecting the fact that the scRNA mode holds significantly richer information compared to the scATAC. This disregard hinders the model benefits from the insights derived from multiple modalities, compromising the overall clustering performance. To this end, we propose an effective multi-modal clustering model scEMC for parallel scRNA and Assay of Transposase Accessible Chromatin data. Concretely, we have devised a skip aggregation network to simultaneously learn global structural information among cells and integrate data from diverse modalities. To safeguard the quality of integrated cell representation against the influence stemming from sparse scATAC data, we connect the scRNA data with the aggregated representation via skip connection. Moreover, to effectively fit the real distribution of cells, we introduced a Zero Inflated Negative Binomial-based denoising autoencoder that accommodates corrupted data containing synthetic noise, concurrently integrating a joint optimization module that employs multiple losses. Extensive experiments serve to underscore the effectiveness of our model. This work contributes significantly to the ongoing exploration of cell subpopulations and tumor microenvironments, and the code of our work will be public at https://github.com/DayuHuu/scEMC.

scEGG: an exogenous gene-guided clustering method for single-cell transcriptomic data.

In recent years, there has been significant advancement in the field of single-cell data analysis, particularly in the development of clustering methods. Despite these advancements, most algorithms continue to focus primarily on analyzing the provided single-cell matrix data. However, within medical contexts, single-cell data often encompasses a wealth of exogenous information, such as gene networks. Overlooking this aspect could result in information loss and produce clustering outcomes lacking significant clinical relevance. To address this limitation, we introduce an innovative deep clustering method for single-cell data that leverages exogenous gene information to generate discriminative cell representations. Specifically, an attention-enhanced graph autoencoder has been developed to efficiently capture topological signal patterns among cells. Concurrently, a random walk on an exogenous protein-protein interaction network enabled the acquisition of the gene's embeddings. Ultimately, the clustering process entailed integrating and reconstructing gene-cell cooperative embeddings, which yielded a discriminative representation. Extensive experiments have demonstrated the effectiveness of the proposed method. This research provides enhanced insights into the characteristics of cells, thus laying the foundation for the early diagnosis and treatment of diseases. The datasets and code can be publicly accessed in the repository at https://github.com/DayuHuu/scEGG.

Systematic screening of protein-coding gene expression identified VWF as a potential key regulator in anthracycline-based chemotherapy-exacerbated metastasis of breast cancer.

BACKGROUND: Breast cancer is the most commonly diagnosed cancer worldwide. Although major treatments represented by chemotherapy have shown effectiveness at the initial period, recurrence and metastasis still occur later after treatments. The alternation of the tumor microenvironment by chemotherapy is confirmed as a trigger of the elevated proliferation and migration of the remaining tumor cells. METHODS: Using bioinformatic methods, differential gene expression analysis was used to determine DEGs between post-chemotherapy and pre-chemotherapy samples of breast cancer patients, followed by survival analysis and ELISA analysis of the potential key genes. An in vitro model of 2 breast cancer cells lines was used to demonstrate the role of VWF in the evasion and migration of breast cancer cells, using cell migration, evasion and wound healing assays, PCR and molecular docking analysis. RESULTS: 19 hub genes were further identified using GO and KEGG pathway analyses and WGCNA. The 5 secreted protein-coding genes with reported carcinogenesis effects (VWF, SVEP1, DPT, ADIPOQ, and LPL) were further analyzed in breast cancer patients and VWF was identified as a potential key regulator in the anthracycline-based chemotherapy-exacerbated metastasis. It was further confirmed that anthracycline-based chemotherapeutics doxorubicin exacerbated VWF upregulation and the evasion and migration of breast cancer cells. Based on molecular docking analysis and previous study, berberine was used as an inhibitor of VWF, and showed an effective inhibition of the doxorubicin-exacerbated VWF upregulation, migration and evasion in breast cancer. CONCLUSIONS: Doxorubicin-exacerbated evasion and migration through VWF upregulation. Berberine as an inhibitor of VWF was able to reversed the doxorubicin-exacerbated VWF upregulation and evasion and migration in breast cancer cells.

Liquid biopsy in prostate cancer: A novel dual biomarker analysis approach integrating circulating tumor cells and circulating tumor DNA data.

This study aimed to explore the potential of liquid biopsy as a diagnostic tool by integrating two key biomarkers, Circulating Tumor Cells (CTCs) and Circulating Tumor DNA (ctDNA), and to enhance the detection fidelity of prostate cancer. A dual biomarker analysis approach was employed to synergize the sensitivities of CTCs and ctDNA. Various genetic mutations of ctDNA and tissues were scrutinized, investigating their prevalence, co-existence, and mutual exclusivity. The findings uncovered a more intricate mutation landscape than previously anticipated, indicating a complex interplay between cellular and genetic aberrations in prostate cancer. Through harnessing the combined power of CTCs and ctDNA, our dual biomarker approach provides a more comprehensive understanding of prostate cancer genetics. This has the potential to revolutionize early detection and guide personalized therapeutic interventions.

Effect of inflammation on association between cancer and coronary artery disease.

BACKGROUND: Cancer and coronary artery disease (CAD) is reported to often co-exist in same individuals, however, whether cancer is directly associated with anatomical severity of CAD is rarely studied. The present study aimed to observe the relationship between newly diagnosed cancer and anatomical severity of CAD, moreover, to investigate effect of inflammation on the relationship of cancer with CAD. METHODS: 374 patients with newly diagnosed cancer who underwent coronary angiography (CAG) were enrolled. Through 1:3 propensity score matching (PSM) to cancer patients based on the age and gender among 51,106 non-cancer patients who underwent CAG, 1122 non-cancer patients were selected as control patients. Anatomical severity of CAD was assessed using SYNTAX score (SXscore) based on coronary angiographic image. SXscore ≤ 22 (highest quartile) was defined as SX-low, and SXscore > 22 as SX-high. The ratio of neutrophil to lymphocyte count (NLR) was used to describe inflammation level. Association between cancer and the anatomical severity of CAD was investigated using logistic regression. RESULTS: Univariate logistic regression analysis showed a correlation between cancer and anatomical severity of CAD (OR: 1.419, 95% CI: 1.083-1.859; P = 0.011). Cancer was associated with increased risk of SX-high after adjusted for common risk factors of CAD (OR: 1.598, 95% CI: 1.172-2.179, P = 0.003). Significant association between cancer and SX-high was revealed among patients with high inflammation (OR: 1.656, 95% CI: 1.099-2.497, P = 0.016), but not among patients with low inflammation (OR: 1.530, 95% CI: 0.973-2.498, P = 0.089). CONCLUSIONS: Cancer was associated with severity of CAD, however, the association between the two diseases was significant among patients with high inflammation rather than among patients with low inflammation.

[Study on methods measuring mechanical properties of arterial wall by macroscopic indentation].

Accurately evaluating the local biomechanics of arterial wall is crucial for diagnosing and treating arterial diseases. Indentation measurement can be used to evaluate the local mechanical properties of the artery. However, the effects of the indenter's geometric structure and the analysis theory on measurement results remain uncertain. In this paper, four kinds of indenters were used to measure the pulmonary aorta, the proximal thoracic aorta and the distal thoracic aorta in pigs, and the arterial elastic modulus was calculated by Sneddon and Sirghi theory to explore the influence of the indenter geometry and analysis theory on the measured elastic modulus. The results showed that the arterial elastic modulus measured by cylindrical indenter was lower than that measured by spherical indenter. In addition, compared with the calculated results of Sirghi theory, the Sneddon theory, which does not take adhesion forces in account, resulted in slightly larger elastic modulus values. In conclusion, this study provides parametric support for effective measurement of arterial local mechanical properties by millimeter indentation technique.

Exercise improves cognitive dysfunction and neuroinflammation in mice through Histone H3 lactylation in microglia.

BACKGROUND: Exercise is postulated to be a promising non-pharmacological intervention for the improvement of neurodegenerative disease pathology. However, the mechanism of beneficial effects of exercise on the brain remains to be further explored. In this study, we investigated the effect of an exercise-induced metabolite, lactate, on the microglia phenotype and its association with learning and memory. RESULTS: Microglia were hyperactivated in the brains of AlCl3/D-gal-treated mice, which was associated with cognitive decline. Running exercise ameliorated the hyperactivation and increased the anti-inflammatory/reparative phenotype of microglia and improved cognition. Mice were injected intraperitoneally with sodium lactate (NaLA) had similar beneficial effects as that of exercise training. Exogenous NaLA addition to cultured BV2 cells promoted their transition from a pro-inflammatory to a reparative phenotype. CONCLUSION: The elevated lactate acted as an "accelerator" of the endogenous "lactate timer" in microglia promoting this transition of microglia polarization balance through lactylation. These findings demonstrate that exercise-induced lactate accelerates the phenotypic transition of microglia, which plays a key role in reducing neuroinflammation and improving cognitive function.

Hepatic neddylation targets and stabilizes electron transfer flavoproteins to facilitate fatty acid ß-oxidation.

Neddylation is a ubiquitination-like pathway that controls cell survival and proliferation by covalently conjugating NEDD8 to lysines in specific substrate proteins. However, the physiological role of neddylation in mammalian metabolism remains elusive, and no mitochondrial targets have been identified. Here, we report that mouse models with liver-specific deficiency of NEDD8 or ubiquitin-like modifier activating enzyme 3 (UBA3), the catalytic subunit of the NEDD8-activating enzyme, exhibit neonatal death with spontaneous fatty liver as well as hepatic cellular senescence. In particular, liver-specific UBA3 deficiency leads to systemic abnormalities similar to glutaric aciduria type II (GA-II), a rare autosomal recessive inherited fatty acid oxidation disorder resulting from defects in mitochondrial electron transfer flavoproteins (ETFs: ETFA and ETFB) or the corresponding ubiquinone oxidoreductase. Neddylation inhibition by various strategies results in decreased protein levels of ETFs in neonatal livers and embryonic hepatocytes. Hepatic neddylation also enhances ETF expression in adult mice and prevents fasting-induced steatosis and mortality. Interestingly, neddylation is active in hepatic mitochondria. ETFs are neddylation substrates, and neddylation stabilizes ETFs by inhibiting their ubiquitination and degradation. Moreover, certain mutations of ETFs found in GA-II patients hinder the neddylation of these substrates. Taken together, our results reveal substrates for neddylation and add insight into GA-II.

Recent Progress in Advanced Polyester Elastomers for Tissue Engineering and Bioelectronics.

Polyester elastomers are highly flexible and elastic materials that have demonstrated considerable potential in various biomedical applications including cardiac, vascular, neural, and bone tissue engineering and bioelectronics. Polyesters are desirable candidates for future commercial implants due to their biocompatibility, biodegradability, tunable mechanical properties, and facile synthesis and fabrication methods. The incorporation of bioactive components further improves the therapeutic effects of polyester elastomers in biomedical applications. In this review, novel structural modification methods that contribute to outstanding mechanical behaviors of polyester elastomers are discussed. Recent advances in the application of polyester elastomers in tissue engineering and bioelectronics are outlined and analyzed. A prospective of the future research and development on polyester elastomers is also provided.

An overview on the two-component systems of Streptomyces coelicolor.

The two-component system (TCS) found in various organisms is a regulatory system, which is involved in the response by the organism to stimuli, thereby regulating the internal behavior of the cell. It is commonly found in prokaryotes and is an important signaling system in bacteria. TCSs are involved in the regulation of physiological and morphological differentiation of the industrially important microbes from the genus Streptomyces, which produce a vast array of bioactive secondary metabolites (SMs). Genetic engineering of TCSs can substantially increase the yield of target SMs, which is valuable for industrial-scale production. Research on TCS has mainly been completed in the model strain Streptomyces coelicolor. In this review, we summarize the recent advances in the functional identification and elucidation of the regulatory mechanisms of various TCSs in S. coelicolor, with a focus on their roles in the biosynthesis of important SMs.

SAR Imaging Algorithm of Ocean Waves Based on Optimum Subaperture.

Synthetic Aperture Radar (SAR) is widely applied to the field of ocean remote sensing. Clear SAR images are the basis for ocean information acquisitions, such as parameter retrieval of ocean waves and wind field inversion of the ocean surface. However, the SAR ocean images are usually blurred, which seriously affects the acquisition of ocean information. The reasons for the wave blurring in SAR images mainly include the following two aspects. One is that when SAR observes the ocean, the motion of ocean waves will have a greater impact on imaging quality. The other is that the ocean's surface is seriously decorrelated within the integration time. In order to obtain clear SAR images of ocean waves, a SAR imaging algorithm of ocean waves based on the optimum subaperture is proposed, aiming at the above two aspects. The optimum focus setting of the ocean waves is calculated, drawing support from the azimuth phase velocity of the dominant wave. The optimum subaperture is further calculated according to the proposed new evaluation, namely, F. Finally, according to the optimum focus setting and the optimum subaperture, the dominant wave is refocused, and a clear SAR image of the dominant wave can be obtained. The proposed algorithm was applied to airborne L-band and P-band SAR data. Furthermore, the proposed algorithm was compared with present methods, and the results sufficiently demonstrated the effectiveness and superiority of the proposed algorithm.

Sea Spike Suppression Method Based on Optimum Polarization Ratio in Airborne SAR Images.

In the condition of ocean observation for high-resolution airborne synthetic aperture radar (SAR), sea spikes will cause serious interference to SAR image interpretation and marine target detection. In order to improve the ability of target detection, it is necessary to suppress sea spikes in SAR images. However, there is no report on sea spike suppression methods in SAR images. As a step forward, a sea spike suppression method based on optimum polarization ratio in airborne SAR images is proposed in this paper. This method is only applicable to the situation where VV and HH dual-polarized SAR data containing sea spikes are acquired at the same time. By calculating the optimum polarization ratio, this method further obtains the difference image of the panoramic area accomplishing sea spike suppression. This method is applied to a field airborne X-band SAR data, including ocean waves, oil spills and ships. The results show that the sea spikes are well suppressed, the contrast of ocean waves and the contrast of oil spills are improved, and the false alarm rate of ship detection is reduced. The discussions on these results demonstrate that the proposed method can effectively suppress sea spikes and improve the interpretability of SAR images.

Analysis of Serum Metabolomics in Rats with Osteoarthritis by Mass Spectrometry.

Osteoarthritis is a common multifactorial chronic disease that occurs in articular cartilage, subchondral bone, and periarticular tissue. The pathogenesis of OA is still unclear. To investigate the differences in serum metabolites between OA and the control group, liquid chromatography/mass spectrometry (LC/MS)-based metabolomics was used. To reveal the pathogenesis of OA, 12 SD male rats were randomly divided into control and OA groups using collagenase to induce OA for modeling, and serum was collected 7 days after modeling for testing. The OA group was distinguished from the control group by principal component analysis and orthogonal partial least squares-discriminant analysis, and six biomarkers were finally identified. These biomarkers were metabolized through tryptophan metabolism, glutamate metabolism, nitrogen metabolism, spermidine metabolism, and fatty acid metabolism pathways. The study identified metabolites that may be altered in OA, suggesting a role in OA through relevant metabolic pathways. Metabolomics, as an important tool for studying disease mechanisms, provides useful information for studying the metabolic mechanisms of OA.

Chemotherapy exacerbates ovarian cancer cell migration and cancer stem cell-like characteristics through GLI1.

BACKGROUND: Despite the great clinical response to the first-line chemotherapeutics, metastasis still happens among most of the ovarian cancer patients within 2 years. METHODS: Using multiple human ovarian cancer cell lines, a transwell co-culture system of the carboplatin or VP-16-challenged feeder and receptor cells was established to demonstrate the chemotherapy-exacerbated migration. The migration and cancer stem cell (CSC)-like characteristics were determined by wound healing, transwell migration, flow cytometry and sphere formation. mRNA and protein expression were identified by qPCR and western blot. Bioinformatics analysis was used to investigate the differentially expressed genes. GLI1 expression in tissue samples was analysed by immunohistochemistry. RESULTS: Chemotherapy was found to not only kill tumour cells, but also trigger the induction of CSC-like traits and the migration of ovarian cancer cells. EMT markers Vimentin and Snail in receptor cells were upregulated in the microenvironment of chemotherapy-challenged feeder cells. The transcription factor GLI1 was upregulated by chemotherapy in both clinical samples and cell lines. Follow-up functional experiments illustrated that inhibiting GLI1 reversed the chemotherapy-exacerbated CSC-like traits, including CD44 and CD133, as well as prevented the migration of ovarian cancer cells. CONCLUSIONS: Targeting GLI1 may improve clinical benefits in the chemotherapy-exacerbated metastasis in ovarian cancer treatment.

Optimization of n-butanol synthesis in Lactobacillus brevis via the functional expression of thl, hbd, crt and ter.

N-butanol is an important chemical and can be naturally synthesized by Clostridium species; however, the poor n-butanol tolerance of Clostridium impedes the further improvement in titer. In this study, Lactobacillus brevis, which possesses a higher butanol tolerance, was selected as host for heterologous butanol production. The Clostridium acetobutylicum genes thl, hbd, and crt which encode thiolase, ß-hydroxybutyryl-CoA dehydrogenase, and crotonase, and the Treponema denticola gene ter, which encodes trans-enoyl-CoA reductase were cloned into a single plasmid to express the butanol synthesis pathway in L. brevis. A titer of 40 mg/L n-butanol was initially achieved with plasmid pLY15-opt, in which all pathway genes are codon-optimized. A titer of 450 mg/L of n-butanol was then synthesized when ter was further overexpressed in this pathway. The role of metabolic flux was reinforced with pLY15, in which only the ter gene was codon-optimized, which greatly increased the n-butanol titer to 817 mg/L. Our strategy significantly improved n-butanol synthesis in L. brevis and the final titer is the highest achieved amongst butanol-tolerant lactic acid bacteria.

Depression promotes hepatocellular carcinoma progression through a glucocorticoid-mediated upregulation of PD-1 expression in tumor-infiltrating NK cells.

There is a growing belief that depression was positively associated with the progression of liver cancer. However, the driving molecular events behind the depression in liver cancer are poorly understood and need to be elucidated. Since hyperactivity of the hypothalamic-pituitary-adrenal axis during depression leads to the excessive release of glucocorticoids (GCs), which suppress the activity of natural killer (NK) cells, we hypothesized that high levels of GCs during depression may inhibit function of tumor-infiltrating NK cells during the progress of the liver cancer. Using chronic unpredictable mild stress-induced depressed mice model, we showed that the progression of liver cancer was significantly accelerated in the depressed mice. The high levels of GCs were observed in both depressed mice and depressed patients with liver cancer. Importantly, the expression of programmed death (PD)-1 on NK cells was specifically increased in the tumor microenvironment rather than that in blood or spleen. Coculture studies demonstrated that the expression of PD-1 was significantly increased and cytotoxicity of NK92 cells was remarkably decreased by the dexamethasone treatment through PD-L1-dependent pathway. To the best of our knowledge, we first found that PD-1/PD-L1-mediated exhaustion of infiltrated NK cells promoted hepatocellular carcinoma progression under depression and provided a novel strategy for GC-mediated antidepressant therapy in patients with liver cancer.

iLoc-lncRNA: predict the subcellular location of lncRNAs by incorporating octamer composition into general PseKNC.

Motivation: Long non-coding RNAs (lncRNAs) are a class of RNA molecules with more than 200 nucleotides. They have important functions in cell development and metabolism, such as genetic markers, genome rearrangements, chromatin modifications, cell cycle regulation, transcription and translation. Their functions are generally closely related to their localization in the cell. Therefore, knowledge about their subcellular locations can provide very useful clues or preliminary insight into their biological functions. Although biochemical experiments could determine the localization of lncRNAs in a cell, they are both time-consuming and expensive. Therefore, it is highly desirable to develop bioinformatics tools for fast and effective identification of their subcellular locations. Results: We developed a sequence-based bioinformatics tool called 'iLoc-lncRNA' to predict the subcellular locations of LncRNAs by incorporating the 8-tuple nucleotide features into the general PseKNC (Pseudo K-tuple Nucleotide Composition) via the binomial distribution approach. Rigorous jackknife tests have shown that the overall accuracy achieved by the new predictor on a stringent benchmark dataset is 86.72%, which is over 20% higher than that by the existing state-of-the-art predictor evaluated on the same tests. Availability and implementation: A user-friendly webserver has been established at http://lin-group.cn/server/iLoc-LncRNA, by which users can easily obtain their desired results. Supplementary information: Supplementary data are available at Bioinformatics online.

5-FU preferably induces apoptosis in BRAF V600E colorectal cancer cells via downregulation of Bcl-xL.

Fluorouracil (5-FU) which has been widely used in postoperative adjuvant therapy in patients with colon cancer, remains the main backbone of combination treatment of patients with colon cancer. However, the efficacy of 5-FU alone in colorectal cancer patients with BRAFV600E is not clear. In this study, we demonstrated that BRAFV600E confers sensitivity to 5-FU in vitro and in vivo xenograft model, using the paired isogenic colorectal cancer cell lines RKO with either BRAF Wild Type (WT)(+/-) or mutant (Mut) (600E/-). Our results revealed 5-FU preferably induces marked apoptosis in BRAF-mutant colorectal cancer cells, through attenuating expression of Bcl-xL and activation caspase-3/9 pathway, eventually conferring the anti-tumor efficacy of 5-FU in vitro and in vivo. Meanwhile, expression of Bcl-xL remained unchanged in BRAF WT group after treatment of 5-FU, although low extent of anti-tumor activity of 5-FU still being observed. In conclusion, these results provided a better understanding of clinical outcome of 5-FU between BRAF WT and mutant colorectal cancer patients, and suggested the inhibition of Bcl-xL might present an alternative strategy to enhance the therapeutic efficacy of 5-FU in colorectal cancer patients with BRAF mutation.

Increased Serum Levels of Cortisol and Inflammatory Cytokines in People With Depression.

This cross-sectional study aimed at measuring the correlation and association between serum levels of cortisol, inflammatory cytokines, and depression and to measure the detection accuracy of serum levels of cortisol in serum samples. In total, 89 male participants were recruited into this study from June 15, 2017, to September 31, 2017. The Hamilton Depression Rating Scale, Beck Anxiety Inventory, and Pittsburgh Sleep Quality Index were used to investigate the mental health status of the participants. Serum concentrations of cortisol and inflammatory cytokines were determined. The serum cortisol concentration, anxiety level, and sleep quality were included in the final logistic regression model. Serum cortisol was able to accurately distinguish between patients with depression and those without depression. There was a significant positive correlation between serum cortisol levels and Hamilton Depression Rating Scale scores.

Multifeature Fusion Neural Network for Oceanic Phenomena Detection in SAR Images.

Oceanic phenomena detection in synthetic aperture radar (SAR) images is important in the fields of fishery, military, and oceanography. The traditional detection methods of oceanic phenomena in SAR images are based on handcrafted features and detection thresholds, which have a problem of poor generalization ability. Methods based on deep learning have good generalization ability. However, most of the deep learning methods currently applied to oceanic phenomena detection only detect one type of phenomenon. To satisfy the requirements of efficient and accurate detection of multiple information of multiple oceanic phenomena in massive SAR images, this paper proposes an oceanic phenomena detection method in SAR images based on convolutional neural network (CNN). The method first uses ResNet-50 to extract multilevel features. Second, it uses the atrous spatial pyramid pooling (ASPP) module to extract multiscale features. Finally, it fuses multilevel features and multiscale features to detect oceanic phenomena. The SAR images acquired from the Sentinel-1 satellite are used to establish a sample dataset of oceanic phenomena. The method proposed can achieve 91% accuracy on the dataset.