The global landscape of immune-derived lncRNA signature in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) is a highly heterogeneous cancer. This heterogeneity has an impact on the efficacy of immunotherapy. Long noncoding RNAs (lncRNAs) have been found to play regulatory functions in cancer immunity. However, the global landscape of immune-derived lncRNA signatures has not yet been explored in colorectal cancer. METHODS: In this study, we applied DESeq2 to identify differentially expressed lncRNAs in colon cancer. Next, we performed an integrative analysis to globally identify immune-driven lncRNA markers in CRC, including immune-associated pathways, tumor immunogenomic features, tumor-infiltrating immune cells, immune checkpoints, microsatellite instability (MSI) and tumor mutation burden (TMB). RESULTS: We also identified dysregulated lncRNAs, such as LINC01354 and LINC02257, and their clinical relevance in CRC. Our findings revealed that the differentially expressed lncRNAs were closely associated with immune pathways. In addition, we found that RP11-354P11.3 and RP11-545G3.1 had the highest association with the immunogenomic signature. As a result, these signatures could serve as markers to assess immunogenomic activity in CRC. Among the immune cells, resting mast cells and M0 macrophages had the highest association with lncRNAs in CRC. The AC006129.2 gene was significantly associated with several immune checkpoints, for example, programmed cell death protein 1 (PD-1) and B and T lymphocyte attenuator (BTLA). Therefore, the AC006129.2 gene could be targeted to regulate the condition of immune cells or immune checkpoints to enhance the efficacy of immunotherapy in CRC patients. Finally, we identified 15 immune-related lncRNA-generated open reading frames (ORFs) corresponding to 15 cancer immune epitopes. CONCLUSION: In conclusion, we provided a genome-wide immune-driven lncRNA signature for CRC that might provide new insights into clinical applications and immunotherapy.

Revealing the conformational dynamics of UDP-GlcNAc recognition by O-GlcNAc transferase via Markov state model.

The O-linked N-acetylglucosamine (O-GlcNAc) glycosylation is a critical post-translational modification and closely linked to various physiological and pathological conditions. The O-GlcNAc transferase (OGT) functions as the only glycosyltransferase of O-GlcNAc glycosylation by transferring GlcNAc from UDP-GlcNAc to serine or threonine residues on protein substrates. The interaction mode of UDP-GlcNAc against OGT has been preliminarily revealed by the crystal structures, yet an atomic-level comprehension for the conformational dynamics of the recognition process remains elusive. Here, we construct the Markov state model based on extensive all-atom molecular dynamics (MD) simulations with an aggregated simulation time of ∼9 µs, and reveal that the UDP-GlcNAc recognition process by OGT encompasses four key metastable states, occurring within an estimated timescale of ∼10 µs. During UDP-GlcNAc recognition process, we find the pyrophosphate moiety (P2O52-) initially anchors to the active pocket via salt bridge and hydrogen bonds, facilitating subsequent binding of the uridine and GlcNAc moieties. Furthermore, the functional roles of K842 involved in the salt bridge with P2O52- were evaluated through extra mutant MD simulations. Overall, our study provides valuable insights into the UDP-GlcNAc recognition mechanism by OGT, which could further aid in mechanistic studies of O-GlcNAc glycosylation and drug development targeting on OGT.

The gut microbial metabolite trimethylamine N-oxide and cardiovascular diseases.

Morbidity and mortality of cardiovascular diseases (CVDs) are exceedingly high worldwide. Researchers have found that the occurrence and development of CVDs are closely related to intestinal microecology. Imbalances in intestinal microecology caused by changes in the composition of the intestinal microbiota will eventually alter intestinal metabolites, thus transforming the host physiological state from healthy mode to pathological mode. Trimethylamine N-oxide (TMAO) is produced from the metabolism of dietary choline and L-carnitine by intestinal microbiota, and many studies have shown that this important product inhibits cholesterol metabolism, induces platelet aggregation and thrombosis, and promotes atherosclerosis. TMAO is directly or indirectly involved in the pathogenesis of CVDs and is an important risk factor affecting the occurrence and even prognosis of CVDs. This review presents the biological and chemical characteristics of TMAO, and the process of TMAO produced by gut microbiota. In particular, the review focuses on summarizing how the increase of gut microbial metabolite TMAO affects CVDs including atherosclerosis, heart failure, hypertension, arrhythmia, coronary artery disease, and other CVD-related diseases. Understanding the mechanism of how increases in TMAO promotes CVDs will potentially facilitate the identification and development of targeted therapy for CVDs.

Rapid discrimination of glycogen particles originated from different eukaryotic organisms.

There are many commercially available glycogen particles in the market due to their bioactive functions as food additive, drug carrier and natural moisturizer, etc. It would be beneficial to rapidly determine the origins of commercially-available glycogen particles, which could facilitate the establishment of quality control methodology for glycogen-containing products. With its non-destructive, label-free and low-cost features, surface enhanced Raman spectroscopy (SERS) is an attractive technique with high potential to discriminate chemical compounds in a rapid mode. In this study, we applied the combination of SERS technique and machine leaning algorithms on glycogen analysis, which successfully predicted the origins of glycogen particles from a variety of organisms with convolutional neural network (CNN) algorithm plus attention mechanism having the best computational performance (5-fold cross validation accuracy = 96.97 %). In sum, this is the first study focusing on the discrimination of commercial glycogen particles originated from different organisms, which holds the application potential in quality control of glycogen-containing products.

Classification of Benign and Malignant Lung Nodules Based on Deep Convolutional Network Feature Extraction.

With the rapid development of detection technology, CT imaging technology has been widely used in the early clinical diagnosis of lung nodules. However, accurate assessment of the nature of the nodule remains a challenging task due to the subjective nature of the radiologist. With the increasing amount of publicly available lung image data, it has become possible to use convolutional neural networks for benign and malignant classification of lung nodules. However, as the network depth increases, network training methods based on gradient descent usually lead to gradient dispersion. Therefore, we propose a novel deep convolutional network approach to classify the benignity and malignancy of lung nodules. Firstly, we segmented, extracted, and performed zero-phase component analysis whitening on images of lung nodules. Then, a multilayer perceptron was introduced into the structure to construct a deep convolutional network. Finally, the minibatch stochastic gradient descent method with a momentum coefficient is used to fine-tune the deep convolutional network to avoid the gradient dispersion. The 750 lung nodules in the lung image database are used for experimental verification. Classification accuracy of the proposed method can reach 96.0%. The experimental results show that the proposed method can provide an objective and efficient aid to solve the problem of classifying benign and malignant lung nodules in medical images.

Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus Species.

Raman spectroscopy (RS) is a widely used analytical technique based on the detection of molecular vibrations in a defined system, which generates Raman spectra that contain unique and highly resolved fingerprints of the system. However, the low intensity of normal Raman scattering effect greatly hinders its application. Recently, the newly emerged surface enhanced Raman spectroscopy (SERS) technique overcomes the problem by mixing metal nanoparticles such as gold and silver with samples, which greatly enhances signal intensity of Raman effects by orders of magnitudes when compared with regular RS. In clinical and research laboratories, SERS provides a great potential for fast, sensitive, label-free, and non-destructive microbial detection and identification with the assistance of appropriate machine learning (ML) algorithms. However, choosing an appropriate algorithm for a specific group of bacterial species remains challenging, because with the large volumes of data generated during SERS analysis not all algorithms could achieve a relatively high accuracy. In this study, we compared three unsupervised machine learning methods and 10 supervised machine learning methods, respectively, on 2,752 SERS spectra from 117 Staphylococcus strains belonging to nine clinically important Staphylococcus species in order to test the capacity of different machine learning methods for bacterial rapid differentiation and accurate prediction. According to the results, density-based spatial clustering of applications with noise (DBSCAN) showed the best clustering capacity (Rand index 0.9733) while convolutional neural network (CNN) topped all other supervised machine learning methods as the best model for predicting Staphylococcus species via SERS spectra (ACC 98.21%, AUC 99.93%). Taken together, this study shows that machine learning methods are capable of distinguishing closely related Staphylococcus species and therefore have great application potentials for bacterial pathogen diagnosis in clinical settings.

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings.

Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

Ethanol extract of mulberry leaves partially restores the composition of intestinal microbiota and strengthens liver glycogen fragility in type 2 diabetic rats.

BACKGROUND: Mulberry leaf as a traditional Chinese medicine is able to treat obesity, diabetes, and dyslipidemia. It is well known that diabetes leads to intestinal microbiota dysbiosis. It is also recently discovered that liver glycogen structure is impaired in diabetic animals. Since mulberry leaves are able to improve the diabetic conditions through reducing blood glucose level, it would be interesting to investigate whether they have any positive effects on intestinal microbiota and liver glycogen structure. METHODS: In this study, we first determined the bioactive components of ethanol extract of mulberry leaves via high-performance liquid chromatography (HPLC) and liquid chromatography/mass spectrometry (LC/MS). Murine animal models were divided into three groups, normal Sprague-Dawley (SD) rats, high-fat diet (HFD) and streptozotocin (STZ) induced type 2 diabetic rats, and HFD/STZ-induced rats administered with ethanol extract of mulberry leaves (200 mg/kg/day). Composition of intestinal microbiota was analyzed via metagenomics by sequencing the V3-V4 region of 16S rDNAs. Liver glycogen structure was characterized through size exclusion chromatography (SEC). Both Student's t-test and Tukey's test were used for statistical analysis. RESULTS: A group of type 2 diabetic rat models were successfully established. Intestinal microbiota analysis showed that ethanol extract of mulberry leaves could partially change intestinal microbiota back to normal conditions. In addition, liver glycogen was restored from fragile state to stable state through administration of ethanol extract of mulberry leaves. CONCLUSIONS: This study confirms that the ethanol extract of mulberry leaves (MLE) ameliorates intestinal microbiota dysbiosis and strengthens liver glycogen fragility in diabetic rats. These finding can be helpful in discovering the novel therapeutic targets with the help of further investigations.

From Prokaryotes to Eukaryotes: Insights Into the Molecular Structure of Glycogen Particles.

Glycogen is a highly-branched polysaccharide that is widely distributed across the three life domains. It has versatile functions in physiological activities such as energy reserve, osmotic regulation, blood glucose homeostasis, and pH maintenance. Recent research also confirms that glycogen plays important roles in longevity and cognition. Intrinsically, glycogen function is determined by its structure that has been intensively studied for many years. The recent association of glycogen α-particle fragility with diabetic conditions further strengthens the importance of glycogen structure in its function. By using improved glycogen extraction procedures and a series of advanced analytical techniques, the fine molecular structure of glycogen particles in human beings and several model organisms such as Escherichia coli, Caenorhabditis elegans, Mus musculus, and Rat rattus have been characterized. However, there are still many unknowns about the assembly mechanisms of glycogen particles, the dynamic changes of glycogen structures, and the composition of glycogen associated proteins (glycogen proteome). In this review, we explored the recent progresses in glycogen studies with a focus on the structure of glycogen particles, which may not only provide insights into glycogen functions, but also facilitate the discovery of novel drug targets for the treatment of diabetes mellitus.

Prediction of Radiosensitivity in Head and Neck Squamous Cell Carcinoma Based on Multiple Omics Data.

Head and neck squamous cell carcinoma (HNSCC) is a malignant tumor. Radiotherapy (RT) is an important treatment for HNSCC, but not all patients derive survival benefit from RT due to the individual differences on radiosensitivity. A prediction model of radiosensitivity based on multiple omics data might solve this problem. Compared with single omics data, multiple omics data can illuminate more systematical associations between complex molecular characteristics and cancer phenotypes. In this study, we obtained 122 differential expression genes by analyzing the gene expression data of HNSCC patients with RT (N = 287) and without RT (N = 189) downloaded from The Cancer Genome Atlas. Then, HNSCC patients with RT were randomly divided into a training set (N = 149) and a test set (N = 138). Finally, we combined multiple omics data of 122 differential genes with clinical outcomes on the training set to establish a 12-gene signature by two-stage regularization and multivariable Cox regression models. Using the median score of the 12-gene signature on the training set as the cutoff value, the patients were divided into the high- and low-score groups. The analysis revealed that patients in the low-score group had higher radiosensitivity and would benefit from RT. Furthermore, we developed a nomogram to predict the overall survival of HNSCC patients with RT. We compared the prognostic value of 12-gene signature with those of the gene signatures based on single omics data. It suggested that the 12-gene signature based on multiple omics data achieved the best ability for predicting radiosensitivity. In conclusion, the proposed 12-gene signature is a promising biomarker for estimating the RT options in HNSCC patients.

Downregulation of CDC20 Increases Radiosensitivity through Mcl-1/p-Chk1-Mediated DNA Damage and Apoptosis in Tumor Cells.

Radiotherapy is an important modality for the local control of human cancers, but the radioresistance induced by aberrant apoptotic signaling is a hallmark of cancers. Restoring the aberrant apoptotic pathways is an emerging strategy for cancer radiotherapy. In this study, we determined that targeting cell division cycle 20 (CDC20) radiosensitized colorectal cancer (CRC) cells through mitochondrial-dependent apoptotic signaling. CDC20 was overexpressed in CRC cells and upregulated after radiation. Inhibiting CDC20 activities genetically or pharmacologically suppressed the proliferation and increased radiation-induced DNA damage and intrinsic apoptosis in CRC cells. Mechanistically, knockdown of CDC20 suppressed the expression of antiapoptotic protein Mcl-1 but not other Bcl-2 family proteins. The expressions of CDC20 and Mcl-1 respond to radiation simultaneously through direct interaction, as evidenced by immunoprecipitation and glutathione S-transferase (GST) pull-down assays. Subsequently, decreased Mcl-1 expression inhibited the expression level of phosphorylated checkpoint kinase 1 (p-Chk1), thereby resulting in impaired DNA damage repair through downregulating the homologous recombination repair protein Rad51 and finally causing apoptotic signaling. In addition, both CDC20 and Chk1 inhibitors together, through in vivo studies, confirmed the radiosensitizing effect of CDC20 via inhibiting Mcl-1 and p-Chk1 expression. In summary, our results indicate that targeting CDC20 is a promising strategy to improve cancer radiotherapy.

Antagonizing CDK8 Sensitizes Colorectal Cancer to Radiation Through Potentiating the Transcription of e2f1 Target Gene apaf1.

Radiotherapy is an essential curative treatment modality for colorectal cancer. Apoptosis is the major mechanism of IR-induced cell death and aberrant apoptotic signaling results in radioresistance, which is a hallmark of most, perhaps all, types of human cancers. Potentiating the induction of apoptosis is an emerging strategy for cancer radiotherapy. Here, we determined that targeting CDK8 selectively radiosensitized colorectal cancer through the mitochondria-dependent intrinsic apoptotic signaling, which was mediated through the induction of the transcription of apaf1 that was e2f1- and not p53-dependent. Importantly, the enhanced transcriptional activity of e2f1 was dependent on the kinase activity of CDK8 itself and not on the assembling of the mediator complex. In addition, clinical inhibitor, and in vivo studies confirmed the radiosensitizing effect of CDK8. Our results provide a new targeting strategy to improve the radiotherapy of CRC.

Pan-Cancer Analysis of Radiotherapy Benefits and Immune Infiltration in Multiple Human Cancers.

Response to radiotherapy (RT) in cancers varies widely among patients. Therefore, it is very important to predict who will benefit from RT before clinical treatment. Consideration of the immune tumor microenvironment (TME) could provide novel insight into tumor treatment options. In this study, we investigated the link between immune infiltration status and clinical RT outcome in order to identify certain leukocyte subsets that could potentially influence the clinical RT benefit across cancers. By integrally analyzing the TCGA data across seven cancers, we identified complex associations between immune infiltration and patients RT outcomes. Besides, immune cells showed large differences in their populations in various cancers, and the most abundant cells were resting memory CD4 T cells. Additionally, the proportion of activated CD4 memory T cells and activated mast cells, albeit at low number, were closely related to RT overall survival in multiple cancers. Furthermore, a prognostic model for RT outcomes was established with good performance based on the immune infiltration status. Summarized, immune infiltration was found to be of significant clinical relevance to RT outcomes. These findings may help to shed light on the impact of tumor-associated immune cell infiltration on cancer RT outcomes, and identify biomarkers and therapeutic targets.

An analysis of mutational signatures of synonymous mutations across 15 cancer types.

BACKGROUND: Synonymous mutations have been identified to play important roles in cancer development, although they do not modify the protein sequences. However, relatively little research has specifically delineated the functionality of synonymous mutations in cancer. RESULTS: We investigated the nucleotide-based and amino acid-based features of synonymous mutations across 15 cancer types from The Cancer Genome Atlas (TCGA), and revealed novel driver candidates by identifying hotspot mutations. Firstly, synonymous mutations were analyzed between TCGA and 1000 Genomes Project at nucleotide and amino acid levels. We found that C:G → T:A transitions were the most frequent single-base substitutions, and leucine underwent the largest number of synonymous mutations in TCGA due to prevalent C → T transition, which induced the transformation between optimal and non-optimal codons. Next, 97 synonymous hotspot mutations in 86 genes were nominated as candidate drivers with potential cancer risk by considering the mutational rates across different sequence contexts. We observed that non-CpG-island GC transition sequence context was positively selected across most of cancer types, and different sequence contexts under which hotspot mutations occur could be significance for genetic differences and functional features. We also found that the hotspots were more conserved than neutral mutations of hotspot-mutation-containing-genes and frequently happened at leucine. In addition, we mapped hotspots, neutral and non-hotspot mutations of hotspot-mutation-containing-genes to their respective protein domains and found ion transport domain was the most frequent one, which could mediate the cell interaction and had relevant implication for tumor therapy. And the signatures of synonymous hotspots were qualitatively similar with those of harmful missense variants. CONCLUSIONS: We illustrated the preferences of cancer associated synonymous mutations, especially hotspots, and laid the groundwork for understanding the synonymous mutations act as drivers in cancer.

Radiation induces apoptosis primarily through the intrinsic pathway in mammalian cells.

Radiation-induced tumor cells death is the theoretical basis of tumor radiotherapy. Death signaling disorder is the most important factor for radioresistance. However, the signaling pathway(s) leading to radiation-triggered cell death is (are) still not completely known. To better understand the cell death signaling induced by radiation, the immortalized mouse embryonic fibroblast (MEF) deficient in "initiator" caspases, "effector" caspases or different Bcl-2 family proteins together with human colon carcinoma cell HCT116 were used. Our data indicated that radiation selectively induced the activation of caspase-9 and caspase-3/7 but not caspase-8 by triggering mitochondrial outer membrane permeabilization (MOMP). Importantly, the role of radiation in MOMP is independent of the activation of both "initiator" and "effector" caspases. Furthermore, both proapoptotic and antiapoptotic Bcl-2 family proteins were involved in radiation-induced apoptotic signaling. Overall, our study indicated that radiation specifically triggered the intrinsic apoptotic signaling pathway through Bcl-2 family protein-dependent mitochondrial permeabilization, which indicates targeting mitochondria is a promising strategy for cancer radiotherapy.

N-(3-oxo-acyl) homoserine lactone induced germ cell apoptosis and suppressed the over-activated RAS/MAPK tumorigenesis via mitochondrial-dependent ROS in C. elegans.

As a quorum-sensing molecule for bacteria-bacteria communication, N-(3-oxododecanoyl)-homoserine lactone (C12) has been found to possess pro-apoptotic activities in various cell culture models. However, the detailed mechanism of how this important signaling molecule function in the cells of live animals still remains largely unclear. In this study, we systematically investigated the mechanism for C12-mediated apoptosis and studied its anti-tumor effect in Caenorhabditis elegans (C. elegans). Our data demonstrated that C12 increased C. elegans germ cell apoptosis, by triggering mitochondrial outer membrane permeabilization (MOMP) and elevating the reactive oxygen species (ROS) level. Importantly, C12-induced ROS increased the expression of genes critical for DNA damage response (hus-1, clk-2 and cep-1) and genes involved in p38 and JNK/MAPK signaling pathway (nsy-1, sek-1, pmk-1, mkk-4 and jnk-1). Furthermore, C12 failed to induce germ cell apoptosis in animals lacking the expression of each of those genes. Finally, in a C. elegans tumor-like symptom model, C12 significantly suppressed tumor growth through inhibiting the expression of RAS/MAPK pathway genes (let-23/EGFR, let-60/RAS, lin-45/RAF, mek-2/MEK and mpk-1/MAPK). Overall, our results indicate that DNA damage response and MAPK activation triggered by mitochondrial ROS play important roles in C12-induced apoptotic signaling in C. elegans, and RAS/MAPK suppression is involved in the tumor inhibition effect of C12. This study provides in vivo evidence that C12 is a potential candidate for cancer therapeutics by exerting its pro-apoptotic and anti-tumor effects via elevating mitochondria-dependent ROS production.

dbCRSR: a manually curated database for regulation of cancer radiosensitivity.

Radiotherapy is used to treat approximately 50% of all cancer patients, with varying prognoses. Intrinsic radiosensitivity is an important factor underlying the radiotherapeutic efficacy of this precise treatment. During the past decades, great efforts have been made to improve radiotherapy treatment through multiple strategies. However, invaluable data remains buried in the extensive radiotherapy literature, making it difficult to obtain an overall view of the detailed mechanisms leading to radiosensitivity, thus limiting advances in radiotherapy. To address this issue, we collected data from the relevant literature contained in the PubMed database and developed a literature-based database that we term the cancer radiosensitivity regulation factors database (dbCRSR). dbCRSR is a manually curated catalogue of radiosensitivity, containing multiple radiosensitivity regulation factors (395 coding genes, 119 non-coding RNAs and 306 chemical compounds) with appropriate annotation. To illustrate the value of the data we collected, data mining was performed including functional annotation and network analysis. In summary, dbCRSR is the first literature-based database to focus on radiosensitivity and provides a resource to better understand the detailed mechanisms of radiosensitivity. We anticipate dbCRSR will be a useful resource to enrich our knowledge and to promote further study of radiosensitivity.Database URL: http://bioinfo.ahu.edu.cn: 8080/dbCRSR/.

dbDSM: a manually curated database for deleterious synonymous mutations.

MOTIVATION: Synonymous mutations (SMs), which changed the sequence of a gene without directly altering the amino acid sequence of the encoded protein, were thought to have no functional consequences for a long time. They are often assumed to be neutral in models of mutation and selection and were completely ignored in many studies. However, accumulating experimental evidence has demonstrated that these mutations exert their impact on gene functions via splicing accuracy, mRNA stability, translation fidelity, protein folding and expression, and some of these mutations are implicated in human diseases. To the best of our knowledge, there is still no database specially focusing on disease-related SMs. RESULTS: We have developed a new database called dbDSM (database of Deleterious Synonymous Mutation), a continually updated database that collects, curates and manages available human disease-related SM data obtained from published literature. In the current release, dbDSM collects 1936 SM-disease association entries, including 1289 SMs and 443 human diseases from ClinVar, GRASP, GWAS Catalog, GWASdb, PolymiRTS database, PubMed database and Web of Knowledge. Additionally, we provided users a link to download all the data in the dbDSM and a link to submit novel data into the database. We hope dbDSM will be a useful resource for investigating the roles of SMs in human disease. AVAILABILITY AND IMPLEMENTATION: dbDSM is freely available online at http://bioinfo.ahu.edu.cn:8080/dbDSM/index.jsp with all major browser supported. CONTACT: jfxia@ahu.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.