A Bayesian model for identifying cancer subtypes from paired methylation profiles.

Aberrant DNA methylation is the most common molecular lesion that is crucial for the occurrence and development of cancer, but has thus far been underappreciated as a clinical tool for cancer classification, diagnosis or as a guide for therapeutic decisions. Partly, this has been due to a lack of proven algorithms that can use methylation data to stratify patients into clinically relevant risk groups and subtypes that are of prognostic importance. Here, we proposed a novel Bayesian model to capture the methylation signatures of different subtypes from paired normal and tumor methylation array data. Application of our model to synthetic and empirical data showed high clustering accuracy, and was able to identify the possible epigenetic cause of a cancer subtype.

A Bayesian approach to estimate MHC-peptide binding threshold.

Major histocompatibility complex (MHC)-peptide binding is a critical step in enabling a peptide to serve as an antigen for T-cell recognition. Accurate prediction of this binding can facilitate various applications in immunotherapy. While many existing methods offer good predictive power for the binding affinity of a peptide to a specific MHC, few models attempt to infer the binding threshold that distinguishes binding sequences. These models often rely on experience-based ad hoc criteria, such as 500 or 1000nM. However, different MHCs may have different binding thresholds. As such, there is a need for an automatic, data-driven method to determine an accurate binding threshold. In this study, we proposed a Bayesian model that jointly infers core locations (binding sites), the binding affinity and the binding threshold. Our model provided the posterior distribution of the binding threshold, enabling accurate determination of an appropriate threshold for each MHC. To evaluate the performance of our method under different scenarios, we conducted simulation studies with varying dominant levels of motif distributions and proportions of random sequences. These simulation studies showed desirable estimation accuracy and robustness of our model. Additionally, when applied to real data, our results outperformed commonly used thresholds.

Selenium nanoparticles decorated with polysaccharides from Sargassum fusiforme protects against 6-OHDA-induced neurotoxicity in PC12 cells and rat model of Parkinson's disease.

Parkinson's disease (PD) is a neurodegenerative disorder and identifying disease-causing pathways and drugs that target them has remained challenging. Herein, selenium nanoparticles decorated with polysaccharides from Sargassum fusiforme (SFPS-SeNPs) were investigated on 6-OHDA-induced neurotoxicity in PC12 cells and rats. 6-OHDA can significantly increase neurotoxicity, oxidative stress and decrease the activity of superoxide dismutase (SOD) and glutathione peroxidase (GPx) both in vitro and vivo. In vitro, treatment with SFPS-SeNPs can significantly decrease 6-OHDA cytotoxicity, reactive oxygen species (ROS) production or malondialdehyde (MDA) levels, and cell apoptosis, significantly increased the activity of SOD and GPx. In vivo, 6-OHDA exposure could also decrease the expression of Nrf2 and OH-1, while treatment with SFPS-SeNPs (1 mg Se/kg) increased. SFPS-SeNPs can protect neurons from 6-OHDA-induced neurotoxicity by regulating apoptosis and Nrf2/ARE pathway. The present study demonstrated that SFPS-SeNPs is a good candidate for developing a new drug against neurodegenerative diseases such as PD.

Automatic block-wise genotype-phenotype association detection based on hidden Markov model.

BACKGROUND: For detecting genotype-phenotype association from case-control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype. RESULTS: In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case-control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher's exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons. CONCLUSIONS: With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case-control GWAS.

Exhaustive Cross-Linking Search with Protein Feedback.

Improving the sensitivity of protein-protein interaction detection and protein structure probing is a principal challenge in cross-linking mass spectrometry (XL-MS) data analysis. In this paper, we propose an exhaustive cross-linking search method with protein feedback (ECL-PF) for cleavable XL-MS data analysis. ECL-PF adopts an optimized α/ß mass detection scheme and establishes protein-peptide association during the identification of cross-linked peptides. Existing major scoring functions can all benefit from the ECL-PF workflow to a great extent. In comparisons using synthetic data sets and hybrid simulated data sets, ECL-PF achieved 3-fold higher sensitivity over standard techniques. In experiments using real data sets, it also identified 65.6% more cross-link spectrum matches and 48.7% more unique cross-links.

Low-cost wavefront shaping via the third-order correlation of light fields.

In this Letter, inspired by the ghost imaging technique, we propose a wavefront shaping technique based on the third-order correlation of light fields (TCLF). Theoretically, we prove that if the light field fluctuation can be modeled by a complex Gaussian random process with a non-zero mean, the conjugate complex amplitude of the object and a focusing phase factor can be obtained by TCLF when using a single-point detector, which can support wavefront shaping. Experiments demonstrate that TCLF can achieve high-resolution wavefront shaping for scattered fields and scattering-assisted holography without additional operations such as optimization and phase shifting.

IRF3-mediated lncRNA FTX promotes cell proliferation, migration, invasion and suppresses cell apoptosis in oral squamous cell carcinoma by up-regulating FCHSD2 via miR-708-5p.

In recent years, researches into the molecular mechanisms of oral squamous cell carcinoma (OSCC) have improved greatly but effective targeted therapies remain elusive. More and more evidence has referred to long non-coding RNAs (lncRNAs) as modulators of carcinomas development. As a novel lncRNA, five prime to Xist (FTX), as reported before, is overexpressed in a variety of cancers. In the present study, we sought to unclose the impacts of FTX and its molecular mechanism in OSCC. Related gene expression levels were disclosed by qRT-PCR and we found that FTX was notably overexpressed in OSCC. The biological functions of FTX in OSCC were measured by functional assays. The results displayed that depletion of FTX hinderedOSCC cell migratory, invasive and proliferative abilities, but promoted cell apoptotic levels. The relationship among interferon regulatory factor 3 (IRF3), FTX, microRNA-708-5p (miR-708-5p) and FCH and double SH3 domains 2 (FCHSD2) was determined by several mechanism assays, from which we discovered that FTX activated by IRF3 regulated FCHSD2 expression by sponging miR-708-5p. Rescue experiments showed that FTX motivated OSCC development by modulating miR-708-5p/FCHSD2 axis. In summary, FTX was an oncogene in OSCC and might provide new insights into OSCC treatment.

miR+Pathway: the integration and visualization of miRNA and KEGG pathways.

miRNAs represent a type of noncoding small molecule RNA. Many studies have shown that miRNAs are widely involved in the regulation of various pathways. The key to fully understanding the regulatory function of miRNAs is the determination of the pathways in which the miRNAs participate. However, the major pathway databases such as KEGG only include information regarding protein-coding genes. Here, we redesigned a pathway database (called miR+Pathway) by integrating and visualizing the 8882 human experimentally validated miRNA-target interactions (MTIs) and 150 KEGG pathways. This database is freely accessible at http://www.insect-genome.com/miR-pathway. Researchers can intuitively determine the pathways and the genes in the pathways that are regulated by miRNAs as well as the miRNAs that target the pathways. To determine the pathways in which targets of a certain miRNA or multiple miRNAs are enriched, we performed a KEGG analysis miRNAs by using the hypergeometric test. In addition, miR+Pathway provides information regarding MTIs, PubMed IDs and the experimental verification method. Users can retrieve pathways regulated by an miRNA or a gene by inputting its names.

miR-2682-3p antagonizes its host lncRNA-MIR137HG by interacting with the same target FUS to regulate the progression of gastric cancer.

BACKGROUND: The mechanism of long non-coding RNA MIR137HG in human gastric cancer (GC) is currently unknown. In the present study, we aimed to explore the function and mechanism of MIR137HG in gastric cancer. METHODS: The expression of lncRNA-MIR137HG in 69 gastric cancer samples and their paired surgical margin (SM) tissue samples were tested by QRT-PCR. UCSC was used to find the gene location relationship among MIR137HG and its embedded miRNAs. TargetScan was used to predict the targets of miR-2682-3p. Starbase was used to predict the candidate proteins that interacted with MIR137HG. Western blot, co-focus, and RIP assay were used to verify the direct interaction between MIR137HG and FUS (fused in sarcoma/translocated in liposarcoma, FUS/TLS), while dual-luciferase reporter assay was used to confirm the interaction between miR-2682-3p and FUS. Cell migration assays, colony formation, and xenografts assay were used to investigate the function of MIR137HG and miR-2682-3p to tumor growth and metastasis. Western blot assay was used to explore the downstream candidate protein of FUS. RESULTS: Data showed that MIR137HG expressed significantly higher in GC than in SM. MIR137HG promoted colony formation and migration in vitro and promoted tumor formation and metastasis in vivo. MIR137HG is distributed in both the nucleus and cytoplasm. It was co-located with FUS and could directly interact with FUS, which might interact with other proteins, such as MET(MET-proto-oncogene, receptor tyrosine kinase), RHOC(ras homolog family member), and CTNNB1(catenin beta1). These proteins may involve different signaling pathways to regulate gastric cancer progression. By contrast, the embedded miR-2682-3p could antagonize the series functions of its host lncRNA-MIR137HG by targeting FUS. CONCLUSIONS: lncRNA-MIR137HG promoted growth and metastasis in gastric cancer by interacting with FUS, while miR-2682-3p could inhibit the function of MIR137HG via the same target FUS.

Deep6mA: A deep learning framework for exploring similar patterns in DNA N6-methyladenine sites across different species.

N6-methyladenine (6mA) is an important DNA modification form associated with a wide range of biological processes. Identifying accurately 6mA sites on a genomic scale is crucial for under-standing of 6mA's biological functions. However, the existing experimental techniques for detecting 6mA sites are cost-ineffective, which implies the great need of developing new computational methods for this problem. In this paper, we developed, without requiring any prior knowledge of 6mA and manually crafted sequence features, a deep learning framework named Deep6mA to identify DNA 6mA sites, and its performance is superior to other DNA 6mA prediction tools. Specifically, the 5-fold cross-validation on a benchmark dataset of rice gives the sensitivity and specificity of Deep6mA as 92.96% and 95.06%, respectively, and the overall prediction accuracy is 94%. Importantly, we find that the sequences with 6mA sites share similar patterns across different species. The model trained with rice data predicts well the 6mA sites of other three species: Arabidopsis thaliana, Fragaria vesca and Rosa chinensis with a prediction accuracy over 90%. In addition, we find that (1) 6mA tends to occur at GAGG motifs, which means the sequence near the 6mA site may be conservative; (2) 6mA is enriched in the TATA box of the promoter, which may be the main source of its regulating downstream gene expression.

Detect differentially methylated regions using non-homogeneous hidden Markov model for bisulfite sequencing data.

DNA methylation plays an important role in many biological processes and diseases. With the rise of the whole genome bisulfite sequencing technique, aberrant methylation patterns can now be detected by comparing paired normal and disease samples at the single nucleotide level. We develop a novel Bayesian method for detecting differentially methylated regions from paired bisulfite sequencing data, and implement it as a R package called BSDMR. Based on a non-homogeneous hidden Markov model, BSDMR provides a better modeling strategy for the spatial correlation between CpG sites and takes into consideration the relationship between methylation signals from normal and disease samples. Simulations show that BSDMR performs well even under low read depth and has a smaller false discovery rates than existing methods. We also apply BSDMR to the colon cancer data from Gene Expression Omnibus. The detected DMRs are well supported by existing biomedical literatures.

Role of pathogen-laden expiratory droplet dispersion and natural ventilation explaining a COVID-19 outbreak in a coach bus.

The influencing mechanism of droplet transmissions inside crowded and poorly ventilated buses on infection risks of respiratory diseases is still unclear. Based on experiments of one-infecting-seven COVID-19 outbreak with an index patient at bus rear, we conducted CFD simulations to investigate integrated effects of initial droplet diameters(tracer gas, 5 µm, 50 µm and 100 µm), natural air change rates per hour(ACH = 0.62, 2.27 and 5.66 h-1 related to bus speeds) and relative humidity(RH = 35% and 95%) on pathogen-laden droplet dispersion and infection risks. Outdoor pressure difference around bus surfaces introduces natural ventilation airflow entering from bus-rear skylight and leaving from the front one. When ACH = 0.62 h-1(idling state), the 30-min-exposure infection risk(TIR) of tracer gas is 15.3%(bus rear) - 11.1%(bus front), and decreases to 3.1%(bus rear)-1.3%(bus front) under ACH = 5.66 h-1(high bus speed).The TIR of large droplets(i.e., 100 µm/50 µm) is almost independent of ACH, with a peak value(∼3.1%) near the index patient, because over 99.5%/97.0% of droplets deposit locally due to gravity. Moreover, 5 µm droplets can disperse further with the increasing ventilation. However, TIR for 5 µm droplets at ACH = 5.66 h-1 stays relatively small for rear passengers(maximum 0.4%), and is even smaller in the bus middle and front(<0.1%). This study verifies that differing from general rooms, most 5 µm droplets deposit on the route through the long-and-narrow bus space with large-area surfaces(L∼11.4 m). Therefore, tracer gas can only simulate fine droplet with little deposition but cannot replace 5-100 µm droplet dispersion in coach buses.

Can ODE gene regulatory models neglect time lag or measurement scaling?

MOTIVATION: Many ordinary differential equation (ODE) models have been introduced to replace linear regression models for inferring gene regulatory relationships from time-course gene expression data. But, since the observed data are usually not direct measurements of the gene products or there is an unknown time lag in gene regulation, it is problematic to directly apply traditional ODE models or linear regression models. RESULTS: We introduce a lagged ODE model to infer lagged gene regulatory relationships from time-course measurements, which are modeled as linear transformation of the gene products. A time-course microarray dataset from a yeast cell-cycle study is used for simulation assessment of the methods and real data analysis. The results show that our method, by considering both time lag and measurement scaling, performs much better than other linear and ODE models. It indicates the necessity of explicitly modeling the time lag and measurement scaling in ODE gene regulatory models. AVAILABILITY AND IMPLEMENTATION: R code is available at https://www.sta.cuhk.edu.hk/xfan/share/lagODE.zip.

MM-6mAPred: identifying DNA N6-methyladenine sites based on Markov model.

MOTIVATION: Recent studies have shown that DNA N6-methyladenine (6mA) plays an important role in epigenetic modification of eukaryotic organisms. It has been found that 6mA is closely related to embryonic development, stress response and so on. Developing a new algorithm to quickly and accurately identify 6mA sites in genomes is important for explore their biological functions. RESULTS: In this paper, we proposed a new classification method called MM-6mAPred based on a Markov model which makes use of the transition probability between adjacent nucleotides to identify 6mA site. The sensitivity and specificity of our method are 89.32% and 90.11%, respectively. The overall accuracy of our method is 89.72%, which is 6.59% higher than that of the previous method i6mA-Pred. It indicated that, compared with the 41 nucleotide chemical properties used by i6mA-Pred, the transition probability between adjacent nucleotides can capture more discriminant sequence information. AVAILABILITY AND IMPLEMENTATION: The web server of MM-6mAPred is freely accessible at http://www.insect-genome.com/MM-6mAPred/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SOMM4mC: a second-order Markov model for DNA N4-methylcytosine site prediction in six species.

MOTIVATION: DNA N4-methylcytosine (4mC) modification is an important epigenetic modification in prokaryotic DNA due to its role in regulating DNA replication and protecting the host DNA against degradation. An efficient algorithm to identify 4mC sites is needed for downstream analyses. RESULTS: In this study, we propose a new prediction method named SOMM4mC based on a second-order Markov model, which makes use of the transition probability between adjacent nucleotides to identify 4mC sites. The results show that the first-order and second-order Markov model are superior to the three existing algorithms in all six species (Caenorhabditis elegans, Drosophila melanogaster, Arabidopsis thaliana, Escherichia coli, Geoalkalibacter subterruneus and Geobacter pickeringii) where benchmark datasets are available. However, the classification performance of SOMM4mC is more outstanding than that of first-order Markov model. Especially, for E.coli and C.elegans, the overall accuracy of SOMM4mC are 91.8% and 87.6%, which are 8.5% and 6.1% higher than those of the latest method 4mcPred-SVM, respectively. This shows that more discriminant sequence information is captured by SOMM4mC through the dependency between adjacent nucleotides. AVAILABILITY AND IMPLEMENTATION: The web server of SOMM4mC is freely accessible at www.insect-genome.com/SOMM4mC. CONTACT: chenyuanyuan@njau.edu.cn or piancong@njau.edu.cn.

The Two-Step Clustering Approach for Metastable States Learning.

Understanding the energy landscape and the conformational dynamics is crucial for studying many biological or chemical processes, such as protein-protein interaction and RNA folding. Molecular Dynamics (MD) simulations have been a major source of dynamic structure. Although many methods were proposed for learning metastable states from MD data, some key problems are still in need of further investigation. Here, we give a brief review on recent progresses in this field, with an emphasis on some popular methods belonging to a two-step clustering framework, and hope to draw more researchers to contribute to this area.

Chemical speciation and risk assessment of heavy metals in biochars derived from sewage sludge and anaerobically digested sludge.

Dewatered sewage sludge (DSS) and anaerobically digested sludge (ADS) were pyrolyzed at 550 °C to investigate the characteristics of derived biochar and evaluate the risk of heavy metals (Cr, Ni, Cu, As, Cd, and Pb). The results showed that the pH value of the biochar derived from DSS (DSS-C) was slightly lower than that of the biochar derived from ADS (ADS-C), while DSS-C presented relatively higher specific surface area and total pore volume. DSS-C also showed higher H/C and lower O/C ratios than ADS-C, indicating a higher aromatic condensation and a lower polarity. Total concentrations of Cr, Ni, Cu, As, Cd, and Pb in DSS and ADS increased significantly after pyrolysis owing to the thermal decomposition of organic matter in the sludge, with corresponding rise of the Nemerow pollution index (NPI) of the biochars compared with the raw sludge. In addition, the sequential extraction procedure (BCR) analysis revealed that the pyrolysis process promoted the transformation of heavy metals from bio-available fractions to stable fractions. The potential environmental risk of heavy metals decreased from moderate and extremely high levels in the DSS and ADS to low risk and moderate levels in DSS-C and ADS-C after pyrolysis, respectively.

Isolation and identification of naphthalene degrading bacteria and their degradation characteristics under rainwater environment in heavily polluted areas.

This study is screened for naphthalene degrading strains from a heavily polluted area with high naphthalene concentration in the rainwater for the effective removal of naphthalene from rainwater. Recently, naphthalene biodegradation has been achieved in water. However, the influences of organics and inorganics in the rainwater on the biodegradation of naphthalene remains unclear. The naphthalene degrading strain Klebsiella sp. (WJ-1) was identified from sewage sludge. The effects of temperature, pH, inoculum size, and rotation speed on the degradation ability of WJ-1 were studied. The results showed that the naphthalene degradation rates of WJ-1 in rainwater were higher than those in aqueous solution at different experimental conditions. The optimal conditions were 30 °C, 10% inoculum size, pH 7.0, and a rotation speed of 150 rpm. The substances in rainwater might be important co-metabolites of naphthalene degradation. Based on intermediate metabolites detected by gas chromatography-mass spectrometer (GC-MS), the naphthalene biodegradation pathway was identified, as being similar to the phthalic acid pathway. These results suggest WJ-1 as a good candidate for the efficient bioremediation of naphthalene from rainwater in heavily polluted areas.

Sunitinib induces primary ectopic endometrial cell apoptosis through up-regulation of STAT1 in vitro.

BACKGROUND: Endometriosis (EMS) is a prevalent gynecological condition characterized by the growth of endometrial tissue outside the uterine cavity. This study aimed to clarify the targeted therapeutic effect of sunitinib in an endometriosis in vitro experiment. METHODS: Primary culture of ectopic endometrial cells and normal endometrial cells. Six tumor targeting drugs were selected to screen. MTT was used to determine the IC50, flow cytometry, and DAPI staining of the targeted drugs, in order to determine the apoptosis. The differential proteins after seeding were analyzed by protein spectrum, the correlation between the specific protein and cell apoptosis was determined by small molecule interference, and the expression of each related protein was detected by Western blot. Immunohistochemistry and ELISA were used to detect the expression of p-PDGFR and p-STAT1 in clinical samples, and the correlation between p-STAT1 expression and ectopic focal size was analyzed by SPSS 19. RESULTS: Through the drug screening, it was found that sunitinib has a significant inhibitory effect on ectopic endometrial cells. It was determined that the IC50 of sunitinib on ectopic stromal endometrial cells was 3.32 µM, while the IC50 on normal endometrium was 7.9 µM. Meanwhile, the flow cytometry and DAPI nuclear dye that took out sunitinib had an inhibition effect on the ectopic endometrium at a concentration of 4 µM. Protein spectrum analysis was conducted on ectopic intimal cells after sunitinib treatment, and it was found that STAT1 is specifically expressed in ectopic endometrial cells. In vitro, and through fludarabine interference, it was revealed that sunitinib specifically inhibited the phosphorylation site Tyr751 of PDGFR, while the expression of STAT1, p-STAT1, and caspase-3 was significantly upregulated, and the expression of STAT1 and p-STAT1 was positively correlated with the expression of caspase-3. Finally, the expression of p-PDGFR and p-STAT1 in ectopic foal tissues was both higher than that in normal endometrium, and p-STAT1 expression was positively with ectopic focal size. CONCLUSION: The in vitro experiments revealed that sunitinib could upregulate the expression of STAT1 by inhibiting the phosphorylation site Tyr751 of PDGFR, thereby specifically inducing the apoptosis of the primary heterotopic mesenchymal endometrium.

Progression of diabetic kidney disease and trajectory of kidney function decline in Chinese patients with Type 2 diabetes.

Diabetes is a major cause of end stage renal disease (ESRD), yet the natural history of diabetic kidney disease is not well understood. We aimed to identify patterns of estimated GFR (eGFR) trajectory and to determine the clinical and genetic factors and their associations of these different patterns with all-cause mortality in patients with type 2 diabetes. Among 6330 patients with baseline eGFR >60 ml/min per 1.73 m2 in the Hong Kong Diabetes Register, a total of 456 patients (7.2%) developed Stage 5 chronic kidney disease or ESRD over a median follow-up of 13 years (incidence rate 5.6 per 1000 person-years). Joint latent class modeling was used to identify different patterns of eGFR trajectory. Four distinct and non-linear trajectories of eGFR were identified: slow decline (84.3% of patients), curvilinear decline (6.5%), progressive decline (6.1%) and accelerated decline (3.1%). Microalbuminuria and retinopathy were associated with accelerated eGFR decline, which was itself associated with all-cause mortality (odds ratio [OR] 6.9; 95% confidence interval [CI]: 5.6-8.4 for comparison with slow eGFR decline). Of 68 candidate genetic loci evaluated, the inclusion of five loci (rs11803049, rs911119, rs1933182, rs11123170, and rs889472) improved the prediction of eGFR trajectories (net reclassification improvement 0.232; 95% CI: 0.057--0.406). Our study highlights substantial heterogeneity in the patterns of eGFR decline among patients with diabetic kidney disease, and identifies associated clinical and genetic factors that may help to identify those who are more likely to experience an accelerated decline in kidney function.