scESI: evolutionary sparse imputation for single-cell transcriptomes from nearest neighbor cells.

The ubiquitous dropout problem in single-cell RNA sequencing technology causes a large amount of data noise in the gene expression profile. For this reason, we propose an evolutionary sparse imputation (ESI) algorithm for single-cell transcriptomes, which constructs a sparse representation model based on gene regulation relationships between cells. To solve this model, we design an optimization framework based on nondominated sorting genetics. This framework takes into account the topological relationship between cells and the variety of gene expression to iteratively search the global optimal solution, thereby learning the Pareto optimal cell-cell affinity matrix. Finally, we use the learned sparse relationship model between cells to improve data quality and reduce data noise. In simulated datasets, scESI performed significantly better than benchmark methods with various metrics. By applying scESI to real scRNA-seq datasets, we discovered scESI can not only further classify the cell types and separate cells in visualization successfully but also improve the performance in reconstructing trajectories differentiation and identifying differentially expressed genes. In addition, scESI successfully recovered the expression trends of marker genes in stem cell differentiation and can discover new cell types and putative pathways regulating biological processes.

Integrative analyses of bulk and single-cell transcriptomics reveals the infiltration and crosstalk of cancer-associated fibroblasts as a novel predictor for prognosis and microenvironment remodeling in intrahepatic cholangiocarcinoma.

BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is a highly malignant neoplasm and characterized by desmoplastic matrix. The heterogeneity and crosstalk of tumor microenvironment remain incompletely understood. METHODS: To address this gap, we performed Weighted Gene Co-expression Network Analysis (WGCNA) to identify and construct a cancer associated fibroblasts (CAFs) infiltration biomarker. We also depicted the intercellular communication network and important receptor-ligand complexes using the single-cell transcriptomics analysis of tumor and Adjacent normal tissue. RESULTS: Through the intersection of TCGA DEGs and WGCNA module genes, 784 differential genes related to CAFs infiltration were obtained. After a series of regression analyses, the CAFs score was generated by integrating the expressions of EVA1A, APBA2, LRRTM4, GOLGA8M, BPIFB2, and their corresponding coefficients. In the TCGA-CHOL, GSE89748, and 107,943 cohorts, the high CAFs score group showed unfavorable survival prognosis (p < 0.001, p = 0.0074, p = 0.028, respectively). Additionally, a series of drugs have been predicted to be more sensitive to the high-risk group (p < 0.05). Subsequent to dimension reduction and clustering, thirteen clusters were identified to construct the single-cell atlas. Cell-cell interaction analysis unveiled significant enhancement of signal transduction in tumor tissues, particularly from fibroblasts to malignant cells via diverse pathways. Moreover, SCENIC analysis indicated that HOXA5, WT1, and LHX2 are fibroblast specific motifs. CONCLUSIONS: This study reveals the key role of fibroblasts - oncocytes interaction in the remodeling of the immunosuppressive microenvironment in intrahepatic cholangiocarcinoma. Subsequently, it may trigger cascade activation of downstream signaling pathways such as PI3K-AKT and Notch in tumor, thus initiating tumorigenesis. Targeted drugs aimed at disrupting fibroblasts-tumor cell interaction, along with associated enrichment pathways, show potential in mitigating the immunosuppressive microenvironment that facilitates tumor progression.

Recall DNA methylation levels at low coverage sites using a CNN model in WGBS.

DNA methylation is an important regulator of gene transcription. WGBS is the gold-standard approach for base-pair resolution quantitative of DNA methylation. It requires high sequencing depth. Many CpG sites with insufficient coverage in the WGBS data, resulting in inaccurate DNA methylation levels of individual sites. Many state-of-arts computation methods were proposed to predict the missing value. However, many methods required either other omics datasets or other cross-sample data. And most of them only predicted the state of DNA methylation. In this study, we proposed the RcWGBS, which can impute the missing (or low coverage) values from the DNA methylation levels on the adjacent sides. Deep learning techniques were employed for the accurate prediction. The WGBS datasets of H1-hESC and GM12878 were down-sampled. The average difference between the DNA methylation level at 12× depth predicted by RcWGBS and that at >50× depth in the H1-hESC and GM2878 cells are less than 0.03 and 0.01, respectively. RcWGBS performed better than METHimpute even though the sequencing depth was as low as 12×. Our work would help to process methylation data of low sequencing depth. It is beneficial for researchers to save sequencing costs and improve data utilization through computational methods.

Predicting active enhancers with DNA methylation and histone modification.

BACKGROUND: Enhancers play a crucial role in gene regulation, and some active enhancers produce noncoding RNAs known as enhancer RNAs (eRNAs) bi-directionally. The most commonly used method for detecting eRNAs is CAGE-seq, but the instability of eRNAs in vivo leads to data noise in sequencing results. Unfortunately, there is currently a lack of research focused on the noise inherent in CAGE-seq data, and few approaches have been developed for predicting eRNAs. Bridging this gap and developing widely applicable eRNA prediction models is of utmost importance. RESULTS: In this study, we proposed a method to reduce false positives in the identification of eRNAs by adjusting the statistical distribution of expression levels. We also developed eRNA prediction models using joint gene expressions, DNA methylation, and histone modification. These models achieved impressive performance with an AUC value of approximately 0.95 for intra-cell prediction and 0.9 for cross-cell prediction. CONCLUSIONS: Our method effectively attenuates the noise generated by stochastic RNA production, resulting in more accurate detection of eRNAs. Furthermore, our eRNA prediction model exhibited significant accuracy in both intra-cell and cross-cell validation, highlighting its robustness and potential application in various cellular contexts.

Effector-GAN: prediction of fungal effector proteins based on pretrained deep representation learning methods and generative adversarial networks.

MOTIVATION: Phytopathogenic fungi secrete effector proteins to subvert host defenses and facilitate infection. Systematic analysis and prediction of candidate fungal effector proteins are crucial for experimental validation and biological control of plant disease. However, two problems are still considered intractable to be solved in fungal effector prediction: one is the high-level diversity in effector sequences that increases the difficulty of protein feature learning, and the other is the class imbalance between effector and non-effector samples in the training dataset. RESULTS: In our study, pretrained deep representation learning methods are presented to represent multiple characteristics of sequences for predicting fungal effectors and generative adversarial networks are adapted to create synthetic feature samples to address the data imbalance problem. Compared with the state-of-the-art fungal effector prediction methods, Effector-GAN shows an overall improvement in accuracy in the independent test set. AVAILABILITY AND IMPLEMENTATION: Effector-GAN offers a user-friendly interface to inspect potential fungal effector proteins (http://lab.malab.cn/~wys/webserver/Effector-GAN). The Python script can be downloaded from http://lab.malab.cn/~wys/gitlab/effector-gan. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effect of Sodium Dodecyl Sulfonate on the Foam Stability and Adsorption Configuration of Dodecylamine at the Gas-Liquid Interface.

In this study, the effect of sodium dodecyl sulfonate (SDS) on the foam stability of dodecylamine (DDA) and on its adsorption configuration at the gas-liquid interface was investigated. Froth stability experiments, surface tension measurements, time-of-flight secondary-ion mass spectrometry measurements, and molecular dynamics simulation calculations were performed in this investigation. The results revealed that the foam stability of DDA solution was extremely strong, and the addition of SDS could decrease the foam stability when the concentration of DDA was less than a certain value. The decrease in foam stability could be ascribed to several reasons, namely, the big cross-sectional area of SDS at the gas-liquid interface and low adsorption capacity of surfactants at the gas-liquid interface, the high surface tension, the change in the double-layer structure, the small thickness of the gas-liquid interfacial layer, the weak interaction intensity between the head groups of the surfactants and the water molecules, the strong movement ability of the water molecules around the head groups, and the sparse and less upright arrangement configuration of molecules at the gas-liquid interface. These findings can greatly help in solving the strong foam stability problem in DDA flotation and provide a method for reducing foam stability.

Identification of methylation states of DNA regions for Illumina methylation BeadChip.

BACKGROUND: Methylation of cytosine bases in DNA is a critical epigenetic mark in many eukaryotes and has also been implicated in the development and progression of normal and diseased cells. Therefore, profiling DNA methylation across the genome is vital to understanding the effects of epigenetic. In recent years the Illumina HumanMethylation450 (HM450K) and MethylationEPIC (EPIC) BeadChip have been widely used to profile DNA methylation in human samples. The methods to predict the methylation states of DNA regions based on microarray methylation datasets are critical to enable genome-wide analyses. RESULT: We report a computational approach based on the two layers two-state hidden Markov model (HMM) to identify methylation states of single CpG site and DNA regions in HM450K and EPIC BeadChip. Using this mothed, all CpGs detected by HM450K and EPIC in H1-hESC and GM12878 cell lines are identified as un-methylated, middle-methylated and full-methylated states. A large number of DNA regions are segmented into three methylation states as well. Comparing the identified regions with the result from the whole genome bisulfite sequencing (WGBS) datasets segmented by MethySeekR, our method is verified. Genome-wide maps of chromatin states show that methylation state is inversely correlated with active histone marks. Genes regulated by un-methylated regions are expressed and regulated by full-methylated regions are repressed. Our method is illustrated to be useful and robust. CONCLUSION: Our method is valuable for DNA methylation genome-wide analyses. It is focusing on identification of DNA methylation states on microarray methylation datasets. For the features of array datasets, using two layers two-state HMM to identify to methylation states on CpG sites and regions creatively, our method which takes into account the distribution of genome-wide methylation levels is more reasonable than segmentation with a fixed threshold.

MeDReaders: a database for transcription factors that bind to methylated DNA.

Understanding the molecular principles governing interactions between transcription factors (TFs) and DNA targets is one of the main subjects for transcriptional regulation. Recently, emerging evidence demonstrated that some TFs could bind to DNA motifs containing highly methylated CpGs both in vitro and in vivo. Identification of such TFs and elucidation of their physiological roles now become an important stepping-stone toward understanding the mechanisms underlying the methylation-mediated biological processes, which have crucial implications for human disease and disease development. Hence, we constructed a database, named as MeDReaders, to collect information about methylated DNA binding activities. A total of 731 TFs, which could bind to methylated DNA sequences, were manually curated in human and mouse studies reported in the literature. In silico approaches were applied to predict methylated and unmethylated motifs of 292 TFs by integrating whole genome bisulfite sequencing (WGBS) and ChIP-Seq datasets in six human cell lines and one mouse cell line extracted from ENCODE and GEO database. MeDReaders database will provide a comprehensive resource for further studies and aid related experiment designs. The database implemented unified access for users to most TFs involved in such methylation-associated binding actives. The website is available at http://medreader.org/.

Histone methyltransferase Ezh2 coordinates mammalian axon regeneration via regulation of key regenerative pathways.

Current treatments for neurodegenerative diseases and neural injuries face major challenges, primarily due to the diminished regenerative capacity of neurons in the mammalian CNS as they mature. Here, we investigated the role of Ezh2, a histone methyltransferase, in regulating mammalian axon regeneration. We found that Ezh2 declined in the mouse nervous system during maturation but was upregulated in adult dorsal root ganglion neurons following peripheral nerve injury to facilitate spontaneous axon regeneration. In addition, overexpression of Ezh2 in retinal ganglion cells in the CNS promoted optic nerve regeneration via both histone methylation-dependent and -independent mechanisms. Further investigation revealed that Ezh2 fostered axon regeneration by orchestrating the transcriptional silencing of genes governing synaptic function and those inhibiting axon regeneration, while concurrently activating various factors that support axon regeneration. Notably, we demonstrated that GABA transporter 2, encoded by Slc6a13, acted downstream of Ezh2 to control axon regeneration. Overall, our study underscores the potential of modulating chromatin accessibility as a promising strategy for promoting CNS axon regeneration.

Novel Insight Into the Role of Immune Dysregulation in Amyotrophic Lateral Sclerosis Based on Bioinformatic Analysis.

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive degeneration of motor neurons. The causative pathogenic mechanisms in ALS remain unclear, limiting the development of treatment strategies. Neuroinflammation and immune dysregulation were involved in the disease onset and progression of several neurodegenerative disorders, including ALS. In this study, we carried out a bioinformatic analysis using publicly available datasets from Gene Expression Omnibus (GEO) to investigate the role of immune cells and genes alterations in ALS. Single-sample gene set enrichment analysis revealed that the infiltration of multiple types of immune cells, including macrophages, type-1/17 T helper cells, and activated CD4 + /CD8 + T cells, was higher in ALS patients than in controls. Weighted gene correlation network analysis identified immune genes associated with ALS. The Gene Ontology analysis revealed that receptor and cytokine activities were the most highly enriched terms. Pathway analysis showed that these genes were enriched not only in immune-related pathways, such as cytokine-cytokine receptor interaction, but also in PI3K-AKT and MAPK signaling pathways. Nineteen immune-related genes (C3AR1, CCR1, CCR5, CD86, CYBB, FCGR2B, FCGR3A, HCK, ITGB2, PTPRC, TLR1, TLR2, TLR7, TLR8, TYROBP, VCAM1, CD14, CTSS, and FCER1G) were identified as hub genes based on least absolute shrinkage and selection operator analysis. This gene signature could differentiate ALS patients from non-neurological controls (p < 0.001) and predict disease occurrence (AUC = 0.829 in training set; AUC = 0.862 in test set). In conclusion, our study provides potential biomarkers of ALS for disease diagnosis and therapeutic monitoring.

Identification of an individualized RNA binding protein-based prognostic signature for diffuse large B-cell lymphoma.

RNA binding proteins (RBPs) are increasingly appreciated as being essential for normal hematopoiesis and have a critical role in the progression of hematological malignancies. However, their functional consequences and clinical significance in diffuse large B-cell lymphoma (DLBCL) remain unknown. Here, we conducted a systematic analysis to identify RBP-related genes affecting DLBCL prognosis based on the Gene Expression Omnibus database. By univariate and multivariate Cox proportional hazards regression (CPHR) methods, six RBPs-related genes (CMSS1, MAEL, THOC5, PSIP1, SNIP1, and ZCCHC7) were identified closely related to the overall survival (OS) of DLBCL patients. The RBPs signature could efficiently distinguished low-risk from high-risk patients and could serve as an independent and reliable factor for predicting OS. Moreover, Gene Set Enrichment Analysis revealed 17 significantly enriched pathways between high- versus low-risk group, including the regulation of autophagy, chronic myeloid leukemia, NOTCH signaling pathway, and B cell receptor signaling pathway. Then we developed an RBP-based nomogram combining other clinical risk factors. The receiver operating characteristic curve analysis demonstrated high prognostic predictive efficiency of this model with the area under the curve values were 0.820 and 0.780, respectively, in the primary set and entire set. In summary, our RBP-based model could be a novel prognostic predictor and had the potential for developing treatment targets for DLBCL.

iEnhancer-EBLSTM: Identifying Enhancers and Strengths by Ensembles of Bidirectional Long Short-Term Memory.

Enhancers are regulatory DNA sequences that could be bound by specific proteins named transcription factors (TFs). The interactions between enhancers and TFs regulate specific genes by increasing the target gene expression. Therefore, enhancer identification and classification have been a critical issue in the enhancer field. Unfortunately, so far there has been a lack of suitable methods to identify enhancers. Previous research has mainly focused on the features of the enhancer's function and interactions, which ignores the sequence information. As we know, the recurrent neural network (RNN) and long short-term memory (LSTM) models are currently the most common methods for processing time series data. LSTM is more suitable than RNN to address the DNA sequence. In this paper, we take the advantages of LSTM to build a method named iEnhancer-EBLSTM to identify enhancers. iEnhancer-ensembles of bidirectional LSTM (EBLSTM) consists of two steps. In the first step, we extract subsequences by sliding a 3-mer window along the DNA sequence as features. Second, EBLSTM model is used to identify enhancers from the candidate input sequences. We use the dataset from the study of Quang H et al. as the benchmarks. The experimental results from the datasets demonstrate the efficiency of our proposed model.

Effects of DNA Methylation on TFs in Human Embryonic Stem Cells.

DNA methylation is an important epigenetic mechanism for gene regulation. The conventional view of DNA methylation is that DNA methylation could disrupt protein-DNA interactions and repress gene expression. Several recent studies reported that DNA methylation could alter transcription factors (TFs) binding sequence specificity in vitro. Here, we took advantage of the large sets of ChIP-seq data for TFs and whole-genome bisulfite sequencing data in many cell types to perform a systematic analysis of the protein-DNA methylation in vivo. We observed that many TFs could bind methylated DNA regions, especially in H1-hESC cells. By locating binding sites, we confirmed that some TFs could bind to methylated CpGs directly. The different proportion of CpGs at TF binding specificity motifs in different methylation statuses shows that some TFs are sensitive to methylation and some could bind to the methylated DNA with different motifs, such as CEBPB and CTCF. At the same time, TF binding could interactively alter local DNA methylation. The TF hypermethylation binding sites extensively overlap with enhancers. And we also found that some DNase I hypersensitive sites were specifically hypermethylated in H1-hESC cells. At last, compared with TFs' binding regions in multiple cell types, we observed that CTCF binding to high methylated regions in H1-hESC were not conservative. These pieces of evidence indicate that TFs that bind to hypermethylation DNA in H1-hESC cells may associate with enhancers to regulate special biological functions.

An all-to-all approach to the identification of sequence-specific readers for epigenetic DNA modifications on cytosine.

Epigenetic modifications of DNA play important roles in many biological processes. Identifying readers of these epigenetic marks is a critical step towards understanding the underlying mechanisms. Here, we present an all-to-all approach, dubbed digital affinity profiling via proximity ligation (DAPPL), to simultaneously profile human TF-DNA interactions using mixtures of random DNA libraries carrying different epigenetic modifications (i.e., 5-methylcytosine, 5-hydroxymethylcytosine, 5-formylcytosine, and 5-carboxylcytosine) on CpG dinucleotides. Many proteins that recognize consensus sequences carrying these modifications in symmetric and/or hemi-modified forms are identified. We further demonstrate that the modifications in different sequence contexts could either enhance or suppress TF binding activity. Moreover, many modifications can affect TF binding specificity. Furthermore, symmetric modifications show a stronger effect in either enhancing or suppressing TF-DNA interactions than hemi-modifications. Finally, in vivo evidence suggests that USF1 and USF2 might regulate transcription via hydroxymethylcytosine-binding activity in weak enhancers in human embryonic stem cells.

Restoration of rare earth mine areas: organic amendments and phytoremediation.

Overexploitation of rare earth mine has caused serious desertification and various environmental issues, and ecological restoration of a mining area is an important concern in China. In this study, experiments involving dry grass landfilling, chicken manure broadcasting, and plant cultivation were carried out to reclaim a rare earth mine area located in Heping County, Guangdong Province, China. The prime focus was to improve soil quality in terms of nutrients, microbial community, enzyme activity, and physicochemical properties so as to reclaim the land. After 2 years of restoration, an increase of organic matter (OM), available potassium (K), available phosphorus (P) levels, and acid phosphatase (ACP) activity and a reduction of the available nitrogen (N) level and urease (URE) activity in soil were achieved compared to the original mined land. The nutrients and enzyme activities in soil with 5 years of restoration were close to or surpass those in the unexploited land as control. The bulk density, total porosity, water holding capacity, pH, and electrical conductivity (EC) of soil were improved, and the number of cultivable microorganisms and the bacterial diversity in soil were greatly increased with time during ecological restoration, especially for surface soil. Furthermore, the artificial vegetation stably grew at the restored mining sites. The results indicated that organic amendments and phytoremediation could ecologically restore the rare earth mining sites and the mined land could finally be planted as farmland.