Network Activity Evaluation Reveals Significant Gene Regulatory Architectures during SARS-CoV-2 Viral Infection from Dynamic scRNA-seq Data.

The key to understand COVID-19 caused by SARS-CoV-2, which has caused massive deaths worldwide, is to reveal the gene activities at molecular level. Single-cell RNA-sequencing (scRNA-seq) technology allows us to capture gene expression at high resolution, thereby delineating cell-specific gene regulatory network (GRN). Network activity refers to the degree of consistency between GRN architectures and gene expression profiles in a specific condition or cellular microenvironment. Currently, numerous experimentally determined molecular interactions, including regulatory relationships closely related to SARS-CoV-2 infection, are documented in knowledge-bases. However, GRN activity is closely related to the cell dynamic environment and the heterogeneity of cell clusters. Therefore, to evaluate the consistency of GRN with gene expression profiles, we propose a single-cell Network Activity Evaluation framework, called scNAE. First, scNAE performs ODE modeling of time-course gene expression data. Then, the loss function with regularization penalty terms is constructed for formulating GRN inference rules from transcriptomic data. Furthermore, we have devised a rapid-convergence alternating direction method of multipliers to solve the regularized and constrained programs. Finally, an empirical P-value is derived based on a permutation statistical testing procedure to quantify the likelihood significance of the network matching with the data. The efficiency and advantage of scNAE have also been demonstrated by extensive numerical experiments, which can clearly depict the dynamic responses underlying GRN architectures triggered by the infection of SARS-CoV-2 in cells. The code and data of scNAE are available at https://github.com/zpliulab/scNAE.

Effects of suspended particles and dispersants on marine oil snow formation of crude oil/diesel oil.

Marine oil snow (MOS) potentially forms after an oil spill. To fully understand the mechanism of its formation, we investigated the effects of suspended particles (SP) and dispersants on MOS formation of crude oil and diesel oil by laboratory experiments. In the crude oil experiment, the SP concentration of 0.2 g L-1 was more suitable for crude oil MOS formation. The addition of dispersants significantly stimulated N and TV during MS/MOS formation of SP at 0.4 g L-1 and 0.8 g L-1 concentration (p < 0.05). Without SP, the dispersants also stimulated crude oil MOS formation. Furthermore, the concentration of SP had a significantly positive effect on the reduction of the total amount of N-alkanes (p < 0.05). In the diesel oil experiment, after adding dispersants to diesel oil, the maximum N, Dm, and TV values at a SP concentration of 0.2 g L-1 were significantly higher than those at 0.4 g L-1 and 0.8 g L-1 (p < 0.05). Besides, we found that dispersants stimulated MOS formation in diesel oil at a SP concentration of 0.2 g L-1. However, the dispersants had an inhibitory effect on diesel oil MOS formation without SP. Notably, the MOS formed by diesel oil appeared white, unlike the black MOS associated with crude oil. These findings are important for the environmental impact of oil spills and elevated SP concentrations.

ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.

MOTIVATION: Protein-protein interactions (PPI) are crucial components of the biomolecular networks that enable cells to function. Biological experiments have identified a large number of PPI, and these interactions are stored in knowledge bases. However, these interactions are often restricted to specific cellular environments and conditions. Network activity can be characterized as the extent of agreement between a PPI network (PPIN) and a distinct cellular environment measured by protein mass spectrometry, and it can also be quantified as a statistical significance score. Without knowing the activity of these PPI in the cellular environments or specific phenotypes, it is impossible to reveal how these PPI perform and affect cellular functioning. RESULTS: To calculate the activity of PPIN in different cellular conditions, we proposed a PPIN activity evaluation framework named ActivePPI to measure the consistency between network architecture and protein measurement data. ActivePPI estimates the probability density of protein mass spectrometry abundance and models PPIN using a Markov-random-field-based method. Furthermore, empirical P-value is derived based on a nonparametric permutation test to quantify the likelihood significance of the match between PPIN structure and protein abundance data. Extensive numerical experiments demonstrate the superior performance of ActivePPI and result in network activity evaluation, pathway activity assessment, and optimal network architecture tuning tasks. To summarize it succinctly, ActivePPI is a versatile tool for evaluating PPI network that can uncover the functional significance of protein interactions in crucial cellular biological processes and offer further insights into physiological phenomena. AVAILABILITY AND IMPLEMENTATION: All source code and data are freely available at https://github.com/zpliulab/ActivePPI.

Tar ball pollution under the influence of ocean circulation in the Bohai Sea, China.

The deposition of tar balls of unknown sources on the coast poses a great threat to the fishery, tourism and coastal biodiversity in the Bohai Sea. This work aimed to identify the sources of tar balls by using oil fingerprint technique. Tar ball samples were collected from the seashore of two islands of the western Bohai Sea and divided into two groups (Group I and Group II). Principal component analysis showed that although Caofeidian oilfield was one of the closest oilfields to the sampling area it was not a source. Fingerprints of characteristic hopanes and steranes showed that samples of Group I were similar to the crude oils from the nearby Jidong oilfield, and samples of Group II were similar to the Penglai-19-3 crude oils. Combined with cross-plots of the samples and the reference oils, it indicated that Group I may come from Jidong and Group II may come from Penglai-19-3. The weathering characteristics of alkanes and polycyclic aromatic hydrocarbons showed that the samples were affected by natural weathering processes (e.g., evaporation, photooxidation and biodegradation). It revealed that oil pollution from the nearby oilfields can also affect other areas under the influence of ocean circulation. It is the first study on source identification of tar balls from the Bohai Sea and the effects of ocean circulation on the tar ball transport. This study can considerably help to further understand the evolution of tar balls and consequently determine their sources.

Chemometric techniques in oil spill identification: A case study in Dalian 7.16 oil spill accident of China.

Chemometric methods have unique advantages regarding comprehensive consideration of multiple parameters and the classification of samples or variables. Classification of oil spill sources was carried out by using chemometric techniques, such as Repeatability Limit, hierarchical cluster analysis (HCA), Student's t-test and Principal component analysis (PCA) Biplot. In addition, this paper takes the fingerprint identification of a Dalian "7.16″ oil spill accident as an example to illustrate the effectiveness of chemometric techniques in oil identification. PCA scores plot (explaining 82.77% of variance accounted for three PCs) showed that samples belong to four clusters and result of HCA method further confirmed that. The residual oil in Jinshatan Beach and Haibei Square may be caused by the explosion of Dalian "7-16" oil pipeline accident. The use of chemometric techniques is significant in providing independent validation for classifying the types of spilled oil in the investigation of oil spill pollution. The results will be of great significance to improve the accuracy and efficiency of oil spill identification based on oil fingerprint.

Source identification and weathering processes of tar ball deposited Qinhuangdao coast along the Bohai Sea, China.

Deposition of tar balls on the Qinhuangdao beaches along the coasts of the Bohai Sea (China) could affect people's leisure activities and tourism, and even affect the marine ecosystem. In 2020, representative tar balls collected from Qinhuangdao beaches, fingerprint analysis based on diagnostic ratios suggested that these tar balls were all very similar and may belong to the same source. Source identification by cross plot and principal component analysis (PCA), showed that the tar balls were likely from Penglai 19-3 crude oil. The weathering characterizations based on polycyclic aromatic hydrocarbons (PAHs), alkanes parameters and unresolved complex mixture (UCM), indicated that the tar balls had been significantly influenced by natural weathering processes such as evaporation, biodegradation and photooxidation. The study of this leakage provides a powerful support for determining the leakage source, evaluating the potential weathering mechanism and determining the accident liability. This is the first time to use fingerprint technology to identify the source of tar balls in Qinhuangdao coastal zone in the Bohai Sea.

Evaluating Gene Regulatory Network Activity From Dynamic Expression Data by Regularized Constraint Programming.

By extracting molecular interactions identified by experiments, gene regulatory networks or gene circuits have documented in a large number of knowledge-based repositories. They provide systematic information and guidance of the functional connections between regulators, e.g., transcription factor proteins and miRNAs, and target genes. Network activity is defined as the degree of consistency between a regulatory network architecture and a specific cellular context of gene expression and can also be measured as a score of statistical significance. The gene network activities are closely related to the dynamics of cell states. To evaluate the activity of regulatory events in the form of network, we propose a network activity evaluation (NAE) framework by measuring the consistency between network architecture and gene expression data across specific states based on mathematical programming. NAE firstly employs the dynamic Bayesian network model to formulate the network structure with time series profiling data. For the constraints of prior knowledge about gene regulatory network, NAE introduces an interpretable general loss function with regularization penalties to calculate the degree of consistency between gene network and gene expression data. Moreover, we design a fast and convergent alternating direction method of multipliers algorithm to optimize the regularized constraint programming. The efficiency and advantage of the NAE framework is deduced through numerous experiments and comparison studies. It reflects the possibility and potential of the match between network and data, thereby helping to reveal the network activity and to explain the dynamic responds underlying the network structure caused by changes in molecular environment of living cells. The code of NAE is freely available for academic use (https://github.com/zpliulab/NAE).

Spatial distribution, compositional pattern and source apportionment of n-alkanes in surface sediments of the Bohai Sea, Yellow Sea, and East China Sea and implications of carbon sink.

China's marginal seas (CMSs, including the Bohai Sea, Yellow Sea, East China Sea) are a significant sink for both terrestrial organic matter (OM) and marine OM, and they play an important role in the global biogeochemical carbon cycle. The spatial distribution and origin of organic matter based on n-alkanes in the surface sediments of CMSs and the implications of carbon sinks were comparatively analyzed. The n-alkane content in surface sediment from the Bohai Sea was higher than that of the Yellow Sea and the East China Sea. The spatial distribution trends of marine and terrestrial organic matter are obviously different in the surface sediments of China's marginal seas. The n-alkanes in the sediments of the Yellow Sea and the East China Sea were mainly derived from terrestrial higher plants, and land-based influence gradually decreased from the near shore to the open sea. Higher concentration of terrigenous OM are concentrated nearshore, especially near estuaries, such as the Yellow River Estuary, the Old Yellow River Estuary and the Yangtze River Estuary. The input of n-alkanes from woody plants in the Bohai Sea area was slightly higher than that of herbaceous plants, and the input of herbaceous plants in the Yellow Sea and East China Sea was slightly dominant. The distribution of marine organic matter is controlled by marine productivity and the sedimentary environment. Due to climate change, the decomposition and enrichment of organic matter also show the climate effect of carbon molecular combinations. As a semiclosed sea area, the Bohai Sea was beneficial to the growth and reproduction of marine phytoplankton. From the perspective of petroleum pollution, the Bohai Sea was the most serious, followed by the Yellow Sea, and the East China Sea was the lightest. The carbon burial amount of terrestrial organic matter accounts for approximately 7% of the terrestrial organic matter burial amount of global marginal sea sediments, indicating that China's marginal sea plays an important role in the global carbon cycle. The result provide a basis for further understanding the source pattern and burial preservation of sedimentary organic matter in this sea area.

Single-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework.

The rapid development of single-cell RNA sequencing (scRNA-seq)technology reveals the gene expression status and gene structure of individual cells, reflecting the heterogeneity and diversity of cells. The traditional methods of scRNA-seq data analysis treat data as the same subspace, and hide structural information in other subspaces. In this paper, we propose a low-rank subspace ensemble clustering framework (LRSEC)to analyze scRNA-seq data. Assuming that the scRNA-seq data exist in multiple subspaces, the low-rank model is used to find the lowest rank representation of the data in the subspace. It is worth noting that the penalty factor of the low-rank kernel function is uncertain, and different penalty factors correspond to different low-rank structures. Moreover, the single cluster model is difficult to find the cellular structure of all datasets. To strengthen the correlation between model solutions, we construct a new ensemble clustering framework LRSEC by using the low-rank model as the basic learner. The LRSEC framework captures the global structure of data through low-rank subspaces, which has better clustering performance than a single clustering model. We validate the performance of the LRSEC framework on seven small datasets and one large dataset and obtain satisfactory results.

Unsupervised Cluster Analysis and Gene Marker Extraction of scRNA-seq Data Based On Non-Negative Matrix Factorization.

The development of single-cell RNA sequencing (scRNA-seq) technology has made it possible to measure gene expression levels at the resolution of a single cell, which further reveals the complex growth processes of cells such as mutation and differentiation. Recognizing cell heterogeneity is one of the most critical tasks in scRNA-seq research. To solve it, we propose a non-negative matrix factorization framework based on multi-subspace cell similarity learning for unsupervised scRNA-seq data analysis (MscNMF). MscNMF includes three parts: data decomposition, similarity learning, and similarity fusion. The three work together to complete the data similarity learning task. MscNMF can learn the gene features and cell features of different subspaces, and the correlation and heterogeneity between cells will be more prominent in multi-subspaces. The redundant information and noise in each low-dimensional feature space are eliminated, and its gene weight information can be further analyzed to calculate the optimal number of subpopulations. The final cell similarity learning will be more satisfactory due to the fusion of cell similarity information in different subspaces. The advantage of MscNMF is that it can calculate the number of cell types and the rank of Non-negative matrix factorization (NMF) reasonably. Experiments on eight real scRNA-seq datasets show that MscNMF can effectively perform clustering tasks and extract useful genetic markers. To verify its clustering performance, the framework is compared with other latest clustering algorithms and satisfactory results are obtained. The code of MscNMF is free available for academic (https://github.com/wangchuanyuan1/project-MscNMF).

Adaptive Total-Variation Regularized Low-Rank Representation for Analyzing Single-Cell RNA-seq Data.

High-throughput sequencing of single-cell gene expression reveals a complex mechanism of individual cell's heterogeneity in a population. An important purpose for analyzing single-cell RNA sequencing (scRNA-seq) data is to identify cell subtypes and functions by cell clustering. To deal with high levels of noise and cellular heterogeneity, we introduced a new single cell data analysis model called Adaptive Total-Variation Regularized Low-Rank Representation (ATV-LRR). In scRNA-seq data, ATV-LRR can reconstruct the low-rank subspace structure to learn the similarity of cells. The low-rank representation can not only segment multiple linear subspaces, but also extract important information. Moreover, adaptive total variation also can remove cell noise and preserve cell feature details by learning the gradient information of the data. At the same time, to analyze scRNA-seq data with unknown prior information, we introduced the maximum eigenvalue method into the ATV-LRR model to automatically identify cell populations. The final clustering results show that the ATV-LRR model can detect cell types more effectively and stably.

Sparse robust graph-regularized non-negative matrix factorization based on correntropy.

Non-negative Matrix Factorization (NMF) is a popular data dimension reduction method in recent years. The traditional NMF method has high sensitivity to data noise. In the paper, we propose a model called Sparse Robust Graph-regularized Non-negative Matrix Factorization based on Correntropy (SGNMFC). The maximized correntropy replaces the traditional minimized Euclidean distance to improve the robustness of the algorithm. Through the kernel function, correntropy can give less weight to outliers and noise in data but give greater weight to meaningful data. Meanwhile, the geometry structure of the high-dimensional data is completely preserved in the low-dimensional manifold through the graph regularization. Feature selection and sample clustering are commonly used methods for analyzing genes. Sparse constraints are applied to the loss function to reduce matrix complexity and analysis difficulty. Comparing the other five similar methods, the effectiveness of the SGNMFC model is proved by selection of differentially expressed genes and sample clustering experiments in three The Cancer Genome Atlas (TCGA) datasets.

Dual Hyper-Graph Regularized Supervised NMF for Selecting Differentially Expressed Genes and Tumor Classification.

Non-negative matrix factorization (NMF) is a dimensionality reduction technique based on high-dimensional mapping. It can learn part-based representations effectively. In this paper, we propose a method called Dual Hyper-graph Regularized Supervised Non-negative Matrix Factorization (HSNMF). To encode the geometric information of the data, the hyper-graph is introduced into the model as a regularization term. The advantage of hyper-graph learning is to find higher order data relationship to enhance data relevance. This method constructs the data hyper-graph and the feature hyper-graph to find the data manifold and the feature manifold simultaneously. The application of hyper-graph theory in cancer datasets can effectively find pathogenic genes. The discrimination information is further introduced into the objective function to obtain more information about the data. Supervised learning with label information greatly improves the classification effect. Furthermore, the real datasets of cancer usually contain sparse noise, so the L2,1-norm is applied to enhance the robustness of HSNMF algorithm. Experiments under The Cancer Genome Atlas (TCGA) datasets verify the feasibility of the HSNMF method.

Effects of Artificial Islands Construction on the Spatial Distribution and Risk Assessment of Heavy Metals in the Surface Sediments from a Semi-closed Bay (Longkou Bay), China.

Artificial islands construction can significantly influence the spatial distribution of heavy metals in inshore sediments. In this study, the distribution and contamination of heavy metals (Cd, Co, Cr, Cu, Ni, Pb, Zn, As and Hg) in inshore sediments of the Longkou Bay and artificial island adjacent areas were investigated in 2013 and 2014, respectively. Results showed that the contents of heavy metals increased in the Longkou Bay and decreased in the west of the artificial island in 2014 compared with 2013. The contamination and potential eco-risk of heavy metals in the sediments were higher in 2014 than those in 2013. Cd and Hg showed a high potential eco-risk in LK02, and other metals were in the lower level. The results indicated that after the construction of artificial islands in the Longkou Bay, the contamination and eco-risk of heavy metals in the sediments markedly increased in the Longkou Bay.

Potential source contributions and risk assessment of PAHs in sediments from the tail-reaches of the Yellow River Estuary, China: PCA model, PMF model, and mean ERM quotient analysis.

Principal component analysis (PCA), positive matrix factorization (PMF), and the mean effects range-median quotient (MERM-Q) models were employed to determine occurrence levels, sources, and potential toxicological significance of polycyclic aromatic hydrocarbons (PAHs) in surface sediments of the Yellow River Estuary, China. Due to the grain size of sediments, cumulative effects, and distribution of oil fields, the total concentration of the 16 U.S. Environmental Protection Agency (US EPA) priority PAHs (T-PAHs) measured in sediments along transects in the offshore area was 119.51 ± 39.58 ng g-1 dry weight (dw), which is notably higher than that measured in rivers (75.00 ± 12.56 ng g-1 dw) and estuaries (67.94 ± 10.20 ng g-1 dw). PAH levels decreased seaward along all the studied transects in coastal Bohai Bay. Multivariate statistical analyses supported that PAHs in sediments were principally derived from coal and biomass combustion, oil pollution, and vehicular emissions. Based on the MERM-Q (0.0050 ± 0.0017), PAHs were at low potential of ecotoxicological contamination level. These results provide helpful information for protecting water resources and serving sustainable development in Construction of Ecological Civilization in the Yellow River Delta.

Quantitative evaluation of in-situ bioremediation of compound pollution of oil and heavy metal in sediments from the Bohai Sea, China.

Owing to the semi-enclosed environment of the Bohai Sea, the ecological effects caused by an oil spill would be significant. A typical in- situ bioremediation engineering project for of oil-spilled marine sediments was performed in the Bohai Sea and a quantitative assessment of the ecological restoration was performed. The bioremediation efficiencies of n-alkane and PAHs in the sediment are 32.84 ± 21.66% and 50.42 ± 17.49% after 70 days of bioremediation, and 60.99 ± 10.14% and 68.01 ± 18.60% after 210 days, respectively. After 210 days of bioremediation, the degradation rates of two- to three ring PAHs and four-ring PAHs are 84.44 ± 23.03% and 26.62 ± 43.76%, respectively. In addition, the concentrations of the heavy metals first increased by 6.00% due to oil spill degradation and release, and then decreased by 72.60% with the degradation of oil caused by bioremediation or vertical migration. According to the continuous tracking monitoring, the composition of the microbial community in the restored area was similar to that in the control area and the clean area in Bohai Sea after 210 days of bioremediation. These results may provide some theoretical and scientific data to understand the degradation mechanism and assessing the ecological remediation efficiency for oil spills in open sea areas.

Sparse Graph Regularization Non-Negative Matrix Factorization Based on Huber Loss Model for Cancer Data Analysis.

Non-negative matrix factorization (NMF) is a matrix decomposition method based on the square loss function. To exploit cancer information, cancer gene expression data often uses the NMF method to reduce dimensionality. Gene expression data usually have some noise and outliers, while the original NMF loss function is very sensitive to non-Gaussian noise. To improve the robustness and clustering performance of the algorithm, we propose a sparse graph regularization NMF based on Huber loss model for cancer data analysis (Huber-SGNMF). Huber loss is a function between L 1-norm and L 2-norm that can effectively handle non-Gaussian noise and outliers. Taking into account the sparsity matrix and data geometry information, sparse penalty and graph regularization terms are introduced into the model to enhance matrix sparsity and capture data manifold structure. Before the experiment, we first analyzed the robustness of Huber-SGNMF and other models. Experiments on The Cancer Genome Atlas (TCGA) data have shown that Huber-SGNMF performs better than other most advanced methods in sample clustering and differentially expressed gene selection.

Effects of diesel oil spill on macrobenthic assemblages at the intertidal zone: A mesocosm experiment in situ.

The oil spill accidents may drastically impact the environment and ecosystem at intertidal zones. The spilled oil will penetrate the sediments and accumulate to cause lethal or sublethal effects on the benthic invertebrates. An M-BACI experiment was manipulated in situ to assess the ecological responses of benthic macrofauna to different degrees of diesel oil spill. Both biotic and abiotic parameters were studied for 126 days, subjected to both "pulse" and "press" oil contaminations. The content of aliphatic hydrocarbons (displayed as ratios of n-C17/Pr and n-C18/Ph) slightly dropped then continuously existed in the sediment during the experiment time. The macrofaunal assemblage structures were dramatically altered in species number, abundance and biomass. In general, it takes longer time for the macrofauna assemblages to recover under high concentration oil spill than that under low concentration. Our results highlight the diversified strategies for survival and recolonization among dominant species, which distinguish themselves between: i) tolerant species, ii) opportunistic species, and iii) equilibrium species.

Biodegradation of marine oil spill residues using aboriginal bacterial consortium based on Penglai 19-3 oil spill accident, China.

Bioremediation, mainly by indigenous bacteria, has been regarded as an effective way to deal with the petroleum pollution after an oil spill accident. The biodegradation of crude oil by microorganisms co-incubated from sediments collected from the Penglai 19-3 oil platform, Bohai Sea, China, was examined. The relative susceptibility of the isomers of alkylnaphthalenes, alkylphenanthrenes and alkyldibenzothiophene to biodegradation was also discussed. The results showed that the relative degradation values of total petroleum hydrocarbon (TPH) are 43.56% and 51.29% for sediments with untreated microcosms (S-BR1) and surfactant-treated microcosms (S-BR2), respectively. TPH biodegradation results showed an obvious decrease in saturates (biodegradation rate: 67.85-77.29%) and a slight decrease in aromatics (biodegradation rate: 47.13-57.21%), while no significant difference of resins and asphaltenes was detected. The biodegradation efficiency of alkylnaphthalenes, alkylphenanthrenes and alkyldibenzothiophene for S-BR1 and S-BR2 samples reaches 1.28-84.43% and 42.56-86.67%, respectively. The efficiency of crude oil degradation in sediment with surfactant-treated microcosms cultures added Tween 20, was higher than that in sediment with untreated microcosms. The biodegradation and selective depletion is not only controlled by thermodynamics but also related to the stereochemical structure of individual isomer compounds. Information on the biodegradation of oil spill residues by the bacterial community revealed in this study will be useful in developing strategies for bioremediation of crude oil dispersed in the marine ecosystem.

In situ microbial remediation of crude oil-soaked marine sediments using zeolite carrier with a polymer coating.

Marine oil spill pollution is an important environmental problem in the world, especially crude oil-soaked marine sediments, because they are difficult to be remediated. In this study, in situ bioremediation of oil-soaked sediment was performed in the middle of the Bohai Sea. Oil-degrading bacteria were adsorbed on powdery zeolite (PZ)/granular zeolites (GZ) surfaces and then wrapped with poly-γ glutamic acid (γ-PGA). Settling column and wave flume experiments were conducted to model marine conditions and to select appropriate biological reagents. The optimal conditions were as follows: the average diameter of GZ 3 mm, mass ratio of GZ/PZ 2:1, and concentration of γ-PGA 7%. After bioremediation, over 50% of most oil-spilled pollutants n-alkanes (C12 to C27) and polycyclic aromatic hydrocarbons were degraded in 70 days. This work resulted in a successful trial of in situ bioremediation of oil-soaked marine sediments.