Identification of cancer driver genes based on hierarchical weak consensus model.

Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-024-00279-6.

Mdwgan-gp: data augmentation for gene expression data based on multiple discriminator WGAN-GP.

BACKGROUND: Although gene expression data play significant roles in biological and medical studies, their applications are hampered due to the difficulty and high expenses of gathering them through biological experiments. It is an urgent problem to generate high quality gene expression data with computational methods. WGAN-GP, a generative adversarial network-based method, has been successfully applied in augmenting gene expression data. However, mode collapse or over-fitting may take place for small training samples due to just one discriminator is adopted in the method. RESULTS: In this study, an improved data augmentation approach MDWGAN-GP, a generative adversarial network model with multiple discriminators, is proposed. In addition, a novel method is devised for enriching training samples based on linear graph convolutional network. Extensive experiments were implemented on real biological data. CONCLUSIONS: The experimental results have demonstrated that compared with other state-of-the-art methods, the MDWGAN-GP method can produce higher quality generated gene expression data in most cases.

Essential proteins discovery based on dominance relationship and neighborhood similarity centrality.

Essential proteins play a vital role in development and reproduction of cells. The identification of essential proteins helps to understand the basic survival of cells. Due to time-consuming, costly and inefficient with biological experimental methods for discovering essential proteins, computational methods have gained increasing attention. In the initial stage, essential proteins are mainly identified by the centralities based on protein-protein interaction (PPI) networks, which limit their identification rate due to many false positives in PPI networks. In this study, a purified PPI network is firstly introduced to reduce the impact of false positives in the PPI network. Secondly, by analyzing the similarity relationship between a protein and its neighbors in the PPI network, a new centrality called neighborhood similarity centrality (NSC) is proposed. Thirdly, based on the subcellular localization and orthologous data, the protein subcellular localization score and ortholog score are calculated, respectively. Fourthly, by analyzing a large number of methods based on multi-feature fusion, it is found that there is a special relationship among features, which is called dominance relationship, then, a novel model based on dominance relationship is proposed. Finally, NSC, subcellular localization score, and ortholog score are fused by the dominance relationship model, and a new method called NSO is proposed. In order to verify the performance of NSO, the seven representative methods (ION, NCCO, E_POC, SON, JDC, PeC, WDC) are compared on yeast datasets. The experimental results show that the NSO method has higher identification rate than other methods.

Identifying driver pathways based on a parameter-free model and a partheno-genetic algorithm.

BACKGROUND: Tremendous amounts of omics data accumulated have made it possible to identify cancer driver pathways through computational methods, which is believed to be able to offer critical information in such downstream research as ascertaining cancer pathogenesis, developing anti-cancer drugs, and so on. It is a challenging problem to identify cancer driver pathways by integrating multiple omics data. RESULTS: In this study, a parameter-free identification model SMCMN, incorporating both pathway features and gene associations in Protein-Protein Interaction (PPI) network, is proposed. A novel measurement of mutual exclusivity is devised to exclude some gene sets with "inclusion" relationship. By introducing gene clustering based operators, a partheno-genetic algorithm CPGA is put forward for solving the SMCMN model. Experiments were implemented on three real cancer datasets to compare the identification performance of models and methods. The comparisons of models demonstrate that the SMCMN model does eliminate the "inclusion" relationship, and produces gene sets with better enrichment performance compared with the classical model MWSM in most cases. CONCLUSIONS: The gene sets recognized by the proposed CPGA-SMCMN method possess more genes engaging in known cancer related pathways, as well as stronger connectivity in PPI network. All of which have been demonstrated through extensive contrast experiments among the CPGA-SMCMN method and six state-of-the-art ones.

Robust electrolysis system divided by bipolar electrode and non-conductive membrane for energy-efficient calcium hardness removal.

In this study, an energy-efficient divided bipolar electrolysis system was developed for water softening, where two PTFE membranes were used as the separating materials and a bipolar electrode was employed to enhance the H2O-splitting reactions. As compared with other two operation modes, the optimum calcium harness removal efficiencies of 85% and 57% could be reached in the induction cathode effluent and terminal effluent, respectively, at 8 mA cm-2 in the mode A. Increasing the current density from 5 to 20 mA cm-2 evidently promoted the removal of calcium hardness from 33% to 65% in the terminal effluent and the CaCO3 precipitation rate from 743 to 1462 gCaCO3 h-1 m-2 with the increased energy consumption from 0.53 to 2.2 kWh kg-1CaCO3. The optimized Ca2+/HCO3- molar ratio was 1:1.2 for the calcium hardness removal. In addition, increasing the flow rate into each cathode chamber from 10 to 40 mL min-1 gradually decreased from 67% to 35%. The calcium hardness was mainly removed in the forms of vaterite and calcite in the alkaline effluents and was marginally precipitated as aragonite and calcite on the cathodes surface. Generally, present energy-efficient electrochemical water softening system showed great potential for application in industrial processes.

Differentiating the reaction mechanism of three-dimensionally electrocatalytic system packed with different particle electrodes: Electro-oxidation versus electro-fenton.

Recently, there are still some controversial mechanisms of the 3D electrocatalytic oxidation system, which would probably confound its industrial application. From the conventional viewpoint, the Ti4O7 material may be the desired particle electrodes in the 3D system since its high oxygen evolution potential favors the production of •OH via H2O splitting reaction at the anode side of Ti4O7 particle electrodes. In fact, the incorporation of Ti4O7 particles showed phenol degradation of 88% and COD removal of 51% within 120 min, under the optimum conditions at energy consumption of 0.668 kWh g-1 COD, the performance of which was much lower than those in many previous literatures. In contrast, the prepared carbon black-polytetrafluoroethylene composite (CB-PTFE) particles with abundant oxygen-containing functional groups could yield considerable amounts of H2O2 (200 mg L-1) in the 3D reactor and achieved a complete degradation of phenol and COD removal of 80% in the presence of Fe2+, accompanying a low energy consumption of only 0.080 kWh g-1 COD. It was estimated that only 20% of Ti4O7 particles near the anode attained the potential over 2.73 V/SCE at 30 mA cm-2 based on the potential test and simulation, responsible for the low yield of •OH via the H2O splitting on Ti4O7 (1.74 × 10-14 M), and the main role of Ti4O7 particle electrodes in phenol degradation was through direct oxidation. For the CB-PTFE-based 3D system, current density of 10 mA cm-2 was sufficient for all the CB-PTFE particles to attain cathodic potential of -0.67 V/SCE, conducive to the high yield of H2O2 and •OH (9.11 × 10-14 M) in the presence of Fe2+, and the •OH-mediated indirect oxidation was mainly responsible for the phenol degradation. Generally, this study can provide a deep insight into the 3D electrocatalytic oxidation technology and help to develop the high-efficiency and cost-efficient 3D technologies for industrial application.

Investigation on the adverse impacts of electrochemically produced ClOx- on assessing the treatment performance of dimensionally stable anode (DSA) for Cl--containing wastewater.

The presence of chloride ions can facilitate the COD removal efficiency due to the involvement of active chlorine species in the electro-oxidation process, but few attentions have been paid to the negative effect of the electro-generated oxychlorides on electro-oxidation performance. In this study, the effects of oxychlorides were investigated as functions of current density and phenol concentration using DSA anodes in terms of the evaluation of the COD removal performance and the biological toxicity. The results show that oxychlorides formed in the electro-oxidation system could result in the over-evaluation of the COD removal performance. Increasing current density (15-50 mA cm-2) aggravated the over-evaluation of COD removal (4%-18%), owing to the enhancement in the electrochemical generation of oxychlorides. The increase of phenol concentration inhibited the production of oxychlorides, but the effect of oxychlorides on COD values at phenol concentration of 200 mg L-1 (82 mg L-1) was higher than that at 100 mg L-1 (51 mg L-1). The ClO3- was predominantly responsible for over-evaluation of the COD removal. In addition, bioassays with chlorella indicated that the electro-generated oxychlorides significantly increased the biological toxicity of the treated Cl--containing wastewater. This work provides new guidance for the correct evaluation of COD treatment performance and highlight the importance of minimizing toxic inorganic chlorinated byproducts during electro-oxidation of Cl--containing wastewater.

Identification of cancer-related module in protein-protein interaction network based on gene prioritization.

With the rapid development of deep sequencing technologies, a large amount of high-throughput data has been available for studying the carcinogenic mechanism at the molecular level. It has been widely accepted that the development and progression of cancer are regulated by modules/pathways rather than individual genes. The investigation of identifying cancer-related active modules has received an extensive attention. In this paper, we put forward an identification method ModFinder by integrating both biological networks and gene expression profiles. More concretely, a gene scoring function is devised by using the regression model with [Formula: see text]-step random walk kernel, and the genes are ranked according to both of their active scores and degrees in the PPI network. Then a greedy algorithm NSEA is introduced to find an active module with high score and strong connectivity. Experiments were performed on both simulated data and real biological one, i.e. breast cancer and cervical cancer. Compared with the previous methods SigMod, LEAN and RegMod, ModFinder shows competitive performance. It can successfully identify a well-connected module that contains a large proportion of cancer-related genes, including some well-known oncogenes or tumor suppressors enriched in cancer-related pathways.

miR-205-3p Functions as a Tumor Suppressor in Ovarian Carcinoma.

Ovarian cancer (OC) represents the most lethal form of gynaecologic cancers in developed countries. To develop a better therapeutic against OC, characterizing new classes of molecular regulators such as microRNAs (miRNAs) involved in OC tumorigenesis becomes immensely important. We used human OC cell lines to study the expression pattern of miRNA-205-3p. We then employed miRNA-205-3p mimic and inhibitor to elucidate its functional role in OC cells. Downstream target of miRNA-205-3p was characterized in OC cells with luciferase gene reporter assay and Western blotting. Its functional role in OC was also investigated with the siRNA approach. Lastly, we assessed the expression change of miRNA-205-3p and its newly identified target in human OC tissues. miR-205-3p was downregulated in five human OC lines tested. Over-expressing miR-205-3p reduced OC cell proliferation and migration. MAPK10 was identified as a direct target of miR-205-3p. Knocking down MAPK10 suppressed OC cell growth and migration. In contrast, knocking down miR-205-3p promoted clonogenicity of primary ovary cells. In clinical samples, miR-205-3p and MAPK10 expressed inversely in accordance with their expression patterns in OC cells. miR-205-3p was shown as a novel tumor suppressor in OC via inhibiting the MAPK10 pathway. This new finding may inspire new personalized treatment for OC.

Natural selection and genetic diversity of domain I of Plasmodium falciparum apical membrane antigen-1 on Bioko Island.

BACKGROUND: Plasmodium falciparum apical membrane antigen-1 (PfAMA-1) is a promising candidate antigen for a blood-stage malaria vaccine. However, antigenic variation and diversity of PfAMA-1 are still major problems to design a universal malaria vaccine based on this antigen, especially against domain I (DI). Detail understanding of the PfAMA-1 gene polymorphism can provide useful information on this potential vaccine component. Here, general characteristics of genetic structure and the effect of natural selection of DIs among Bioko P. falciparum isolates were analysed. METHODS: 214 blood samples were collected from Bioko Island patients with P. falciparum malaria between 2011 and 2017. A fragment spanning DI of PfAMA-1 was amplified by nested polymerase chain reaction and sequenced. Polymorphic characteristics and the effect of natural selection were analysed using MEGA 5.0, DnaSP 6.0 and Popart programs. Genetic diversity in 576 global PfAMA-1 DIs were also analysed. Protein function prediction of new amino acid mutation sites was performed using PolyPhen-2 program. RESULTS: 131 different haplotypes of PfAMA-1 were identified in 214 Bioko Island P. falciparum isolates. Most amino acid changes identified on Bioko Island were found in C1L. 32 amino acid changes identified in PfAMA-1 sequences from Bioko Island were found in predicted RBC-binding sites, B cell epitopes or IUR regions. Overall patterns of amino acid changes of Bioko PfAMA-1 DIs were similar to those in global PfAMA-1 isolates. Differential amino acid substitution frequencies were observed for samples from different geographical regions. Eight new amino acid changes of Bioko island isolates were also identified and their three-dimensional protein structural consequences were predicted. Evidence for natural selection and recombination event were observed in global isolates. CONCLUSIONS: Patterns of nucleotide diversity and amino acid polymorphisms of Bioko Island isolates were similar to those of global PfAMA-1 DIs. Balancing natural selection across DIs might play a major role in generating genetic diversity in global isolates. Most amino acid changes in DIs occurred in predicted B-cell epitopes. Novel sites mapped on a three dimensional structure of PfAMA-1 showed that these regions were located at the corner. These results may provide significant value in the design of a malaria vaccine based on this antigen.

Identifying mutated driver pathways in cancer by integrating multi-omics data.

Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.

MiR-144-3p: a novel tumor suppressor targeting MAPK6 in cervical cancer.

Cervical cancer is the third most common gynecologic cancer in the world. Exploration of the molecular mechanism underlying cervical cancer pathogenesis will provide new insights into the development of novel therapies. In this study, we were aimed to characterize a novel miRNA in cervical cancer tumorigenesis. First, we measured the expressional change of miR-144-3p in clinical tissues and cancer cells. Second, we employed cell proliferation, cell migration, and invasion assays to understand its functional role in cervical cancer. Then, we confirmed in vitro findings in xenograft cancer model. Last, we mapped out a downstream target of miR-144-3p and validated its functional role in cancer cells. In the results, miR-144-3p was found significantly downregulated in cervical cancer cells and tissues. Over-expressing miR-144-3p suppressed cancer cells growth and metastasis. Consistent with in vitro results, over-expressing miR-144-3p led to tumor growth inhibition in vivo. Further on, MAPK6 was identified as an endogenous target of miR-144-3p in cervical cancer. Knocking down MAPK6 inhibited cervical cancer cells proliferation, migration, and invasion potential. Our investigation was the first time to report miR-144-3p as a tumor suppressive miRNA in cervical cancer. It inhibited tumor growth by targeting MAKP6. The newly identified signalling axis may serve as novel therapeutic targets to manage cervical cancer.

A fast and accurate enumeration-based algorithm for haplotyping a triploid individual.

BACKGROUND: Haplotype assembly, reconstructing haplotypes from sequence data, is one of the major computational problems in bioinformatics. Most of the current methodologies for haplotype assembly are designed for diploid individuals. In recent years, genomes having more than two sets of homologous chromosomes have attracted many research groups that are interested in the genomics of disease, phylogenetics, botany and evolution. However, there is still a lack of methods for reconstructing polyploid haplotypes. RESULTS: In this work, the minimum error correction with genotype information (MEC/GI) model, an important combinatorial model for haplotyping a single individual, is used to study the triploid individual haplotype reconstruction problem. A fast and accurate enumeration-based algorithm enumeration haplotyping triploid with least difference (EHTLD) is proposed for solving the MEC/GI model. The EHTLD algorithm tries to reconstruct the three haplotypes according to the order of single nucleotide polymorphism (SNP) loci along them. When reconstructing a given SNP site, the EHTLD algorithm enumerates three kinds of SNP values in terms of the corresponding site's genotype value, and chooses the one, which leads to the minimum difference between the reconstructed haplotypes and the sequenced fragments covering that SNP site, to fill the SNP loci being reconstructed. CONCLUSION: Extensive experimental comparisons were performed between the EHTLD algorithm and the well known HapCompass and HapTree. Compared with algorithms HapCompass and HapTree, the EHTLD algorithm can reconstruct more accurate haplotypes, which were proven by a number of experiments.

The purification and characterization of a novel alkali-stable pectate lyase produced by Bacillus subtilis PB1.

Pectinase is an important kind of enzyme with many industrial applications, among which pectinases produced by bacteria were scarce compared with fungal sources. In this study, a novel bacterium which produced extracellular pectinase was firstly isolated from flue-cured tobacco leaves and identified as Bacillus subtilis PB1 according to its 16S rRNA gene. The pectinolytic enzyme was purified by ammonium sulfate precipitation, ion-exchange and gel filtration chromatography, after which molecular weight was determined as 43.1 ± 0.5 kDa by SDS-PAGE. Peptide mass fingerprinting of the pectinase by MALDI-TOF MS showed that the purified enzyme shared homology with pectate lyase and was designated as BsPel-PB1. The optimal temperature for BsPel-PB1 was 50 °C. The optimal pH was pH 9.5 for BsPel-PB1 while it had a broad pH stability from 5 to 11. The values of K m and V max were 0.312 mg/mL and 1248 U/mL, respectively. Accordingly, the BsPel-PB1 was a novel alkaline pectate lyase which could find potential application as a commercial candidate in the pectinolytic related industries.

Improvement of simultaneous determination of neutral monosaccharides and uronic acids by gas chromatography.

Although pre-column derivatization with n-propylamine and acetic anhydride combined with the gas chromatograph was a useful method for the analysis of monosaccharide composition, failure often occurs because of lack of detail information on the mechanism as well as the operating key point in derivatization process. In this study, the key points in the derivatization (lactonization time, the amount of n-propylamine and acetic anhydride) were investigated and optimized to improve the method. Under the optimal conditions, the derivatives of seven neutral monosaccharides and two uronic acids were simultaneouly obtained, after which they were well separated and detected by GC. It was also found that all derivatives of monosaccharides were stable even stored for 20days. The linearity, sensitivity, precision, reproducibility and recovery rate of the improved method were evaluated. Thereafter, five polysaccharide samples from different sources were analyzed to validate the improved method.

Predicting essential proteins based on subcellular localization, orthology and PPI networks.

BACKGROUND: Essential proteins play an indispensable role in the cellular survival and development. There have been a series of biological experimental methods for finding essential proteins; however they are time-consuming, expensive and inefficient. In order to overcome the shortcomings of biological experimental methods, many computational methods have been proposed to predict essential proteins. The computational methods can be roughly divided into two categories, the topology-based methods and the sequence-based ones. The former use the topological features of protein-protein interaction (PPI) networks while the latter use the sequence features of proteins to predict essential proteins. Nevertheless, it is still challenging to improve the prediction accuracy of the computational methods. RESULTS: Comparing with nonessential proteins, essential proteins appear more frequently in certain subcellular locations and their evolution more conservative. By integrating the information of subcellular localization, orthologous proteins and PPI networks, we propose a novel essential protein prediction method, named SON, in this study. The experimental results on S.cerevisiae data show that the prediction accuracy of SON clearly exceeds that of nine competing methods: DC, BC, IC, CC, SC, EC, NC, PeC and ION. CONCLUSIONS: We demonstrate that, by integrating the information of subcellular localization, orthologous proteins with PPI networks, the accuracy of predicting essential proteins can be improved. Our proposed method SON is effective for predicting essential proteins.

Purification and characterization of a novel bacteriocin produced by Lactobacillus crustorum MN047 isolated from koumiss from Xinjiang, China.

The growing emergence of antibiotic-resistant bacteria in the food industry needs to be controlled with effective antimicrobials. In this study, bacteriocin MN047 A (BMA) was found to have antibacterial activity against multidrug-resistant bacteria. It was produced by Lactobacillus crustorum MN047, which was first isolated from koumiss, a traditional fermented dairy product from Xinjiang Autonomous Region, China. It was purified by ammonium sulfate precipitation, ion-exchange chromatography, and reversed-phase chromatography. It had a low molecular mass of 1,770.89 Da according to matrix-assisted laser desorption/ionization time-of-flight mass spectrometry, and the sequence was identified as QLPWQILGIVAGMFQA by liquid chromatography-tandem mass spectrometry analysis and MASCOT searching. It was proteinaceous in nature: the bacteriocin was digested by protease but not by α-amylase or lipase. It showed broad pH toleration (pH 2-11), good thermostability, and good storage stability. It had a broad inhibitory spectrum, including both gram-positive and gram-negative bacteria. Growth curve and time-kill kinetics indicated that it was bactericidal to the indicator strains, and this finding was verified by scanning electron microscope and transmission electron microscope after treatment with BMA. As well, BMA halted the growth of Staphylococcus aureus and Escherichia coli in the G1 and G2/M phases according to cell-cycle analysis by flow cytometry, indicating that BMA had comprehensive inhibitory effects against foodborne pathogens.

TG-MS analysis and kinetic study for thermal decomposition of six representative components of municipal solid waste under steam atmosphere.

Thermal decomposition of six representative components of municipal solid waste (MSW, including lignin, printing paper, cotton, rubber, polyvinyl chloride (PVC) and cabbage) was investigated by thermogravimetric-mass spectroscopy (TG-MS) under steam atmosphere. Compared with TG and derivative thermogravimetric (DTG) curves under N2 atmosphere, thermal decomposition of MSW components under steam atmosphere was divided into pyrolysis and gasification stages. In the pyrolysis stage, the shapes of TG and DTG curves under steam atmosphere were almost the same with those under N2 atmosphere. In the gasification stage, the presence of steam led to a greater mass loss because of the steam partial oxidation of char residue. The evolution profiles of H2, CH4, CO and CO2 were well consistent with DTG curves in terms of appearance of peaks and relevant stages in the whole temperature range, and the steam partial oxidation of char residue promoted the generation of more gas products in high temperature range. The multi-Gaussian distributed activation energy model (DAEM) was proved plausible to describe thermal decomposition behaviours of MSW components under steam atmosphere.

Haplotyping a single triploid individual based on genetic algorithm.

The minimum error correction model is an important combinatorial model for haplotyping a single individual. In this article, triploid individual haplotype reconstruction problem is studied by using the model. A genetic algorithm based method GTIHR is presented for reconstructing the triploid individual haplotype. A novel coding method and an effectual hill-climbing operator are introduced for the GTIHR algorithm. This relatively short chromosome code can lead to a smaller solution space, which plays a positive role in speeding up the convergence process. The hill-climbing operator ensures algorithm GTIHR converge at a good solution quickly, and prevents premature convergence simultaneously. The experimental results prove that algorithm GTIHR can be implemented efficiently, and can get higher reconstruction rate than previous algorithms.

Corn stalks char from fast pyrolysis as precursor material for preparation of activated carbon in fluidized bed reactor.

Corn stalks char from fast pyrolysis was activated by physical and chemical activation process in a fluidized bed reactor. The structure and morphology of the carbons were characterized by N2 adsorption and SEM. Effects of activation time and activation agents on the structure of activation carbon were investigated. The physically activated carbons with CO2 have BET specific surface area up to 880 m(2)/g, and exhibit microporous structure. The chemically activated carbons with H3PO4 have BET specific surface area up to 600 m(2)/g, and exhibit mesoporous structure. The surface morphology shows that physically activated carbons exhibit fibrous like structure in nature with long ridges, resembling parallel lines. Whereas chemically activated carbons have cross-interconnected smooth open pores without the fibrous like structure.