Preparation and Hydration Properties of Sodium Silicate-Activated Municipal Solid Waste Incineration Bottom Ash Composite Ground-Granulated Blast Furnace Slag Cementitious Materials.

The production of municipal solid waste incineration bottom ash (MSWIBA) is substantial and has the potential to replace cement, despite challenges such as complex composition, uneven particle size distribution, and low reactivity. This paper employs sodium silicate activation of MSWIBA composite Ground-granulated Blast Furnace slag (GGBS) to improve the reactivity in preparing composite cementitious materials. It explores the hydration performance of the composite cementitious materials using isothermal calorimetric analysis, Fourier-transform infrared (FTIR) spectroscopy, XRD physical diffraction analysis, and SEM tests. SEM tests were used to explore the hydration properties of the composite gelling. The results show that with an increase in MSWIBA doping, the porosity between the materials increased, the degree of hydration decreased, and the compressive strength decreased. When the sodium silicate concentration increased from 25% to 35%, excessive alkaline material occurred, impacting the alkaline effect. This inhibited particle hydration, leading to a decrease in the degree of hydration and, consequently, the compressive strength. The exothermic process of hydration can be divided into five main stages; quartz and calcite did not fully participate in the hydration reaction, while aluminum did. The vibrational peaks of Si-O-Ti (T = Si and Al) were present in the material. The vibrational peaks of XRD, FTIR, and SEM all indicate the presence of alumosilicate network structures in the hydration products, mainly N-A-S-H and C-A-S-H gels.

TAGET: a toolkit for analyzing full-length transcripts from long-read sequencing.

Single-molecule Real-time Isoform Sequencing (Iso-seq) of transcriptomes by PacBio can generate very long and accurate reads, thus providing an ideal platform for full-length transcriptome analysis. We present an integrated computational toolkit named TAGET for Iso-seq full-length transcript data analyses, including transcript alignment, annotation, gene fusion detection, and quantification analyses such as differential expression gene analysis and differential isoform usage analysis. We evaluate the performance of TAGET using a public Iso-seq dataset and newly sequenced Iso-seq datasets from tumor patients. TAGET gives significantly more precise novel splice site prediction and enables more accurate novel isoform and gene fusion discoveries, as validated by experimental validations and comparisons with RNA-seq data. We identify and experimentally validate a differential isoform usage gene ECM1, and further show that its isoform ECM1b may be a tumor-suppressor in laryngocarcinoma. Our results demonstrate that TAGET provides a valuable computational toolkit and can be applied to many full-length transcriptome studies.

High-throughput screening of spike variants uncovers the key residues that alter the affinity and antigenicity of SARS-CoV-2.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has elicited a worldwide pandemic since late 2019. There has been ~675 million confirmed coronavirus disease 2019 (COVID-19) cases, leading to more than 6.8 million deaths as of March 1, 2023. Five SARS-CoV-2 variants of concern (VOCs) were tracked as they emerged and were subsequently characterized. However, it is still difficult to predict the next dominant variant due to the rapid evolution of its spike (S) glycoprotein, which affects the binding activity between cellular receptor angiotensin-converting enzyme 2 (ACE2) and blocks the presenting epitope from humoral monoclonal antibody (mAb) recognition. Here, we established a robust mammalian cell-surface-display platform to study the interactions of S-ACE2 and S-mAb on a large scale. A lentivirus library of S variants was generated via in silico chip synthesis followed by site-directed saturation mutagenesis, after which the enriched candidates were acquired through single-cell fluorescence sorting and analyzed by third-generation DNA sequencing technologies. The mutational landscape provides a blueprint for understanding the key residues of the S protein binding affinity to ACE2 and mAb evasion. It was found that S205F, Y453F, Q493A, Q493M, Q498H, Q498Y, N501F, and N501T showed a 3-12-fold increase in infectivity, of which Y453F, Q493A, and Q498Y exhibited at least a 10-fold resistance to mAbs REGN10933, LY-CoV555, and REGN10987, respectively. These methods for mammalian cells may assist in the precise control of SARS-CoV-2 in the future.

Patient-derived tumor-like cell clusters for drug testing in cancer therapy.

Several patient-derived tumor models emerged recently as robust preclinical drug-testing platforms. However, their potential to guide clinical therapy remained unclear. Here, we report a model called patient-derived tumor-like cell clusters (PTCs). PTCs result from the self-assembly and proliferation of primary epithelial, fibroblast, and immune cells, which structurally and functionally recapitulate original tumors. PTCs enabled us to accomplish personalized drug testing within 2 weeks after obtaining the tumor samples. The defined culture conditions and drug concentrations in the PTC model facilitate its clinical application in precision oncology. PTC tests of 59 patients with gastric, colorectal, or breast cancers revealed an overall accuracy of 93% in predicting their clinical outcomes. We implemented PTC to guide chemotherapy selection for a patient with mucinous rectal adenocarcinoma who experienced recurrence with metastases after conventional therapy. After three cycles of a nonconventional therapy identified by the PTC, the patient showed a positive response. These findings need to be validated in larger clinical trials, but they suggest that the PTC model could be prospectively implemented in clinical decision-making for therapy selection.

CNV-BAC: Copy number Variation Detection in Bacterial Circular Genome.

MOTIVATION: Whole-genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias. RESULTS: We show that the replication bias is widespread using ∼200 bacterial WGS data. We develop CNV-BAC (CNV-Bacteria) that can properly normalize the replication bias and other known biases in bacterial WGS data and can accurately detect CNVs. Simulation and real data analysis show that CNV-BAC achieves the best performance in CNV detection compared with available algorithms. AVAILABILITY AND IMPLEMENTATION: CNV-BAC is available at https://github.com/XiDsLab/CNV-BAC. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Detecting virus integration sites based on multiple related sequencing data by VirTect.

BACKGROUND: Since tumor often has a high level of intra-tumor heterogeneity, multiple tumor samples from the same patient at different locations or different time points are often sequenced to study tumor intra-heterogeneity or tumor evolution. In virus-related tumors such as human papillomavirus- and Hepatitis B Virus-related tumors, virus genome integrations can be critical driving events. It is thus important to investigate the integration sites of the virus genomes. Currently, a few algorithms for detecting virus integration sites based on high-throughput sequencing have been developed, but their insufficient performance in their sensitivity, specificity and computational complexity hinders their applications in multiple related tumor sequencing. RESULTS: We develop VirTect for detecting virus integration sites simultaneously from multiple related-sample data. This algorithm is mainly based on the joint analysis of short reads spanning breakpoints of integration sites from multiple samples. To achieve high specificity and breakpoint accuracy, a local precise sandwich alignment algorithm is used. Simulation and real data analyses show that, compared with other algorithms, VirTect is significantly more sensitive and has a similar or lower false discovery rate. CONCLUSIONS: VirTect can provide more accurate breakpoint position and is computationally much more efficient in terms both memory requirement and computational time.

Somatic mutations in renal cell carcinomas from Chinese patients revealed by whole exome sequencing.

BACKGROUND: While the somatic mutation profiles of renal cell carcinoma (RCC) have been revealed by several studies worldwide, the overwhelming majority of those were not derived from Chinese patients. The landscape of somatic alterations in RCC from Chinese patients still needs to be elucidated to determine whether discrepancies exist between Chinese patients and sufferers from other countries and regions. METHODS: We collected specimens from 26 Chinese patients with primary RCC, including 15 clear cell renal cell carcinoma (ccRCC) samples, 5 papillary renal cell carcinoma (PRCC) samples and 6 chromophobe renal cell carcinoma (ChRCC) samples. Genomic DNAs were isolated from paired tumor-normal tissues and subjected to whole exome sequencing (WES). Immunohistochemistry analysis was performed to detect the programmed death ligand 1 (PD-L1) expression in tumor tissues. RESULTS: A total of 1920 nonsynonymous somatic variants in exons and 86 mutations at splice junctions were revealed. The tumor mutation burden of ccRCC was significantly higher than that of ChRCC (P < 0.05). For both ccRCC and PRCC, the most frequent substitution in somatic missense mutations was T:A > A:T, which was different from that recorded in the COSMIC database. Among eight significantly mutated genes in ccRCC in the TCGA database, six genes were verified in our study including VHL (67%), BAP1 (13%), SETD2 (13%), PBRM1 (7%), PTEN (7%) and MTOR (7%). All the mutations detected in those genes had not been reported in ccRCC before, except for alterations in VHL and PBRM1. Regarding the frequently mutated genes in PRCC in our study, DEPDC4 (p.E293A, p.T279A), PNLIP (p.N401Y, p.F342L) and SARDH (p.H554Q, p.M1T) were newly detected gene mutations predicted to be deleterious. As the most recurrently mutated gene in ChRCC in the TCGA dataset, TP53 (p.R81Q) was somatically altered only in one ChRCC case in this study. The HIF-1 signaling pathway was the most affected pathway in ccRCC, while the PI3K-Akt signaling pathway was altered in all of the three RCC types. Membranous PD-L1 expression was positive in tumor cells from 6/26 (23%) RCC specimens. The PD-L1-positive rate was higher in RCC samples with the somatically mutated genes CSPG4, DNAH11, INADL and TMPRSS13 than in specimens without those (P < 0.05). CONCLUSIONS: Using WES, we identified somatic mutations in 26 Chinese patients with RCC, which enriched the racial diversity of the somatic mutation profiles of RCC subjects, and revealed a few discrepancies in molecular characterizations between our study and published datasets. We also identified numerous newly detected somatic mutations, which further supplements the somatic mutation landscape of RCC. Moreover, 4 somatically mutated genes, including CSPG4, DNAH11, INADL and TMPRSS13, might be promising predictive factors of PD-L1-positive expression in RCC tumor cells.

SVmine improves structural variation detection by integrative mining of predictions from multiple algorithms.

MOTIVATION: Structural variation (SV) is an important class of genomic variations in human genomes. A number of SV detection algorithms based on high-throughput sequencing data have been developed, but they have various and often limited level of sensitivity, specificity and breakpoint resolution. Furthermore, since overlaps between predictions of algorithms are low, SV detection based on multiple algorithms, an often-used strategy in real applications, has little effect in improving the performance of SV detection. RESULTS: We develop a computational tool called SVmine for further mining of SV predictions from multiple tools to improve the performance of SV detection. SVmine refines SV predictions by performing local realignment and assess quality of SV predictions based on likelihoods of the realignments. The local realignment is performed against a set of sequences constructed from the reference sequence near the candidate SV by incorporating nearby single nucleotide variations, insertions and deletions. A sandwich alignment algorithm is further used to improve the accuracy of breakpoint positions. We evaluate SVmine on a set of simulated data and real data and find that SVmine has superior sensitivity, specificity and breakpoint estimation accuracy. We also find that SVmine can significantly improve overlaps of SV predictions from other algorithms. AVAILABILITY AND IMPLEMENTATION: SVmine is available at https://github.com/xyc0813/SVmine. CONTACT: ruibinxi@math.pku.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic analysis of oesophageal squamous-cell carcinoma identifies alcohol drinking-related mutation signature and genomic alterations.

Approximately half of the world's 500,000 new oesophageal squamous-cell carcinoma (ESCC) cases each year occur in China. Here, we show whole-genome sequencing of DNA and RNA in 94 Chinese individuals with ESCC. We identify six mutational signatures (E1-E6), and Signature E4 is unique in ESCC linked to alcohol intake and genetic variants in alcohol-metabolizing enzymes. We discover significantly recurrent mutations in 20 protein-coding genes, 4 long non-coding RNAs and 10 untranslational regions. Functional analyses show six genes that have recurrent copy-number variants in three squamous-cell carcinomas (oesophageal, head and neck and lung) significantly promote cancer cell proliferation, migration and invasion. The most frequently affected genes by structural variation are LRP1B and TTC28. The aberrant cell cycle and PI3K-AKT pathways seem critical in ESCC. These results establish a comprehensive genomic landscape of ESCC and provide potential targets for precision treatment and prevention of the cancer.

Pysim-sv: a package for simulating structural variation data with GC-biases.

BACKGROUND: Structural variations (SVs) are wide-spread in human genomes and may have important implications in disease-related and evolutionary studies. High-throughput sequencing (HTS) has become a major platform for SV detection and simulation serves as a powerful and cost-effective approach for benchmarking SV detection algorithms. Accurate performance assessment by simulation requires the simulator capable of generating simulation data with all important features of real data, such GC biases in HTS data and various complexities in tumor data. However, no available package has systematically addressed all issues in data simulation for SV benchmarking. RESULTS: Pysim-sv is a package for simulating HTS data to evaluate performance of SV detection algorithms. Pysim-sv can introduce a wide spectrum of germline and somatic genomic variations. The package contains functionalities to simulate tumor data with aneuploidy and heterogeneous subclones, which is very useful in assessing algorithm performance in tumor studies. Furthermore, Pysim-sv can introduce GC-bias, the most important and prevalent bias in HTS data, in the simulated HTS data. CONCLUSIONS: Pysim-sv provides an unbiased toolkit for evaluating HTS-based SV detection algorithms.

Cell Culture System for Analysis of Genetic Heterogeneity Within Hepatocellular Carcinomas and Response to Pharmacologic Agents.

BACKGROUND & AIMS: No targeted therapies have been found to be effective against hepatocellular carcinoma (HCC), possibly due to the large degree of intratumor heterogeneity. We performed genetic analyses of different regions of HCCs to evaluate levels of intratumor heterogeneity and associate alterations with responses to different pharmacologic agents. METHODS: We obtained samples of HCCs (associated with hepatitis B virus infection) from 10 patients undergoing curative resection, before adjuvant therapy, at hospitals in China. We collected 4-9 spatially distinct samples from each tumor (55 regions total), performed histologic analyses, isolated cancer cells, and carried them low-passage culture. We performed whole-exome sequencing, copy-number analysis, and high-throughput screening of the cultured primary cancer cells. We tested responses of an additional 105 liver cancer cell lines to a fibroblast growth factor receptor (FGFR) 4 inhibitor. RESULTS: We identified a total of 3670 non-silent mutations (3192 missense, 94 splice-site variants, and 222 insertions or deletions) in the tumor samples. We observed considerable intratumor heterogeneity and branched evolution in all 10 tumors; the mean percentage of heterogeneous mutations in each tumor was 39.7% (range, 12.9%-68.5%). We found significant mutation shifts toward C>T and C>G substitutions in branches of phylogenetic trees among samples from each tumor (P < .0001). Of note, 14 of the 26 oncogenic alterations (53.8%) varied among subclones that mapped to different branches. Genetic alterations that can be targeted by existing pharmacologic agents (such as those in FGF19, DDR2, PDGFRA, and TOP1) were identified in intratumor subregions from 4 HCCs and were associated with sensitivity to these agents. However, cells from the remaining subregions, which did not have these alterations, were not sensitive to these drugs. High-throughput screening identified pharmacologic agents to which these cells were sensitive, however. Overexpression of FGF19 correlated with sensitivity of cells to an inhibitor of FGFR 4; this observation was validated in 105 liver cancer cell lines (P = .0024). CONCLUSIONS: By analyzing genetic alterations in different tumor regions of 10 HCCs, we observed extensive intratumor heterogeneity. Our patient-derived cell line-based model, integrating genetic and pharmacologic data from multiregional cancer samples, provides a platform to elucidate how intratumor heterogeneity affects sensitivity to different therapeutic agents.

A microscopic landscape of the invasive breast cancer genome.

Histologic grade is one of the most important microscopic features used to predict the prognosis of invasive breast cancer and may serve as a marker for studying cancer driving genomic abnormalities in vivo. We analyzed whole genome sequencing data from 680 cases of TCGA invasive ductal carcinomas of the breast and correlated them to corresponding pathology information. Ten genetic abnormalities were found to be statistically associated with histologic grade, including three most prevalent cancer driver events, TP53 and PIK3CA mutations and MYC amplification. A distinct genetic interaction among these genomic abnormalities was revealed as measured by the histologic grading score. While TP53 mutation and MYC amplification were synergistic in promoting tumor progression, PIK3CA mutation was found to have alleviated the oncogenic effect of either the TP53 mutation or MYC amplification, and was associated with a significant reduction in mitotic activity in TP53 mutated and/or MYC amplified breast cancer. Furthermore, we discovered that different types of genetic abnormalities (mutation versus amplification) within the same cancer driver gene (PIK3CA or GATA3) were associated with opposite histologic changes in invasive breast cancer. In conclusion, our study suggests that histologic grade may serve as a biomarker to define cancer driving genetic events in vivo.

Copy number analysis of whole-genome data using BIC-seq2 and its application to detection of cancer susceptibility variants.

Whole-genome sequencing data allow detection of copy number variation (CNV) at high resolution. However, estimation based on read coverage along the genome suffers from bias due to GC content and other factors. Here, we develop an algorithm called BIC-seq2 that combines normalization of the data at the nucleotide level and Bayesian information criterion-based segmentation to detect both somatic and germline CNVs accurately. Analysis of simulation data showed that this method outperforms existing methods. We apply this algorithm to low coverage whole-genome sequencing data from peripheral blood of nearly a thousand patients across eleven cancer types in The Cancer Genome Atlas (TCGA) to identify cancer-predisposing CNV regions. We confirm known regions and discover new ones including those covering KMT2C, GOLPH3, ERBB2 and PLAG1 Analysis of colorectal cancer genomes in particular reveals novel recurrent CNVs including deletions at two chromatin-remodeling genes RERE and NPM2 This method will be useful to many researchers interested in profiling CNVs from whole-genome sequencing data.

PDEGEM: Modeling non-uniform read distribution in RNA-Seq data.

BACKGROUND: RNA-Seq is a powerful new technology to comprehensively analyze the transcriptome of any given cells. An important task in RNA-Seq data analysis is quantifying the expression levels of all transcripts. Although many methods have been introduced and much progress has been made, a satisfactory solution remains be elusive. RESULTS: In this article, we borrow the idea from the Positional Dependent Nearest Neighborhood (PDNN) model, originally developed for analyzing microarray data, to model the non-uniformity of read distribution in RNA-seq data. We propose a robust nonlinear regression model named PDEGEM, a Positional Dependent Energy Guided Expression Model to estimate the abundance of transcripts. Using real data, we find that the PDEGEM fits the data better than mseq in all three real datasets we tested. We also find that the expression measure obtained using PDEGEM showed higher correlation with that obtained from alterative assays for quantifying gene and isoform expressions. CONCLUSIONS: Based on these results, we believe that our PDEGEM can improve the accuracy in modeling and estimating the transcript abundance and isoform expression in RNA-Seq data. Additionally, although the stacking energy and positional weight of the PDEGEM are relatively related to sequencing platforms and species, they share some common trends, which indicates that the PDEGEM could partly reflect the mechanism of DNA binding between the template strain and the new synthesized read.