Genome Of Cancer Cells Research Articles

DNA Hypomethylation Underlies Epigenetic Swapping between AGO1 and AGO1-V2 Isoforms in Tumors.

Human tumors progress in part by accumulating epigenetic alterations, which include gains and losses of DNA methylation in different parts of the cancer cell genome. Recent work has revealed a link between these two opposite alterations by showing that DNA hypomethylation in tumors can induce the expression of transcripts that overlap downstream gene promoters and thereby induce their hypermethylation. Preliminary in silico evidence prompted us to investigate if this mechanism applies to the locus harboring AGO1, a gene that plays a central role in miRNA biogenesis and RNA interference. Inspection of public RNA-Seq datasets and RT-qPCR experiments show that an alternative transcript starting 13.4 kb upstream of AGO1 (AGO1-V2) is expressed specifically in testicular germ cells, and becomes aberrantly activated in different types of tumors, particularly in tumors of the esophagus, stomach, and lung. This expression pattern classifies AGO1-V2 into the group of "Cancer-Germline" (CG) genes. Analysis of transcriptomic and methylomic datasets provided evidence that transcriptional activation of AGO1-V2 depends on DNA demethylation of its promoter region. Western blot experiments revealed that AGO1-V2 encodes a shortened isoform of AGO1, corresponding to a truncation of 75 aa in the N-terminal domain, and which we therefore referred to as "∆NAGO1". Interestingly, significant correlations between hypomethylation/activation of AGO1-V2 and hypermethylation/repression of AGO1 were observed upon examination of tumor cell lines and tissue datasets. Overall, our study reveals the existence of a process of interdependent epigenetic alterations in the AGO1 locus, which promotes swapping between two AGO1 protein-coding mRNA isoforms in tumors.

Identification of genes related to ribosomal proteins in colorectal cancer: exploring their potential as biomarkers, prognostic indicators, and therapeutic targets.

Colorectal cancer (CRC) ranks as the third most commonly diagnosed cancer in both females and males, underscoring the need for the identification of effective biomarkers. We assessed the expression levels of ribosomal proteins (RPs) at both mRNA and protein levels. Subsequently, leveraging the STRING database, we constructed a protein-protein interaction network and identified hub genes. The co-expression network of differentially expressed genes associated with CRC and their target hub RPs was constructed using the weighted gene co-expression network analysis algorithm. Gene ontology and molecular signatures database were conducted to gain insights into the biological roles of genes associated with the identified module. To confirm the results, the expression level of the candidate genes in the CRC samples compared to the adjacent healthy was evaluated by the RT-qPCR method. Our findings indicated that the genes related to RPs were predominantly enriched in biological processes associated with Myc Targets, Oxidative Phosphorylation, and cell proliferation. Also, results demonstrated that elevated levels of GRWD1, MCM5, IMP4, and RABEPK that related to RPs were associated with poor prognostic outcomes for CRC patients. Notably, IMP4 and RABEPK exhibited higher diagnostic value. Moreover, the expression of IMP4 and RABEPK showed a significant association with drug resistance using cancer cell line encyclopedia and genomics of drug sensitivity in cancer databases. Also, the results showed that the expression level of IMP4 and RABEPK in cancerous samples was significantly higher compared to the adjacent healthy ones. The general results of this study have shown that many genes related to RPs are increased in cancer and could be associated with the death rate of patients. We also highlighted the therapeutic and prognostic potentials of RPs genes in CRC.

The prognostic value of MEK pathway-associated estrogen receptor signaling activity for female cancers.

Other than for breast cancer, endocrine therapy has not been highly effective for gynecologic cancers. Endocrine therapy resistance in estrogen receptor positive gynecologic cancers is still poorly understood. In this retrospective study, we examined the estrogen receptor (ER) signaling pathway activities of breast, ovarian, endometrial, and cervical cancers to identify those that may predict endocrine therapy responsiveness. Clinical and genomic data of women with breast and gynecological cancers were downloaded from cBioPortal for Cancer Genomics. Estrogen receptor alpha (ESR1) expression level and sample-level pathway enrichment scores (EERES) were calculated to classify patients into four groups (low/high ESR1 and low/high EERES). Correlation between ESR1/EERES score and survival was further validated with RNAseq data from low-grade serous ovarian cancer. Pathway analyses were performed among different ESR1/EERES groups to identify genes that correlate with endocrine resistance, which are validated using Cancer Cell Line Encyclopedia gene expression and Genomics of Drug Sensitivity in Cancer data. We identified a novel combined prognostic value of ESR1 expression and the corresponding estrogen response signaling (EERES score) for breast cancer. The combined prognostic value (ESR1/EERES) may be applicable to other gynecologic cancers. More importantly, we discovered that ER signaling can cross-regulate MEK pathway activation. We identified downstream genes in the MEK pathway (EPHA2, INAVA, MALL, MPZL2, PCDH1, and TNFRSF21) that are potential endocrine therapy response biomarkers. This study demonstrated that targeting both the ER and the ER signaling activity related MEK pathway may aid the development of endocrine therapy strategies for personalized medicine.

DBDNMF: A Dual Branch Deep Neural Matrix Factorization method for drug response prediction.

Anti-cancer response of cell lines to drugs is in urgent need for individualized precision medical decision-making in the era of precision medicine. Measurements with wet-experiments is time-consuming and expensive and it is almost impossible for wide ranges of application. The design of computational models that can precisely predict the responses between drugs and cell lines could provide a credible reference for further research. Existing methods of response prediction based on matrix factorization or neural networks have revealed that both linear or nonlinear latent characteristics are applicable and effective for the precise prediction of drug responses. However, the majority of them consider only linear or nonlinear relationships for drug response prediction. Herein, we propose a Dual Branch Deep Neural Matrix Factorization (DBDNMF) method to address the above-mentioned issues. DBDNMF learns the latent representation of drugs and cell lines through flexible inputs and reconstructs the partially observed matrix through a series of hidden neural network layers. Experimental results on the datasets of Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) show that the accuracy of drug prediction exceeds state-of-the-art drug response prediction algorithms, demonstrating its reliability and stability. The hierarchical clustering results show that drugs with similar response levels tend to target similar signaling pathway, and cell lines coming from the same tissue subtype tend to share the same pattern of response, which are consistent with previously published studies.

Identification of biological markers in cancer disease using explainable artificial intelligence

AbstractThe research aims to improve the prediction of drug sensitivity on cancer cell lines using gene expression data and molecular fingerprints of drugs. The proposed study uses a deep learning model, BioMarkerX, trained on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) datasets utilizing Particle Swarm Optimization technique to select specific genes as features. The model achieves high prediction accuracy with a Root Mean Square Error (RMSE) of 0.40 ± 0.02 and R2 of 0.83 ± 0.03 on the CCLE dataset, and an RMSE of 0.36 ± 0.05 and R2 of 0.83 ± 0.03 on the GDSC dataset. The approach also used an explainable artificial intelligence model to discover biological markers linked to cancer development. This can provide insights into targeted therapies for improving cancer treatment outcomes. Overall, the study presents an effective approach for identifying important biological markers relevant to cancer disease, aiding in the development of more efficient anticancer medications.

It took a long, long time: Ras and the race to cure cancer

Discoveries of the roles of RAS oncogenes in cancer development four decades ago opened the door to proving that tumor development is driven by somatic mutations' altering the genomes of cancer cells. These discoveries led to illusions about the simplicity of cancer pathogenesis and how cancer could be cured.

DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery.

Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facilitate oncogenic transcription programs by cooperating with transcription factors bound at flanking distal regions. Identification of DNA sequence features from a broad genomic region that distinguish cancer-specific CTCF binding sites from regular CTCF binding sites can help find oncogenic transcription factors in a cancer type. However, the presence of long DNA sequences without localization information makes it difficult to perform conventional motif analysis. Here, we present DNAResDualNet (DARDN), a computational method that utilizes convolutional neural networks (CNNs) for predicting cancer-specific CTCF binding sites from long DNA sequences and employs DeepLIFT, a method for interpretability of deep learning models that explains the model's output in terms of the contributions of its input features. The method is used for identifying DNA sequence features associated with cancer-specific CTCF binding. Evaluation on DNA sequences associated with CTCF binding sites in T-cell acute lymphoblastic leukemia (T-ALL) and other cancer types demonstrates DARDN's ability in classifying DNA sequences surrounding cancer-specific CTCF binding from control constitutive CTCF binding and identifying sequence motifs for transcription factors potentially active in each specific cancer type. We identify potential oncogenic transcription factors in T-ALL, acute myeloid leukemia (AML), breast cancer (BRCA), colorectal cancer (CRC), lung adenocarcinoma (LUAD), and prostate cancer (PRAD). Our work demonstrates the power of advanced machine learning and feature discovery approach in finding biologically meaningful information from complex high-throughput sequencing data.

Метилирование промоторов генов клеточного цикла и апоптоза у облученных лиц, впоследствии заболевших злокачественными новообразованиями

DNA methylation plays an important role in carcinogenesis; there are many studies that investigate the degree of methylation of the entire genome, gene promoters, and non-coding elements in cancer cells, but much less information about changes of the methylation patterns in blood cells and links with the development of malignant neoplasms (MN). This study aimed to investigate the degree of methylation of promoter regions of cell cycle control and apoptosis genes (BAX, MDM2, TP53, NFkB1) in peripheral blood cells of persons chronically exposed to radiation with MN developing latently. The study included 200 persons chronically exposed to radiation from the Techa River, contaminated with nuclear wastes dumped into it. The level of methylation was assessed by real-time PCR. The participants were divided into exposed and control groups; comparing them, we found that in the former, the distribution of exposed individuals with latent MN by the degree of methylation of promoter regions of BAX, MDM2 and NFkB1 genes was significantly different from that in the latter (p < 0.001; p < 0.001; p = 0.004, respectively). It was established that, compared to the control group, the share of the test group participants with subsequent MN who had up to 10% of the BAX gene promoter regions methylated was significantly higher, and amounted to 98%, while in the control group this figure did not exceed 73% (p < 0.00001).

Enhancers dysfunction in the 3D genome of cancer cells.

Eukaryotic genomes are spatially organized inside the cell nucleus, forming a threedimensional (3D) architecture that allows for spatial separation of nuclear processes and for controlled expression of genes required for cell identity specification and tissue homeostasis. Hence, it is of no surprise that mis-regulation of genome architecture through rearrangements of the linear genome sequence or epigenetic perturbations are often linked to aberrant gene expression programs in tumor cells. Increasing research efforts have shed light into the causes and consequences of alterations of 3D genome organization. In this review, we summarize the current knowledge on how 3D genome architecture is dysregulated in cancer, with a focus on enhancer highjacking events and their contribution to tumorigenesis. Studying the functional effects of genome architecture perturbations on gene expression in cancer offers a unique opportunity for a deeper understanding of tumor biology and sets the basis for the discovery of novel therapeutic targets.

Engineered bacteria detect tumor DNA.

Synthetic biology has developed sophisticated cellular biosensors to detect and respond to human disease. However, biosensors have not yet been engineered to detect specific extracellular DNA sequences and mutations. Here, we engineered naturally competent Acinetobacter baylyi to detect donor DNA from the genomes of colorectal cancer (CRC) cells, organoids, and tumors. We characterized the functionality of the biosensors in vitro with coculture assays and then validated them in vivo with sensor bacteria delivered to mice harboring colorectal tumors. We observed horizontal gene transfer from the tumor to the sensor bacteria in our mouse model of CRC. This cellular assay for targeted, CRISPR-discriminated horizontal gene transfer (CATCH) enables the biodetection of specific cell-free DNA.

Causes of DNA Damage and Genomic Instability: A Review

Genomic instability defines all genetic alterations resulting from excessive or high frequency of mutation; base pairs sequence alterations known as microsatellite instability (MSI) and aneuploidy also called chromosome instability (CIN), chromosomal re- arrangements within the genome or the susceptibility of genome to alterations that occur during cell division cycle. The genome of most cancer cells is highly unstable, mostly due to damage to tumour suppressor genes example tp53 which encodes p53 or other genes coordinating cell division. Telomere is a nucleoprotein complex which extends the physical ends of eukaryotic chromosomes, protecting it from degradation, counteracting sequence loss, protecting genes closer to the chromosomes end and inhibiting cell cycle arrest. Studies have implicated the dysfunction of telomere to the formation of sub-tetraploid aneuploid cells that exhibit tumourigenic capacity and formation of anueploid cells that can elude programmed cell death (apoptosis) and form tumour cells, thus initiating genomic instability. In this review, we presented an insight to the causes of DNA damage and its associated genomic instability. We found that many factors such as fragile sites, end replication problem, DNA double strand breaks, DNA replication defects, deletion, insertion and translocation all are responsible to the fragile nature of the genome.

Machine learning-driven exploration of drug therapies for triple-negative breast cancer treatment.

Breast cancer is the second leading cause of cancer death in women among all cancer types. It is highly heterogeneous in nature, which means that the tumors have different morphologies and there is heterogeneity even among people who have the same type of tumor. Several staging and classifying systems have been developed due to the variability of different types of breast cancer. Due to high heterogeneity, personalized treatment has become a new strategy. Out of all breast cancer subtypes, triple-negative breast cancer (TNBC) comprises ∼10%-15%. TNBC refers to the subtype of breast cancer where cells do not express estrogen receptors, progesterone receptors, or human epidermal growth factor receptors (ERs, PRs, and HERs). Tumors in TNBC have a diverse set of genetic markers and prognostic indicators. We scanned the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases for potential drugs using human breast cancer cell lines and drug sensitivity data. Three different machine-learning approaches were used to evaluate the prediction of six effective drugs against the TNBC cell lines. The top biomarkers were then shortlisted on the basis of their involvement in breast cancer and further subjected to testing for radion resistance using data from the Cleveland database. It was observed that Panobinostat, PLX4720, Lapatinib, Nilotinib, Selumetinib, and Tanespimycin were six effective drugs against the TNBC cell lines. We could identify potential derivates that may be used against approved drugs. Only one biomarker (SETD7) was sensitive to all six drugs on the shortlist, while two others (SRARP and YIPF5) were sensitive to both radiation and drugs. Furthermore, we did not find any radioresistance markers for the TNBC. The proposed biomarkers and drug sensitivity analysis will provide potential candidates for future clinical investigation.

The use of plant-derived exosome-like nanoparticles as a delivery system of CRISPR/Cas9-based therapeutics for editing long non-coding RNAs in cancer colon cells.

Colon cancer is one of the leading causes of cancer in the United States. Colon cancer develops from the many gene mutations found in the genomes of colon cancer cells. Long non-coding RNAs (lncRNAs) can cause the development and progression of many cancers, including colon cancer. LncRNAs have been and could be corrected through the gene-editing technology of the clustered repeats of the clustered regularly interspaced short palindromic repeats (CRISPR)-associated nuclease 9 (CRISPR/Cas9) system to reduce the proliferation of cancer cells in the colon. However, many current delivery systems for transporting CRISPR/Cas9-based therapeutics in vivo need more safety and efficiency. CRISPR/Cas9-based therapeutics require a safe and effective delivery system to more directly and specifically target cancer cells present in the colon. This review will present pertinent evidence for the increased efficiency and safety of using plant-derived exosome-like nanoparticles as nanocarriers for delivering CRISPR/Cas9-based therapeutics to target colon cancer cells directly.

Overexpressed kinetochore genes are used by cancer cells as genome destabilizers and transformation catalysts

Cancer cells have an altered transcriptome, which contributes to their abnormal behavior. Many tumors have high levels of kinetochore genes, which play important roles in genome stability. This overexpression could be utilized to destabilize cancer cell genomes, however this has not been proven specifically. We investigated the link between kinetochore gene overexpression, chromosomal number variations (CNVs) and genomic instability.Data on RNA expression and CNV from 12 different cancer types were evaluated using information theory. In all cancer types, we looked at the relationship between RNA expression and CNVs. Kinetochore gene expression was found to be substantially linked with CNV levels. In all cancer types, with the exception of thyroid cancer, highly expressed kinetochore genes were enriched in the most dominant cancer-specific co-expression subnetworks characterizing the largest patient subgroups. Except for thyroid cancer, kinetochore inner protein CENPA was among the transcripts most strongly associated with CNV values in all cancer types studied, with significantly higher expression levels in patients with high CNVs than in patients with low CNVs. CENPA function was investigated further in cell models by transfecting genomically stable (HCT116) and unstable (MCF7 and HT29) cancer cell lines using CENPA overexpression vectors. This overexpression increased the number of abnormal cell divisions in the stable cancer cell line HCT116 and, to a lesser extent, in the unstable cell lines MCF7 and HT29. Overexpression improved anchorage-independent growth properties of all cell lines.Our findings suggest that overexpression of kinetochore genes in general, and CENPA in particular, can cause genomic instability and cancer progression.

CTDN (Convolutional Temporal Based Deep‐ Neural Network): An Improvised Stacked Hybrid Computational Approach for Anticancer Drug Response Prediction

The characterization of drug - metabolizing enzymes is a significant problem for customized therapy. It is important to choose the right drugs for cancer victims, and the ability to forecast how those drugs will react is usually based on the available information, genetic sequence, and structural properties. To the finest of our knowledge, this is the first study to evaluate optimization algorithms for selection of features and pharmacogenetics categorization using classification methods based on a successful evolutionary algorithm using datasets from the Cancer Cell Line Encyclopaedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC). The study proposes the uses of Firefly and Grey Wolf Optimization techniques for feature extraction, while comparing the traditional Machine Learning (ML), ensemble ML and Stacking Algorithm with the proposed Convolutional Temporal Deep Neural Network or CTDN. With the potential to increase efficiency from the suggested intelligible classifier model for a suggestive chemotherapeutic drugs response prediction, our study is important in particular for selecting an acceptable feature selection method. The comparison analysis demonstrates that the proposed model not only surpasses the prior state-of-the-art methods, but also uses Grey Wolf and Fire Fly Optimization to lessen multicollinearity and overfitting.

Ranking Breast Cancer Drugs and Biomarkers Identification Using Machine Learning and Pharmacogenomics.

Breast cancer is one of the major causes of death in women worldwide. It is a diverse illness with substantial intersubject heterogeneity, even among individuals with the same type of tumor, and customized therapy has become increasingly important in this sector. Because of the clinical and physical variability of different kinds of breast cancers, multiple staging and classification systems have been developed. As a result, these tumors exhibit a wide range of gene expression and prognostic indicators. To date, no comprehensive investigation of model training procedures on information from numerous cell line screenings has been conducted together with radiation data. We used human breast cancer cell lines and drug sensitivity information from Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) databases to scan for potential drugs using cell line data. The results are further validated through three machine learning approaches: Elastic Net, LASSO, and Ridge. Next, we selected top-ranked biomarkers based on their role in breast cancer and tested them further for their resistance to radiation using the data from the Cleveland database. We have identified six drugs named Palbociclib, Panobinostat, PD-0325901, PLX4720, Selumetinib, and Tanespimycin that significantly perform on breast cancer cell lines. Also, five biomarkers named TNFSF15, DCAF6, KDM6A, PHETA2, and IFNGR1 are sensitive to all six shortlisted drugs and show sensitivity to the radiations. The proposed biomarkers and drug sensitivity analysis are helpful in translational cancer studies and provide valuable insights for clinical trial design.

Non-interfacial self-assembly of synthetic protocells

BackgroundProtocell refers to the basic unit of life and synthetic molecular assembly with cell structure and function. The protocells have great applications in the field of biomedical technology. Simulating the morphology and function of cells is the key to the preparation of protocells. However, some organic solvents used in the preparation process of protocells would damage the function of the bioactive substance. Perfluorocarbon, which has no toxic effect on bioactive substances, is an ideal solvent for protocell preparation. However, perfluorocarbon cannot be emulsified with water because of its inertia.MethodsSpheroids can be formed in nature even without emulsification, since liquid can reshape the morphology of the solid phase through the scouring action, even if there is no stable interface between the two phases. Inspired by the formation of natural spheroids such as pebbles, we developed non-interfacial self-assembly (NISA) of microdroplets as a step toward synthetic protocells, in which the inert perfluorocarbon was utilized to reshape the hydrogel through the scouring action.ResultsThe synthetic protocells were successfully obtained by using NISA-based protocell techniques, with the morphology very similar to native cells. Then we simulated the cell transcription process in the synthetic protocell and used the protocell as an mRNA carrier to transfect 293T cells. The results showed that protocells delivered mRNAs, and successfully expressed proteins in 293T cells. Further, we used the NISA method to fabricate an artificial cell by extracting and reassembling the membrane, proteins, and genomes of ovarian cancer cells. The results showed that the recombination of tumor cells was successfully achieved with similar morphology as tumor cells. In addition, the synthetic protocell prepared by the NISA method was used to reverse cancer chemoresistance by restoring cellular calcium homeostasis, which verified the application value of the synthetic protocell as a drug carrier.ConclusionThis synthetic protocell fabricated by the NISA method simulates the occurrence and development process of primitive life, which has great potential application value in mRNA vaccine, cancer immunotherapy, and drug delivery.Graphical

Transforming Diagnosis and Therapeutics Using Cancer Genomics.

In past quarter of the century, much has been understood about the genetic variation and abnormal genes that activate cancer in humans. All the cancers somehow possess alterations in the DNA sequence of cancer cell's genome. In present, we are heading toward the era where it is possible to obtain complete genome of the cancer cells for their better diagnosis, categorization and to explore treatment options.

Increased copy number of imprinted genes in the chromosomal region 20q11-q13.32 is associated with resistance to antitumor agents in cancer cell lines

BackgroundParent of origin-specific allelic expression of imprinted genes is epigenetically controlled. In cancer, imprinted genes undergo both genomic and epigenomic alterations, including frequent copy number changes. We investigated whether copy number loss or gain of imprinted genes in cancer cell lines is associated with response to chemotherapy treatment.ResultsWe analyzed 198 human imprinted genes including protein-coding genes and noncoding RNA genes using data from tumor cell lines from the Cancer Cell Line Encyclopedia and Genomics of Drug Sensitivity in Cancer datasets. We examined whether copy number of the imprinted genes in 35 different genome locations was associated with response to cancer drug treatment. We also analyzed associations of pretreatment expression and DNA methylation of imprinted genes with drug response. Higher copy number of BLCAP, GNAS, NNAT, GNAS-AS1, HM13, MIR296, MIR298, and PSIMCT-1 in the chromosomal region 20q11-q13.32 was associated with resistance to multiple antitumor agents. Increased expression of BLCAP and HM13 was also associated with drug resistance, whereas higher methylation of gene regions of BLCAP, NNAT, SGK2, and GNAS was associated with drug sensitivity. While expression and methylation of imprinted genes in several other chromosomal regions was also associated with drug response and many imprinted genes in different chromosomal locations showed a considerable copy number variation, only imprinted genes at 20q11-q13.32 had a consistent association of their copy number with drug response. Copy number values among the imprinted genes in the 20q11-q13.32 region were strongly correlated. They were also correlated with the copy number of cancer-related non-imprinted genes MYBL2, AURKA, and ZNF217 in that chromosomal region. Expression of genes at 20q11-q13.32 was associated with ex vivo drug response in primary tumor samples from the Beat AML 1.0 acute myeloid leukemia patient cohort. Association of the increased copy number of the 20q11-q13.32 region with drug resistance may be complex and could involve multiple genes.ConclusionsCopy number of imprinted and non-imprinted genes in the chromosomal region 20q11-q13.32 was associated with cancer drug resistance. The genes in this chromosomal region may have a modulating effect on tumor response to chemotherapy.

Multilevel view on chromatin architecture alterations in cancer.

Chromosomes inside the nucleus are not located in the form of linear molecules. Instead, there is a complex multilevel genome folding that includes nucleosomes packaging, formation of chromatin loops, domains, compartments, and finally, chromosomal territories. Proper spatial organization play an essential role for the correct functioning of the genome, and is therefore dynamically changed during development or disease. Here we discuss how the organization of the cancer cell genome differs from the healthy genome at various levels. A better understanding of how malignization affects genome organization and long-range gene regulation will help to reveal the molecular mechanisms underlying cancer development and evolution.