jMorp: Japanese Multi-Omics Reference Panel update report 2023.

Modern medicine is increasingly focused on personalized medicine, and multi-omics data is crucial in understanding biological phenomena and disease mechanisms. Each ethnic group has its unique genetic background with specific genomic variations influencing disease risk and drug response. Therefore, multi-omics data from specific ethnic populations are essential for the effective implementation of personalized medicine. Various prospective cohort studies, such as the UK Biobank, All of Us and Lifelines, have been conducted worldwide. The Tohoku Medical Megabank project was initiated after the Great East Japan Earthquake in 2011. It collects biological specimens and conducts genome and omics analyses to build a basis for personalized medicine. Summary statistical data from these analyses are available in the jMorp web database (https://jmorp.megabank.tohoku.ac.jp), which provides a multidimensional approach to the diversity of the Japanese population. jMorp was launched in 2015 as a public database for plasma metabolome and proteome analyses and has been continuously updated. The current update will significantly expand the scale of the data (metabolome, genome, transcriptome, and metagenome). In addition, the user interface and backend server implementations were rewritten to improve the connectivity between the items stored in jMorp. This paper provides an overview of the new version of the jMorp.

Gene Rearrangement and Expression of PRKACA and PRKACB Govern Morphobiology of Pancreatobiliary Oncocytic Neoplasms.

Intraductal oncocytic papillary neoplasms (IOPNs) are distinct from intraductal papillary mucinous neoplasms based on characteristic morphologic and genetic features represented by fusion genes involving PRKACA or PRKACB (PRKACA/B). However, pancreatic and biliary tumors with partial oncocytic features are often encountered clinically, and their molecular features are yet to be clarified. This study included 80 intraductal papillary neoplasms: 32 tumors with mature IOPN morphology (typical), 28 with partial or subclonal oncocytic features (atypical), and 20 without oncocytic features (control). We analyzed PRKACA/B fusion genes, including ATP1B1::PRKACA, DNAJB1::PRKACA, and ATP1B1::PRKACB, by reverse-transcription PCR; mRNA expression of fusion genes and nonrearranged PRKACA/B genes by quantitative reverse-transcription PCR; mutations in KRAS, BRAF, and GNAS by targeted sequencing or droplet digital PCR; and the expression of cyclic adenosine monophosphate (cAMP)-dependent protein kinase catalytic subunits α (PRKACA) and ß (PRKACB), phosphorylated cAMP response element-binding protein, and aberrations of p16, p53, SMAD4, STK11, and ß-catenin by immunohistochemistry. PRKACA/B fusion genes were detected in 100% (32/32) of typical, 46% (13/28) of atypical, and 0% (0/20) of control (P < .05). Expression of PRKACA, PRKACB, and phosphorylated cAMP response element-binding protein was upregulated in neoplasms with PRKACA/B fusion genes (P < .05). mRNA expression of the PRKACA/B fusion genes and protein expression of PRKACA or PRKACB tended to be higher in typical than in atypical cases (mRNA, P = .002; protein expression, P = .054). In some atypical neoplasms with mixed subtypes, PRKACA/B fusion genes were superimposed exclusively on oncocytic components. Typical IOPNs harbored fewer KRAS and GNAS mutations than control samples and fewer alterations in p53 and STK11 than atypical samples (P < .05). In conclusion, PRKACA/B fusion genes not only are the characteristic drivers of IOPNs but also play a crucial role in the development of subclonal oncocytic neoplasms. Moreover, oncocytic morphology is strongly associated with upregulation of PRKACA/B, which may provide clues for potential therapeutic options.

Two-stage strategy using denoising autoencoders for robust reference-free genotype imputation with missing input genotypes.

Widely used genotype imputation methods are based on the Li and Stephens model, which assumes that new haplotypes can be represented by modifying existing haplotypes in a reference panel through mutations and recombinations. These methods use genotypes from SNP arrays as inputs to estimate haplotypes that align with the input genotypes by analyzing recombination patterns within a reference panel, and then infer unobserved variants. While these methods require reference panels in an identifiable form, their public use is limited due to privacy and consent concerns. One strategy to overcome these limitations is to use de-identified haplotype information, such as summary statistics or model parameters. Advances in deep learning (DL) offer the potential to develop imputation methods that use haplotype information in a reference-free manner by handling it as model parameters, while maintaining comparable imputation accuracy to methods based on the Li and Stephens model. Here, we provide a brief introduction to DL-based reference-free genotype imputation methods, including RNN-IMP, developed by our research group. We then evaluate the performance of RNN-IMP against widely-used Li and Stephens model-based imputation methods in terms of accuracy (R2), using the 1000 Genomes Project Phase 3 dataset and corresponding simulated Omni2.5 SNP genotype data. Although RNN-IMP is sensitive to missing values in input genotypes, we propose a two-stage imputation strategy: missing genotypes are first imputed using denoising autoencoders; RNN-IMP then processes these imputed genotypes. This approach restores the imputation accuracy that is degraded by missing values, enhancing the practical use of RNN-IMP.

jMorp updates in 2020: large enhancement of multi-omics data resources on the general Japanese population.

In the Tohoku Medical Megabank project, genome and omics analyses of participants in two cohort studies were performed. A part of the data is available at the Japanese Multi Omics Reference Panel (jMorp; https://jmorp.megabank.tohoku.ac.jp) as a web-based database, as reported in our previous manuscript published in Nucleic Acid Research in 2018. At that time, jMorp mainly consisted of metabolome data; however, now genome, methylome, and transcriptome data have been integrated in addition to the enhancement of the number of samples for the metabolome data. For genomic data, jMorp provides a Japanese reference sequence obtained using de novo assembly of sequences from three Japanese individuals and allele frequencies obtained using whole-genome sequencing of 8,380 Japanese individuals. In addition, the omics data include methylome and transcriptome data from ∼300 samples and distribution of concentrations of more than 755 metabolites obtained using high-throughput nuclear magnetic resonance and high-sensitivity mass spectrometry. In summary, jMorp now provides four different kinds of omics data (genome, methylome, transcriptome, and metabolome), with a user-friendly web interface. This will be a useful scientific data resource on the general population for the discovery of disease biomarkers and personalized disease prevention and early diagnosis.

X chromosome aneuploidies and schizophrenia: association analysis and phenotypic characterization.

AIM: The aims of the present study were: (i) to examine the association between schizophrenia (SCZ) and 47, XXY or 47, XXX in a large case-control sample; and (ii) to characterize the clinical features of patients with SCZ with these X chromosome aneuploidies. METHODS: To identify 47, XXY and 47, XXX, array comparative genomic hybridization (aCGH) was performed in 3188 patients with SCZ and 3586 controls. We examined the association between 47, XXY and 47, XXX and SCZ in males and females separately using exact conditional tests to control for platform effects. Clinical data were retrospectively examined for patients with SCZ with X chromosome aneuploidies. RESULTS: Of the analyzed samples, 3117 patients (97.8%) and 3519 controls (98.1%) passed our quality control. X chromosome aneuploidies were exclusively identified in patients: 47, XXY in seven patients (0.56%), 47, XXX in six patients (0.42%). Statistical analysis revealed a significant association between SCZ and 47, XXY (P = 0.028) and 47, XXX (P = 0.011). Phenotypic data were available from 12 patients. Treatment-resistance to antipsychotics and manic symptoms were observed in six patients each (four with 47, XXY and two with 47, XXX for both), respectively. Statistical analysis revealed that treatment-resistance to antipsychotics, mood stabilizer use, and manic symptoms were significantly more common in patients with 47, XXY than in male patients without pathogenic copy number variations. CONCLUSION: These findings indicate that both 47, XXY and 47, XXX are significantly associated with risk for SCZ. Patients with SCZ with 47, XXY may be characterized by treatment-resistance and manic symptoms.

The association between ERK inhibitor sensitivity and molecular characteristics in colorectal cancer.

The mitogen-activated protein kinase (MAPK) pathway plays an important role in the colorectal cancer (CRC) progression, being supposed to be activated by the gene mutations, such as BRAF or KRAS. Although the inhibitors of extracellular signal-regulated kinase (ERK) have demonstrated efficacy in the cells with the BRAF or KRAS mutations, a clinical response is not always associated with the molecular signature. The patient-derived organoids (PDO) have emerged as a powerful in vitro model system to study cancer, and it has been widely applied for the drug screening. The present study aims to analyze the association between the molecular characteristics which analyzed by next-generation sequencing (NGS) and sensitivity to the ERK inhibitor (i.e., SCH772984) in PDO derived from CRC specimens. A drug sensitivity test for the SCH772984 was conducted using 14 CRC cell lines, and the results demonstrated that the sensitivity was in agreement with the BRAF mutation, but was not completely consistent with the KRAS status. In the drug sensitivity test for PDO, 6 out of 7 cases with either BRAF or KRAS mutations showed sensitivity to the SCH772984, while 5 out of 6 cases of both BRAF and KRAS wild-types were resistant. The results of this study suggested that the molecular status of the clinical specimens are likely to represent the sensitivity in the PDOs but is not necessarily absolutely overlapping. PDO might be able to complement the limitations of the gene panel and have the potential to provide a novel precision medicine.

A 12-kb structural variation in progressive myoclonic epilepsy was newly identified by long-read whole-genome sequencing.

We report a family with progressive myoclonic epilepsy who underwent whole-exome sequencing but was negative for pathogenic variants. Similar clinical courses of a devastating neurodegenerative phenotype of two affected siblings were highly suggestive of a genetic etiology, which indicates that the survey of genetic variation by whole-exome sequencing was not comprehensive. To investigate the presence of a variant that remained unrecognized by standard genetic testing, PacBio long-read sequencing was performed. Structural variant (SV) detection using low-coverage (6×) whole-genome sequencing called 17,165 SVs (7,216 deletions and 9,949 insertions). Our SV selection narrowed down potential candidates to only five SVs (two deletions and three insertions) on the genes tagged with autosomal recessive phenotypes. Among them, a 12.4-kb deletion involving the CLN6 gene was the top candidate because its homozygous abnormalities cause neuronal ceroid lipofuscinosis. This deletion included the initiation codon and was found in a GC-rich region containing multiple repetitive elements. These results indicate the presence of a causal variant in a difficult-to-sequence region and suggest that such variants that remain enigmatic after the application of current whole-exome sequencing technology could be uncovered by unbiased application of long-read whole-genome sequencing.

Matataki: an ultrafast mRNA quantification method for large-scale reanalysis of RNA-Seq data.

BACKGROUND: Data generated by RNA sequencing (RNA-Seq) is now accumulating in vast amounts in public repositories, especially for human and mouse genomes. Reanalyzing these data has emerged as a promising approach to identify gene modules or pathways. Although meta-analyses of gene expression data are frequently performed using microarray data, meta-analyses using RNA-Seq data are still rare. This lag is partly due to the limitations in reanalyzing RNA-Seq data, which requires extensive computational resources. Moreover, it is nearly impossible to calculate the gene expression levels of all samples in a public repository using currently available methods. Here, we propose a novel method, Matataki, for rapidly estimating gene expression levels from RNA-Seq data. RESULTS: The proposed method uses k-mers that are unique to each gene for the mapping of fragments to genes. Since aligning fragments to reference sequences requires high computational costs, our method could reduce the calculation cost by focusing on k-mers that are unique to each gene and by skipping uninformative regions. Indeed, Matataki outperformed conventional methods with regards to speed while demonstrating sufficient accuracy. CONCLUSIONS: The development of Matataki can overcome current limitations in reanalyzing RNA-Seq data toward improving the potential for discovering genes and pathways associated with disease at reduced computational cost. Thus, the main bottleneck of RNA-Seq analyses has shifted to achieving the decompression of sequenced data. The implementation of Matataki is available at https://github.com/informationsea/Matataki .

Inflammatory responses induce an identity crisis of alveolar macrophages, leading to pulmonary alveolar proteinosis.

Pulmonary alveolar proteinosis (PAP) is a severe respiratory disease characterized by dyspnea caused by accumulation of surfactant protein. Dysfunction of alveolar macrophages (AMs), which regulate the homeostasis of surfactant protein, leads to the development of PAP; for example, in mice lacking BTB and CNC homology 2 (Bach2). However, how Bach2 helps prevent PAP is unknown, and the cell-specific effects of Bach2 are undefined. Using mice lacking Bach2 in specific cell types, we found that the PAP phenotype of Bach2-deficient mice is due to Bach2 deficiency in more than two types of immune cells. Depletion of hyperactivated T cells in Bach2-deficient mice restored normal function of AMs and ameliorated PAP. We also found that, in Bach2-deficient mice, hyperactivated T cells induced gene expression patterns that are specific to other tissue-resident macrophages and dendritic cells. Moreover, Bach2-deficient AMs exhibited a reduction in cell cycle progression. IFN-γ released from T cells induced Bach2 expression in AMs, in which Bach2 then bound to regulatory regions of inflammation-associated genes in myeloid cells. Of note, in AMs, Bach2 restricted aberrant responses to excessive T cell-induced inflammation, whereas, in T cells, Bach2 puts a brake on T cell activation. Moreover, Bach2 stimulated the expression of multiple histone genes in AMs, suggesting a role of Bach2 in proper histone expression. We conclude that Bach2 is critical for the maintenance of AM identity and self-renewal in inflammatory environments. Treatments targeting T cells may offer new therapeutic strategies for managing secondary PAP.

Genomic testing for pancreatic cancer in clinical practice as real-world evidence.

BACKGROUND: Precision medicine guided by comprehensive genome sequencing represents a potential treatment strategy for pancreatic cancer. However, clinical sequencing for pancreatic cancer entails several practical difficulties. We have launched an in-house clinical sequencing system and started genomic testing for patients with cancer in clinical practice. We have analyzed the clinical utility of this system in pancreatic cancer. METHODS: We retrospectively reviewed 20 patients with pancreatic cancer who visited our division. Genomic DNA was extracted from both tumor tissue and peripheral blood mononuclear cells obtained from the patients. We performed a comprehensive genomic testing using targeted amplicon sequencing for 160 cancer-related genes. The primary endpoints were the detection rates of potential actionable and druggable gene alterations. The secondary endpoints were the detection rate of secondary germline findings, the rate of re-biopsy required for genome sequencing, survival time after the initial visit (post-sequencing survival time), and turnaround time. RESULTS: Although re-biopsy was required for 25% (5/20) of all patients, genomic testing was performed in all patients. Actionable and druggable gene alterations were detected in 100% (20/20) and 35% (7/20) of patients, respectively, whereas secondary germline findings were detected in 5% (1/20) of patients. The median turnaround times for physicians and patients were 20 and 26 days, respectively. The median post-sequencing survival time was 10.3 months. Only 10% (2/20) of all patients were treated with therapeutic agents based on the outcomes of genomic testing. CONCLUSIONS: The clinical application of comprehensive genomic testing for pancreatic cancer was feasible and promising in clinical practice.

COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems.

The COXPRESdb (http://coxpresdb.jp) provides gene coexpression relationships for animal species. Here, we report the updates of the database, mainly focusing on the following two points. For the first point, we added RNAseq-based gene coexpression data for three species (human, mouse and fly), and largely increased the number of microarray experiments to nine species. The increase of the number of expression data with multiple platforms could enhance the reliability of coexpression data. For the second point, we refined the data assessment procedures, for each coexpressed gene list and for the total performance of a platform. The assessment of coexpressed gene list now uses more reasonable P-values derived from platform-specific null distribution. These developments greatly reduced pseudo-predictions for directly associated genes, thus expanding the reliability of coexpression data to design new experiments and to discuss experimental results.

ATTED-II in 2016: A Plant Coexpression Database Towards Lineage-Specific Coexpression.

ATTED-II (http://atted.jp) is a coexpression database for plant species with parallel views of multiple coexpression data sets and network analysis tools. The user can efficiently find functional gene relationships and design experiments to identify gene functions by reverse genetics and general molecular biology techniques. Here, we report updates to ATTED-II (version 8.0), including new and updated coexpression data and analysis tools. ATTED-II now includes eight microarray- and six RNA sequencing-based coexpression data sets for seven dicot species (Arabidopsis, field mustard, soybean, barrel medick, poplar, tomato and grape) and two monocot species (rice and maize). Stand-alone coexpression analyses tend to have low reliability. Therefore, examining evolutionarily conserved coexpression is a more effective approach from the viewpoints of reliability and evolutionary importance. In contrast, the reliability of species-specific coexpression data remains poor. Our assessment scores for individual coexpression data sets indicated that the quality of the new coexpression data sets in ATTED-II is higher than for any previous coexpression data set. In addition, five species (Arabidopsis, soybean, tomato, rice and maize) in ATTED-II are now supported by both microarray- and RNA sequencing-based coexpression data, which has increased the reliability. Consequently, ATTED-II can now provide lineage-specific coexpression information. As an example of the use of ATTED-II to explore lineage-specific coexpression, we demonstrate monocot- and dicot-specific coexpression of cell wall genes. With the expanded coexpression data for multilevel evaluation, ATTED-II provides new opportunities to investigate lineage-specific evolution in plants.

ALCOdb: Gene Coexpression Database for Microalgae.

In the era of energy and food shortage, microalgae have gained much attention as promising sources of biofuels and food ingredients. However, only a small fraction of microalgal genes have been functionally characterized. Here, we have developed the Algae Gene Coexpression database (ALCOdb; http://alcodb.jp), which provides gene coexpression information to survey gene modules for a function of interest. ALCOdb currently supports two model algae: the green alga Chlamydomonas reinhardtii and the red alga Cyanidioschyzon merolae. Users can retrieve coexpression information for genes of interest through three unique data pages: (i) Coexpressed Gene List; (ii) Gene Information; and (iii) Coexpressed Gene Network. In addition to the basal coexpression information, ALCOdb also provides several advanced functionalities such as an expression profile viewer and a differentially expressed gene search tool. Using these user interfaces, we demonstrated that our gene coexpression data have the potential to detect functionally related genes and are useful in extrapolating the biological roles of uncharacterized genes. ALCOdb will facilitate molecular and biochemical studies of microalgal biological phenomena, such as lipid metabolism and organelle development, and promote the evolutionary understanding of plant cellular systems.

COXPRESdb: a database of comparative gene coexpression networks of eleven species for mammals.

Coexpressed gene databases are valuable resources for identifying new gene functions or functional modules in metabolic pathways and signaling pathways. Although coexpressed gene databases are a fundamental platform in the field of plant biology, their use in animal studies is relatively limited. The COXPRESdb (http://coxpresdb.jp) provides coexpression relationships for multiple animal species, as comparisons of coexpressed gene lists can enhance the reliability of gene coexpression determinations. Here, we report the updates of the database, mainly focusing on the following two points. First, we updated our coexpression data by including recent microarray data for the previous seven species (human, mouse, rat, chicken, fly, zebrafish and nematode) and adding four new species (monkey, dog, budding yeast and fission yeast), along with a new human microarray platform. A reliability scoring function was also implemented, based on coexpression conservation to filter out coexpression with low reliability. Second, the network drawing function was updated, to implement automatic cluster analyses with enrichment analyses in Gene Ontology and in cis elements, along with interactive network analyses with Cytoscape Web. With these updates, COXPRESdb will become a more powerful tool for analyses of functional and regulatory networks of genes in a variety of animal species.

ATTED-II in 2014: evaluation of gene coexpression in agriculturally important plants.

ATTED-II (http://atted.jp) is a database of coexpressed genes that was originally developed to identify functionally related genes in Arabidopsis and rice. Herein, we describe an updated version of ATTED-II, which expands this resource to include additional agriculturally important plants. To improve the quality of the coexpression data for Arabidopsis and rice, we included more gene expression data from microarray and RNA sequencing studies. The RNA sequencing-based coexpression data now cover 94% of the Arabidopsis protein-encoding genes, representing a substantial increase from previously available microarray-based coexpression data (76% coverage). We also generated coexpression data for four dicots (soybean, poplar, grape and alfalfa) and one monocot (maize). As both the quantity and quality of expression data for the non-model species are generally poorer than for the model species, we verified coexpression data associated with these new species using multiple methods. First, the overall performance of the coexpression data was evaluated using gene ontology annotations and the coincidence of a genomic feature. Secondly, the reliability of each guide gene was determined by comparing coexpressed gene lists between platforms. With the expanded and newly evaluated coexpression data, ATTED-II represents an important resource for identifying functionally related genes in agriculturally important plants.

Morphometric analysis of nuclear shape irregularity as a novel predictor of programmed death-ligand 1 expression in lung squamous cell carcinoma.

Immune checkpoint inhibitor (ICI) therapy has been established as one of the key treatment strategies for lung squamous cell carcinoma (LUSQ). The status of programmed death-ligand 1 (PD-L1) in tumor cells and/or immune cells using immunohistochemistry has been primarily used as a surrogate marker for determining ICI treatment; however, when the tissues to be examined are small, false-negative results could be unavoidable due to the heterogeneity of PD-L1 immunoreactivity. To overcome this practical limitation, we attempted to explore the status of nuclear atypia evaluated using morphometry as a potential predictor of PD-L1 status in LUSQ. We correlated the parameters related to nuclear atypia with PD-L1 status using two different cohorts of LUSQ patients (95 cases from The Cancer Genome Atlas database and 30 cases from the Miyagi Cancer Center). Furthermore, we studied the gene mutation status to elucidate the genetic profile of PD-L1 predictable cases. The results revealed that nuclear atypia, especially morphometric parameters related to nuclear shape irregularity, including aspect ratio, circularity, roundness, and solidity, were all significantly associated with PD-L1 status. Additionally, LUSQ cases with high PD-L1 expression and pronounced nuclear atypia were significantly associated with C10orf71 and COL14A1 mutations compared with those with low PD-L1 expression and mild nuclear atypia. We demonstrated for the first time that nuclear shape irregularity could represent a novel predictor of PD-L1 expression in LUSQ. Including the morphometric parameters related to nuclear atypia in conjunction with PD-L1 status could help determine an effective ICI therapeutic strategy; however, further investigation is required.

Salivary gland cancer organoids are valid for preclinical genotype-oriented medical precision trials.

Salivary gland cancers (SGCs) are heterogeneous tumors, and precision oncology represents a promising therapeutic approach; however, its impact on SGCs remains obscure. This study aimed to establish a translational model for testing molecular-targeted therapies by combining patient-derived organoids and genomic analyses of SGCs. We enrolled 29 patients, including 24 with SGCs and 5 with benign tumors. Resected tumors were subjected to organoid and monolayer cultures, as well as whole-exome sequencing. Organoid and monolayer cultures of SGCs were successfully established in 70.8% and 62.5% of cases, respectively. Organoids retained most histopathological and genetic profiles of their original tumors. In contrast, 40% of the monolayer-cultured cells did not harbor somatic mutations of their original tumors. The efficacy of molecular-targeted drugs tested on organoids depended on their oncogenic features. Organoids recapitulated the primary tumors and were useful for testing genotype-oriented molecular targeted therapy, which is valuable for precision medicine in patients with SGCs.

Construction of a trio-based structural variation panel utilizing activated T lymphocytes and long-read sequencing technology.

Long-read sequencing technology enable better characterization of structural variants (SVs). To adapt the technology to population-scale analyses, one critical issue is to obtain sufficient amount of high-molecular-weight genomic DNA. Here, we propose utilizing activated T lymphocytes, which can be established efficiently in a biobank to stably supply high-grade genomic DNA sufficiently. We conducted nanopore sequencing of 333 individuals constituting 111 trios with high-coverage long-read sequencing data (depth 22.2x, N50 of 25.8 kb) and identified 74,201 SVs. Our trio-based analysis revealed that more than 95% of the SVs were concordant with Mendelian inheritance. We also identified SVs associated with clinical phenotypes, all of which appear to be stably transmitted from parents to offspring. Our data provide a catalog of SVs in the general Japanese population, and the applied approach using the activated T-lymphocyte resource will contribute to biobank-based human genetic studies focusing on SVs at the population scale.

Plasma metabolic disturbances during pregnancy and postpartum in women with depression.

Examining plasma metabolic profiling during pregnancy and postpartum could help clinicians understand the risk factors for postpartum depression (PPD) development. This analysis targeted paired plasma metabolites in mid-late gestational and 1 month postpartum periods in women with (n = 209) or without (n = 222) PPD. Gas chromatogram-mass spectrometry was used to analyze plasma metabolites at these two time points. Among the 170 objected plasma metabolites, principal component analysis distinguished pregnancy and postpartum metabolites but failed to discriminate women with and without PPD. Compared to women without PPD, those with PPD exhibited 37 metabolites with disparate changes during pregnancy and the 1-month postpartum period and an enriched citrate cycle. Machine learning and multivariate statistical analysis identified two or three compounds that could be potential biomarkers for PPD prediction during pregnancy. Our findings suggest metabolic disturbances in women with depression and may help to elucidate metabolic processes associated with PPD development.