Stability and Equilibrium Structures of Unknown Ternary Metal Oxides Explored by Machine-Learned Potentials.

Ternary metal oxides are crucial components in a wide range of applications and have been extensively cataloged in experimental materials databases. However, there still exist cation combinations with unknown stability and structures of their compounds in oxide forms. In this study, we employ extensive crystal structure prediction methods, accelerated by machine-learned potentials, to investigate these untapped chemical spaces. We examine 181 ternary metal oxide systems, encompassing most cations except for partially filled 3d or f shells, and determine their lowest-energy crystal structures with representative stoichiometry derived from prevalent oxidation states or recommender systems. Consequently, we discover 45 ternary oxide systems containing stable compounds against decomposition into binary or elemental phases, the majority of which incorporate noble metals. Comparisons with other theoretical databases highlight the strengths and limitations of informatics-based material searches. With a relatively modest computational resource requirement, we contend that heuristic-based structure searches, as demonstrated in this study, offer a promising approach for future materials discovery endeavors.

Transcriptome-metabolome-wide association study (TMWAS) in rats revealed a potential carcinogenic effect of DEHP in thyroid associated with eicosanoids.

The incidence of thyroid cancer (TC) has increased considerably in the last few decades. Environmental factors, including plasticizers, are recognized as potential risks leading to thyroid cancer in humans. In this study, we used a transcriptome-metabolome-wide association study to find the unidentified carcinogenic mechanism of di-2-ethylhexyl phthalate (DEHP) in thyroid and biomarkers for non-invasive diagnosis. Rats were treated with different doses of DEHP (0, 0.3, 3, 30, 150 mg DEHP/kg bw/day) for 13 weeks. Then, the thyroids were processed for Ki67 staining and RNA-seq. Also, 17-h urine samples were collected for high-resolution metabolomics analysis. After a high dose of DEHP exposure, the terminal body weights and the thyroid and parathyroid glands weights were not altered. However, the liver weights and numbers of Ki67-positive cells were increased. Further, multivariate statistical analysis revealed that metabolic shifts were considerably altered above 30 mg DEHP/kg bw/day. In RNA-seq analysis, some cancer-related genes were altered, including 18 upregulated and 9 downregulated transcripts. These cancer transcripts and whole metabolome data were integrated to uncover thyroid cancer-related metabolic pathways, which revealed that cancer-related transcripts had a network structure linked to eicosanoids such as leukotriene D4 and prostaglandin. In brief, our study demonstrated that DEHP can induce thyroid hyperplasia through the eicosanoid-associated pathway, providing further insight into the mechanism of DEHP-associated thyroid cancer.

Whole genome sequencing of an ethnic Pathan (Pakhtun) from the north-west of Pakistan.

BACKGROUND: Pakistan covers a key geographic area in human history, being both part of the Indus River region that acted as one of the cradles of civilization and as a link between Western Eurasia and Eastern Asia. This region is inhabited by a number of distinct ethnic groups, the largest being the Punjabi, Pathan (Pakhtuns), Sindhi, and Baloch. RESULTS: We analyzed the first ethnic male Pathan genome by sequencing it to 29.7-fold coverage using the Illumina HiSeq2000 platform. A total of 3.8 million single nucleotide variations (SNVs) and 0.5 million small indels were identified by comparing with the human reference genome. Among the SNVs, 129,441 were novel, and 10,315 nonsynonymous SNVs were found in 5,344 genes. SNVs were annotated for health consequences and high risk diseases, as well as possible influences on drug efficacy. We confirmed that the Pathan genome presented here is representative of this ethnic group by comparing it to a panel of Central Asians from the HGDP-CEPH panels typed for ~650 k SNPs. The mtDNA (H2) and Y haplogroup (L1) of this individual were also typical of his geographic region of origin. Finally, we reconstruct the demographic history by PSMC, which highlights a recent increase in effective population size compatible with admixture between European and Asian lineages expected in this geographic region. CONCLUSIONS: We present a whole-genome sequence and analyses of an ethnic Pathan from the north-west province of Pakistan. It is a useful resource to understand genetic variation and human migration across the whole Asian continent.

Variation block-based genomics method for crop plants.

BACKGROUND: In contrast with wild species, cultivated crop genomes consist of reshuffled recombination blocks, which occurred by crossing and selection processes. Accordingly, recombination block-based genomics analysis can be an effective approach for the screening of target loci for agricultural traits. RESULTS: We propose the variation block method, which is a three-step process for recombination block detection and comparison. The first step is to detect variations by comparing the short-read DNA sequences of the cultivar to the reference genome of the target crop. Next, sequence blocks with variation patterns are examined and defined. The boundaries between the variation-containing sequence blocks are regarded as recombination sites. All the assumed recombination sites in the cultivar set are used to split the genomes, and the resulting sequence regions are termed variation blocks. Finally, the genomes are compared using the variation blocks. The variation block method identified recurring recombination blocks accurately and successfully represented block-level diversities in the publicly available genomes of 31 soybean and 23 rice accessions. The practicality of this approach was demonstrated by the identification of a putative locus determining soybean hilum color. CONCLUSIONS: We suggest that the variation block method is an efficient genomics method for the recombination block-level comparison of crop genomes. We expect that this method will facilitate the development of crop genomics by bringing genomics technologies to the field of crop breeding.

MetaBase--the wiki-database of biological databases.

Biology is generating more data than ever. As a result, there is an ever increasing number of publicly available databases that analyse, integrate and summarize the available data, providing an invaluable resource for the biological community. As this trend continues, there is a pressing need to organize, catalogue and rate these resources, so that the information they contain can be most effectively exploited. MetaBase (MB) (http://MetaDatabase.Org) is a community-curated database containing more than 2000 commonly used biological databases. Each entry is structured using templates and can carry various user comments and annotations. Entries can be searched, listed, browsed or queried. The database was created using the same MediaWiki technology that powers Wikipedia, allowing users to contribute on many different levels. The initial release of MB was derived from the content of the 2007 Nucleic Acids Research (NAR) Database Issue. Since then, approximately 100 databases have been manually collected from the literature, and users have added information for over 240 databases. MB is synchronized annually with the static Molecular Biology Database Collection provided by NAR. To date, there have been 19 significant contributors to the project; each one is listed as an author here to highlight the community aspect of the project.

Scalable Parallel Algorithm for Graph Neural Network Interatomic Potentials in Molecular Dynamics Simulations.

Message-passing graph neural network interatomic potentials (GNN-IPs), particularly those with equivariant representations such as NequIP, are attracting significant attention due to their data efficiency and high accuracy. However, parallelizing GNN-IPs poses challenges because multiple message-passing layers complicate data communication within the spatial decomposition method, which is preferred by many molecular dynamics (MD) packages. In this article, we propose an efficient parallelization scheme compatible with GNN-IPs and develop a package, SevenNet (Scalable EquiVariance-Enabled Neural NETwork), based on the NequIP architecture. For MD simulations, SevenNet interfaces with the LAMMPS package. Through benchmark tests on a 32-GPU cluster with examples of SiO2, SevenNet achieves over 80% parallel efficiency in weak-scaling scenarios and exhibits nearly ideal strong-scaling performance as long as GPUs are fully utilized. However, the strong-scaling performance significantly declines with suboptimal GPU utilization, particularly affecting parallel efficiency in cases involving lightweight models or simulations with small numbers of atoms. We also pretrain SevenNet with a vast data set from the Materials Project (dubbed "SevenNet-0") and assess its performance on generating amorphous Si3N4 containing more than 100,000 atoms. By developing scalable GNN-IPs, this work aims to bridge the gap between advanced machine-learning models and large-scale MD simulations, offering researchers a powerful tool to explore complex material systems with high accuracy and efficiency.

KoNA: Korean Nucleotide Archive as A New Data Repository for Nucleotide Sequence Data.

During the last decade, the generation and accumulation of petabase-scale high-throughput sequencing data have resulted in great challenges, including access to human data, as well as transfer, storage, and sharing of enormous amounts of data. To promote data-driven biological research, the Korean government announced that all biological data generated from government-funded research projects should be deposited at the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. Here, we introduce the Korean Nucleotide Archive (KoNA), a repository of nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next-generation sequencing data from national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure was adopted, which is similar to that of the International Nucleotide Sequence Database Collaboration. The standard operating procedure includes quality control processes for submitted data and metadata using an automated pipeline, followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed using a cloud computing service called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances the data experience, including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea but also provides datasets to researchers globally and contributes to advances in genomics. The KoNA is available at https://www.kobic.re.kr/kona/.

Microarray analysis of Drosophila dicer-2 mutants reveals potential regulation of mitochondrial metabolism by endogenous siRNAs.

RNA interference is a eukaryotic regulatory mechanism by which small non-coding RNAs typically mediate specific silencing of their cognate genes. In Drosophila, the RNase III enzyme Dicer-2 (Dcr-2) is essential for biogenesis of endogenous small interfering RNAs (endo-siRNAs), which have been implicated in regulation of endogenous protein-coding genes. Although much is known about microRNA-based regulatory networks, the biological functions of endo-siRNAs in animals remain poorly understood. We performed gene expression profiling on Drosophila dcr-2 null mutant pupae to investigate transcriptional effects caused by a severe defect in endo-siRNA production, and found 306 up-regulated and 357 down-regulated genes with at least a twofold change in expression compared with the wild type. Most of these up-regulated and down-regulated genes were associated with energy metabolism and development, respectively. Importantly, mRNA sequences of 39% of the up-regulated genes were perfectly complementary to the sequences of previously reported endo-siRNAs, suggesting they may be direct targets of endo-siRNAs. We confirmed up-regulation of five selected genes matching endo-siRNAs and concomitant down-regulation of the corresponding endo-siRNAs in dcr-2 mutant pupae. Most of the potential endo-siRNA target genes were associated with energy metabolism, including the citric acid cycle and oxidative phosphorylation in mitochondria, implying that these are major metabolic processes directly affected by endo-siRNAs in Drosophila. Consistent with this finding, dcr-2 null mutant pupae had lower ATP content compared with controls, indicating that mitochondrial energy production is impaired in these mutants. Our data support a potential role for the endo-siRNA pathway in energy homeostasis through regulation of mitochondrial metabolism.

Introduction of the Korea BioData Station (K-BDS) for sharing biological data.

A wave of new technologies has created opportunities for the cost-effective generation of high-throughput profiles of biological systems, foreshadowing a "data-driven science" era. The large variety of data available from biological research is also a rich resource that can be used for innovative endeavors. However, we are facing considerable challenges in big data deposition, integration, and translation due to the complexity of biological data and its production at unprecedented exponential rates. To address these problems, in 2020, the Korean government officially announced a national strategy to collect and manage the biological data produced through national R&D fund allocations and provide the collected data to researchers. To this end, the Korea Bioinformation Center (KOBIC) developed a new biological data repository, the Korea BioData Station (K-BDS), for sharing data from individual researchers and research programs to create a data-driven biological study environment. The K-BDS is dedicated to providing free open access to a suite of featured data resources in support of worldwide activities in both academia and industry.

Comparison and evaluation of pathway-level aggregation methods of gene expression data.

BACKGROUND: Microarray experiments produce expression measurements in genomic scale. A way to derive functional understanding of the data is to focus on functional sets of genes, such as pathways, instead of individual genes. While a common practice for the pathway-level analysis has been functional enrichment analysis such as over-representation analysis and gene set enrichment analysis, an alternative approach has also been explored. In this approach, gene expression data are first aggregated at pathway level to transform the original data into a compact representation in which each row corresponds to a pathway instead of a gene. Thereafter the pathway expression data can be used for differential expression and classification analyses in pathway space, leveraging existing algorithms usually applied to gene expression data. While several studies have proposed the pathway-level aggregation methods, it remains unclear how they compare with one another, since the evaluations were done to a limited extent. Thus this study presents a comprehensive evaluation of six most prominent aggregation methods. RESULTS: The compared methods include five existing methods--mean of all member genes (Mean all), mean of condition-responsive genes (Mean CORGs), analysis of sample set enrichment scores (ASSESS), principal component analysis (PCA), and partial least squares (PLS)--and a variant of an existing method (Mean top 50%, averaging top half of member genes). Comprehensive and stringent benchmarking was performed by collecting seven pairs of related but independent datasets encompassing various phenotypes. Aggregation was done in the space of KEGG pathways. Performance of the methods was assessed by classification accuracy validated both internally and externally, and by examining the correlative extent of pathway signatures between the dataset pairs. The assessment revealed that (i) the best accuracy and correlation were obtained from ASSESS and Mean top 50%, (ii) Mean all showed the lowest accuracy, and (iii) Mean CORGs and PLS gave rise to the largest extent of discordance in the pathway signature correlation. CONCLUSIONS: The two best performing method (ASSESS and Mean top 50%) are suggested to be preferred. The benchmarking analysis also suggests that there is both room and necessity for developing a novel method for pathway-level aggregation.

Whole transcriptome analyses of six thoroughbred horses before and after exercise using RNA-Seq.

BACKGROUND: Thoroughbred horses are the most expensive domestic animals, and their running ability and knowledge about their muscle-related diseases are important in animal genetics. While the horse reference genome is available, there has been no large-scale functional annotation of the genome using expressed genes derived from transcriptomes. RESULTS: We present a large-scale analysis of whole transcriptome data. We sequenced the whole mRNA from the blood and muscle tissues of six thoroughbred horses before and after exercise. By comparing current genome annotations, we identified 32,361 unigene clusters spanning 51.83 Mb that contained 11,933 (36.87%) annotated genes. More than 60% (20,428) of the unigene clusters did not match any current equine gene model. We also identified 189,973 single nucleotide variations (SNVs) from the sequences aligned against the horse reference genome. Most SNVs (171,558 SNVs; 90.31%) were novel when compared with over 1.1 million equine SNPs from two SNP databases. Using differential expression analysis, we further identified a number of exercise-regulated genes: 62 up-regulated and 80 down-regulated genes in the blood, and 878 up-regulated and 285 down-regulated genes in the muscle. Six of 28 previously-known exercise-related genes were over-expressed in the muscle after exercise. Among the differentially expressed genes, there were 91 transcription factor-encoding genes, which included 56 functionally unknown transcription factor candidates that are probably associated with an early regulatory exercise mechanism. In addition, we found interesting RNA expression patterns where different alternative splicing forms of the same gene showed reversed expressions before and after exercising. CONCLUSION: The first sequencing-based horse transcriptome data, extensive analyses results, deferentially expressed genes before and after exercise, and candidate genes that are related to the exercise are provided in this study.

Genome-wide identification of palmitate-regulated immediate early genes and target genes in pancreatic beta-cells reveals a central role of NF-κB.

Free fatty acid-induced pancreatic ß-cell dysfunction plays a key role in the pathogenesis of type 2 diabetes. We conducted gene expression microarray analysis to comprehensively investigate the transcription machinery of palmitate-regulated genes in pancreatic ß-cells in vitro. In particular, mouse pancreatic ßTC3 cells were treated with palmitate in the presence or absence of cycloheximide (CHX), which blocks protein synthesis and thereby allows us to distinguish immediate early genes (IEGs) from their target genes. The microarray experiments identified 34 palmitate-regulated IEGs and 74 palmitate-regulated target genes. In silico promoter analysis revealed that transcription factor binding sites for NF-κB were over-represented, regulating approximately one-third of the palmitate-regulated target genes. In cells treated with CHX, nfkb1 showed an up-regulation by palmitate, suggesting that NF-κB could be an IEG. Functional enrichment analysis of 27 palmitate-regulated genes with NF-κB binding sites showed an over-representation of genes involved in immune response, inflammatory response, defense response, taxis, regulation of cell proliferation, and regulation of cell death pathways. Electrophoretic mobility shift assay showed that palmitate stimulates NF-κB activity both in the presence and absence of CHX. In conclusion, by identifying IEGs and target genes, the present study depicted a comprehensive view of transcription machinery underlying palmitate-induced inflammation and cell proliferation/death in pancreatic ß-cells and our data demonstrated the central role of NF-κB.

Liverome: a curated database of liver cancer-related gene signatures with self-contained context information.

BACKGROUND: Hepatocellular carcinoma (HCC) is the fifth most common cancer worldwide. A number of molecular profiling studies have investigated the changes in gene and protein expression that are associated with various clinicopathological characteristics of HCC and generated a wealth of scattered information, usually in the form of gene signature tables. A database of the published HCC gene signatures would be useful to liver cancer researchers seeking to retrieve existing differential expression information on a candidate gene and to make comparisons between signatures for prioritization of common genes. A challenge in constructing such database is that a direct import of the signatures as appeared in articles would lead to a loss or ambiguity of their context information that is essential for a correct biological interpretation of a gene's expression change. This challenge arises because designation of compared sample groups is most often abbreviated, ad hoc, or even missing from published signature tables. Without manual curation, the context information becomes lost, leading to uninformative database contents. Although several databases of gene signatures are available, none of them contains informative form of signatures nor shows comprehensive coverage on liver cancer. Thus we constructed Liverome, a curated database of liver cancer-related gene signatures with self-contained context information. DESCRIPTION: Liverome's data coverage is more than three times larger than any other signature database, consisting of 143 signatures taken from 98 HCC studies, mostly microarray and proteome, and involving 6,927 genes. The signatures were post-processed into an informative and uniform representation and annotated with an itemized summary so that all context information is unambiguously self-contained within the database. The signatures were further informatively named and meaningfully organized according to ten functional categories for guided browsing. Its web interface enables a straightforward retrieval of known differential expression information on a query gene and a comparison of signatures to prioritize common genes. The utility of Liverome-collected data is shown by case studies in which useful biological insights on HCC are produced. CONCLUSION: Liverome database provides a comprehensive collection of well-curated HCC gene signatures and straightforward interfaces for gene search and signature comparison as well. Liverome is available at http://liverome.kobic.re.kr.

Novel genes exhibiting DNA methylation alterations in Korean patients with chronic lymphocytic leukaemia: a methyl-CpG-binding domain sequencing study.

Chronic lymphocytic leukaemia (CLL) exhibits differences between Asians and Caucasians in terms of incidence rate, age at onset, immunophenotype, and genetic profile. We performed genome-wide methylation profiling of CLL in an Asian cohort for the first time. Eight Korean patients without somatic immunoglobulin heavy chain gene hypermutations underwent methyl-CpG-binding domain sequencing (MBD-seq), as did five control subjects. Gene Ontology, pathway analysis, and network-based prioritization of differentially methylated genes were also performed. More regions were hypomethylated (2,062 windows) than were hypermethylated (777 windows). Promoters contained the highest proportion of differentially methylated regions (0.08%), while distal intergenic and intron regions contained the largest number of differentially methylated regions. Protein-coding genes were the most abundant, followed by long noncoding and short noncoding genes. The most significantly over-represented signalling pathways in the differentially methylated gene list included immune/cancer-related pathways and B-cell receptor signalling. Among the top 10 hub genes identified via network-based prioritization, four (UBC, GRB2, CREBBP, and GAB2) had no known relevance to CLL, while the other six (STAT3, PTPN6, SYK, STAT5B, XPO1, and ABL1) have previously been linked to CLL in Caucasians. As such, our analysis identified four novel candidate genes of potential significance to Asian patients with CLL.

IL-10 Protects Mice From the Lung Infection of Acinetobacter baumannii and Contributes to Bacterial Clearance by Regulating STAT3-Mediated MARCO Expression in Macrophages.

Interleukin-10 plays important, yet contrasting, roles in host protection against bacterial infections and in the septic response. To determine the role of IL-10 in the host defense against Acinetobacter baumannii infection, wild-type (WT) and IL-10-deficient mice were infected intranasally with the bacteria. IL-10-deficient mice exhibited increased mortality, severe pathology, and excess production of proinflammatory cytokines and chemokines in the lungs, and increased bacterial burdens in bronchoalveolar lavage (BAL) fluids and lung homogenates after A. baumannii infection, compared to WT mice. Intranasal administration of recombinant IL-10 rescued mice from the lethality of the bacterial infection by promoting bacterial clearance and reducing production of cytokines and chemokines in the lungs. In vitro experiments revealed that IL-10 enhanced phagocytosis and bacterial killing by macrophages by upregulating the macrophage receptor with collagenous structure (MARCO). In addition, A. baumannii-induced activation of STAT3 was impaired in IL-10-deficient macrophages, which was essential for expression of MARCO. Intranasal adoptive transfer of WT macrophages resulted in significant increases in mice survival and bacterial clearance in IL-10-deficient mice infected with A. baumannii. Our results show that IL-10 played an important role in the host defense against pulmonary infection of A. baumannii by promoting the antibacterial function of macrophages by regulating MARCO expression through the STAT3-mediated pathway.

Characterization of Distinct CyanoHABs-Related Modules in Microbial Recurrent Association Network.

To elucidate the interspecies connectivity between cyanobacteria and other bacteria (non-cyanobacteria) during cyanobacterial harmful algal blooms (cyanoHABs), samples were collected from the Nakdong River, Korea, from June 2016 to August 2017, and microbial recurrent association network (MRAN) analysis was performed to overcome the limitations of conventional network analysis. Microcystis blooms were tightly linked with Pseudanabaena in summer and were accompanied by significant changes in the non-cyanobacterial community composition (nCCC) compared to non-bloom period. Riverine bacterial communities could be clearly separated into modules that were involved in the formation, maintenance, and decomposition of cyanoHABs. Roseomonas and Herbaspirillum were directly linked with major cyanobacteria and assigned to connector and module hub in cyanoHABs-related modules, respectively. The functional profiles of the cyanoHABs-related modules suggested that nitrate reduction, aerobic ammonia oxidation, fermentation, and hydrocarbon degradation could be increased during the Microcystis bloom periods. In conclusion, MRAN analysis revealed that specific bacteria belonging to cyanoHABs-related module, including connectors and module hubs, appeared to contribute to the development and collapse of cyanoHABs. Therefore, to understand cyanoHABs, a modular microbial perspective may be more helpful than a single bacterial species perspective.

The water depth-dependent co-occurrence patterns of marine bacteria in shallow and dynamic Southern Coast, Korea.

To investigate the interactions between bacterial species in relation to the biotic and abiotic environmental fluctuations, free-living (FL), nanoparticle-associated (NP), and microparticle-associated (MP) bacterial community compositions (BCCs) were analyzed. A total of 267 samples were collected from July to December 2016 in the dynamic and shallow southern coastal water of Korea. The variations in BCC mostly depended on planktonic size fraction. Network analysis revealed water depth-dependent co-occurrence patterns of coastal bacterial communities. Higher interspecies connectivity was observed within FL bacteria than NP/MP bacteria, suggesting that FL bacteria with a streamlined genome may need other bacterial metabolites for survival, while the NP/MP copiotrophs may have the self-supporting capacity to produce the vital nutrients. The analysis of topological roles of individual OTUs in the network revealed that several groups of metabolically versatile bacteria (the marine Roseobacters, Flavobacteriales, Desulfobacterales, and SAR406 clade) acted as module hubs in different water depth. In conclusion, interspecies interactions dominated in FL bacteria, compared to NP and MP bacteria; modular structures of bacterial communities and keystone species strongly depended on the water depth-derived environmental factors. Furthermore, the multifunctional, versatile FL bacteria could play pivotal roles in dynamic shallow coastal ecosystems.

DP-Bind: a web server for sequence-based prediction of DNA-binding residues in DNA-binding proteins.

UNLABELLED: This article describes DP-Bind, a web server for predicting DNA-binding sites in a DNA-binding protein from its amino acid sequence. The web server implements three machine learning methods: support vector machine, kernel logistic regression and penalized logistic regression. Prediction can be performed using either the input sequence alone or an automatically generated profile of evolutionary conservation of the input sequence in the form of PSI-BLAST position-specific scoring matrix (PSSM). PSSM-based kernel logistic regression achieves the accuracy of 77.2%, sensitivity of 76.4% and specificity of 76.6%. The outputs of all three individual methods are combined into a consensus prediction to help identify positions predicted with high level of confidence. AVAILABILITY: Freely available at http://lcg.rit.albany.edu/dp-bind. SUPPLEMENTARY INFORMATION: http://lcg.rit.albany.edu/dp-bind/dpbind_supplement.html.

Long-Term Stability and Integrity of Plasmid-Based DNA Data Storage.

Validation of long-term DNA stability and integrity are essential for the use of DNA in data storage applications. Because of this, we evaluated the plasmid-based DNA data storage in a manner that preserves DNA stability and integrity. A document consisting of 2046 words was encoded with DNA sequences using Perl script, and the encoded DNA sequences were synthesized for information storage. The DNA comprised a total of 22 chemically synthesized DNA fragments with 400 nucleotides each, which were incorporated into a plasmid vector. A long-term DNA stability study demonstrated that 3-year stored plasmid containing text information showed DNA stability at controlled conditions of -20 °C. The plasmid DNA under accelerated aging conditions (AAC) up to 65 °C for 20 days, which corresponds to approximately 20 years of storage at -20 °C, also exhibited no significant differences in DNA stability compared to newly produced plasmid. Also, the 3-year old plasmid stored at -20 °C and the AAC-tested plasmid stored up to 65 °C for 20 days had functional integrity and nucleotide integrity comparable to control sample, thereby allowing for retrieval of the original error-free text data. Finally, the nucleotides were sequenced, and then decoded to retrieve the original data, thereby allowing us to read the text with 100% accuracy, and amplify the DNA with a simple and quick bacterial transformation. To the best of our knowledge, this is the first report on examining the long-term stability and integrity of plasmid-based DNA data storage. Taken together, our results indicate that plasmid DNA data storage can be useful for long-term archival storage to recover the source text in a reproducible and accountable manner.

Low Dose Exposure to Di-2-Ethylhexylphthalate in Juvenile Rats Alters the Expression of Genes Related with Thyroid Hormone Regulation.

Phthalates widely used in the manufacture of plastics have deeply penetrated into our everyday lives. Recently, a concern over the toxicity of phthalates on thyroid, has been raised but in most of cases, the doses employed were unrealistically high. To investigate the effects of phthalates on thyroid, we investigated the effects of the repeated oral exposure to low to high doses (0.3, 3, 30 and 150 mg/kg) di-2-ethylhexylphthalate (DEHP) from weaning to maturity for 90 days in juvenile rats on the thyroid. The histological examination revealed that DEHP significantly induced hyperplasia in the thyroid from the doses of 30 mg/kg, which was confirmed with Ki67 staining. In line with this finding, increased mRNA expression of thyrotropin releasing hormone (Trh) was observed in the thyroid of female at 0.3 mg/kg and 150 mg/kg as determined by RNAseq analysis. Moreover, significantly increased expression of parathyroid hormone (Pth) in the female at 0.3 mg/kg, and thyroglobulin (Tg) and thyroid hormone responsive (Thrsp) in the male at 0.3 mg/kg were noted in the blood, of which changes were substantially attenuated at 150 m/kg, alluding the meaningful effects of low dose DEHP on the thyroid hormone regulation. Urinary excretion of mono-2-ethylhexyl-phthalate (MEHP), a major metabolite of DEHP was determined to be 4.10 and 12.26 ppb in male, 6.65 and 324 ppb in female at 0.3 and 30 mg/kg DEHP, respectively, which fell within reported human urine levels. Collectively, these results suggest a potential adverse effects of low dose phthalates on the thyroid.