KEAP1-NRF2 system regulates age-related spermatogenesis dysfunction.

Purpose: The average fatherhood age has been consistently increasing in developed countries. Aging has been identified as a risk factor for male infertility. However, its impact on various mechanisms remains unclear. This study focused on the KEAP1-NRF2 oxidative stress response system, by investigating the relationship between the KEAP1-NRF2 system and age-related changes in spermatogenesis. Methods: For examination of age-related changes, we used 10-, 30-, 60-, and 90-week-old mice to compare sperm count, sperm motility, and protein expression. For assessment of Keap1 inhibition, 85-week-old C57BL/6J mice were randomly assigned to the following groups: control and bardoxolone methyl (KEAP1 inhibitor). Whole-exome sequencing of a Japanese cohort of patients with non-obstructive azoospermia was performed for evaluating. Results: Sperm count decreased significantly with aging. Oxidative stress and KEAP1 expression in the testes were elevated. Inhibition of KEAP1 in aging mice significantly increased sperm count compared with that in the control group. In the human study, the frequency of a missense-type SNP (rs181294188) causing changes in NFE2L2 (NRF2) activity was significantly higher in patients with non-obstructive azoospermia than in healthy control group. Conclusions: The KEAP1-NRF2 system, an oxidative stress response system, is associated with age-related spermatogenesis dysfunction.

Genetic analysis of low-grade adenosquamous carcinoma of the breast progressing to high-grade metaplastic carcinoma.

PURPOSE: Low-grade adenosquamous carcinoma (LGASC) is a rare type of metaplastic carcinoma of the breast (MBC) with an indolent clinical course. A few LGASC cases with high-grade transformation have been reported; however, the genetics underlying malignant progression of LGASC remain unclear. METHODS: We performed whole-genome sequencing analysis on five MBCs from four patients, including one case with matching primary LGASC and a lymph node metastatic tumor consisting of high-grade MBC with a predominant metaplastic squamous cell carcinoma component (MSC) that progressed from LGASC and three cases of independent de novo MSC. RESULTS: Unlike de novo MSC, LGASC and its associated MSC showed no TP53 mutation and tended to contain fewer structural variants than de novo MSC. Both LGASC and its associated MSC harbored the common GNAS c.C2530T:p.Arg844Cys mutation, which was more frequently detected in the cancer cell fraction of MSC. MSC associated with LGASC showed additional pathogenic deletions of multiple tumor-suppressor genes, such as KMT2D and BTG1. Copy number analysis revealed potential 18q loss of heterozygosity in both LGASC and associated MSC. The frequency of SMAD4::DCC fusion due to deletions increased with progression to MSC; however, chimeric proteins were not detected. SMAD4 protein expression was already decreased at the LGASC stage due to unknown mechanisms. CONCLUSION: Not only LGASC but also its associated high-grade MBC may be genetically different from de novo high-grade MBC. Progression from LGASC to high-grade MBC may involve the concentration of driver mutations caused by clonal selection and inactivation of tumor-suppressor genes.

Identification of bacteriophage genome sequences with representation learning.

MOTIVATION: Bacteriophages/phages are the viruses that infect and replicate within bacteria and archaea, and rich in human body. To investigate the relationship between phages and microbial communities, the identification of phages from metagenome sequences is the first step. Currently, there are two main methods for identifying phages: database-based (alignment-based) methods and alignment-free methods. Database-based methods typically use a large number of sequences as references; alignment-free methods usually learn the features of the sequences with machine learning and deep learning models. RESULTS: We propose INHERIT which uses a deep representation learning model to integrate both database-based and alignment-free methods, combining the strengths of both. Pre-training is used as an alternative way of acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compare INHERIT with four existing methods on a third-party benchmark dataset. Our experiments show that INHERIT achieves a better performance with the F1-score of 0.9932. In addition, we find that pre-training two species separately helps the non-alignment deep learning model make more accurate predictions. AVAILABILITY AND IMPLEMENTATION: The codes of INHERIT are now available in: https://github.com/Celestial-Bai/INHERIT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses.

BACKGROUND : There are several types of pancreatic mass, so it is important to distinguish between them before treatment. Artificial intelligence (AI) is a mathematical technique that automates learning and recognition of data patterns. This study aimed to investigate the efficacy of our AI model using endoscopic ultrasonography (EUS) images of multiple types of pancreatic mass (pancreatic ductal adenocarcinoma [PDAC], pancreatic adenosquamous carcinoma [PASC], acinar cell carcinoma [ACC], metastatic pancreatic tumor [MPT], neuroendocrine carcinoma [NEC], neuroendocrine tumor [NET], solid pseudopapillary neoplasm [SPN], chronic pancreatitis, and autoimmune pancreatitis [AIP]). METHODS : Patients who underwent EUS were included in this retrospective study. The included patients were divided into training, validation, and test cohorts. Using these cohorts, an AI model that can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions was developed using a deep-learning architecture and the diagnostic performance of the AI model was evaluated. RESULTS : 22 000 images were generated from 933 patients. The area under the curve, sensitivity, specificity, and accuracy (95 %CI) of the AI model for the diagnosis of pancreatic carcinomas in the test cohort were 0.90 (0.84-0.97), 0.94 (0.88-0.98), 0.82 (0.68-0.92), and 0.91 (0.85-0.95), respectively. The per-category sensitivities (95 %CI) of each disease were PDAC 0.96 (0.90-0.99), PASC 1.00 (0.05-1.00), ACC 1.00 (0.22-1.00), MPT 0.33 (0.01-0.91), NEC 1.00 (0.22-1.00), NET 0.93 (0.66-1.00), SPN 1.00 (0.22-1.00), chronic pancreatitis 0.78 (0.52-0.94), and AIP 0.73 (0.39-0.94). CONCLUSIONS : Our developed AI model can distinguish pancreatic carcinomas from noncarcinomatous pancreatic lesions, but external validation is needed.

MEK/ERK-mediated oncogenic signals promote secretion of extracellular vesicles by controlling lysosome function.

Cancer cells secrete large amounts of extracellular vesicles (EVs) originating from multivesicular bodies (MVBs). Mature MVBs fuse either with the plasma membrane for release as EVs, often referred as to exosomes or with lysosomes for degradation. However, the mechanisms regulating MVB fate remain unknown. Here, we investigated the regulators of MVB fate by analyzing the effects of signaling inhibitors on EV secretion from cancer cells engineered to secrete luciferase-labeled EVs. Inhibition of the oncogenic MEK/ERK pathway suppressed EV release and activated lysosome formation. MEK/ERK-mediated lysosomal inactivation impaired MVB degradation, resulting in increased EV secretion from cancer cells. Moreover, MEK/ERK inhibition prevented c-MYC expression and induced the nuclear translocation of MiT/TFE transcription factors, thereby promoting the activation of lysosome-related genes, including the gene encoding a subunit of vacuolar-type H+ -ATPase, which is responsible for lysosomal acidification and function. Furthermore, c-MYC upregulation was associated with lysosomal gene downregulation in MEK/ERK-activated renal cancer cells/tissues. These findings suggest that the MEK/ERK/c-MYC pathway controls MVB fate and promotes EV production in human cancers by inactivating lysosomal function.

Association between germline pathogenic variants and breast cancer risk in Japanese women: The HERPACC study.

Approximately 5%-10% of breast cancers are hereditary, caused by germline pathogenic variants (GPVs) in breast cancer predisposition genes. To date, most studies of the prevalence of GPVs and risk of breast cancer for each gene based on cases and noncancer controls have been conducted in Europe and the United States, and little information from Japanese populations is available. Furthermore, no studies considered confounding by established environmental factors and single-nucleotide polymorphisms (SNPs) identified in genome-wide association studies (GWAS) together in GPV evaluation. To evaluate the association between GPVs in nine established breast cancer predisposition genes including BRCA1/2 and breast cancer risk in Japanese women comprehensively, we conducted a case-control study within the Hospital-based Epidemiologic Research Program at Aichi Cancer Center (629 cases and 1153 controls). The associations between GPVs and the risk of breast cancer were assessed by odds ratios (OR) and 95% confidence intervals (CI) using logistic regression models adjusted for potential confounders. A total of 25 GPVs were detected among all cases (4.0%: 95% CI: 2.6-5.9), whereas four individuals carried GPVs in all controls (0.4%). The OR for breast cancer by all GPVs and by GPVs in BRCA1/2 was 12.2 (4.4-34.0, p = 1.74E-06) and 16.0 (4.2-60.9, p = 5.03E-0.5), respectively. A potential confounding with GPVs was observed for the GWAS-identified SNPs, whereas not for established environmental risk factors. In conclusion, GPVs increase the risk of breast cancer in Japanese women regardless of environmental factors and GWAS-identified SNPs. Future studies investigating interactions with environment and SNPs are warranted.

Perioperative circulating tumor DNA enables the identification of patients with poor prognosis in upper tract urothelial carcinoma.

Perioperative systemic chemotherapy improves the prognosis of upper tract urothelial carcinoma (UTUC). The first objective of this study was to verify whether perioperative circulating tumor DNA (ctDNA) analysis using a pan-cancer gene panel and next-generation sequencing could identify patients with poor prognosis who require perioperative chemotherapy. Second, we investigated whether ctDNA is useful for minimal residual disease (MRD) detection and treatment monitoring in UTUC. This study included 50 patients with untreated UTUC, including 43 cases of localized UTUC. We performed targeted ultradeep sequencing of plasma cell-free DNA (cfDNA) and buffy coat DNA and whole-exome sequencing of cancer tissues, allowing exclusion of possible false positives. We attempted to stratify the prognosis according to the perioperative ctDNA levels in patients with localized UTUC. In patients with metastatic UTUC, ctDNA was evaluated before, during, and after systemic treatment. In total, 23 (46%) of 50 patients with untreated UTUC were ctDNA positive, and 17 (40%) of 43 patients with localized UTUC were ctDNA positive. Of the detected TP53 mutations, 19% were false positives due to clonal hematopoiesis of indeterminate potential. Among preoperative risk factors, only the preoperative ctDNA fraction>2% was a significant and independent risk factor associated with worse recurrence-free survival (RFS). Furthermore, the existence of ctDNA early points after the operation was significantly associated with worse RFS, suggesting the presence of MRD. ctDNA also showed a potential as a real-time marker for systemic therapy in patients with metastatic UTUC. Detection of ctDNA may indicate potential metastasis and guide decisions on perioperative chemotherapy.

Functional Restoration of Bacteriomes and Viromes by Fecal Microbiota Transplantation.

BACKGROUND & AIMS: Fecal microbiota transplantation (FMT) is an effective therapy for recurrent Clostridioides difficile infection (rCDI). However, the overall mechanisms underlying FMT success await comprehensive elucidation, and the safety of FMT has recently become a serious concern because of the occurrence of drug-resistant bacteremia transmitted by FMT. We investigated whether functional restoration of the bacteriomes and viromes by FMT could be an indicator of successful FMT. METHODS: The human intestinal bacteriomes and viromes from 9 patients with rCDI who had undergone successful FMT and their donors were analyzed. Prophage-based and CRISPR spacer-based host bacteria-phage associations in samples from recipients before and after FMT and in donor samples were examined. The gene functions of intestinal microorganisms affected by FMT were evaluated. RESULTS: Metagenomic sequencing of both the viromes and bacteriomes revealed that FMT does change the characteristics of intestinal bacteriomes and viromes in recipients after FMT compared with those before FMT. In particular, many Proteobacteria, the fecal abundance of which was high before FMT, were eliminated, and the proportion of Microviridae increased in recipients. Most temperate phages also behaved in parallel with the host bacteria that were altered by FMT. Furthermore, the identification of bacterial and viral gene functions before and after FMT revealed that some distinctive pathways, including fluorobenzoate degradation and secondary bile acid biosynthesis, were significantly represented. CONCLUSIONS: The coordinated action of phages and their host bacteria restored the recipients' intestinal flora. These findings show that the restoration of intestinal microflora functions reflects the success of FMT.

Halcyon: an accurate basecaller exploiting an encoder-decoder model with monotonic attention.

MOTIVATION: In recent years, nanopore sequencing technology has enabled inexpensive long-read sequencing, which promises reads longer than a few thousand bases. Such long-read sequences contribute to the precise detection of structural variations and accurate haplotype phasing. However, deciphering precise DNA sequences from noisy and complicated nanopore raw signals remains a crucial demand for downstream analyses based on higher-quality nanopore sequencing, although various basecallers have been introduced to date. RESULTS: To address this need, we developed a novel basecaller, Halcyon, that incorporates neural-network techniques frequently used in the field of machine translation. Our model employs monotonic-attention mechanisms to learn semantic correspondences between nucleotides and signal levels without any pre-segmentation against input signals. We evaluated performance with a human whole-genome sequencing dataset and demonstrated that Halcyon outperformed existing third-party basecallers and achieved competitive performance against the latest Oxford Nanopore Technologies' basecallers. AVAILABILITYAND IMPLEMENTATION: The source code (halcyon) can be found at https://github.com/relastle/halcyon.

Enhancing breakpoint resolution with deep segmentation model: A general refinement method for read-depth based structural variant callers.

Read-depths (RDs) are frequently used in identifying structural variants (SVs) from sequencing data. For existing RD-based SV callers, it is difficult for them to determine breakpoints in single-nucleotide resolution due to the noisiness of RD data and the bin-based calculation. In this paper, we propose to use the deep segmentation model UNet to learn base-wise RD patterns surrounding breakpoints of known SVs. We integrate model predictions with an RD-based SV caller to enhance breakpoints in single-nucleotide resolution. We show that UNet can be trained with a small amount of data and can be applied both in-sample and cross-sample. An enhancement pipeline named RDBKE significantly increases the number of SVs with more precise breakpoints on simulated and real data. The source code of RDBKE is freely available at https://github.com/yaozhong/deepIntraSV.

Early dynamics of circulating tumor DNA predict clinical response to immune checkpoint inhibitors in metastatic renal cell carcinoma.

OBJECTIVES: Detection of genomic alterations in circulating tumor deoxyribonucleic acid of peripheral blood can guide the selection of systemic therapy in cancer patients. The predictive significance of circulating tumor deoxyribonucleic acid in metastatic renal cell carcinoma remains unclear, especially for patients treated with immune checkpoint inhibitors. METHODS: In this study, we collected plasma samples before and 1 month after commencing nivolumab monotherapy or nivolumab plus ipilimumab therapy from 14 metastatic renal cell carcinoma patients. We performed circulating tumor deoxyribonucleic acid genomic profiling in plasma cell-free deoxyribonucleic acid by next-generation sequencing using a commercially available pan-cancer panel (Guardant360 CDx). Additionally, we also performed whole exome sequencing of tumor tissues and compared the concordance of genomic profiles with circulating tumor deoxyribonucleic acid. RESULTS: Nine patients had circulating tumor deoxyribonucleic acid in pretreatment plasma samples with a total of 20 mutations (15 single nucleotide variants, three insertions/deletions, and two copy number amplification). VHL (30.0%) was the most frequently mutated gene, followed by TP53 (20.0%), and 45.0% of circulating tumor deoxyribonucleic acid mutations were concordant with somatic mutations in tumor tissues. Patients with decreasing circulating tumor deoxyribonucleic acid mutant allele frequency had better progression free survival when compared to those with increasing mutant allele frequency (P = 0.0441). CONCLUSIONS: Our findings revealed that early circulating tumor deoxyribonucleic acid dynamics can serve as a predictive biomarker for response to immune checkpoint inhibitors in metastatic renal cell carcinoma patients.

Neoantimon: a multifunctional R package for identification of tumor-specific neoantigens.

SUMMARY: It is known that some mutant peptides, such as those resulting from missense mutations and frameshift insertions, can bind to the major histocompatibility complex and be presented to antitumor T cells on the surface of a tumor cell. These peptides are termed neoantigen, and it is important to understand this process for cancer immunotherapy. Here, we introduce an R package termed Neoantimon that can predict a list of potential neoantigens from a variety of mutations, which include not only somatic point mutations but insertions, deletions and structural variants. Beyond the existing applications, Neoantimon is capable of attaching and reflecting several additional information, e.g. wild-type binding capability, allele specific RNA expression levels, single nucleotide polymorphism information and combinations of mutations to filter out infeasible peptides as neoantigen. AVAILABILITY AND IMPLEMENTATION: The R package is available at http://github/hase62/Neoantimon.

Application of targeted nanopore sequencing for the screening and determination of structural variants in patients with Lynch syndrome.

Lynch syndrome is a hereditary disease characterized by an increased risk of colorectal and other cancers. Germline variants in the mismatch repair (MMR) genes are responsible for this disease. Previously, we screened the MMR genes in colorectal cancer patients who fulfilled modified Amsterdam II criteria, and multiplex ligation-dependent probe amplification (MPLA) identified 11 structural variants (SVs) of MLH1 and MSH2 in 17 patients. In this study, we have tested the efficacy of long read-sequencing coupled with target enrichment for the determination of SVs and their breakpoints. DNA was captured by array probes designed to hybridize with target regions including four MMR genes and then sequenced using MinION, a nanopore sequencing platform. Approximately, 1000-fold coverage was obtained in the target regions compared with other regions. Application of this system to four test cases among the 17 patients correctly mapped the breakpoints. In addition, we newly found a deletion across an 84 kb region of MSH2 in a case without the pathogenic single nucleotide variants. These data suggest that long read-sequencing combined with hybridization-based enrichment is an efficient method to identify both SVs and their breakpoints. This strategy might replace MLPA for the screening of SVs in hereditary diseases.

Prognostic impact of circulating tumor DNA status post-allogeneic hematopoietic stem cell transplantation in AML and MDS.

This study was performed to assess the utility of tumor-derived fragmentary DNA, or circulating tumor DNA (ctDNA), for identifying high-risk patients for relapse of acute myeloid leukemia and myelodysplastic syndrome (AML/MDS) after undergoing myeloablative allogeneic hematopoietic stem cell transplantation (alloSCT). We retrospectively collected tumor and available matched serum samples at diagnosis and 1 and 3 months post-alloSCT from 53 patients with AML/MDS. After identifying driver mutations in 51 patients using next-generation sequencing, we designed at least 1 personalized digital polymerase chain reaction assay per case. Diagnostic ctDNA and matched tumor DNA exhibited excellent correlations with variant allele frequencies. Sixteen patients relapsed after a median of 7 months post-alloSCT. Both mutation persistence (MP) in bone marrow (BM) at 1 and 3 months post-alloSCT and corresponding ctDNA persistence (CP) in the matched serum (MP1 and MP3; CP1 and CP3, respectively) were comparably associated with higher 3-year cumulative incidence of relapse (CIR) rates (MP1 vs non-MP1, 72.9% vs 13.8% [P = .0012]; CP1 vs non-CP1, 65.6% vs 9.0% [P = .0002]; MP3 vs non-MP3, 80% vs 11.6% [P = .0002]; CP3 vs non-CP3, 71.4% vs 8.4% [P < .0001]). We subsequently evaluated whether subset analysis of patients with 3 genes associated with clonal hematopoiesis, DNMT3A, TET2, and ASXL1 (DTA), could also be helpful in relapse prediction. As a result, CP based on DTA gene mutations also had the prognostic effect on CIR. These results, for the first time, support the utility of ctDNA as a noninvasive prognostic biomarker in patients with AML/MDS undergoing alloSCT.

Metagenomic profiling of gut microbiome in early chronic kidney disease.

BACKGROUND: The relationship between chronic kidney disease (CKD) and the gut microbiome, which interact through chronic inflammation, uraemic toxin production and immune response regulation, has gained interest in the development of CKD therapies. However, reports using shotgun metagenomic analysis of the gut microbiome are scarce, especially for early CKD. Here we characterized gut microbiome differences between non-CKD participants and ones with early CKD using metagenomic sequencing. METHODS: In total, 74 non-CKD participants and 37 participants with early CKD were included based on propensity score matching, controlling for various factors including dietary intake. Stool samples were collected from participants and subjected to shotgun sequencing. Bacterial and pathway abundances were profiled at the species level with MetaPhlAn2 and HUMAnN2, respectively, and overall microbiome differences were determined using Bray-Curtis dissimilarities. Diabetic and non-diabetic populations were analysed separately. RESULTS: For diabetic and non-diabetic participants, the mean estimated glomerular filtration rates of the CKD group were 53.71 [standard deviation (SD) 3.87] and 53.72 (SD 4.44), whereas those of the non-CKD group were 72.63 (SD 7.72) and 76.10 (SD 9.84), respectively. Alpha and beta diversities were not significantly different between groups. Based on taxonomic analysis, butyrate-producing species Roseburia inulinivorans, Ruminococcus torques and Ruminococcus lactaris were more abundant in the non-CKD group, whereas Bacteroides caccae and Bacteroides coprocora were more abundant in the non-diabetic CKD group. CONCLUSIONS: Although gut microbiome changes in individuals with early CKD were subtle, the results suggest that changes related to producing short-chain fatty acids can already be observed in early CKD.

[A Future Perspective on Cancer Precision Medicine Accelerated by AI and Data Science].

In this article, we give a perspective for the future of cancer precision medicine that has been accelerated by AI and data science with massive production of personal omics data by new measurement technologies.

Nanopore basecalling from a perspective of instance segmentation.

BACKGROUND: Nanopore sequencing is a rapidly developing third-generation sequencing technology, which can generate long nucleotide reads of molecules within a portable device in real-time. Through detecting the change of ion currency signals during a DNA/RNA fragment's pass through a nanopore, genotypes are determined. Currently, the accuracy of nanopore basecalling has a higher error rate than the basecalling of short-read sequencing. Through utilizing deep neural networks, the-state-of-the art nanopore basecallers achieve basecalling accuracy in a range from 85% to 95%. RESULT: In this work, we proposed a novel basecalling approach from a perspective of instance segmentation. Different from previous approaches of doing typical sequence labeling, we formulated the basecalling problem as a multi-label segmentation task. Meanwhile, we proposed a refined U-net model which we call UR-net that can model sequential dependencies for a one-dimensional segmentation task. The experiment results show that the proposed basecaller URnano achieves competitive results on the in-species data, compared to the recently proposed CTC-featured basecallers. CONCLUSION: Our results show that formulating the basecalling problem as a one-dimensional segmentation task is a promising approach, which does basecalling and segmentation jointly.

A Bayesian model integration for mutation calling through data partitioning.

MOTIVATION: Detection of somatic mutations from tumor and matched normal sequencing data has become among the most important analysis methods in cancer research. Some existing mutation callers have focused on additional information, e.g. heterozygous single-nucleotide polymorphisms (SNPs) nearby mutation candidates or overlapping paired-end read information. However, existing methods cannot take multiple information sources into account simultaneously. Existing Bayesian hierarchical model-based methods construct two generative models, the tumor model and error model, and limited information sources have been modeled. RESULTS: We proposed a Bayesian model integration framework named as partitioning-based model integration. In this framework, through introducing partitions for paired-end reads based on given information sources, we integrate existing generative models and utilize multiple information sources. Based on that, we constructed a novel Bayesian hierarchical model-based method named as OHVarfinDer. In both the tumor model and error model, we introduced partitions for a set of paired-end reads that cover a mutation candidate position, and applied a different generative model for each category of paired-end reads. We demonstrated that our method can utilize both heterozygous SNP information and overlapping paired-end read information effectively in simulation datasets and real datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/takumorizo/OHVarfinDer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

TRIM28/TIF1ß and Fli-1 negatively regulate peroxynitrite generation via DUOX2 to decrease the shedding of membrane-bound fractalkine in human macrophages after exposure to substance P.

The chemokine fractalkine is synthesized as a membrane-bound protein, but studies have shown that serum levels of soluble fractalkine are elevated in inflammatory and autoimmune diseases. Patients with autoimmune diseases also have increased serum levels of neuropeptide substance P (SP). The shedding activity of the ADAM family is induced by peroxynitrite, but that of SP is unclear. Treatment of human macrophages with SP upregulated levels of membrane-bound fractalkine. Interestingly, small interfering RNA (siRNA) for DUOX2 further increased membrane-bound fractalkine but decreased soluble fractalkine compared with cells treated with SP alone. SP induced nitric oxide 2/inducible nitric oxide synthase (NOS2/iNOS) mRNA and increased levels of nitrotyrosine, a biomarker of peroxynitrite, whereas transfection with DUOX2 siRNA blunted upregulation of nitrotyrosine. Most importantly, N(ω)-nitro-L-arginine methyl ester (L-NAME, a nitric oxide synthase inhibitor) decreased protein levels of nitrotyrosine and concomitantly increased expression of membrane-bound fractalkine after exposure to SP. As for the signaling pathway of TGFß1 (an inhibitor of iNOS mRNA expression), silencing of RNA for TAK-1 upregulated membrane-bound fractalkine, but silencing of RNA for the Smad family did not. Interfering RNA of transcription factor specificity protein 1 (Sp1) upregulated protein levels of TGFß1/LAP. Most importantly, double transfection with siRNA for Sp1 and TRIM28/TIF1ßor Fli-1 led to a significant increase in TGFß1/LAP levels and a corresponding reduction of NOS2/iNOS, which inhibited the shedding of membrane-bound fractalkine. In conclusion, TRIM28/TIF1ß and Fli-1 negatively regulate TGFß1 expression to upregulate the generation of peroxynitrite, leading to increased shedding of membrane-bound fractalkine induced by SP.

Virtual Grid Engine: a simulated grid engine environment for large-scale supercomputers.

BACKGROUND: Supercomputers have become indispensable infrastructures in science and industries. In particular, most state-of-the-art scientific results utilize massively parallel supercomputers ranked in TOP500. However, their use is still limited in the bioinformatics field due to the fundamental fact that the asynchronous parallel processing service of Grid Engine is not provided on them. To encourage the use of massively parallel supercomputers in bioinformatics, we developed middleware called Virtual Grid Engine, which enables software pipelines to automatically perform their tasks as MPI programs. RESULT: We conducted basic tests to check the time required to assign jobs to workers by VGE. The results showed that the overhead of the employed algorithm was 246 microseconds and our software can manage thousands of jobs smoothly on the K computer. We also tried a practical test in the bioinformatics field. This test included two tasks, the split and BWA alignment of input FASTQ data. 25,055 nodes (2,000,440 cores) were used for this calculation and accomplished it in three hours. CONCLUSION: We considered that there were four important requirements for this kind of software, non-privilege server program, multiple job handling, dependency control, and usability. We carefully designed and checked all requirements. And this software fulfilled all the requirements and achieved good performance in a large scale analysis.