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Understanding the genomic features of dedifferentiated liposarcoma (DDLPS) is likely to uncover new options for management. Here, the authors reveal three prognostic groups, and highlight molecular markers associated with malignant transformation.

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.

Summary: Background: There are an estimated 1·3–4·0 million cases of cholera and 20 000–140 000 cholera-related deaths worldwide each year. The rice-based cholera toxin B subunit (CTB) vaccine, MucoRice-CTB, is an oral candidate vaccine that does not require a cold chain, has shown efficacy in animal models, and could be of benefit in places where there is a paucity of medical infrastructure. We aim to assess the safety, tolerability, and immunogenicity of MucoRice-CTB in humans. Methods: We did a double-blind, randomised, placebo-controlled, dose-escalation, phase 1 study at one centre in Tokyo, Japan. Eligible participants were healthy adult men with measurable serum and faecal antibodies against CTB at screening. Participants were excluded if they had allergy to rice; history of cholera or travellers' diarrhoea; poorly controlled constipation; abnormal results on hepatic, renal, or haematological screening tests; use of any over-the-counter drugs within 7 days before first administration; inability to use a medically acceptable means of contraception; or other reasons by medical judgment of the investigator. Three dose cohorts of participants were randomly assigned by block to receive oral MucoRice-CTB (1 g, 3 g, or 6 g) or placebo (1 g, 3 g, or 6 g), once every 2 weeks for 8 weeks (for a total of 4 doses). The dose groups were performed sequentially, and each dose cohort was completed before the higher dose cohort began. All medical staff, participants, and most trial staff were masked to treatment allocation. The primary outcomes were safety and tolerability, measured by 12-lead electrocardiogram; vital signs; haematology, biochemistry, and urinalysis; rice protein-specific serum IgE antibody concentration; and monitoring of adverse events. Participants were assessed at baseline and at 1, 2, 4, 6, 8, and 16 weeks after the first administration of vaccine or placebo. The safety analysis set included all participants enrolled in the trial who received at least one dose of the study drug or placebo and were compliant with good clinical practice. The full analysis population included all participants enrolled in the trial who received at least one dose of the study drug and for whom any data were obtained after the start of study drug administration. Meta-genomic analysis of study participants was performed using bacterial DNA from faecal samples before vaccination. This trial is registered with UMIN.ac.jp, UMIN000018001. Findings: Between June 23, 2015, and May 31, 2016, 226 participants were recruited and assessed for eligibility. 166 participants were excluded based on health condition or schedule. We then randomly selected 60 male volunteers aged 20–40 years who were enrolled and assigned to MucoRice-CTB (10 participants assigned to 1 g, 10 participants assigned to 3 g, and 10 participants assigned to 6 g), or placebo (10 participants assigned to 1 g, 10 participants assigned to 3 g, and 10 participants assigned to 6 g). All participants received at least one dose of study drug or placebo and were included in the safety analyses. Two participants given MucoRice-CTB 3 g and one participant given MucoRice-CTB 6 g were lost to follow-up and excluded from the efficacy analysis. Serum CTB-specific IgG and IgA antibody concentrations in participants who received 6 g MucoRice-CTB increased significantly in both a time-dependent and dose-dependent manner compared with those in the placebo groups (p for interaction=0·002 for IgG, p=0·004 for IgA). Genome analysis of subjects' faeces before vaccination revealed that compared to non-responders, responders had a gut microbiota of higher diversity with the presence of Escherichia coli and Shigella spp. 28 (93%) of 30 participants who received MucoRice-CTB at any dose had at least one adverse event during the study period, compared with 30 (100%) of 30 participants given placebo. Grade 3 or higher adverse events were reported in four participants in the MucoRice-CTB group (5 events) and four participants in the placebo group (10 events). The most common serious adverse event was haemoglobin decreased (2 events in 2 participants in the pooled MucoRice-CTB group, 2 events in 2 participants in the placebo group; all grade 3). Interpretation: Participants given MucoRice-CTB showed increased CTB-specific serum IgG and IgA antibody concentrations without inducing serious adverse events, indicating that MucoRice-CTB could be a safe and potent vaccine to prevent diarrhoeal disease. MucoRice-CTB induced neutralising antibodies against diarrhoeal toxins in a gut microbiota-dependent manner. A similar phase 1 trial will be done with participants of other ethnicities to substantiate our findings. Funding: Translational Research Acceleration Network Program of Japan Agency for Medical Research and Development; Ministry of Education, Culture, Sports, Science and Technology, Japan; Science and Technology Research Partnership for Sustainable Development; Grant-in-Aid for Scientific Research (S) (18H05280) (to H K) from the Japan Society for the Promotion of Science (JSPS); Grant-in-Aid for Young Scientists (B) (16K16144) (to Y K) from JSPS; Grant-in-Aid for Young Scientists (18K18148) (to Y K) from JSPS; Grant from International Joint Usage/Research Center (K3002), the Institute of Medical Science, University of Tokyo

ABSTRACT Chronic inflammation is a hallmark of human immunodeficiency virus (HIV) infection and a risk factor for the development and progression of age-related comorbidities. Although HIV-associated gut dysbiosis has been suggested to be involved in sustained chronic inflammation, there remains a limited understanding of the association between gut dysbiosis and chronic inflammation during HIV infection. Here, we investigated compositional changes in the gut microbiome and its role in chronic inflammation in patients infected with HIV. We observed that the gut microbiomes of patients with low CD4 counts had reduced alpha diversity compared to those in uninfected controls. Following CD4 recovery, alpha diversity was restored, but intergroup dissimilarity of bacterial composition remained unchanged between patients and uninfected controls. Patients with HIV had higher abundance of the classes Negativicutes, Bacilli, and Coriobacteriia, as well as depletion of the class Clostridia. These relative abundances positively correlated with inflammatory cytokines and negatively correlated with anti-inflammatory cytokines. We found that gut dysbiosis accompanying HIV infection was characterized by a depletion of obligate anaerobic Clostridia and enrichment of facultative anaerobic bacteria, reflecting increased intestinal oxygen levels and intestinal permeability. Furthermore, it is likely that HIV-associated dysbiosis shifts the immunological balance toward inflammatory Th1 responses and encourages proinflammatory cytokine production. Our results suggest that gut dysbiosis contributes to sustaining chronic inflammation in patients with HIV infection despite effective antiretroviral therapy and that correcting gut dysbiosis will be effective in improving long-term outcomes in patients. IMPORTANCE Chronic inflammation is a hallmark of HIV infection and is associated with the development and progression of age-related comorbidities. Although the gastrointestinal tract is a major site of HIV replication and CD4+ T-cell depletion, the role of HIV-associated imbalance of gut microbiome in chronic inflammation is unclear. Here, we aimed to understand the causal relationship between abnormalities in the gut microbiome and chronic inflammation in patients with HIV. Our results suggest HIV-associated gut dysbiosis presents a more aerobic environment than that of healthy individuals, despite prolonged viral suppression. This dysbiosis likely results from a sustained increase in intestinal permeability, which supports sustained bacterial translocation in HIV patients, despite effective therapy. Additionally, we observed that several bacterial taxa enriched in HIV patients were associated with increased expression of inflammatory cytokines. Collectively, these results suggest that gut dysbiosis plays an important role in chronic inflammation in HIV patients.

HIV-1 infected individuals under antiretroviral therapy can control viremia but often develop non-AIDS diseases such as cardiovascular and metabolic disorders. Gut microbiome dysbiosis has been indicated to be associated with progression of these diseases. Analyses of gut/fecal microbiome in individual regions are important for our understanding of pathogenesis in HIV-1 infections. However, data on gut/fecal microbiome has not yet been accumulated in West Africa. In the present study, we examined fecal microbiome compositions in HIV-1 infected adults in Ghana, where approximately two-thirds of infected adults are females. In a cross-sectional case-control study, age- and gender-matched HIV-1 infected adults (HIV+; n = 55) and seronegative controls (HIV-; n = 55) were enrolled. Alpha diversity of fecal microbiome in HIV+ was significantly reduced compared to HIV- and associated with CD4 counts. HIV+ showed reduction in varieties of bacteria including Faecalibacterium, the most abundant in seronegative controls, but enrichment of Proteobacteria. Ghanaian HIV+ exhibited enrichment of Dorea and Blautia; bacteria groups whose depletion has been reported in HIV-1 infected individuals in several other cohorts. Furthermore, HIV+ in our cohort exhibited a depletion of Prevotella, a genus whose enrichment has recently been shown in men having sex with men (MSM) regardless of HIV-1 status. The present study revealed the characteristics of dysbiotic fecal microbiome in HIV-1 infected adults in Ghana, a representative of West African populations.

Abstract Background The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor tissues have high affinities for tumor-specific antigens, the patterns of their amino acid sequences and other sequence-independent features such as the number of somatic hypermutations (SHMs) may differ between the normal and tumor microenvironments. However, given the high diversity of BCRs/Igs and the rarity of recurrent sequences among individuals, it is far more difficult to capture such differences in BCR/Ig sequences than in TCR sequences. The aim of this study was to explore the possibility of discriminating BCRs/Igs in tumor and in normal tissues, by capturing these differences using supervised machine learning methods applied to RNA sequences of BCRs/Igs. Results RNA sequences of BCRs/Igs were obtained from matched normal and tumor specimens from 90 gastric cancer patients. BCR/Ig-features obtained in Rep-Seq were used to classify individual BCR/Ig sequences into normal or tumor classes. Different machine learning models using various features were constructed as well as gradient boosting machine (GBM) classifier combining these models. The results demonstrated that BCR/Ig sequences between normal and tumor microenvironments exhibit their differences. Next, by using a GBM trained to classify individual BCR/Ig sequences, we tried to classify sets of BCR/Ig sequences into normal or tumor classes. As a result, an area under the curve (AUC) value of 0.826 was achieved, suggesting that BCR/Ig repertoires have distinct sequence-level features in normal and tumor tissues. Conclusions To the best of our knowledge, this is the first study to show that BCR/Ig sequences derived from tumor and normal tissues have globally distinct patterns, and that these tissues can be effectively differentiated using BCR/Ig repertoires.

Abstract Background Although human leukocyte antigen (HLA) genotyping based on amplicon, whole exome sequence (WES), and RNA sequence data has been achieved in recent years, accurate genotyping from whole genome sequence (WGS) data remains a challenge due to the low depth. Furthermore, there is no method to identify the sequences of unknown HLA types not registered in HLA databases. Results We developed a Bayesian model, called ALPHLARD, that collects reads potentially generated from HLA genes and accurately determines a pair of HLA types for each of HLA-A, -B, -C, -DPA1, -DPB1, -DQA1, -DQB1, and -DRB1 genes at 3rd field resolution. Furthermore, ALPHLARD can detect rare germline variants not stored in HLA databases and call somatic mutations from paired normal and tumor sequence data. We illustrate the capability of ALPHLARD using 253 WES data and 25 WGS data from Illumina platforms. By comparing the results of HLA genotyping from SBT and amplicon sequencing methods, ALPHLARD achieved 98.8% for WES data and 98.5% for WGS data at 2nd field resolution. We also detected three somatic point mutations and one case of loss of heterozygosity in the HLA genes from the WGS data. Conclusions ALPHLARD showed good performance for HLA genotyping even from low-coverage data. It also has a potential to detect rare germline variants and somatic mutations in HLA genes. It would help to fill in the current gaps in HLA reference databases and unveil the immunological significance of somatic mutations identified in HLA genes.

[This corrects the article DOI: 10.1371/journal.pone.0241508.].

Intestinal microflora has been associated with obesity. While visceral fat is more strongly associated with cardiovascular disorder, a complication linked to obesity, than the body mass index (BMI), the association between intestinal microflora and obesity (as defined in terms of BMI) has been studied widely. However, the link between visceral fat area (VFA) and intestinal microflora has been little studied. In this study, we investigate the association between intestinal microflora and VFA and BMI using a longitudinal study on Japanese subjects with different VFA statuses (N = 767). Principal component analysis of the changes in intestinal microflora composition over the one-year study period revealed the different associations between intestinal microflora and VFA and BMI. As determined by 16S rRNA amplicon sequencing, changes in the abundance ratio of two microbial genera—Blautia and Flavonifractor—were significantly associated with VFA changes and changes in the abundance ratio of four different microbial genera were significantly associated with BMI changes, suggesting that the associated intestinal microbes are different. Furthermore, as determined by metagenomic shotgun sequences, changes in the abundance ratios of two Blautia species—Blautia hansenii and Blautia producta—were significantly and negatively associated with VFA changes. Our findings might be used to develop a new treatment for visceral fat.

Background: The tumor microenvironment can be classified into immunologically active “inflamed” tumors and inactive “non-inflamed” tumors based on the infiltration of cytotoxic immune cells. Previous studies on liver cancer have reported a superior prognosis for inflamed tumors compared to non-inflamed tumors. However, liver cancer is highly heterogeneous immunologically and genetically, and a finer classification of the liver cancer microenvironment may improve our understanding of its immunological diversity and response to immune therapy. Methods: We characterized the immune gene signatures of 234 primary liver cancers, mainly virus-related, from a Japanese population using RNA-Seq of tumors and matched non-tumorous hepatitis livers. We then compared them with the somatic alterations detected using the whole-genome sequencing. Findings: Liver cancers expressed lower levels of immune marker genes than non-tumorous hepatitis livers, indicating immunosuppression in the tumor microenvironment. Several immunosuppression mechanisms functioned actively and mutually exclusively, resulting in four immune subclasses of liver cancer: tumor-associated macrophage (TAM), CTNNB1, cytolytic activity (CYT), and regulatory T cell (Treg). The CYT and Treg subclasses represented inflamed tumors, while the TAM and CTNNB1 subclasses represented non-inflamed tumors. The TAM subclass, which comprised 31% of liver cancers, showed a poor survival, expressed elevated levels of extracellular matrix genes, and was associated with somatic mutations of chromatin regulator ARID2. The results of cell line experiments suggested a functional link between ARID2 and chemokine production by liver cancer cells. Interpretation: Primary liver cancer was classified into four subclasses based on mutually exclusive mechanisms for immunosuppression. This classification indicate the importance of immunosuppression mechanisms, such as TAM and Treg, as therapeutic targets for liver cancer. Funding: The Japan Agency for Medical Research and Development (AMED). Keywords: Liver cancer, Tumor microenvironment, Tumor-associated macrophage, Regulatory T cell

Background: The oral microbiome, which consists of various habitats, has been shown to be influenced by smoking. However, differences in the tongue microbiomes of current and former smokers, as well as their resultant functional consequences, have rarely been investigated in East Asian populations. Methods: We used 16S rRNA amplicon sequencing of tongue-coating samples obtained from East Asian subjects who were current, former, or never smokers to identify differences in their tongue microbiomes and related metagenomic functions. Two sets of participants from 2016 to 2017 (n = 657 and n = 187, respectively) were analyzed separately. Results: We found significant differences between the overall microbiome compositions of current versus never smokers (p = 0.0015), but not between former versus never smokers (p = 0.43) based on the weighted UniFrac distance. Twenty-nine of 43 investigated genera showed significantly different expression levels in current versus never smokers. Neisseria and Capnocytophaga were less abundant, and Streptococcus and Megasphaera were more abundant in current smokers. Moreover, the abundances of metagenomic pathways, including those related to nitrate reduction and the tricarboxylic acid cycle, were significantly different between current and never smokers. Conclusions: The tongue microbiomes and related metagenomic pathways of current smokers differ from those of never smokers among East Asians.

Owing to increasing medical expenses, researchers have attempted to detect clinical signs and preventive measures of diseases using electronic health record (EHR). In particular, time-series EHRs collected by periodic medical check-up enable us to clarify the relevance among check-up results and individual environmental factors such as lifestyle. However, usually such time-series data have many missing observations and some results are strongly correlated to each other. These problems make the analysis difficult and there exists strong demand to detect clinical findings beyond them. We focus on blood test values in medical check-up results and apply a time-series analysis methodology using a state space model. It can infer the internal medical states emerged in blood test values and handle missing observations. The estimated models enable us to predict one's blood test values under specified condition and predict the effect of intervention, such as changes of body composition and lifestyle. We use time-series data of EHRs periodically collected in the Hirosaki cohort study in Japan and elucidate the effect of 17 environmental factors to 38 blood test values in elderly people. Using the estimated model, we then simulate and compare time-transitions of participant's blood test values under several lifestyle scenarios. It visualizes the impact of lifestyle changes for the prevention of diseases. Finally, we exemplify that prediction errors under participant's actual lifestyle can be partially explained by genetic variations, and some of their effects have not been investigated by traditional association studies.

PURPOSE:Pulse wave velocity (PWV), an indicator of vascular stiffness, increases with age and is increasingly recognized as an independent risk factor for cardiovascular disease (CVD). Although many mechanical and chemical factors underlie the stiffness of the elastic artery, genetic risk factors related to age-dependent increases in PWV in apparently healthy people are largely unknown. The transcription factor nuclear factor E2 (NF-E2)-related factor 2 (Nrf2), which is activated by unidirectional vascular pulsatile shear stress or oxidative stress, regulates vascular redox homeostasis. Previous reports have shown that a SNP in the NRF2 gene regulatory region (-617C>A; hereafter called SNP-617) affects NRF2 gene expression such that the minor A allele confers lower gene expression compared to the C allele, and it is associated with various diseases, including CVD. We aimed to investigate whether SNP-617 affects vascular stiffness with aging in apparently healthy people. METHODS:Analyzing wide-ranging data obtained from a public health survey performed in Japan, we evaluated whether SNP-617 affected brachial-ankle PWV (baPWV) in never-smoking healthy subjects (n = 642). We also evaluated the effects of SNP-617 on other cardiovascular and blood test measurements. RESULTS:We have shown that not only AA carriers (n = 55) but also CA carriers (n = 247) show arterial stiffness compared to CC carriers (n = 340). Furthermore, SNP-617 also affected blood pressure indexes such as systolic blood pressure and mean arterial pressure but not the ankle brachial pressure index, an indicator of atherosclerosis. Multivariate analysis showed that SNP-617 accelerates the incremental ratio of baPWV with age. CONCLUSIONS:This study is the first to show that SNP-617 affects the age-dependent increase in vascular stiffness. Our results indicate that low NRF2 activity induces premature vascular aging and could be targeted for the prevention of cardiovascular diseases associated with aging.

In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statistical and machine learning approaches have been developed and applied to reveal sample-specific regulatory pathways, integrative understanding of the massive multilayer networks remains a challenge. To resolve this problem, we propose a novel artificial intelligence (AI) strategy for comprehensive gene regulatory network analysis. In our strategy, personalized gene networks corresponding specific clinical characteristic are constructed and the constructed network is considered as a second-order tensor. Then, an explainable AI method based on deep learning is applied to decompose the multilayer networks, thus we can reveal all-encompassing gene regulatory systems characterized by clinical features of patients. To evaluate the proposed methodology, we apply our method to the multilayer gene networks under varying conditions of an epithelial-mesenchymal transition (EMT) process. From the comprehensive analysis of multilayer networks, we identified novel markers, and the biological mechanisms of the identified genes and their reciprocal mechanisms are verified through the literature. Although any biological knowledge about the identified genes was not incorporated in our analysis, our data-driven approach based on AI approach provides biologically reliable results. Furthermore, the results provide crucial evidences to reveal biological mechanism related to various diseases, e.g., keratinocyte proliferation. The use of explainable AI method based on the tensor decomposition enables us to reveal global and novel mechanisms of gene regulatory system from the massive multiple networks, which cannot be demonstrated by existing methods. We expect that the proposed method provides a new insight into network biology and it will be a useful tool to integrative gene network analysis related complex architectures of diseases.

Progranulin (PGRN) mutations cause frontotemporal lobe dementia with TDP-43 pathology. Here the authors develop a mutant PGRN knock-in mouse model of the disease, and show that Tyro3, a tyrosine kinase membrane receptor that acts upstream of PKC and MAPK, is inhibited by PGRN which contributes to pathology in this model.

We determined the bone mineral density (BMD) and the expression of serum bone formation marker (procollagen type I N-terminal propeptide: PINP) and bone resorption marker (C-terminal telopeptide of collagen: CTX) by ELISA to evaluate ovariectomy-induced osteoporosis in ovariectomized (OVX) mice. The intestinal microbiota of the mice was assessed using 16S rRNA gene sequencing. OVX mice exhibited a lower BMD of 87% with higher serum levels of CTX and PINP compared to sham-operated (sham) mice. The cecum microbiome of OVX mice showed lower bacterial diversity than that of sham mice. TNFα mRNA levels in the colon were 1.6 times higher, and zonula occludens-1 mRNA and protein expression were lower in OVX mice than in sham mice, suggesting that ovariectomy induced inflammation and increased intestinal permeability. Next, we used antibiotic treatment followed by fecal microbiota transplantation (FMT) to remodel the gut microbiota in the OVX mice. A decrease in PINP was observed in antibiotic-treated mice, while there was no change in BMD or CTX between mice with and without antibiotic treatment. Oral transplantation of the luminal cecal content of OVX or sham mice to antibiotic-treated mice did not affect the BMD or PINP and CTX expression. Additionally, transplantation of the luminal contents of OVX or sham mice to antibiotic-treated OVX mice had similar effects on BMD, PINP, and CTX. In conclusion, although ovariectomy induces dysbiosis in the colon, the changes in the gut microbiota may only have a minor role in ovariectomy-induced osteoporosis.

Patient-derived xenograft models reportedly represent original tumor morphology and gene mutation profiles. In addition, patient-derived xenografts are expected to recapitulate the parental tumor drug responses. In this study, we analyzed the pathways involved in gemcitabine resistance using patient-derived xenograft models of pancreatic cancer. The patient-derived xenograft models were established using samples from patients with pancreatic cancer. The models were treated with gemcitabine to better understand the mechanism of resistance to this anti-cancer drug. We performed comparative gene analysis through the next-generation sequencing of tumor tissues from gemcitabine-treated or non-treated patient-derived xenograft mice and gene set enrichment analysis to analyze mRNA profiling data. Pathway analysis of gemcitabine-treated patient-derived xenografts disclosed the upregulation of multiple gene sets and identified several specific gene pathways that could potentially be related to gemcitabine resistance in pancreatic cancer. Further, we conducted an in vitro analysis to validate these results. The mRNA expression of cytochrome P450 1B1 and cytochrome P450 2A6 was upregulated in a concentration-dependent manner following gemcitabine treatment. Moreover, the sensitivity to gemcitabine increased, and viable cells were decreased by the cytochrome P450 1B1 inhibitor, indicating that the cytochrome P450 1B1 pathway may be related to gemcitabine resistance in pancreatic cancer.

With the recent advances in noninvasive approaches for cancer diagnosis and surveillance, the term “liquid biopsy” has become more familiar to clinicians, including hematologists. Liquid biopsy provides a variety of clinically useful genetic data. In this era of personalized medicine, genetic information is critical to early diagnosis, aiding risk stratification, directing therapeutic options, and monitoring disease relapse. The validity of circulating tumor DNA (ctDNA)-mediated liquid biopsies has received increasing attention. This review summarizes the current knowledge of liquid biopsy ctDNA in hematological malignancies, focusing on the feasibility, limitations, and key areas of clinical application. We also highlight recent advances in the minimal residual disease monitoring of leukemia using ctDNA. This article will be useful to those involved in the clinical practice of hematopoietic oncology.

Although recent cancer genomics studies have identified a large number of genes that were mutated in human cancers, p53 remains as the most frequently mutated gene. To further elucidate the p53-signalling network, we performed transcriptome analysis on 24 tissues in p53+/+ or p53−/− mice after whole-body X-ray irradiation. Here we found transactivation of a total of 3551 genes in one or more of the 24 tissues only in p53+/+ mice, while 2576 genes were downregulated. p53 mRNA expression level in each tissue was significantly associated with the number of genes upregulated by irradiation. Annotation using TCGA (The Cancer Genome Atlas) database revealed that p53 negatively regulated mRNA expression of several cancer therapeutic targets or pathways such as BTK, SYK, and CTLA4 in breast cancer tissues. In addition, stomach exhibited the induction of Krt6, Krt16, and Krt17 as well as loricrin, an epidermal differentiation marker, after the X-ray irradiation only in p53+/+ mice, implying a mechanism to protect damaged tissues by rapid induction of differentiation. Our comprehensive transcriptome analysis elucidated tissue specific roles of p53 and its signalling networks in DNA-damage response that will enhance our understanding of cancer biology.

Therapy-related myeloid neoplasm (t-MN) is a late and lethal complication induced by chemotherapy and/or radiation therapy. Hematological malignancy is one of the most common primary diseases in patients with t-MN. However, the occurrence of t-MN in adult T-cell leukemia/lymphoma (ATL) patients is rarely reported, possibly due to the dismal prognosis of ATL per se. Here, we report a 62-year-old female who developed t-MN only three months after the completion of conventional chemotherapy and anti-CCR4 antibody for ATL acute type. The patient presented with persistent fever and monocytosis without any evidence of infectious diseases. Bone marrow examinations revealed chronic myelomonocytic leukemia-like disease with a chromosomal translocation of t(11;22)(q23;q13) as a solo cytogenetic abnormality, resulting in the diagnosis of t-MN. Next-generation sequencing analysis identified a rare chimeric transcript, MLL-EP300, without any additional somatic mutations. Although the patient underwent allogenic hematopoietic stem cell transplantation, she died of viral encephalomyelitis at 7 months after diagnosis of t-MN. Since recent therapeutic advances have extended the survival of patients with ATL, further evaluation of the long-term risks of developing t-MN in these patients is warranted.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

High visceral fat area (VFA) is a stronger predictor of cardiovascular disease and overall mortality than body mass index or waist circumference. VFA may be decreased by proper dietary habits. Although previous epidemiologic studies demonstrated an association between nutritional components or foodstuffs and VFA, only the associations of a few nutrients, such as dietary fiber and calcium, are reported. We performed a comprehensive 2-year longitudinal study in more than 624 healthy people and analyzed 33 micronutrients to investigate nutrients that contribute to changes in visceral fat. Our analyses revealed that “macronutrients” and “micronutrients” were “mutual confounders”. Therefore, when evaluating the association between VFA and micronutrients, associations were adjusted by macronutrients. The ingestion of 7 nutrients: soluble dietary fiber, manganese, potassium, magnesium, vitamin K, folic acid, and pantothenic acid, which are abundant components in vegetable diets, was significantly inversely correlated with a change in VFA. Additionally, a change in the ingestion of one nutrient, monounsaturated fat, was significantly positively correlated with a change in VFA. These associations were independent of body mass index and waist circumference. Thus, a predominantly vegetable diet may decrease VFA. In addition, adjusting the intake of macronutrients might help to clarify the association of micronutrients with VFA.

The CXCR3 ligands CXCL9, 10, and 11 play critical roles in the amplification of immune responses by recruiting CXCR3+ immune effector cells to the tumor site. Taking advantage of this property of CXCR3 ligands, we aimed to establish a novel approach to identify immunogenic mutated-antigens. We examined the feasibility of using CXCR3 ligand mRNAs as sensors for detection of specific immune responses in human and murine systems. We further investigated whether this approach is applicable for the identification of immunogenic mutated-antigens by using murine sarcoma lines. Rapid synthesis of CXCR3 ligand mRNAs occurred shortly after specific immune responses in both human and murine immune systems. Particularly, in CMS5 tumor-bearing mice, we detected specific immune responses to mutated mitogen-activated protein kinase 2 (ERK2), which has previously been identified as an immunogenic mutated-antigen. Furthermore, by combining this approach with whole-exome and transcriptome sequencing analyses, we identified an immunogenic neo-epitope derived from mutated staphylococcal nuclease domain-containing protein 1 (Snd1) in CMS7 tumor-bearing mice. Most importantly, we successfully detected the specific immune response to this neo-epitope even without co-administration of anti-cytotoxic T-lymphocyte protein-4 (CTLA-4), anti-programmed cell death-1 (PD-1) and anti-glucocorticoid-induced TNFR-related protein (GITR) antibodies, which vigorously augmented the immune response and consequently enabled us to detect the specific immune response to this neo-epitope by conventional IFNγ intracellular staining method. Our data indicate the potential usefulness of this strategy for the identification of immunogenic mutated-antigens. We propose that this approach would be of great help for the development of personalized cancer vaccine therapies in future.

Uncovering driver genes is crucial for understanding heterogeneity in cancer. L1-type regularization approaches have been widely used for uncovering cancer driver genes based on genome-scale data. Although the existing methods have been widely applied in the field of bioinformatics, they possess several drawbacks: subset size limitations, erroneous estimation results, multicollinearity, and heavy time consumption. We introduce a novel statistical strategy, called a Recursive Random Lasso (RRLasso), for high dimensional genomic data analysis and investigation of driver genes. For time-effective analysis, we consider a recursive bootstrap procedure in line with the random lasso. Furthermore, we introduce a parametric statistical test for driver gene selection based on bootstrap regression modeling results. The proposed RRLasso is not only rapid but performs well for high dimensional genomic data analysis. Monte Carlo simulations and analysis of the "Sanger Genomics of Drug Sensitivity in Cancer dataset from the Cancer Genome Project" show that the proposed RRLasso is an effective tool for high dimensional genomic data analysis. The proposed methods provide reliable and biologically relevant results for cancer driver gene selection.

With the exponential increase in the number of sequenced organisms, automated annotation of proteins is becoming increasingly important. Intrinsically disordered regions are known to play a significant role in protein function. Despite their abundance, especially in eukaryotes, they are rarely used to inform function prediction systems. In this study, we extracted seven sequence features in intrinsically disordered regions and developed a scheme to use them to predict Gene Ontology Slim terms associated with proteins. We evaluated the function prediction performance of each feature. Our results indicate that the residue composition based features have the highest precision while bigram probabilities, based on sequence profiles of intrinsically disordered regions obtained from PSIBlast, have the highest recall. Amino acid bigrams and features based on secondary structure show an intermediate level of precision and recall. Almost all features showed a high prediction performance for GO Slim terms related to extracellular matrix, nucleus, RNA and DNA binding. However, feature performance varied significantly for different GO Slim terms emphasizing the need for a unique classifier optimized for the prediction of each functional term. These findings provide a first comprehensive and quantitative evaluation of sequence features in intrinsically disordered regions and will help in the development of a more informative protein function predictor.

Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction generates false regulations that are not permitted biologically. Thus, when dealing with several tens of genes of which the relationships are partially known, a method that can infer regulatory relationships based on a model with low abstraction and that can emulate the dynamics of ODE-based models while incorporating prior knowledge is urgently required. To accomplish this, we propose a method for inference of GRNs using a state space representation of a vector auto-regressive (VAR) model with L1 regularization. This method can estimate the dynamic behavior of genes based on linear time-series modeling constructed from an ODE-based model and can infer the regulatory structure among several tens of genes maximizing prediction ability for the observational data. Furthermore, the method is capable of incorporating various types of existing biological knowledge, e.g., drug kinetics and literature-recorded pathways. The effectiveness of the proposed method is shown through a comparison of simulation studies with several previous methods. For an application example, we evaluated mRNA expression profiles over time upon corticosteroid stimulation in rats, thus incorporating corticosteroid kinetics/dynamics, literature-recorded pathways and transcription factor (TF) information.

The personal genomics era has attracted a large amount of attention for anti-cancer therapy by patient-specific analysis. Patient-specific analysis enables discovery of individual genomic characteristics for each patient, and thus we can effectively predict individual genetic risk of disease and perform personalized anti-cancer therapy. Although the existing methods for patient-specific analysis have successfully uncovered crucial biomarkers, their performance takes a sudden turn for the worst in the presence of outliers, since the methods are based on non-robust manners. In practice, clinical and genomic alterations datasets usually contain outliers from various sources (e.g., experiment error, coding error, etc.) and the outliers may significantly affect the result of patient-specific analysis. We propose a robust methodology for patient-specific analysis in line with the NetwrokProfiler. In the proposed method, outliers in high dimensional gene expression levels and drug response datasets are simultaneously controlled by robust Mahalanobis distance in robust principal component space. Thus, we can effectively perform for predicting anti-cancer drug sensitivity and identifying sensitivity-specific biomarkers for individual patients. We observe through Monte Carlo simulations that the proposed robust method produces outstanding performances for predicting response variable in the presence of outliers. We also apply the proposed methodology to the Sanger dataset in order to uncover cancer biomarkers and predict anti-cancer drug sensitivity, and show the effectiveness of our method.

Ctf18-replication factor C complex including Dscc1 (DNA replication and sister chromatid cohesion 1) is implicated in sister chromatid cohesion, DNA replication, and genome stability in S. cerevisiae and C. elegans. We previously performed gene expression profiling in primary colorectal cancer cells in order to identify novel molecular targets for the treatment of colorectal cancer. A feature of the cancer-associated transcriptional signature revealed from this effort is the elevated expression of the proto-oncogene DSCC1. Here, we have interrogated the molecular basis for deviant expression of human DSCC1 in colorectal cancer and its ability to promote survival of cancer cells. Quantitative PCR and immunohistochemical analyses corroborated that the expression level of DSCC1 is elevated in 60-70% of colorectal tumors compared to their matched noncancerous colonic mucosa. An in silico evaluation of the presumptive DSCC1 promoter region for consensus DNA transcriptional regulatory elements revealed a potential role for the E2F family of DNA-binding proteins in controlling DSCC1 expression. RNAi-mediated reduction of E2F1 reduced expression of DSCC1 in colorectal cancer cells. Gain- and loss-of-function experiments demonstrated that DSCC1 is involved in the viability of cancer cells in response to genotoxic stimuli. We reveal that E2F-dependent expression of DSCC1 confers anti-apoptotic properties in colorectal cancer cells, and that its suppression may be a useful option for the treatment of colorectal cancer.

Vaccination is a preventive measure against influenza that does not require placing restrictions on social activities. However, since the stockpile of vaccine that can be prepared before the arrival of an emerging pandemic strain is generally quite limited, one has to select priority target groups to which the first stockpile is distributed. In this paper, we study a simulation-based priority target selection method with the goal of enhancing the collective immunity of the whole population. To model the region in which the disease spreads, we consider an urban area composed of suburbs and central areas connected by a single commuter train line. Human activity is modelled following an agent-based approach. The degree to which collective immunity is enhanced is judged by the attack rate in unvaccinated people. The simulation results show that if students and office workers are given exclusive priority in the first three months, the attack rate can be reduced from [Formula: see text] in the baseline case down to 1-2%. In contrast, random vaccination only slightly reduces the attack rate. It should be noted that giving preference to active social groups does not mean sacrificing those at high risk, which corresponds to the elderly in our simulation model. Compared with the random administration of vaccine to all social groups, this design successfully reduces the attack rate across all age groups.

INTRODUCTION: The quantification of social media impacts on societal and political events is a difficult undertaking. The Japanese Society of Oriental Medicine started a signature-collecting campaign to oppose a medical policy of the Government Revitalization Unit to exclude a traditional Japanese medicine, "Kampo," from the public insurance system. The signature count showed a series of aberrant bursts from November 26 to 29, 2009. In the same interval, the number of messages on Twitter including the keywords "Signature" and "Kampo," increased abruptly. Moreover, the number of messages on an Internet forum that discussed the policy and called for signatures showed a train of spikes. METHODS AND FINDINGS: In order to estimate the contributions of social media, we developed a statistical model with state-space modeling framework that distinguishes the contributions of multiple social media in time-series of collected public opinions. We applied the model to the time-series of signature counts of the campaign and quantified contributions of two social media, i.e., Twitter and an Internet forum, by the estimation. We found that a considerable portion (78%) of the signatures was affected from either of the social media throughout the campaign and the Twitter effect (26%) was smaller than the Forum effect (52%) in total, although Twitter probably triggered the initial two bursts of signatures. Comparisons of the estimated profiles of the both effects suggested distinctions between the social media in terms of sustainable impact of messages or tweets. Twitter shows messages on various topics on a time-line; newer messages push out older ones. Twitter may diminish the impact of messages that are tweeted intermittently. CONCLUSIONS: The quantification of social media impacts is beneficial to better understand people's tendency and may promote developing strategies to engage public opinions effectively. Our proposed method is a promising tool to explore information hidden in social phenomena.

BackgroundOur understanding of the molecular pathways that underlie melanoma remains incomplete. Although several published microarray studies of clinical melanomas have provided valuable information, we found only limited concordance between these studies. Therefore, we took an in vitro functional genomics approach to understand melanoma molecular pathways.Methodology/principal findingsAffymetrix microarray data were generated from A375 melanoma cells treated in vitro with siRNAs against 45 transcription factors and signaling molecules. Analysis of this data using unsupervised hierarchical clustering and Bayesian gene networks identified proliferation-association RNA clusters, which were co-ordinately expressed across the A375 cells and also across melanomas from patients. The abundance in metastatic melanomas of these cellular proliferation clusters and their putative upstream regulators was significantly associated with patient prognosis. An 8-gene classifier derived from gene network hub genes correctly classified the prognosis of 23/26 metastatic melanoma patients in a cross-validation study. Unlike the RNA clusters associated with cellular proliferation described above, co-ordinately expressed RNA clusters associated with immune response were clearly identified across melanoma tumours from patients but not across the siRNA-treated A375 cells, in which immune responses are not active. Three uncharacterised genes, which the gene networks predicted to be upstream of apoptosis- or cellular proliferation-associated RNAs, were found to significantly alter apoptosis and cell number when over-expressed in vitro.Conclusions/significanceThis analysis identified co-expression of RNAs that encode functionally-related proteins, in particular, proliferation-associated RNA clusters that are linked to melanoma patient prognosis. Our analysis suggests that A375 cells in vitro may be valid models in which to study the gene expression modules that underlie some melanoma biological processes (e.g., proliferation) but not others (e.g., immune response). The gene expression modules identified here, and the RNAs predicted by Bayesian network inference to be upstream of these modules, are potential prognostic biomarkers and drug targets.

PURPOSE: To identify stage I lung adenocarcinoma patients with a poor prognosis who will benefit from adjuvant therapy. PATIENTS AND METHODS: Whole gene expression profiles were obtained at 19 time points over a 48-hour time course from human primary lung epithelial cells that were stimulated with epidermal growth factor (EGF) in the presence or absence of a clinically used EGF receptor tyrosine kinase (RTK)-specific inhibitor, gefitinib. The data were subjected to a mathematical simulation using the State Space Model (SSM). "Gefitinib-sensitive" genes, the expressional dynamics of which were altered by addition of gefitinib, were identified. A risk scoring model was constructed to classify high- or low-risk patients based on expression signatures of 139 gefitinib-sensitive genes in lung cancer using a training data set of 253 lung adenocarcinomas of North American cohort. The predictive ability of the risk scoring model was examined in independent cohorts of surgical specimens of lung cancer. RESULTS: The risk scoring model enabled the identification of high-risk stage IA and IB cases in another North American cohort for overall survival (OS) with a hazard ratio (HR) of 7.16 (P = 0.029) and 3.26 (P = 0.0072), respectively. It also enabled the identification of high-risk stage I cases without bronchioalveolar carcinoma (BAC) histology in a Japanese cohort for OS and recurrence-free survival (RFS) with HRs of 8.79 (P = 0.001) and 3.72 (P = 0.0049), respectively. CONCLUSION: The set of 139 gefitinib-sensitive genes includes many genes known to be involved in biological aspects of cancer phenotypes, but not known to be involved in EGF signaling. The present result strongly re-emphasizes that EGF signaling status in cancer cells underlies an aggressive phenotype of cancer cells, which is useful for the selection of early-stage lung adenocarcinoma patients with a poor prognosis. TRIAL REGISTRATION: The Gene Expression Omnibus (GEO) GSE31210.

INTRODUCTION: Japan is rapidly becoming a full-fledged aged society, and physician shortage is a significant concern. The Japanese government has increased the number of medical school enrollments since 2008, but some researchers warn that this increase could lead to physician surplus in the future. It is unknown how many physicians will be required to accommodate future healthcare needs. MATERIALS AND METHODS: We simulated changes in age/sex composition of the population, fatalities (the number of fatalities for the consecutive five years), and number of physicians from 2010 to 2035. Two indicators were defined: fatalities per physician and fatalities by physician working hour, based on the data of the working hours of physicians for each tuple of sex and age groups. We estimated the necessary number of physicians in 2035 and the number of new physicians to maintain the indicator levels in 2010. RESULTS: The number of physicians per 1,000 population is predicted to rise from 2·00 in 2010 to 3·14 in 2035. The number of physicians aged 60 years or older is expected to increase from 55,375 (20% of physicians) to 141,711 (36%). In 2010 and 2035, fatalities per physician were 23·1 and 24·0 for the total population, and 13·9 and 19·2 for 75 years or older, respectively. Fatalities per physician working hour are predicted to rise from 0·128 to 0·138. If working hours are limited to 48 hours per week in 2035, the number of fatalities per physician working hour is expected to be 0·196, and the number of new physicians must be increased by 53% over the current pace. DISCUSSION: The number of physicians per population continues to rise, but the estimated supply will not fulfill the demand for healthcare in the aging society. Strategies to increase the number of physicians and improve working conditions are urgently needed.

Patient-specific analysis of molecular networks is a promising strategy for making individual risk predictions and treatment decisions in cancer therapy. Although systems biology allows the gene network of a cell to be reconstructed from clinical gene expression data, traditional methods, such as bayesian networks, only provide an averaged network for all samples. Therefore, these methods cannot reveal patient-specific differences in molecular networks during cancer progression. In this study, we developed a novel statistical method called NetworkProfiler, which infers patient-specific gene regulatory networks for a specific clinical characteristic, such as cancer progression, from gene expression data of cancer patients. We applied NetworkProfiler to microarray gene expression data from 762 cancer cell lines and extracted the system changes that were related to the epithelial-mesenchymal transition (EMT). Out of 1732 possible regulators of E-cadherin, a cell adhesion molecule that modulates the EMT, NetworkProfiler, identified 25 candidate regulators, of which about half have been experimentally verified in the literature. In addition, we used NetworkProfiler to predict EMT-dependent master regulators that enhanced cell adhesion, migration, invasion, and metastasis. In order to further evaluate the performance of NetworkProfiler, we selected Krueppel-like factor 5 (KLF5) from a list of the remaining candidate regulators of E-cadherin and conducted in vitro validation experiments. As a result, we found that knockdown of KLF5 by siRNA significantly decreased E-cadherin expression and induced morphological changes characteristic of EMT. In addition, in vitro experiments of a novel candidate EMT-related microRNA, miR-100, confirmed the involvement of miR-100 in several EMT-related aspects, which was consistent with the predictions obtained by NetworkProfiler.

Decoding transcriptional programs governing transcriptomic diversity across human multiple tissues is a major challenge in bioinformatics. To address this problem, a number of computational methods have focused on cis-regulatory codes driving overexpression or underexpression in a single tissue as compared to others. On the other hand, we recently proposed a different approach to mine cis-regulatory codes: starting from gene sets sharing common cis-regulatory motifs, the method screens for expression modules based on expression coherence. However, both approaches seem to be insufficient to capture transcriptional programs that control gene expression in a subset of all samples. Especially, this limitation would be serious when analyzing multiple tissue data. To overcome this limitation, we developed a new module discovery method termed BEEM (Biclusering-based Extraction of Expression Modules) in order to discover expression modules that are functional in a subset of tissues. We showed that, when applied to expression profiles of human multiple tissues, BEEM finds expression modules missed by two existing approaches that are based on the coherent expression and the single tissue-specific differential expression. From the BEEM results, we obtained new insights into transcriptional programs controlling transcriptomic diversity across various types of tissues. This study introduces BEEM as a powerful tool for decoding regulatory programs from a compendium of gene expression profiles.