PI3Kγ inhibition suppresses microglia/TAM accumulation in glioblastoma microenvironment to promote exceptional temozolomide response.

Precision medicine in oncology leverages clinical observations of exceptional response. Toward an understanding of the molecular features that define this response, we applied an integrated, multiplatform analysis of RNA profiles derived from clinically annotated glioblastoma samples. This analysis suggested that specimens from exceptional responders are characterized by decreased accumulation of microglia/macrophages in the glioblastoma microenvironment. Glioblastoma-associated microglia/macrophages secreted interleukin 11 (IL11) to activate STAT3-MYC signaling in glioblastoma cells. This signaling induced stem cell states that confer enhanced tumorigenicity and resistance to the standard-of-care chemotherapy, temozolomide (TMZ). Targeting a myeloid cell restricted an isoform of phosphoinositide-3-kinase, phosphoinositide-3-kinase gamma isoform (PI3Kγ), by pharmacologic inhibition or genetic inactivation disrupted this signaling axis by reducing microglia/macrophage-associated IL11 secretion in the tumor microenvironment. Mirroring the clinical outcomes of exceptional responders, PI3Kγ inhibition synergistically enhanced the anti-neoplastic effects of TMZ in orthotopic murine glioblastoma models. Moreover, inhibition or genetic inactivation of PI3Kγ in murine glioblastoma models recapitulated expression profiles observed in clinical specimens isolated from exceptional responders. Our results suggest key contributions from tumor-associated microglia/macrophages in exceptional responses and highlight the translational potential for PI3Kγ inhibition as a glioblastoma therapy.

An Iterative Scale Purification Procedure on lz for the Detection of Aberrant Responses.

Many person-fit statistics have been proposed to detect aberrant response behaviors (e.g., cheating, guessing). Among them, lz is one of the most widely used indices. The computation of lz assumes the item and person parameters are known. In reality, they often have to be estimated from data. The better the estimation, the better lz will perform. When aberrant behaviors occur, the person and item parameter estimations are inaccurate, which in turn degrade the performance of lz. In this study, an iterative procedure was developed to attain more accurate person parameter estimates for improved performance of lz. A series of simulations were conducted to evaluate the iterative procedure under two conditions of item parameters, known and unknown, and three aberrant response styles of difficulty-sharing cheating, random-sharing cheating, and random guessing. The results demonstrated the superiority of the iterative procedure over the non-iterative one in maintaining control of Type-I error rates and improving the power of detecting aberrant responses. The proposed procedure was applied to a high-stake intelligence test.

Inferring circadian gene regulatory relationships from gene expression data with a hybrid framework.

BACKGROUND: The central biological clock governs numerous facets of mammalian physiology, including sleep, metabolism, and immune system regulation. Understanding gene regulatory relationships is crucial for unravelling the mechanisms that underlie various cellular biological processes. While it is possible to infer circadian gene regulatory relationships from time-series gene expression data, relying solely on correlation-based inference may not provide sufficient information about causation. Moreover, gene expression data often have high dimensions but a limited number of observations, posing challenges in their analysis. METHODS: In this paper, we introduce a new hybrid framework, referred to as Circadian Gene Regulatory Framework (CGRF), to infer circadian gene regulatory relationships from gene expression data of rats. The framework addresses the challenges of high-dimensional data by combining the fuzzy C-means clustering algorithm with dynamic time warping distance. Through this approach, we efficiently identify the clusters of genes related to the target gene. To determine the significance of genes within a specific cluster, we employ the Wilcoxon signed-rank test. Subsequently, we use a dynamic vector autoregressive method to analyze the selected significant gene expression profiles and reveal directed causal regulatory relationships based on partial correlation. CONCLUSION: The proposed CGRF framework offers a comprehensive and efficient solution for understanding circadian gene regulation. Circadian gene regulatory relationships are inferred from the gene expression data of rats based on the Aanat target gene. The results show that genes Pde10a, Atp7b, Prok2, Per1, Rhobtb3 and Dclk1 stand out, which have been known to be essential for the regulation of circadian activity. The potential relationships between genes Tspan15, Eprs, Eml5 and Fsbp with a circadian rhythm need further experimental research.

mUSP: a high-accuracy map of the in situ crosstalk of ubiquitylation and SUMOylation proteome predicted via the feature enhancement approach.

Reversible post-translational modification (PTM) orchestrates various biological processes by changing the properties of proteins. Since many proteins are multiply modified by PTMs, identification of PTM crosstalk site has emerged to be an intriguing topic and attracted much attention. In this study, we systematically deciphered the in situ crosstalk of ubiquitylation and SUMOylation that co-occurs on the same lysine residue. We first collected 3363 ubiquitylation-SUMOylation (UBS) crosstalk site on 1302 proteins and then investigated the prime sequence motifs, the local evolutionary degree and the distribution of structural annotations at the residue and sequence levels between the UBS crosstalk and the single modification sites. Given the properties of UBS crosstalk sites, we thus developed the mUSP classifier to predict UBS crosstalk site by integrating different types of features with two-step feature optimization by recursive feature elimination approach. By using various cross-validations, the mUSP model achieved an average area under the curve (AUC) value of 0.8416, indicating its promising accuracy and robustness. By comparison, the mUSP has significantly better performance with the improvement of 38.41 and 51.48% AUC values compared to the cross-results by the previous single predictor. The mUSP was implemented as a web server available at http://bioinfo.ncu.edu.cn/mUSP/index.html to facilitate the query of our high-accuracy UBS crosstalk results for experimental design and validation.

Development of an integrated predictive model for postoperative glioma-related epilepsy using gene-signature and clinical data.

BACKGROUND: This study aimed to develop an integrated model for predicting the occurrence of postoperative seizures in patients with diffuse high-grade gliomas (DHGGs) using clinical and RNA-seq data. METHODS: Patients with DHGGs, who received prophylactic anti-epileptic drugs (AEDs) for three months following surgery, were enrolled into the study. The patients were assigned randomly into training (n = 166) and validation (n = 42) cohorts. Differentially expressed genes (DEGs) were identified based on preoperative glioma-related epilepsy (GRE) history. Least absolute shrinkage and selection operator (LASSO) logistic regression analysis was used to construct a predictive gene-signature for the occurrence of postoperative seizures. The final integrated prediction model was generated using the gene-signature and clinical data. Receiver operating characteristic analysis and calibration curve method were used to evaluate the accuracy of the gene-signature and prediction model using the training and validation cohorts. RESULTS: A seven-gene signature for predicting the occurrence of postoperative seizures was developed using LASSO logistic regression analysis of 623 DEGs. The gene-signature showed satisfactory predictive capacity in the training cohort [area under the curve (AUC) = 0.842] and validation cohort (AUC = 0.751). The final integrated prediction model included age, temporal lobe involvement, preoperative GRE history, and gene-signature-derived risk score. The AUCs of the integrated prediction model were 0.878 and 0.845 for the training and validation cohorts, respectively. CONCLUSION: We developed an integrated prediction model for the occurrence of postoperative seizures in patients with DHGG using clinical and RNA-Seq data. The findings of this study may contribute to the development of personalized management strategies for patients with DHGGs and improve our understanding of the mechanisms underlying GRE in these patients.

Does one subgenome become dominant in the formation and evolution of a polyploid?

BACKGROUND: Polyploids are common in flowering plants and they tend to have more expanded ranges of distributions than their diploid progenitors. Possible mechanisms underlying polyploid success have been intensively investigated. Previous studies showed that polyploidy generates novel changes and that subgenomes in allopolyploid species often differ in gene number, gene expression levels and levels of epigenetic alteration. It is widely believed that such differences are the results of conflicts among the subgenomes. These differences have been treated by some as subgenome dominance, and it is claimed that the magnitude of subgenome dominance increases in polyploid evolution. SCOPE: In addition to changes which occurred during evolution, differences between subgenomes of a polyploid species may also be affected by differences between the diploid donors and changes which occurred during polyploidization. The variable genome components in many plant species are extensive, which would result in exaggerated differences between a subgenome and its progenitor when a single genotype or a small number of genotypes are used to represent a polyploid or its donors. When artificially resynthesized polyploids are used as surrogates for newly formed genotypes which have not been exposed to evolutionary selection, differences between diploid genotypes available today and those involved in the formation of the natural polyploid genotypes must also be considered. CONCLUSIONS: Contrary to the now widely held views that subgenome biases in polyploids are the results of conflicts among the subgenomes and that one of the parental subgenomes generally retains more genes which are more highly expressed, available results show that subgenome biases mainly reflect legacy from the progenitors and that they can be detected before the completion of polyploidization events. Further, there is no convincing evidence that the magnitudes of subgenome biases have significantly changed during evolution for any of the allopolyploid species assessed.

Differences between diploid donors are the main contributing factor for subgenome asymmetry measured in either gene ratio or relative diversity in allopolyploids.

Subgenome asymmetry (SA) has routinely been attributed to different responses between the subgenomes of a polyploid to various stimuli during evolution. Here, we compared subgenome differences in gene ratio and relative diversity between artificial and natural genotypes of several allopolyploid species. Surprisingly, consistent differences were not detected between these two types of polyploid genotypes, although they differ in times exposed to evolutionary selection. The estimated ratio of shared genes between a subgenome and its diploid donor was invariably higher for the artificial allopolyploid genotypes than those for the natural genotypes, which is expected as it is now well-known that many genes in a species are not shared among all individuals. As the exact diploid parent for a given subgenome is unknown, the estimated ratios of shared genes for the natural genotypes would also include difference among individual genotypes of the diploid donor species. Further, we detected the presence of SA in genotypes before the completion of the polyploidization events as well as in those which were not formed via polyploidization. These results indicate that SA may, to a large degree, reflect differences between its diploid donors or that changes occurred during polyploid evolution are defined by their donor genomes.

Robust approach for variable selection with high dimensional longitudinal data analysis.

This article proposes a new robust smooth-threshold estimating equation to select important variables and automatically estimate parameters for high dimensional longitudinal data. A novel working correlation matrix is proposed to capture correlations within the same subject. The proposed procedure works well when the number of covariates pn increases as the number of subjects n increases. The proposed estimates are competitive with the estimates obtained with the true correlation structure, especially when the data are contaminated. Moreover, the proposed method is robust against outliers in the response variables and/or covariates. Furthermore, the oracle properties for robust smooth-threshold estimating equations under "large n, diverging pn " are established under some regularity conditions. Extensive simulation studies and a yeast cell cycle data are used to evaluate the performance of the proposed method, and results show that the proposed method is competitive with existing robust variable selection procedures.

An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data.

In medical studies, the collected covariates contain underlying outliers. For clustered/longitudinal data with censored observations, the traditional Gehan-type estimator is robust to outliers in response but sensitive to outliers in the covariate domain, and it also ignores the within-cluster correlations. To take account of within-cluster correlations, varying cluster sizes, and outliers in covariates, we propose weighted Gehan-type estimating functions for parameter estimation in the accelerated failure time model for clustered data. We provide the asymptotic properties of the resulting estimators and carry out simulation studies to evaluate the performance of the proposed method under a variety of realistic settings. The simulation results demonstrate that the proposed method is robust to the outliers existing in the covariate domain and lead to much more efficient estimators when a strong within-cluster correlation exists. Finally, the proposed method is applied to two medical datasets and more reliable and convincing results are hence obtained.

Significance tests for analyzing gene expression data with small sample sizes.

MOTIVATION: Under two biologically different conditions, we are often interested in identifying differentially expressed genes. It is usually the case that the assumption of equal variances on the two groups is violated for many genes where a large number of them are required to be filtered or ranked. In these cases, exact tests are unavailable and the Welch's approximate test is most reliable one. The Welch's test involves two layers of approximations: approximating the distribution of the statistic by a t-distribution, which in turn depends on approximate degrees of freedom. This study attempts to improve upon Welch's approximate test by avoiding one layer of approximation. RESULTS: We introduce a new distribution that generalizes the t-distribution and propose a Monte Carlo based test that uses only one layer of approximation for statistical inferences. Experimental results based on extensive simulation studies show that the Monte Carol based tests enhance the statistical power and performs better than Welch's t-approximation, especially when the equal variance assumption is not met and the sample size of the sample with a larger variance is smaller. We analyzed two gene-expression datasets, namely the childhood acute lymphoblastic leukemia gene-expression dataset with 22 283 genes and Golden Spike dataset produced by a controlled experiment with 13 966 genes. The new test identified additional genes of interest in both datasets. Some of these genes have been proven to play important roles in medical literature. AVAILABILITY AND IMPLEMENTATION: R scripts and the R package mcBFtest is available in CRAN and to reproduce all reported results are available at the GitHub repository, https://github.com/iullah1980/MCTcodes. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Accurate prediction of species-specific 2-hydroxyisobutyrylation sites based on machine learning frameworks.

Lysine 2-hydroxyisobutyrylation (Khib) is a newly discovered post-translational modification (PTM) across eukaryotes and prokaryotes in recent years, which plays a significant role in diverse cellular functions. Accurate prediction of Khib sites is a first-crucial step to decipher its molecular mechanism and urgently needed. In this work, based on a large benchmark datasets in multi-species, a novel online species-specific prediction tool, namely KhibPred, was developed to identify Khib sites. Four types of feature strategies, including sequence-based information, physicochemical properties and evolutionary-derived information, were applied to represent a wide range of protein sequences, and the random forest was used to build the optimal feature datasets. Moreover, six representative machine learning (ML) methods were trained and comprehensively discussed and compared for each organism. Data analyses suggested that the unique protein sequence preferences were discovered for each species. When evaluated on independent test datasets, the area under the receiver operating characteristic curves (AUCs) achieved 0.807, 0.781, 0.825 and 0.831 for Saccharomyces cerevisiaes, Physcomitrella patens, Rice Seeds and HeLa cells, respectively. The satisfactory results imply that KhibPred is a promising computational tool. The online predictor can be freely available at: http://bioinfo.ncu.edu.cn/KhibPred.aspx.

Identifying barley pan-genome sequence anchors using genetic mapping and machine learning.

KEY MESSAGE: We identified 1.844 million barley pan-genome sequence anchors from 12,306 genotypes using genetic mapping and machine learning. There is increasing evidence that genes from a given crop genotype are far to cover all genes in that species; thus, building more comprehensive pan-genomes is of great importance in genetic research and breeding. Obtaining a thousand-genotype scale pan-genome using deep-sequencing data is currently impractical for species like barley which has a huge and highly repetitive genome. To this end, we attempted to identify barley pan-genome sequence anchors from a large quantity of genotype-by-sequencing (GBS) datasets by combining genetic mapping and machine learning algorithms. Based on the GBS sequences from 11,166 domesticated and 1140 wild barley genotypes, we identified 1.844 million pan-genome sequence anchors. Of them, 532,253 were identified as presence/absence variation (PAV) tags. Through aligning these PAV tags to the genome of hulless barley genotype Zangqing320, our analysis resulted in a validation of 83.6% of them from the domesticated genotypes and 88.6% from the wild barley genotypes. Association analyses against flowering time, plant height and kernel size showed that the relative importance of the PAV and non-PAV tags varied for different traits. The pan-genome sequence anchors based on GBS tags can facilitate the construction of a comprehensive pan-genome and greatly assist various genetic studies including identification of structural variation, genetic mapping and breeding in barley.

Correlation of alteration of HLA-F expression and clinical characterization in 593 brain glioma samples.

BACKGROUND: Human gliomas are highly fatal tumors with a significant feature of immune suppression. The association of the immune system in gliomas is gradually revealed, and immunotherapy is expected to improve the survival of glioma patients. In-depth understanding of the immune microenvironment of gliomas and their associated immunotherapy was increased exponentially in recent years. Gliomas provide clinical targets for immunotherapy during the search of key regulators of immune response. Our study focused on the human leukocyte antigen (HLA) system that is responsible for regulating the immune system, and discovered the relationship between HLA-F expression and clinical prognosis in gliomas. METHODS: A total of 593 patients with gliomas were included in our research. Of these, 325 patients were from the Chinese Glioma Genome Atlas (CGGA) and 268 were from the GSE 16011 set. Kaplan-Meier (KM) analysis was performed to explore the prognostic value of HLA-F. t test analysis was used to find the distribution difference in various groups. R language packages are used for other statistical computations and figure drawing. RESULTS: HLA-F was negatively correlated with overall survival (OS) in all grades of glioma and glioblastoma (GBM). Moreover, HLA-F was enriched in GBM and isocitrate dehydrogenase 1 wild-type (IDH1 wt) group and considered HLA-F as a mesenchymal subtype marker. Pearson correlation test showed that HLA-F was correlated with other HLA-I molecules. CONCLUSION: HLA-F expression was positively correlated with malignant phenotype and negatively correlated with OS, indicating that HLA-F could predict the immune state of gliomas and might be a clinical target of glioma immunotherapy.

Sweepstakes reproductive success is absent in a New Zealand snapper (Chrysophrus auratus) population protected from fishing despite "tiny" Ne /N ratios elsewhere.

A landmark study published in 2002 estimated a very small Ne /N ratio (around 10-5 ) in a population of pink snapper (Chrysophrys auratus, Forster, 1801) in the Hauraki Gulf in New Zealand. It epitomized the tiny Ne /N ratios (<10-3 ) reported in marine species due to the hypothesized operation of sweepstakes reproductive success (SRS). Here we re-evaluate the occurrence of SRS in marine species and the potential effect of fishing on the Ne /N ratio by studying the same species in the same region, but in a population that has been protected from fishing since 1975. We combine empirical, simulation and model-based approaches to estimate Ne (and Nb ) from genotypes of 1,044 adult fish and estimate N using recapture-probabilities. The estimated Ne /N ratio was much larger (0.33, SE: 0.14) than expected. The magnitude of estimates of population-wide variance in individual lifetime reproductive success (10-18) suggested that the sweepstakes effect was negligible in the study population. After evaluating factors that could explain the contrast between studies - experimental design, life history differences, environmental effects and the influence of exploitation on the Ne /N ratio - we conclude that the low Ne of the Hauraki Gulf population is associated with demographic instability in the harvested compared to the protected population despite circumstantial evidence that the 2002 study may have underestimated Ne . This study has broad implications for the prevailing view that reproductive success in the sea is largely driven by chance, and for genetic monitoring of populations using the Ne /N ratio and Nb .

Predicting the likelihood of postoperative seizure status based on mRNA sequencing in low-grade gliomas.

AIM: No comprehensive and objective methods yet exist for predicting postoperative seizure. PATIENTS & METHODS: mRNA-seq data and corresponding postoperative seizure status of 109 low-grade glioma samples were obtained from Chinese Glioma Genome Atlas database and divided into two sets randomly. Logistic regression and receiver operating characteristic analysis with risk score method were used to develop a ten-gene prediction model. RESULTS: Considering gene number and area under the curve of receiver operating characteristic, a ten-gene model was generated which showed an area under the curve of 0.9965 in training set. Patients with high-risk scores had higher probability of postoperative seizure compared with those with low-risk scores. CONCLUSION: This is the first prediction model for postoperative seizures in gliomas, integrating multiple genes. Clinical application may help patients with postoperative seizure control.

Selection of working correlation structure in generalized estimating equations.

The well-known generalized estimating equations is a very popular approach for analyzing longitudinal data. Selecting an appropriate correlation structure in the generalized estimating equations framework is a key step for estimating parameters efficiently and deriving reliable statistical inferences. We present two new criteria for selecting the best among the candidates with any arbitrary structures, even for irregularly timed measurements. The simulation results demonstrate that the new criteria perform more similarly to EAIC and EBIC as the sample size becomes large. However, their performance is much enhanced when the sample size is small and the number of measurements is large. Finally, three real datasets are used to illustrate the proposed criteria. Copyright © 2017 John Wiley & Sons, Ltd.

Mixture of time-dependent growth models with an application to blue swimmer crab length-frequency data.

Understanding how aquatic species grow is fundamental in fisheries because stock assessment often relies on growth dependent statistical models. Length-frequency-based methods become important when more applicable data for growth model estimation are either not available or very expensive. In this article, we develop a new framework for growth estimation from length-frequency data using a generalized von Bertalanffy growth model (VBGM) framework that allows for time-dependent covariates to be incorporated. A finite mixture of normal distributions is used to model the length-frequency cohorts of each month with the means constrained to follow a VBGM. The variances of the finite mixture components are constrained to be a function of mean length, reducing the number of parameters and allowing for an estimate of the variance at any length. To optimize the likelihood, we use a minorization-maximization (MM) algorithm with a Nelder-Mead sub-step. This work was motivated by the decline in catches of the blue swimmer crab (BSC) (Portunus armatus) off the east coast of Queensland, Australia. We test the method with a simulation study and then apply it to the BSC fishery data.

A single aspergillus fumigatus intracranial abscess in an immunocompetent patient with parietal lobe tumorectomy.

Aspergillosis of the central nervous system is a rare fungal infection that is mainly reported in patients with immune deficiency, such as AIDS patients and organ transplant patients treated with immunosuppressive agents, and is uncommon among patients with intact immune function. We report here a rare case of intracranial aspergillosis in a patient who had previously undergone a parietal lobe tumorectomy. Aspergillus fumigatus was confirmed by histopathology, and susceptibility tests reported that this infection should respond to voriconazole. We believe the immunosuppression resulting from surgical trauma and glucocorticosteroid treatment may be contributing to the infection, and therefore management of these two factors may improve the prognosis.