Bo Zhou, Xuejiao Dou, Wei Wang, Hongxiang Yao, Feng Feng, Pan Wang, Zhengyi Yang, Ningyu An, Bing Liu, Xi Zhang, Yong Liu <<<

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia, and amnestic mild cognitive impairment (aMCI) has been defined as a transitional stage between normal aging and AD. Accumulating evidence has shown that altered functional connectivity (FC) and structural connectivity (SC) in the default mode network (DMN) is the prominent hallmarks of AD. However, the relationship between the changes in SC and FC of the DMN is not yet clear. In the present study, we derived the FC and SC matrices of the DMN with functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI) data and further assessed FC and SC abnormalities within a discovery dataset of 120 participants (39 normal controls, 34 patients with aMCI and 47 patients with AD), as well as a replication dataset of 122 participants (43 normal controls, 37 patients with aMCI and 42 patients with AD). Disrupted SC and FC were found among DMN components (e.g., the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), and hippocampus) in patients in the aMCI and AD groups in the discovery dataset; most of the disrupted connections were also identified in the replication dataset. More importantly, some SC and FC elements were significantly correlated with the cognitive ability of patients with aMCI and AD. In addition, we found structural-functional decoupling between the PCC and the right hippocampus in patients in the aMCI and AD groups. These findings of the alteration of DMN connectivity in neurodegenerative cohorts deepen our understanding of the pathophysiological mechanisms of AD. <<<

Omar Yossofzai, Aria Fallah, Cassia Maniquis, Shelly Wang, John Ragheb, Alexander G Weil, Tristan Brunette-Clement, Andrea Andrade, George M Ibrahim, Nicholas Mitsakakis, Elysa Widjaja <<<

OBJECTIVE: There is substantial variability in reported seizure outcome following pediatric epilepsy surgery, and lack of individualized predictive tools that could evaluate the probability of seizure freedom postsurgery. The aim of this study was to develop and validate a supervised machine learning (ML) model for predicting seizure freedom after pediatric epilepsy surgery.METHODS: This is a multicenter retrospective study of children who underwent epilepsy surgery at five pediatric epilepsy centers in North America. Clinical information, diagnostic investigations, and surgical characteristics were collected, and used as features to predict seizure-free outcome 1 year after surgery. The dataset was split randomly into 80% training and 20% testing data. Thirty-five combinations of five feature sets with seven ML classifiers were assessed on the training cohort using 10-fold cross-validation for model development. The performance of the optimal combination of ML classifier and feature set was evaluated in the testing cohort, and compared with logistic regression, a classical statistical approach.RESULTS: Of the 801 patients included, 61.3% were seizure-free 1 year postsurgery. During model development, the best combination was XGBoost ML algorithm with five features from the univariate feature set, including number of antiseizure medications, magnetic resonance imaging lesion, age at seizure onset, video-electroencephalography concordance, and surgery type, with a mean area under the curve (AUC) of .73 (95% confidence interval [CI] = .69-.77). The combination of XGBoost and univariate feature set was then evaluated on the testing cohort and achieved an AUC of .74 (95% CI = .66-.82; sensitivity = .87, 95% CI = .81-.94; specificity = .58, 95% CI = .47-.71). The XGBoost model outperformed the logistic regression model (AUC = .72, 95% CI = .63-.80; sensitivity = .72, 95% CI = .63-.82; specificity = .66, 95% CI = .53-.77) in the testing cohort (p = .005).SIGNIFICANCE: This study identified important features and validated an ML algorithm, XGBoost, for predicting the probability of seizure freedom after pediatric epilepsy surgery. Improved prognostication of epilepsy surgery is critical for presurgical counseling and will inform treatment decisions. <<<

C++ is a widely used programming language and the C++ front-end is a critical part of a C++ compiler. Although many techniques have been proposed to test compilers, few studies are devoted to detecting bugs in C++ compiler. In this study, we take the first step to detect bugs in C++ compiler front-ends. To do so, two main challenges need to be addressed, namely, the acquisition of test programs that are more likely to trigger bugs in compiler front-ends and the bug identification from complicated compiler outputs. In this article, we propose a novel framework named Ccoft to detect bugs in C++ compiler front-ends. To address the first challenge, Ccoft implements a practical program generator. The generator first transforms C++ grammars into a flexible structured format and then utilizes an equal-chance selection (ECS) strategy to conduct structure-aware grammar mutation to generate diverse C++ programs. Next, Ccoft employs a set of differential testing strategies to identify various kinds of bugs in C++ compiler front-ends by comparing complex outputs emitted by C++ compilers, thus tackling the second challenge. Empirical evaluation results over two mainstream compilers (i.e., GCC and Clang) show that Ccoft greatly improves two state-of-the-art approaches (i.e., Dharma and Grammarinator) by 135% and 111% in terms of the numbers of detected bugs, respectively. By running Ccoft for three months, we have successfully reported 136 bugs for two C++ compilers, of which 78 (57 confirmed, assigned, or fixed) for GCC and 58 (10 confirmed or fixed) for Clang. <<<

BACKGROUND: Immune checkpoint blockade (ICB) therapy has revolutionized the treatment of lung squamous cell carcinoma (LUSC). However, a significant proportion of patients with high tumour PD-L1 expression remain resistant to immune checkpoint inhibitors. To understand the underlying resistance mechanisms, characterization of the immunosuppressive tumour microenvironment and identification of biomarkers to predict resistance in patients are urgently needed.METHODS: Our study retrospectively analysed RNA sequencing data of 624 LUSC samples. We analysed gene expression patterns from tumour microenvironment by unsupervised clustering. We correlated the expression patterns with a set of T cell exhaustion signatures, immunosuppressive cells, clinical characteristics, and immunotherapeutic responses. Internal and external testing datasets were used to validate the presence of exhausted immune status.RESULTS: Approximately 28 to 36% of LUSC patients were found to exhibit significant enrichments of T cell exhaustion signatures, high fraction of immunosuppressive cells (M2 macrophage and CD4 Treg), co-upregulation of 9 inhibitory checkpoints (CTLA4, PDCD1, LAG3, BTLA, TIGIT, HAVCR2, IDO1, SIGLEC7, and VISTA), and enhanced expression of anti-inflammatory cytokines (e.g. TGFβ and CCL18). We defined this immunosuppressive group of patients as exhausted immune class (EIC). Although EIC showed a high density of tumour-infiltrating lymphocytes, these were associated with poor prognosis. EIC had relatively elevated PD-L1 expression, but showed potential resistance to ICB therapy. The signature of 167 genes for EIC prediction was significantly enriched in melanoma patients with ICB therapy resistance. EIC was characterized by a lower chromosomal alteration burden and a unique methylation pattern. We developed a web application ( http://lilab2.sysu.edu.cn/tex & http://liwzlab.cn/tex ) for researchers to further investigate potential association of ICB resistance based on our multi-omics analysis data.CONCLUSIONS: We introduced a novel LUSC immunosuppressive class which expressed high PD-L1 but showed potential resistance to ICB therapy. This comprehensive characterization of immunosuppressive tumour microenvironment in LUSC provided new insights for further exploration of resistance mechanisms and optimization of immunotherapy strategies. <<<

The rapid development of information technology has generated substantial data, which urgently requires new storage media and storage methods. DNA, as a storage medium with high density, high durability, and ultra-long storage time characteristics, is promising as a potential solution. However, DNA storage is still in its infancy and suffers from low space utilization of DNA strands, high read coverage, and poor coding coupling. Therefore, in this work, an adaptive coding DNA storage system is proposed to use different coding schemes for different coding region locations, and the method of adaptively generating coding constraint thresholds is used to optimize at the system level to ensure the efficient operation of each link. Images, videos, and PDF files of size 698 KB were stored in DNA using adaptive coding algorithms. The data were sequenced and losslessly decoded into raw data. Compared with previous work, the DNA storage system implemented by adaptive coding proposed in this paper has high storage density and low read coverage, which promotes the development of carbon-based storage systems. <<<

There are major efforts underway to make genome sequencing a routine part of clinical practice. A critical barrier to these is achieving practical solutions for data ownership and integrity. Blockchain provides solutions to these challenges in other realms, such as finance. However, its use in genomics is stymied due to the difficulty in storing large-scale data on-chain, slow transaction speeds, and limitations on querying. To overcome these roadblocks, we developed a private blockchain network to store genomic variants and reference-aligned reads on-chain. It uses nested database indexing with an accompanying tool suite to rapidly access and analyze the data. <<<

Investigating the functions and activities of genes requires proper annotation of the transcribed units. However, transcript assembly efforts have produced a surprisingly large variation in the number of transcripts, and especially so for noncoding transcripts. This heterogeneity in assembled transcript sets might be partially explained by sequencing depth. Here, we used real and simulated short-read sequencing data as well as long-read data to systematically investigate the impact of sequencing depths on the accuracy of assembled transcripts. We assembled and analyzed transcripts from 671 human short-read data sets and four long-read data sets. At the first level, there is a positive correlation between the number of reads and the number of recovered transcripts. However, the effect of the sequencing depth varied based on cell or tissue type, the type of read and the nature and expression levels of the transcripts. The detection of coding transcripts saturated rapidly with both short and long-reads, however, there was no sign of early saturation for noncoding transcripts at any sequencing depth. Increasing long-read sequencing depth specifically benefited transcripts containing transposable elements. Finally, we show how single-cell RNA-seq can be guided by transcripts assembled from bulk long-read samples, and demonstrate that noncoding transcripts are expressed at similar levels to coding transcripts but are expressed in fewer cells. This study highlights the impact of sequencing depth on transcript assembly. <<<

Our representation of the physical world requires judgments of magnitudes, such as loudness, distance, or time. Interestingly, magnitude estimates are often not veridical but subject to characteristic biases. These biases are strikingly similar across different sensory modalities, suggesting common processing mechanisms that are shared by different sensory systems. However, the search for universal neurobiological principles of magnitude judgments requires guidance by formal theories. Here, we discuss a unifying Bayesian framework for understanding biases in magnitude estimation. This Bayesian perspective enables a re-interpretation of a range of established psychophysical findings, reconciles seemingly incompatible classical views on magnitude estimation, and can guide future investigations of magnitude estimation and its neurobiological mechanisms in health and in psychiatric diseases, such as schizophrenia. <<<

Bipolar disorder (BIP) is one of the most common hereditary psychiatric disorders worldwide. Elucidating the genetic basis of BIP will play a pivotal role in mechanistic delineation. Genome-wide association studies (GWAS) have successfully reported multiple susceptibility loci conferring BIP risk, thus providing insight into the effects of its underlying pathobiology. However, difficulties remain in the extrication of important and biologically relevant data from genetic discoveries related to psychiatric disorders such as BIP. There is an urgent need for an integrated and comprehensive online database with unified access to genetic and multi-omics data for in-depth data mining. Here, we developed the dbBIP, a database for BIP genetic research based on published data. The dbBIP consists of several modules, i.e.: (i) single nucleotide polymorphism (SNP) module, containing large-scale GWAS genetic summary statistics and functional annotation information relevant to risk variants; (ii) gene module, containing BIP-related candidate risk genes from various sources and (iii) analysis module, providing a simple and user-friendly interface to analyze one's own data. We also conducted extensive analyses, including functional SNP annotation, integration (including summary-data-based Mendelian randomization and transcriptome-wide association studies), co-expression, gene expression, tissue expression, protein-protein interaction and brain expression quantitative trait loci analyses, thus shedding light on the genetic causes of BIP. Finally, we developed a graphical browser with powerful search tools to facilitate data navigation and access. The dbBIP provides a comprehensive resource for BIP genetic research as well as an integrated analysis platform for researchers and can be accessed online at http://dbbip.xialab.info. Database URL: http://dbbip.xialab.info. <<<

Long diagnostic wait times hinder international efforts to address antibiotic resistance in M. tuberculosis. Pathogen whole genome sequencing, coupled with statistical and machine learning models, offers a promising solution. However, generalizability and clinical adoption have been limited by a lack of interpretability, especially in deep learning methods. Here, we present two deep convolutional neural networks that predict antibiotic resistance phenotypes of M. tuberculosis isolates: a multi-drug CNN (MD-CNN), that predicts resistance to 13 antibiotics based on 18 genomic loci, with AUCs 82.6-99.5% and higher sensitivity than state-of-the-art methods; and a set of 13 single-drug CNNs (SD-CNN) with AUCs 80.1-97.1% and higher specificity than the previous state-of-the-art. Using saliency methods to evaluate the contribution of input sequence features to the SD-CNN predictions, we identify 18 sites in the genome not previously associated with resistance. The CNN models permit functional variant discovery, biologically meaningful interpretation, and clinical applicability. <<<

Alport syndrome is an inherited disorder of the kidneys that results from variants in three collagen IV genes-COL4A3, COL4A4, and COL4A5. Early diagnosis and pharmacologic intervention can delay the progression of chronic kidney disease and the onset of kidney failure in patients with Alport syndrome. This article describes the evolution of approaches to the diagnosis and early treatment of Alport syndrome. <<<

Human papillomavirus type 16 (HPV16) belongs to the high-risk-group of HPVs and is causing a variety of anogenital cancers and head and neck cancer. The two HPV16 oncoproteins E6 and E7 prevent apoptosis and promote mitosis and are essential for completion of the HPV16 life cycle and for transformation of the infected cell and maintenance of malignancy. E6 and E7 are produced from two mRNAs that are generated in a mutually exclusive manner by alternative splicing. While E6 protein is made from the unspliced mRNA, E7 is made from the spliced version of the same pre-mRNA. Since sufficient quantities of both E6 and E7 are required for malignant transformation, this intricate arrangement of gene expression renders E6 and E7 expression vulnerable to external interference. Since antiviral drugs to HPV16 are not available, a detailed knowledge of the regulation of HPV16 E6 and E7 mRNA splicing may uncover novel targets for therapy. <<<

BACKGROUND: Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation. Traditional methods for detecting RBPs binding sites are time-consuming and computationally-intensive. Recently, the computational method has been incorporated in researches of RBPs. Nevertheless, lots of them not only rely on the sequence data of RNA but also need additional data, for example the secondary structural data of RNA, to improve the performance of prediction, which needs the pre-work to prepare the learnable representation of structural data.RESULTS: To reduce the dependency of those pre-work, in this paper, we introduce DeepPN, a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutional network (GCN) for detecting RBPs binding sites. It includes a two-layer CNN and GCN in parallel to extract the hidden features, followed by a fully connected layer to make the prediction. DeepPN discriminates the RBP binding sites on learnable representation of RNA sequences, which only uses the sequence data without using other data, for example the secondary or tertiary structure data of RNA. DeepPN is evaluated on 24 datasets of RBPs binding sites with other state-of-the-art methods. The results show that the performance of DeepPN is comparable to the published methods.CONCLUSION: The experimental results show that DeepPN can effectively capture potential hidden features in RBPs and use these features for effective prediction of binding sites. <<<

The diversity of microbial insertion sequences, crucial mobile genetic elements in generating diversity in microbial genomes, needs to be better represented in current microbial databases. Identification of these sequences in microbiome communities presents some significant problems that have led to their underrepresentation. Here, we present a software tool called PaliDIS that recognises insertion sequences in metagenomic sequence data rapidly by identifying inverted terminal repeat regions from mixed microbial community genomes. Applying this software to 266 human metagenomes identifies 11,681 unique insertion sequences. Querying this catalogue against a large database of isolate genomes reveals evidence of horizontal gene transfer events of clinically relevant antimicrobial resistance genes between classes of bacteria. We will continue to apply this tool more widely, building the Insertion Sequence Catalogue, a valuable resource for researchers wishing to query their microbial genomes for insertion sequences. <<<

Epithelial organoids are stem cell–derived tissues that approximate aspects of real organs, and thus they have potential as powerful tools in basic and translational research. By definition, they self-organize, but the structures formed are often heterogeneous and irreproducible, which limits their use in the lab and clinic. We describe methodologies for spatially and temporally controlling organoid formation, thereby rendering a stochastic process more deterministic. Bioengineered stem cell microenvironments are used to specify the initial geometry of intestinal organoids, which in turn controls their patterning and crypt formation. We leveraged the reproducibility and predictability of the culture to identify the underlying mechanisms of epithelial patterning, which may contribute to reinforcing intestinal regionalization in vivo. By controlling organoid culture, we demonstrate how these structures can be used to answer questions not readily addressable with the standard, more variable, organoid models. <<<

The heterogeneous therapy response observed in colorectal cancer is in part due to cancer stem cells (CSCs) that resist chemotherapeutic insults. The anti-apoptotic protein BCL-XL plays a critical role in protecting CSCs from cell death, where its inhibition with high doses of BH3 mimetics can induce apoptosis. Here, we screen a compound library for synergy with low-dose BCL-XL inhibitor A-1155463 to identify pathways that regulate sensitivity to BCL-XL inhibition and reveal that fibroblast growth factor receptor (FGFR)4 inhibition effectively sensitizes to A-1155463 both in vitro and in vivo. Mechanistically, we identify a rescue response that is activated upon BCL-XL inhibition and leads to rapid FGF2 secretion and subsequent FGFR4-mediated post-translational stabilization of MCL-1. FGFR4 inhibition prevents MCL-1 upregulation and thereby sensitizes CSCs to BCL-XL inhibition. Altogether, our findings suggest a cell transferable induction of a FGF2/FGFR4 rescue response in CRC that is induced upon BCL-XL inhibition and leads to MCL-1 upregulation. <<<

Ryan T Davis, Kerrigan Blake, Dennis Ma, Mari B Ishak Gabra, Grace A Hernandez, Anh T Phung, Ying Yang, Dustin Maurer, Austin E Y T Lefebvre, Hamad Alshetaiwi, Zhengtao Xiao, Juan Liu, Jason W Locasale, Michelle A Digman, Eric Mjolsness, Mei Kong, Zena Werb, Devon A Lawson <<<

Although metastasis remains the cause of most cancer-related mortality, mechanisms governing seeding in distal tissues are poorly understood. Here, we establish a robust method for the identification of global transcriptomic changes in rare metastatic cells during seeding using single-cell RNA sequencing and patient-derived-xenograft models of breast cancer. We find that both primary tumours and micrometastases display transcriptional heterogeneity but micrometastases harbour a distinct transcriptome program conserved across patient-derived-xenograft models that is highly predictive of poor survival of patients. Pathway analysis revealed mitochondrial oxidative phosphorylation as the top pathway upregulated in micrometastases, in contrast to higher levels of glycolytic enzymes in primary tumour cells, which we corroborated by flow cytometric and metabolomic analyses. Pharmacological inhibition of oxidative phosphorylation dramatically attenuated metastatic seeding in the lungs, which demonstrates the functional importance of oxidative phosphorylation in metastasis and highlights its potential as a therapeutic target to prevent metastatic spread in patients with breast cancer. <<<

Maintenance of tissue homeostasis is dependent on the communication between stem cells and supporting cells in the same niche. Regulatory T cells (T cells) are emerging as a critical component of the stem-cell niche for supporting their differentiation. How T cells sense dynamic signals in this microenvironment and communicate with stem cells is mostly unknown. In the present study, by using hair follicles (HFs) to study T cell-stem cell crosstalk, we show an unrecognized function of the steroid hormone glucocorticoid in instructing skin-resident T cells to facilitate HF stem-cell (HFSC) activation and HF regeneration. Ablation of the glucocorticoid receptor (GR) in T cells blocks hair regeneration without affecting immune homeostasis. Mechanistically, GR and Foxp3 cooperate in T cells to induce transforming growth factor β3 (TGF-β3), which activates Smad2/3 in HFSCs and facilitates HFSC proliferation. The present study identifies crosstalk between T cells and HFSCs mediated by the GR-TGF-β3 axis, highlighting a possible means of manipulating T cells to support tissue regeneration. <<<

Smruti Pushalkar, Mautin Hundeyin, Donnele Daley, Constantinos P Zambirinis, Emma Kurz, Ankita Mishra, Navyatha Mohan, Berk Aykut, Mykhaylo Usyk, Luisana E Torres, Gregor Werba, Kevin Zhang, Yuqi Guo, Qianhao Li, Neha Akkad, Sarah Lall, Benjamin Wadowski, Johana Gutierrez, Juan Andres Kochen Rossi, Jeremy W Herzog, Brian Diskin, Alejandro Torres-Hernandez, Josh Leinwand, Wei Wang, Pardeep S Taunk, Shivraj Savadkar, Malvin Janal, Anjana Saxena, Xin Li, Deirdre Cohen, R Balfour Sartor, Deepak Saxena, George Miller <<<

We found that the cancerous pancreas harbors a markedly more abundant microbiome compared with normal pancreas in both mice and humans, and select bacteria are differentially increased in the tumorous pancreas compared with gut. Ablation of the microbiome protects against preinvasive and invasive pancreatic ductal adenocarcinoma (PDA), whereas transfer of bacteria from PDA-bearing hosts, but not controls, reverses tumor protection. Bacterial ablation was associated with immunogenic reprogramming of the PDA tumor microenvironment, including a reduction in myeloid-derived suppressor cells and an increase in M1 macrophage differentiation, promoting TH1 differentiation of CD4 T cells and CD8 T-cell activation. Bacterial ablation also enabled efficacy for checkpoint-targeted immunotherapy by upregulating PD-1 expression. Mechanistically, the PDA microbiome generated a tolerogenic immune program by differentially activating select Toll-like receptors in monocytic cells. These data suggest that endogenous microbiota promote the crippling immune-suppression characteristic of PDA and that the microbiome has potential as a therapeutic target in the modulation of disease progression. We found that a distinct and abundant microbiome drives suppressive monocytic cellular differentiation in pancreatic cancer via selective Toll-like receptor ligation leading to T-cell anergy. Targeting the microbiome protects against oncogenesis, reverses intratumoral immune tolerance, and enables efficacy for checkpoint-based immunotherapy. These data have implications for understanding immune suppression in pancreatic cancer and its reversal in the clinic. . <<<

Yongqiang Liu, Hongru Wang, Zhimin Jiang, Wei Wang, Ruineng Xu, Qihui Wang, Zhihua Zhang, Aifu Li, Yan Liang, Shujun Ou, Xiujie Liu, Shouyun Cao, Hongning Tong, Yonghong Wang, Feng Zhou, Hong Liao, Bin Hu, Chengcai Chu <<<

The intensive application of inorganic nitrogen underlies marked increases in crop production, but imposes detrimental effects on ecosystems: it is therefore crucial for future sustainable agriculture to improve the nitrogen-use efficiency of crop plants. Here we report the genetic basis of nitrogen-use efficiency associated with adaptation to local soils in rice (Oryza sativa L.). Using a panel of diverse rice germplasm collected from different ecogeographical regions, we performed a genome-wide association study on the tillering response to nitrogen-the trait that is most closely correlated with nitrogen-use efficiency in rice-and identified OsTCP19 as a modulator of this tillering response through its transcriptional response to nitrogen and its targeting to the tiller-promoting gene DWARF AND LOW-TILLERING (DLT). A 29-bp insertion and/or deletion in the OsTCP19 promoter confers a differential transcriptional response and variation in the tillering response to nitrogen among rice varieties. The allele of OsTCP19 associated with a high tillering response to nitrogen is prevalent in wild rice populations, but has largely been lost in modern cultivars: this loss correlates with increased local soil nitrogen content, which suggests that it might have contributed to geographical adaptation in rice. Introgression of the allele associated with a high tillering response into modern rice cultivars boosts grain yield and nitrogen-use efficiency under low or moderate levels of nitrogen, which demonstrates substantial potential for rice breeding and the amelioration of negative environment effects by reducing the application of nitrogen to crops. <<<