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. <<<

M Ryan Corces, Jeffrey M Granja, Shadi Shams, Bryan H Louie, Jose A Seoane, Wanding Zhou, Tiago C Silva, Clarice Groeneveld, Christopher K Wong, Seung Woo Cho, Ansuman T Satpathy, Maxwell R Mumbach, Katherine A Hoadley, A Gordon Robertson, Nathan C Sheffield, Ina Felau, Mauro A A Castro, Benjamin P Berman, Louis M Staudt, Jean C Zenklusen, Peter W Laird, Christina Curtis, Cancer Genome Atlas Analysis Network, William J Greenleaf, Howard Y Chang <<<

We present the genome-wide chromatin accessibility profiles of 410 tumor samples spanning 23 cancer types from The Cancer Genome Atlas (TCGA). We identify 562,709 transposase-accessible DNA elements that substantially extend the compendium of known cis-regulatory elements. Integration of ATAC-seq (the assay for transposase-accessible chromatin using sequencing) with TCGA multi-omic data identifies a large number of putative distal enhancers that distinguish molecular subtypes of cancers, uncovers specific driving transcription factors via protein-DNA footprints, and nominates long-range gene-regulatory interactions in cancer. These data reveal genetic risk loci of cancer predisposition as active DNA regulatory elements in cancer, identify gene-regulatory interactions underlying cancer immune evasion, and pinpoint noncoding mutations that drive enhancer activation and may affect patient survival. These results suggest a systematic approach to understanding the noncoding genome in cancer to advance diagnosis and therapy. <<<

移民通过尽早进城,在青少年时期获得城市生活经历,可以提升其非认知能力,增加进入现代服务业就业的概率,改善其劳动力市场收入。基于流动人口监测数据的实证研究表明,农村移民首次进城年龄越小,其小时工资越高。以农村移民进城当年户籍地春季旱涝等级作为首次进城年龄的工具变量,两阶段最小二乘法估计表明,户籍地当年春季降水量越多,农民越早进城,其日后的劳动力市场表现越好。机制分析表明,农村移民早进城能提高其非认知能力,提升其进入收入较高的现代服务业的概率。本文的政策含义是,破除体制障碍让更多农村人口尽早进城,以改善其日后的就业和收入。 <<<

Nathan G Skene, Julien Bryois, Trygve E Bakken, Gerome Breen, James J Crowley, Héléna A Gaspar, Paola Giusti-Rodriguez, Rebecca D Hodge, Jeremy A Miller, Ana B Muñoz-Manchado, Michael C O'Donovan, Michael J Owen, Antonio F Pardiñas, Jesper Ryge, James T R Walters, Sten Linnarsson, Ed S Lein, Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium, Patrick F Sullivan, Jens Hjerling-Leffler <<<

With few exceptions, the marked advances in knowledge about the genetic basis of schizophrenia have not converged on findings that can be confidently used for precise experimental modeling. By applying knowledge of the cellular taxonomy of the brain from single-cell RNA sequencing, we evaluated whether the genomic loci implicated in schizophrenia map onto specific brain cell types. We found that the common-variant genomic results consistently mapped to pyramidal cells, medium spiny neurons (MSNs) and certain interneurons, but far less consistently to embryonic, progenitor or glial cells. These enrichments were due to sets of genes that were specifically expressed in each of these cell types. We also found that many of the diverse gene sets previously associated with schizophrenia (genes involved in synaptic function, those encoding mRNAs that interact with FMRP, antipsychotic targets, etc.) generally implicated the same brain cell types. Our results suggest a parsimonious explanation: the common-variant genetic results for schizophrenia point at a limited set of neurons, and the gene sets point to the same cells. The genetic risk associated with MSNs did not overlap with that of glutamatergic pyramidal cells and interneurons, suggesting that different cell types have biologically distinct roles in schizophrenia. <<<

Why do fathers matter? Recent conceptual and theoretical advances regarding father–child relationships have demonstrated that fathers affect children's outcomes both directly and indirectly. To attain a complete developmental account of the ecologically rich contexts of child development, in this article, we recommend best practices regarding the conceptualization and assessment of father–child relationships that reflect contemporary family life. We also discuss conceptual and measurement issues pertaining to father–child relationships in different family configurations, including those with resident and nonresident fathers. We conclude with recommendations that can help developmental researchers advance our understanding of fathering, parenting, and children's development. <<<

Diffeomorphic deformable image registration is crucial in many medical image studies, as it offers unique, special features including topology preservation and invertibility of the transformation. Recent deep learning-based deformable image registration methods achieve fast image registration by leveraging a convolutional neural network (CNN) to learn the spatial transformation from the synthetic ground truth or the similarity metric. However, these approaches often ignore the topology preservation of the transformation and the smoothness of the transformation which is enforced by a global smoothing energy function alone. Moreover, deep learning-based approaches often estimate the displacement field directly, which cannot guarantee the existence of the inverse transformation. In this paper, we present a novel, efficient unsupervised symmetric image registration method which maximizes the similarity between images within the space of diffeomorphic maps and estimates both forward and inverse transformations simultaneously. We evaluate our method on 3D image registration with a large scale brain image dataset. Our method achieves state-of-the-art registration accuracy and running time while maintaining desirable diffeomorphic properties. <<<

Vision-Language Pre-training (VLP) has advanced the performance for many vision-language tasks. However, most existing pre-trained models only excel in either understanding-based tasks or generation-based tasks. Furthermore, performance improvement has been largely achieved by scaling up the dataset with noisy image-text pairs collected from the web, which is a suboptimal source of supervision. In this paper, we propose BLIP, a new VLP framework which transfers flexibly to both vision-language understanding and generation tasks. BLIP effectively utilizes the noisy web data by bootstrapping the captions, where a captioner generates synthetic captions and a filter removes the noisy ones. We achieve state-of-the-art results on a wide range of vision-language tasks, such as image-text retrieval (+2.7% in average recall@1), image captioning (+2.8% in CIDEr), and VQA (+1.6% in VQA score). BLIP also demonstrates strong generalization ability when directly transferred to video-language tasks in a zero-shot manner. Code, models, and datasets are released at this https URL. <<<

Research on child development increasingly includes data on both parents and from different cultures. However, the relative importance of fathers versus mothers for child adjustment is still under debate. The present review compares the contributions of perceived paternal and maternal acceptance to various child adjustment indicators among samples of families around the world. We reviewed 127 published studies that included child-reported paternal and maternal acceptance and developmental outcomes. Regardless of the sex of parent, children benefited from perceived parental acceptance. Fathers and mothers were often found to both predict adjustment significantly to varying degrees. Paternal acceptance tended to be related to children's problem behavior and psychopathology, whereas maternal acceptance was more likely to contribute to socioemotional development. Paternal and maternal acceptance also often jointly contributed to child adjustment through their interaction with each other and with other predictors. Moreover, the link between parental acceptance and adjustment was often moderated by child gender and cultural context. <<<

Lei Zhong, Yueshan Li, Liang Xiong, Wenjing Wang, Ming Wu, Ting Yuan, Wei Yang, Chenyu Tian, Zhuang Miao, Tianqi Wang, Shengyong Yang <<<

Due to the advantages in efficacy and safety compared with traditional chemotherapy drugs, targeted therapeutic drugs have become mainstream cancer treatments. Since the first tyrosine kinase inhibitor imatinib was approved to enter the market by the US Food and Drug Administration (FDA) in 2001, an increasing number of small-molecule targeted drugs have been developed for the treatment of malignancies. By December 2020, 89 small-molecule targeted antitumor drugs have been approved by the US FDA and the National Medical Products Administration (NMPA) of China. Despite great progress, small-molecule targeted anti-cancer drugs still face many challenges, such as a low response rate and drug resistance. To better promote the development of targeted anti-cancer drugs, we conducted a comprehensive review of small-molecule targeted anti-cancer drugs according to the target classification. We present all the approved drugs as well as important drug candidates in clinical trials for each target, discuss the current challenges, and provide insights and perspectives for the research and development of anti-cancer drugs. <<<

One of the most salient features of life is its capacity to handle novelty and namely to thrive and adapt to new circumstances and changes in both the environment and internal components. An understanding of this capacity is central to several fields: the evolution of form and function, the design of effective strategies for biomedicine, and the creation of novel life forms via chimeric and bioengineering technologies. Here, we review instructive examples of living organisms solving diverse problems and propose competent navigation in arbitrary spaces as an invariant for thinking about the scaling of cognition during evolution. We argue that our innate capacity to recognize agency and intelligence in unfamiliar guises lags far behind our ability to detect it in familiar behavioral contexts. The multi-scale competency of life is essential to adaptive function, potentiating evolution and providing strategies for top-down control (not micromanagement) to address complex disease and injury. We propose an observer-focused viewpoint that is agnostic about scale and implementation, illustrating how evolution pivoted similar strategies to explore and exploit metabolic, transcriptional, morphological, and finally 3D motion spaces. By generalizing the concept of behavior, we gain novel perspectives on evolution, strategies for system-level biomedical interventions, and the construction of bioengineered intelligences. This framework is a first step toward relating to intelligence in highly unfamiliar embodiments, which will be essential for progress in artificial intelligence and regenerative medicine and for thriving in a world increasingly populated by synthetic, bio-robotic, and hybrid beings. <<<

Within computational neuroscience, the algorithmic and neural basis of structure learning remains poorly understood. Concept learning is one primary example, which requires both a type of internal model expansion process (adding novel hidden states that explain new observations), and a model reduction process (merging different states into one underlying cause and thus reducing model complexity via meta-learning). Although various algorithmic models of concept learning have been proposed within machine learning and cognitive science, many are limited to various degrees by an inability to generalize, the need for very large amounts of training data, and/or insufficiently established biological plausibility. Using concept learning as an example case, we introduce a novel approach for modeling structure learning-and specifically state-space expansion and reduction-within the active inference framework and its accompanying neural process theory. Our aim is to demonstrate its potential to facilitate a novel line of active inference research in this area. The approach we lay out is based on the idea that a generative model can be equipped with extra (hidden state or cause) "slots" that can be engaged when an agent learns about novel concepts. This can be combined with a Bayesian model reduction process, in which any concept learning-associated with these slots-can be reset in favor of a simpler model with higher model evidence. We use simulations to illustrate this model's ability to add new concepts to its state space (with relatively few observations) and increase the granularity of the concepts it currently possesses. We also simulate the predicted neural basis of these processes. We further show that it can accomplish a simple form of "one-shot" generalization to new stimuli. Although deliberately simple, these simulation results highlight ways in which active inference could offer useful resources in developing neurocomputational models of structure learning. They provide a template for how future active inference research could apply this approach to real-world structure learning problems and assess the added utility it may offer. <<<

Understanding information processing in the brain-and creating general-purpose artificial intelligence-are long-standing aspirations of scientists and engineers worldwide. The distinctive features of human intelligence are high-level cognition and control in various interactions with the world including the self, which are not defined in advance and are vary over time. The challenge of building human-like intelligent machines, as well as progress in brain science and behavioural analyses, robotics, and their associated theoretical formalisations, speaks to the importance of the world-model learning and inference. In this article, after briefly surveying the history and challenges of internal model learning and probabilistic learning, we introduce the free energy principle, which provides a useful framework within which to consider neuronal computation and probabilistic world models. Next, we showcase examples of human behaviour and cognition explained under that principle. We then describe symbol emergence in the context of probabilistic modelling, as a topic at the frontiers of cognitive robotics. Lastly, we review recent progress in creating human-like intelligence by using novel probabilistic programming languages. The striking consensus that emerges from these studies is that probabilistic descriptions of learning and inference are powerful and effective ways to create human-like artificial intelligent machines and to understand intelligence in the context of how humans interact with their world. <<<