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Poly (ADP-ribose) polymerase inhibitor (PARPi)-based therapies initially reduce tumor burden but eventually lead to acquired resistance in cancer patients with BRCA1 or BRCA2 mutation. To understand the potential PARPi resistance mechanisms, we performed whole-genome CRISPR screens to discover genetic alterations that change the gene essentiality in cells with inducible depletion of BRCA2. We identified that several RNA Polymerase II transcription Mediator complex components, especially Cyclin C (CCNC) as synthetic survival targets upon BRCA2 loss. Total mRNA sequencing demonstrated that loss of CCNC could activate the transforming growth factor (TGF)-beta signaling pathway and extracellular matrix (ECM)-receptor interaction pathway, however the inhibition of these pathways could not reverse cell survival in BRCA2 depleted CCNC-knockout cells, indicating that the activation of these pathways is not required for the resistance. Moreover, we showed that the improved survival is not due to restoration of homologous recombination repair although decreased DNA damage signaling was observed. Interestingly, loss of CCNC could restore replication fork stability in BRCA2 deficient cells, which may contribute to PARPi resistance. Taken together, our data reveal CCNC as a critical genetic determinant upon BRCA2 loss of function, which may help the development of novel therapeutic strategies that overcome PARPi resistance.

It has been established in the mouse model that during embryogenesis joint cartilage is generated from a specialized progenitor cell type, distinct from that responsible for the formation of growth plate cartilage. We recently found that mesodermal progeny of human pluripotent stem cells gave rise to two types of chondrogenic mesenchymal cells in culture: SOX9+ and GDF5+ cells. The fast-growing SOX9+ cells formed in vitro cartilage that expressed chondrocyte hypertrophy markers and readily underwent mineralization after ectopic transplantation. In contrast, the slowly growing GDF5+ cells derived from SOX9+ cells formed cartilage that tended to express low to undetectable levels of chondrocyte hypertrophy markers, but expressed PRG4, a marker of embryonic articular chondrocytes. The GDF5+-derived cartilage remained largely unmineralized in vivo. Interestingly, chondrocytes derived from the GDF5+ cells seemed to elicit these activities via non-cell-autonomous mechanisms. Genome-wide transcriptomic analyses suggested that GDF5+ cells might contain a teno/ligamento-genic potential, whereas SOX9+ cells resembled neural crest-like progeny-derived chondroprogenitors. Thus, human pluripotent stem cell-derived GDF5+ cells specified to generate permanent-like cartilage seem to emerge coincidentally with the commitment of the SOX9+ progeny to the tendon/ligament lineage.

The development of chromatin immunoprecipitation (ChIP) with massively parallel DNA sequencing (ChIP-seq) technologies has promoted generation of large-scale epigenomics data, providing us unprecedented opportunities to explore the landscape of epigenomic profiles at scales across both histone marks and tissue types. In addition to many tools directly for data analysis, advanced computational approaches, such as deep learning, have recently become promising to deeply mine the data structures and identify important regulators from complex functional genomics data. We implemented a neural network framework, a Variational Auto-Encoder (VAE) model, to explore the epigenomic data from the Roadmap Epigenomics Project and the Encyclopedia of DNA Elements (ENCODE) project. Our model is applied to 935 reference samples, covering 28 tissues and 12 histone marks. We used the enhancer and promoter regions as the annotation features and ChIP-seq signal values in these regions as the feature values. Through a parameter sweep process, we identified the suitable hyperparameter values and built a VAE model to represent the epigenomics data and to further explore the biological regulation. The resultant Roadmap-ENCODE VAE (RE-VAE) model contained data compression and feature representation. Using the compressed data in the latent space, we found that the majority of histone marks were well clustered but not for tissues or cell types. Tissue or cell specificity was observed only in some histone marks (e.g., H3K4me3 and H3K27ac) and could be characterized when the number of tissue samples is large (e.g., blood and brain). In blood, the contributive regions and genes identified by RE-VAE model were confirmed by tissue-specificity enrichment analysis with an independent tissue expression panel. Finally, we demonstrated that RE-VAE model could detect cancer cell lines with similar epigenomics profiles. In conclusion, we introduced and implemented a VAE model to represent large-scale epigenomics data. The model could be used to explore classifications of histone modifications and tissue/cell specificity and to classify new data with unknown sources.

Purpose:Here we investigated the impact of oncolytic herpes simplex virus (HSV) treatment on cisplatin sensitivity of platinum-resistant ovarian cancer, and the impact of the combination on immunotherapy.Experimental Design:Therapeutic efficacy of the combination was assessed in platinum-resistant human and murine ovarian cancer peritoneal metastatic mouse models (n = 9–10/group). RNA sequencing along with flow cytometry of splenocytes from treated mice was employed to examine the effect of antitumor immune response (n = 3/group). Anti-PD-1 antibody was performed to evaluate impact on checkpoint inhibition in vivo.Results:Gene Ontology pathway analysis uncovered disruption of cellular extracellular vesicle (EV)-related pathways in infected cells (FDR = 2.97E-57). Mechanistically, we identified reduced expression of transporters expressed on EV implicated in cisplatin efflux. The increased cisplatin retention led to increased cisplatin–DNA adducts, which resulted in micronuclei and the subsequent activation of cGAS–STING pathway with a significant activation of innate immune cells and translated to an increase in antitumor immunity and efficacy. In mice bearing platinum-resistant ovarian cancer, we also observed a feedback induction of PD-L1 on tumor cells, which sensitized combination-treated mice to anti-PD-1 immune checkpoint therapy.Conclusions:To our knowledge, this is the first report to show HSV-induced cisplatin retention in infected cells. The consequential increased damaged DNA was then expelled from cells as micronuclei which resulted in induction of inflammatory responses and education of antitumor immunity. The combination therapy also created an environment that sensitized tumors to immune checkpoint therapy.

Exploiting cancer vulnerabilities is critical for the discovery of anticancer drugs. However, tumor suppressors cannot be directly targeted because of their loss of function. To uncover specific vulnerabilities for cells with deficiency in any given tumor suppressor(s), we performed genome-scale CRISPR loss-of-function screens using a panel of isogenic knockout cells we generated for 12 common tumor suppressors. Here, we provide a comprehensive and comparative dataset for genetic interactions between the whole-genome protein-coding genes and a panel of tumor suppressor genes, which allows us to uncover known and new high-confidence synthetic lethal interactions. Mining this dataset, we uncover essential paralog gene pairs, which could be a common mechanism for interpreting synthetic lethality. Moreover, we propose that some tumor suppressors could be targeted to suppress proliferation of cells with deficiency in other tumor suppressors. This dataset provides valuable information that can be further exploited for targeted cancer therapy.

Millions of somatic mutations have recently been discovered in cancer genomes. These mutations in cancer genomes occur due to internal and external mutagenesis forces. Decoding the mutational processes by examining their unique patterns has successfully revealed many known and novel signatures from whole exome data, but many still remain undiscovered. Here, we developed a deep learning approach, DeepMS, to decompose mutational signatures using 52,671,908 somatic mutations from 2780 highly curated cancer genomes with whole genome sequencing (WGS) in 37 cancer types/subtypes. With rigorous model training and comparison, we characterized 54 signatures for single base substitutions (SBSs), 11 for doublet base substitutions (DBSs) and 16 for small insertions and deletions (Indels). Compared to the previous methods, DeepMS could discover 37 SBS, 5 DBS, and 9 Indel new signatures, many of which represent associations with DNA mismatch or base excision repair and cisplatin therapy mechanisms. We further developed a regression-based model to estimate the correlation between signatures and clinical and demographical phenotypes. The first deep learning model DeepMS on WGS somatic mutational profiles enable us identify more comprehensive context-based mutational signatures than traditional NMF approaches. Our work substantially expands the landscape of the naturally occurring mutational signatures in cancer genomes, and provides new insights into cancer biology.

An important mechanism of oncolytic virotherapy in ameliorating cancer immunotherapy is by inducing significant changes in the immune landscape in the tumor microenvironment (TME). Despite this notion and the potential therapeutic implications, a comprehensive analysis of the immune changes in carcinomas induced by virotherapy has not yet been elucidated. We conducted single-cell RNA sequencing analysis on carcinomas treated with an HSV-2-based oncolytic virus to characterize the immunogenic changes in the TME. We specifically analyzed and compared the immune cell composition between viral treated and untreated tumors. We also applied CellChat to analyze the complex interactions among the infiltrated immune cells. Our data revealed significant infiltration of B cells in addition to other important immune cells, including CD4+, CD8+, and NK cells following virotherapy. Further analysis identified distinct subset compositions of the infiltrated immune cells and their activation status upon virotherapy. The intensive interactions among the infiltrated immune cells as revealed by CellChat analysis may further shape the immune landscape in favor of generating antitumor immunity. Our findings will facilitate the design of new strategies in incorporating immunotherapy into virotherapy for clinical translation. Moreover, the significant infiltration of B cells makes it suitable for combining virotherapy with immune checkpoint inhibitors.

BackgroundAlthough oncolytic virotherapy has shown substantial promises as a new treatment modality for many malignancies, further improvement on its therapeutic efficacy will likely bring more clinical benefits. One plausible way of enhancing the therapeutic effect of virotherapy is to enable it with the ability to concurrently engage the infiltrating immune cells to provide additional antitumor mechanisms. Here, we report the construction and evaluation of two novel chimeric molecules (bispecific chimeric engager proteins, BiCEP and trispecific chimeric engager protein, TriCEP) that can engage both natural killer (NK) and T cells with tumor cells for enhanced antitumor activities.MethodsBiCEP was constructed by linking orthopoxvirus major histocompatibility complex class I-like protein, which can selectively bind to NKG2D with a high affinity to a mutant form of epidermal growth factor (EGF) that can strongly bind to EGF receptor. TriCEP is similarly constructed except that it also contains a modified form of interleukin-2 that can only function as a tethered form. As NKG2D is expressed on both NK and CD8+ T cells, both of which can thus be engaged by BiCEP and TriCEP.ResultsBoth BiCEP and TriCEP showed the ability to engage NK and T cells to kill tumor cells in vitro. Coadministration of BiCEP and TriCEP with an oncolytic herpes simplex virus enhanced the overall antitumor effect. Furthermore, single-cell RNA sequencing analysis revealed that TriCEP not only engaged NK and T cells to kill tumor cells, it also promotes the infiltration and activation of these important immune cells.ConclusionsThese novel chimeric molecules exploit the ability of the oncolytic virotherapy in altering the tumor microenvironment with increased infiltration of important immune cells such as NK and T cells for cancer immunotherapy. The ability of BiCEP and TriCEP to engage both NK and T cells makes them an ideal choice for arming an oncolytic virotherapy.

Purpose: Oncolytic herpes simplex virus-1 (oHSV) infection of brain tumors activates NOTCH, however the consequences of NOTCH on oHSV-induced immunotherapy is largely unknown. Here we evaluated the impact of NOTCH blockade on virus-induced immunotherapy. Experimental Design: RNA sequencing (RNA-seq), TCGA data analysis, flow cytometry, Luminex- and ELISA-based assays, brain tumor animal models, and serum analysis of patients with recurrent glioblastoma (GBM) treated with oHSV was used to evaluate the effect of NOTCH signaling on virus-induced immunotherapy. Results: TCGA data analysis of patients with grade IV glioma and oHSV treatment of experimental brain tumors in mice showed that NOTCH signaling significantly correlated with a higher myeloid cell infiltration. Immunofluorescence staining and RNA-seq uncovered a significant induction of Jag1 (NOTCH ligand) expression in infiltrating myeloid cells upon oHSV infection. Jag1-expressing macrophages further spread NOTCH activation in the tumor microenvironment (TME). NOTCH-activated macrophages increased the secretion of CCL2, which further amplified myeloid-derived suppressor cells. CCL2 and IL10 induction was also observed in serum of patients with recurrent GBM treated with oHSV (rQnestin34.5; NCT03152318). Pharmacologic blockade of NOTCH signaling rescued the oHSV-induced immunosuppressive TME and activated a CD8-dependent antitumor memory response, resulting in a therapeutic benefit. Conclusions: NOTCH-induced immunosuppressive myeloid cell recruitment limited antitumor immunity. Translationally, these findings support the use of NOTCH inhibition in conjunction with oHSV therapy.

Assessing the causal tissues of human traits and diseases is important for better interpreting trait-associated genetic variants, understanding disease etiology, and improving treatment strategies. Here, we present a reference database for trait-associated tissue specificity based on genome-wide association study (GWAS) results, named Tissue-Specific Enrichment Analysis DataBase (TSEA-DB, available at https://bioinfo.uth.edu/TSEADB/). We collected GWAS summary statistics data for a wide range of human traits and diseases followed by rigorous quality control. The current version of TSEA-DB includes 4423 data sets from the UK Biobank (UKBB) and 596 from other resources (GWAS Catalog and literature mining), totaling 5019 unique GWAS data sets and 15 770 trait-associated gene sets. TSEA-DB aims to provide reference tissue(s) enriched with the genes from GWAS. To this end, we systematically performed a tissue-specific enrichment analysis using our recently developed tool deTS and gene expression profiles from two reference tissue panels: the GTEx panel (47 tissues) and the ENCODE panel (44 tissues). The comprehensive trait–tissue association results can be easily accessed, searched, visualized, analyzed, and compared across the studies and traits through our web site. TSEA-DB represents one of the many timely and comprehensive approaches in exploring human trait–tissue association.

The genetic landscape of clear cell renal cell carcinoma (ccRCC) had been investigated extensively but its evolution patterns remained unclear. Here we analyze the clonal architectures of 473 patients from three different populations. We find that the mutational signatures vary substantially across different populations and evolution stages. The evolution patterns of ccRCC have great inter-patient heterogeneities, with del(3p) being regarded as the common earliest event followed by three early departure points: VHL and PBRM1 mutations, del(14q) and other somatic copy number alterations (SCNAs) including amp(7), del(1p) and del(6q). We identify three prognostic subtypes of ccRCC with distinct clonal architectures and immune infiltrates: long-lived patients, enriched with VHL but depleted of BAP1 mutations, have high levels of Th17 and CD8+ T cells while short-lived patients with high burden of SCNAs have high levels of Tregs and Th2 cells, highlighting the importance of evaluating evolution patterns in the clinical management of ccRCC., Clear cell renal cell carcinoma (ccRCC) is a urogenital cancer with a well-defined genetic landscape. Here, the authors analyse the clonal architecture of ccRCC patients from three populations, and find prognostic subtypes linked to immune infiltrates and clonal architecture.

Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outcome. In this study, we develop a deep variational autoencoder (VAE) model to compress thousands of genes into latent vectors in a low-dimensional space. We then demonstrate that these encoded vectors could accurately impute drug response, outperform standard signature-gene based approaches, and appropriately control the overfitting problem. We apply rigorous quality assessment and validation, including assessing the impact of cell line lineage, cross-validation, cross-panel evaluation, and application in independent clinical data sets, to warrant the accuracy of the imputed drug response in both cell lines and cancer samples. Specifically, the expression-regulated component (EReX) of the observed drug response achieves high correlation across panels. Using the well-trained models, we impute drug response of The Cancer Genome Atlas data and investigate the features and signatures associated with the imputed drug response, including cell line origins, somatic mutations and tumor mutation burdens, tumor microenvironment, and confounding factors. In summary, our deep learning method and the results are useful for the study of signatures and markers of drug response., Drug response in cancer patients vary dramatically due to inter- and intra-tumor heterogeneity and transcriptome context plays a significant role in shaping the actual treatment outcome. Here, the authors develop a deep variational autoencoder model to compress gene signatures into latent vectors and accurately impute drug response.

Sepsis and sepsis‐associated lung inflammation significantly contribute to the morbidity and mortality of critical illness. Here, we examined the hypothesis that neuronal guidance proteins could orchestrate inflammatory events during endotoxin‐induced lung injury. Through a targeted array, we identified netrin‐1 as the top upregulated neuronal guidance protein in macrophages treated with lipopolysaccharide (LPS). Furthermore, we found that netrin‐1 is highly enriched in infiltrating myeloid cells, particularly in macrophages during LPS‐induced lung injury. Transcriptional studies implicate hypoxia‐inducible factor HIF‐1α in the transcriptional induction of netrin‐1 during LPS treatment. Subsequently, the deletion of netrin‐1 in the myeloid compartment (Ntn1loxp/loxp LysM Cre) resulted in exaggerated mortality and lung inflammation. Surprisingly, further studies revealed enhanced natural killer cells (NK cells) infiltration in Ntn1loxp/loxp LysM Cre mice, and neutralization of NK cell chemoattractant chemokine (C‐C motif) ligand 2 (CCL2) reversed the exaggerated lung inflammation. Together, these studies provide functional insight into myeloid cell‐derived netrin‐1 in controlling lung inflammation through the modulation of CCL2‐dependent infiltration of NK cells.

Assessing the causal tissues of human complex diseases is important for the prioritization of trait-associated genetic variants. Yet, the biological underpinnings of trait-associated variants are extremely difficult to infer due to statistical noise in genome-wide association studies (GWAS), and because >90% of genetic variants from GWAS are located in non-coding regions. Here, we collected the largest human epigenomic map from ENCODE and Roadmap consortia and implemented a deep-learning-based convolutional neural network (CNN) model to predict the regulatory roles of genetic variants across a comprehensive list of epigenomic modifications. Our model, called DeepFun, was built on DNA accessibility maps, histone modification marks, and transcription factors. DeepFun can systematically assess the impact of non-coding variants in the most functional elements with tissue or cell-type specificity, even for rare variants or de novo mutations. By applying this model, we prioritized trait-associated loci for 51 publicly-available GWAS studies. We demonstrated that CNN-based analyses on dense and high-resolution epigenomic annotations can refine important GWAS associations in order to identify regulatory loci from background signals, which yield novel insights for better understanding the molecular basis of human complex disease. We anticipate our approaches will become routine in GWAS downstream analysis and non-coding variant evaluation.

BackgroundPsychiatric disorders such as schizophrenia (SCZ), bipolar disorder (BIP), major depressive disorder (MDD), attention deficit-hyperactivity disorder (ADHD), and autism spectrum disorder (ASD) are often related to brain development. Both shared and unique biological and neurodevelopmental processes have been reported to be involved in these disorders.MethodsIn this work, we developed an integrative analysis framework to seek for the sensitive spatiotemporal point during brain development underlying each disorder. Specifically, we first identified spatiotemporal gene co-expression modules for four brain regions three developmental stages (prenatal, birth to 11 years old, and older than 13 years), totaling 12 spatiotemporal sites. By integrating GWAS summary statistics and the spatiotemporal co-expression modules, we characterized the risk genes and their co-expression partners for five disorders.ResultsWe found that SCZ and BIP, ASD and ADHD tend to cluster with each other and keep a distance from other psychiatric disorders. At the gene level, we identified several genes that were shared among the most significant modules, such asCTNNB1andLNX1, and a hub gene,ATF2,in multiple modules. Moreover, we pinpointed two spatiotemporal points in the prenatal stage with active expression activities and highlighted one postnatal point for BIP. Further functional analysis of the disorder-related module highlighted the apoptotic signaling pathway for ASD and the immune-related and cell-cell adhesion function for SCZ, respectively.ConclusionOur study demonstrated the dynamic changes of disorder-related genes at the network level, shedding light on the spatiotemporal regulation during brain development.

Cancer-associated fibroblasts (CAFs) constitute a prominent component of the tumor microenvironment and play critical roles in cancer progression and drug resistance. Although recent studies indicate CAFs may consist of several CAF subtypes, the breadth of CAF heterogeneity and functional roles of CAF subtypes in cancer progression remain unclear. In this study, we implemented a cell-type deconvolutional approach to comprehensively characterize cell-type alternations across 18 cancer types from The Cancer Genome Atlas (TCGA). Pan-cancer survival analysis using deconvoluted CAF subtypes revealed myofibroblastic CAF (myCAF) composition as a poor prognostic factor in nine cancer types. Patients with higher myCAF compositions tend to have worse response to six antineoplastic drugs predicted by a lncRNA-based Elastic Net prediction model (LENP). In addition, integrative mutational analysis identified 14 and 413 genes associated with the differentiation degree of myCAF and inflammatory CAF (iCAF), respectively, with significant enrichment of genes involved in fibroblast and extracellular matrix (ECM)-related pathways. In summary, our findings systematically illustrated the complex roles of CAF subtypes in patient prognosis and drug response, and identified putative driver genes in CAF-subtype differentiation. These results provided novel therapeutic perspectives for targeting CAF subtypes in tumor microenvironment and arranging treatment scheme based on the CAF compositions in different cancer types.

SUMMARY 53BP1 plays a central role in dictating DNA repair choice between non-homologous end joining (NHEJ) and homologous recombination (HR), which is important for the sensitivity to poly(ADP-ribose) polymerase inhibitors (PARPis) of BRCA1-deficient cancers. In this study, we show that FOXK1 associates with 53BP1 and regulates 53BP1-dependent functions. FOXK1–53BP1 interaction is significantly enhanced upon DNA damage during the S phase in an ATM/CHK2-dependent manner, which reduces the association of 53BP1 with its downstream factors RIF1 and PTIP. Depletion of FOXK1 impairs DNA repair and induces compromised cell survival upon DNA damage. Overexpression of FOXK1 diminishes 53BP1 foci formation, which leads to resistance to PARPis and elevation of HR in BRCA1-deficient cells and decreased telomere fusion in TRF2-depleted cells. Collectively, our findings demonstrate that FOXK1 negatively regulates 53BP1 function by inhibiting 53BP1 localization to sites of DNA damage, which alters the DSB-induced protein complexes centering on 53BP1 and thus influences DNA repair choice., In Brief 53BP1 plays a critical role in DNA double-strand break repair choice. Tang et al. report that FOXK1 acts together with 53BP1 and participates in proper DNA repair pathway choice during various cell cycle phases., Graphical Abstract

As the most complex organ of the human body, the brain is composed of diverse regions, each consisting of distinct cell types and their respective cellular interactions. Human brain development involves a finely tuned cascade of interactive events. These include spatiotemporal gene expression changes and dynamic alterations in cell-type composition. However, our understanding of this process is still largely incomplete owing to the difficulty of brain spatiotemporal transcriptome collection. In this study, we developed a tensor-based approach to impute gene expression on a transcriptome-wide level. After rigorous computational benchmarking, we applied our approach to infer missing data points in the widely used BrainSpan resource and completed the entire grid of spatiotemporal transcriptomics. Next, we conducted deconvolutional analyses to comprehensively characterize major cell-type dynamics across the entire BrainSpan resource to estimate the cellular temporal changes and distinct neocortical areas across development. Moreover, integration of these results with GWAS summary statistics for 13 brain-associated traits revealed multiple novel trait–cell-type associations and trait-spatiotemporal relationships. In summary, our imputed BrainSpan transcriptomic data provide a valuable resource for the research community and our findings help further studies of the transcriptional and cellular dynamics of the human brain and related diseases.

Human-induced pluripotent stem cells (hiPSCs) allow for the establishment of brain cellular models of psychiatric disorders that account for a patient’s genetic background. Here, we conducted an RNA-sequencing profiling study of hiPSC-derived cell lines from schizophrenia (SCZ) subjects, most of which are from a multiplex family, from the population isolate of the Central Valley of Costa Rica. hiPSCs, neural precursor cells, and cortical neurons derived from six healthy controls and seven SCZ subjects were generated using standard methodology. Transcriptome from these cells was obtained using Illumina HiSeq 2500, and differential expression analyses were performed using DESeq2 (|fold change|>1.5 and false discovery rate

BackgroundAlcohol use disorder (AUD) is one of the most common forms of substance use disorders with a strong contribution of genetic (50%–60%) and environmental factors. Genome-wide association studies (GWAS) have identified a number of AUD-associated variants, including those in alcohol metabolism genes. These genetic variants may modulate gene expression, making individuals more susceptible to AUD. A long-term alcohol consumption can also change the transcriptome patterns of subjects via epigenetic modulations.MethodsTo explore the interactive effect of genetic and epigenetic factors on AUD, we conducted a secondary analysis by integrating GWAS, CNV, brain transcriptome and DNA methylation data to unravel novel AUD-associated genes/variants. We applied the mega-analysis of OR (MegaOR) method to prioritise AUD candidate genes (AUDgenes).ResultsWe identified a consensus set of 206 AUDgenes based on the multi-omics data. We demonstrated that these AUDgenes tend to interact with each other more frequent than chance expectation. Functional annotation analysis indicated that these AUDgenes were involved in substance dependence, synaptic transmission, glial cell proliferation and enriched in neuronal and liver cells. We obtained a multidimensional evidence that AUD is a polygenic disorder influenced by both genetic and epigenetic factors as well as the interaction of them.ConclusionWe characterised multidimensional evidence of genetic, epigenetic and transcriptomic data in AUD. We found that 206 AUD associated genes were highly expressed in liver, brain cerebellum, frontal cortex, hippocampus and pituitary. Our studies provides important insights into the molecular mechanism of AUD and potential target genes for AUD treatment.

Background Genome-wide association studies (GWAS) have been successful in identifying disease-associated genetic variants. Recently, an increasing number of GWAS summary statistics have been made available to the research community, providing extensive repositories for studies of human complex diseases. In particular, cross-trait associations at the genetic level can be beneficial from large-scale GWAS summary statistics by using genetic variants that are associated with multiple traits. However, direct assessment of cross-trait associations using susceptibility loci has been challenging due to the complex genetic architectures in most diseases, calling for advantageous methods that could integrate functional interpretation and imply biological mechanisms. Results We developed an analytical framework for systematic integration of cross-trait associations. It incorporates two different approaches to detect enriched pathways and requires only summary statistics. We demonstrated the framework using 25 traits belonging to four phenotype groups. Our results revealed an average of 54 significantly associated pathways (ranged between 18 and 175) per trait. We further proved that pathway-based analysis provided increased power to estimate cross-trait associations compared to gene-level analysis. Based on Fisher’s Exact Test (FET), we identified a total of 24 (53) pairs of trait-trait association at adjusted pFET

Biapenem is a parenteral carbapenem antibiotic that has powerful antibacterial activity. The aim of this study is to evaluate the efficacy and safety of biapenem for the treatment of infection diseases.We performed a meta-analysis of published randomized-controlled trials (RCTs) identified in Embase, PubMed, and Cochrane library that compared the efficacy and safety of biapenem with other antibiotic regimes for the treatment of patients with infections.Eight RCTs were included in the meta-analysis, involving totally 1685 patients with lower respiratory tract infections (LRTIs), complicated urinary tract infections (cUTIs), and complicated intra-abdominal infections (cIAIs). There was no difference found between the patients with LRTIs, cUTIs, or cIAIs treated with biapenem and comparators, regarding treatment success and adverse events.This meta-analysis provides evidence that biapenem can be used as effectively and safely as imipenem-cilstatin or meropenem, for the treatment of patients with LRTIs, cUTIs, and cIAIs. It may be a considerable option for the treatment of these infections.

Motivation Genome-wide multi-omics profiling of complex diseases provides valuable resources and opportunities to discover associations between various measures of genes and diseases. Currently, a pressing challenge is how to effectively detect functional genes associated with or causing phenotypic outcomes. We developed CNet to identify groups of genomic signatures whose combinatory effect is significantly associated with clinical and phenotypical outcomes. Results CNet builds on a generalized sequential feedforward method, augmented by a down-sampling bootstrap strategy to reduce random hitchhiking signatures. It further applies a dynamic trimming procedure to remove relatively less informative signatures at every step. CNet can manage heterogeneous genomic signature profiles simultaneously and select the best signature to represent a specific gene. To deal with various forms of clinical and phenotypical measurements, we introduced four models to deal with continuous, categorical and censored data. We tested CNet using drug-response data, multidimensional cancer genomics data and genome-wide association study data for multiple traits. Our results demonstrated that in various scenarios, CNet could effectively identify signatures that are associated with the outcomes. In addition, we applied CNet to identify likely disease-causing chains involving somatic mutations, pathway activities and patient outcomes. With appropriate setting, CNet can be applied in many biological conditions. Availability and implementation CNet can be downloaded at https://github.com/bsml320/CNet. Supplementary information Supplementary data are available at Bioinformatics online.

Intratumoral infiltration of myeloid-derived suppressor cells (MDSCs) is known to promote neoplastic growth by inhibiting the tumoricidal activity of T cells. However, direct interactions between patient-derived MDSCs and circulating tumors cells (CTCs) within the microenvironment of blood remain unexplored. Dissecting interplays between CTCs and circulatory MDSCs by heterotypic CTC/MDSC clustering is critical as a key mechanism to promote CTC survival and sustain the metastatic process. We characterized CTCs and polymorphonuclear-MDSCs (PMN-MDSCs) isolated in parallel from peripheral blood of metastatic melanoma and breast cancer patients by multi-parametric flow cytometry. Transplantation of both cell populations in the systemic circulation of mice revealed significantly enhanced dissemination and metastasis in mice co-injected with CTCs and PMN-MDSCs compared to mice injected with CTCs or MDSCs alone. Notably, CTC/PMN-MDSC clusters were detected in vitro and in vivo either in patients&rsquo, blood or by longitudinal monitoring of blood from animals. This was coupled with in vitro co-culturing of cell populations, demonstrating that CTCs formed physical clusters with PMN-MDSCs, and induced their pro-tumorigenic differentiation through paracrine Nodal signaling, augmenting the production of reactive oxygen species (ROS) by PMN-MDSCs. These findings were validated by detecting significantly higher Nodal and ROS levels in blood of cancer patients in the presence of na&iuml, ve, heterotypic CTC/PMN-MDSC clusters. Augmented PMN-MDSC ROS upregulated Notch1 receptor expression in CTCs through the ROS-NRF2-ARE axis, thus priming CTCs to respond to ligand-mediated (Jagged1) Notch activation. Jagged1-expressing PMN-MDSCs contributed to enhanced Notch activation in CTCs by engagement of Notch1 receptor. The reciprocity of CTC/PMN-MDSC bi-directional paracrine interactions and signaling was functionally validated in inhibitor-based analyses, demonstrating that combined Nodal and ROS inhibition abrogated CTC/PMN-MDSC interactions and led to a reduction of CTC survival and proliferation. This study provides seminal evidence showing that PMN-MDSCs, additive to their immuno-suppressive roles, directly interact with CTCs and promote their dissemination and metastatic potency. Targeting CTC/PMN-MDSC heterotypic clusters and associated crosstalks can therefore represent a novel therapeutic avenue for limiting hematogenous spread of metastatic disease.

We sought to evaluate the efficacy and safety of capecitabine-based therapy as first-line chemotherapy in advanced breast cancer. Randomised controlled trials of capecitabine monotherapy or combined treatment were included in the meta-analysis. PubMed, EMBASE, the Cochrane Library database and important meeting summaries were searched systematically. Outcomes were progression-free survival (PFS), overall survival (OS), overall response rate (ORR) and grades 3–4 drug-related adverse events. Nine trials with 1798 patients were included. The results indicated a significant improvement with capecitabine-based chemotherapy compared with capecitabine-free chemotherapy in ORR (relative risk [RR] 1.14, 95% confidence interval [CI] 1.03 to 1.26, P = 0.013) and PFS (hazard ratio [HR] 0.77, 95% CI 0.69 to 0.87, P < 0.0001). Overall survival favoured capecitabine-based chemotherapy, but this was not significant. There were more incidences of neutropenia and neutropenic fever in the capecitabine-free chemotherapy group and more vomiting, diarrhoea and hand–foot syndrome in the capecitabine-based chemotherapy group. There were no significant differences in nausea, fatigue, cardiotoxicity or mucositis/stomatitis between the two treatment regimens. Capecitabine-based chemotherapy significantly improves ORR and PFS in patients with advanced breast cancer, but has no demonstrable impact on OS. Capecitabine-based regimens are suitable as first-line treatment for patients with advanced breast cancer.

The aim of this study was to investigate the in vitro activity of minocycline combined with cefoperazone-sulbactam against carbapenem-resistant Acinetobacter baumannii (CRAB). A total of 53 nonduplicate CRAB were collected from inpatients of intensive care units in three hospitals in China from February 2009 to August 2011. Minimum inhibitory concentrations were determined by the broth microdilution method. The checkerboard method was used to determine whether combinations of the two agents act synergistically. Among the 53 A. baumannii, 64.2% of the isolates were susceptible to amikacin and 69.8% were susceptible to cefoperazone-sulbactam. More than 80% of isolates were resistant to the other agents and no isolates were found resistant to polymyxin B and minocycline. The combination of minocycline and cefoperazone-sulbactam demonstrated synergism in 39 isolates, partial synergism in 11 isolates, and indifference in 3 isolates. No antagonistic interactions were observed. Our study demonstrated that minocycline has good activity against CRAB and the combination of minocycline with cefoperazone-sulbactam had significant synergistic activity against these strains in vitro. The combination of minocycline and cefoperazone-sulbactam may be an alternative option for the treatment of infections caused by CRAB.