Unraveling the causal genes and transcriptomic determinants of human telomere length.

Telomere length (TL) shortening is a pivotal indicator of biological aging and is associated with many human diseases. The genetic determinates of human TL have been widely investigated, however, most existing studies were conducted based on adult tissues which are heavily influenced by lifetime exposure. Based on the analyses of terminal restriction fragment (TRF) length of telomere, individual genotypes, and gene expressions on 166 healthy placental tissues, we systematically interrogate TL-modulated genes and their potential functions. We discover that the TL in the placenta is comparatively longer than in other adult tissues, but exhibiting an intra-tissue homogeneity. Trans-ancestral TL genome-wide association studies (GWASs) on 644,553 individuals identify 20 newly discovered genetic associations and provide increased polygenic determination of human TL. Next, we integrate the powerful TL GWAS with placental expression quantitative trait locus (eQTL) mapping to prioritize 23 likely causal genes, among which 4 are functionally validated, including MMUT, RRM1, KIAA1429, and YWHAZ. Finally, modeling transcriptomic signatures and TRF-based TL improve the prediction performance of human TL. This study deepens our understanding of causal genes and transcriptomic determinants of human TL, promoting the mechanistic research on fine-grained TL regulation.

N6-methyladenosine-mediated overexpression of long noncoding RNA ADAMTS9-AS2 triggers neuroblastoma differentiation via regulating LIN28B/let-7/MYCN signaling.

Neuroblastomas have shed light on the differentiation disorder that is associated with spontaneous regression or differentiation in the same tumor at the same time. Long noncoding RNAs (lncRNAs) actively participate in a broad spectrum of biological processes. However, the detailed molecular mechanisms underlying lncRNA regulation of differentiation in neuroblastomas remain largely unknown. Here, we sequenced clinical samples of ganglioneuroma, ganglioneuroblastoma, and neuroblastoma. We compared transcription profiles of neuroblastoma cells, ganglion cells, and intermediate state cells; verified the profiles in a retinoic acid-induced cell differentiation model and clinical samples; and screened out the lncRNA ADAMTS9 antisense RNA 2 (ADAMTS9-AS2), which contributed to neuroblastoma differentiation. ADAMTS9-AS2 upregulation in neuroblastoma cell lines inhibited proliferation and metastatic potential. Additional mechanistic studies illustrated that the interactions between ADAMTS9-AS2 and LIN28B inhibited the association between LIN28B and primary let-7 (pri-let-7) miRNA, then released pri-let-7 into cytoplasm to form mature let-7, resulting in the inhibition of oncogene MYCN activity that subsequently affected cancer stemness and differentiation. Furthermore, we showed that the observed differential expression of ADAMTS9-AS2 in neuroblastoma cells was due to N6-methyladenosine methylation. Finally, ADAMTS9-AS2 upregulation inhibited proliferation and cancer stem-like capabilities in vivo. Taken together, these results show that ADAMTS9-AS2 loss leads to malignant neuroblastoma by increasing metastasis and causing dysfunctional differentiation.

Landscape of enhancer disruption and functional screen in melanoma cells.

BACKGROUND: The high mutation rate throughout the entire melanoma genome presents a major challenge in stratifying true driver events from the background mutations. Numerous recurrent non-coding alterations, such as those in enhancers, can shape tumor evolution, thereby emphasizing the importance in systematically deciphering enhancer disruptions in melanoma. RESULTS: Here, we leveraged 297 melanoma whole-genome sequencing samples to prioritize highly recurrent regions. By performing a genome-scale CRISPR interference (CRISPRi) screen on highly recurrent region-associated enhancers in melanoma cells, we identified 66 significant hits which could have tumor-suppressive roles. These functional enhancers show unique mutational patterns independent of classical significantly mutated genes in melanoma. Target gene analysis for the essential enhancers reveal many known and hidden mechanisms underlying melanoma growth. Utilizing extensive functional validation experiments, we demonstrate that a super enhancer element could modulate melanoma cell proliferation by targeting MEF2A, and another distal enhancer is able to sustain PTEN tumor-suppressive potential via long-range interactions. CONCLUSIONS: Our study establishes a catalogue of crucial enhancers and their target genes in melanoma growth and progression, and illuminates the identification of novel mechanisms of dysregulation for melanoma driver genes and new therapeutic targeting strategies.

Inferring CTCF-binding patterns and anchored loops across human tissues and cell types.

CCCTC-binding factor (CTCF) is a transcription regulator with a complex role in gene regulation. The recognition and effects of CTCF on DNA sequences, chromosome barriers, and enhancer blocking are not well understood. Existing computational tools struggle to assess the regulatory potential of CTCF-binding sites and their impact on chromatin loop formation. Here we have developed a deep-learning model, DeepAnchor, to accurately characterize CTCF binding using high-resolution genomic/epigenomic features. This has revealed distinct chromatin and sequence patterns for CTCF-mediated insulation and looping. An optimized implementation of a previous loop model based on DeepAnchor score excels in predicting CTCF-anchored loops. We have established a compendium of CTCF-anchored loops across 52 human tissue/cell types, and this suggests that genomic disruption of these loops could be a general mechanism of disease pathogenesis. These computational models and resources can help investigate how CTCF-mediated cis-regulatory elements shape context-specific gene regulation in cell development and disease progression.

Shc3 facilitates breast cancer drug resistance by interacting with ErbB2 to initiate ErbB2/COX2/MDR1 axis.

Multidrug resistance (MDR) is a primary limitation of breast cancer chemotherapy. The common mechanism of MDR is various anticancer drugs can be effluxed by the cell membrane protein P-glycoprotein (P-gp). Here, we found that ectopic overexpression of Shc3 was detected specifically in drug-resistant breast cancer cells, consequently reducing sensitivity to chemotherapy and promoting cell migration by mediating P-gp expression. However, the molecular mechanism underlying the interplay between P-gp and Shc3 in breast cancer is unknown. We reported an additional resistance mechanism involving an increase in the active form of P-gp after Shc3 upregulation. MCF-7/ADR and SK-BR-3 cells can be sensitive to doxorubicin after knockdown of Shc3. Our results indicated that the interaction between ErbB2 and EphA2 is indirect and regulated by Shc3, and also, this complex is essential for activation of the MAPK and AKT pathways. Meanwhile, Shc3 promotes ErbB2 nuclear translocation, followed by a subsequent increase of the COX2 expression through ErbB2 binding to the COX2 promoter. We further demonstrated that COX2 expression was positively correlated with P-gp expression and the Shc3/ErbB2/COX2 axis upregulates P-gp activity in vivo. Our results show the crucial roles of Shc3 and ErbB2 in modulating P-gp efficacy in breast cancer cells and suggest that Shc3 inhibition may enhance the sensitivity to chemotherapeutic drugs that target oncogene addiction pathways.

QTLbase2: an enhanced catalog of human quantitative trait loci on extensive molecular phenotypes.

Deciphering the fine-scale molecular mechanisms that shape the genetic effects at disease-associated loci from genome-wide association studies (GWAS) remains challenging. The key avenue is to identify the essential molecular phenotypes that mediate the causal variant and disease under particular biological conditions. Therefore, integrating GWAS signals with context-specific quantitative trait loci (QTLs) (such as different tissue/cell types, disease states, and perturbations) from extensive molecular phenotypes would present important strategies for full understanding of disease genetics. Via persistent curation and systematic data processing of large-scale human molecular trait QTLs (xQTLs), we updated our previous QTLbase database (now QTLbase2, http://mulinlab.org/qtlbase) to comprehensively analyze and visualize context-specific QTLs across 22 molecular phenotypes and over 95 tissue/cell types. Overall, the resource features the following major updates and novel functions: (i) 960 more genome-wide QTL summary statistics from 146 independent studies; (ii) new data for 10 previously uncompiled QTL types; (iii) variant query scope expanded to fit 195 QTL datasets based on whole-genome sequencing; (iv) supports filtering and comparison of QTLs for different biological conditions, such as stimulation types and disease states; (v) a new linkage disequilibrium viewer to facilitate variant prioritization across tissue/cell types and QTL types.

Interrogating cell type-specific cooperation of transcriptional regulators in 3D chromatin.

Context-specific activities of transcription regulators (TRs) in the nucleus modulate spatiotemporal gene expression precisely. Using the largest ChIP-seq data and chromatin loops in the human K562 cell line, we initially interrogated TR cooperation in 3D chromatin via a graphical model and revealed many known and novel TRs manipulating context-specific pathways. To explore TR cooperation across broad tissue/cell types, we systematically leveraged large-scale open chromatin profiles, computational footprinting, and high-resolution chromatin interactions to investigate tissue/cell type-specific TR cooperation. We first delineated a landscape of TR cooperation across 40 human tissue/cell types. Network modularity analyses uncovered the commonality and specificity of TR cooperation in different conditions. We also demonstrated that TR cooperation information can better interpret the disease-causal variants identified by genome-wide association studies and recapitulate cell states during neural development. Our study characterizes shared and unique patterns of TR cooperation associated with the cell type specificity of gene regulation in 3D chromatin.

Human Gene Functional Network-Informed Prediction of HIV-1 Host Dependency Factors.

Human immunodeficiency virus type 1 (HIV-1) depends on a class of host proteins called host dependency factors (HDFs) to facilitate its infection. So far experimental efforts have detected a certain number of HDFs, but the gene inventory of HIV-1 HDFs remains incomplete. Here, we implemented an existing network-based gene discovery strategy to predict HIV-1 HDFs. First, an encoding scheme based on a publicly available human tissue-specific gene functional network (GIANT; http://giant.princeton.edu/) was designed to convert each human gene into a 25,825-dimensional feature vector. Then, a random forest-based predictive model was trained on a data set containing 868 known HDFs and 1,736 non-HDFs. Through 5-fold cross-validation, an independent test, and comparison with one existing method, the proposed prediction method consistently revealed accurate and competitive performance. The highlight of our method should be ascribed to the introduction of the GIANT encoding scheme, which contains rich information regarding gene interactions. By merging known HDFs and genome-wide HDF prediction results, network analysis was conducted to catch the common patterns of HDFs in the context of the GIANT network. Interestingly, HDFs reveal significantly lower betweenness than HIV-1-interacting human proteins (i.e., HIV targets). In the meantime, the functional roles of HDFs were also examined by mapping all the HDF candidates into human protein complexes. Especially, we observed the frequent co-occurrence of HDFs and HIV targets at the protein complex level. Collectively, we hope the proposed prediction method not only can accelerate the HDF identification and antiviral drug target discovery, but also can provide some mechanistic insights into human-virus relationships.IMPORTANCE Identification of HIV-1 HDFs remains a crucial step to understand the complicated relationships between human and HIV-1. To complement the experimental identification of HDFs, we have implemented an existing network-based gene discovery strategy to predict HDFs from the human genome. The core idea of the proposed method is that the rich information deposited in host gene functional networks can be effectively utilized to infer the potential HDFs. We hope the proposed prediction method could further guide hypothesis-driven experimental efforts to interrogate human-HIV-1 relationships and provide new hints for the development of antiviral drugs to combat HIV-1 infection.

Diversity spectrum analysis identifies mutation-specific effects of cancer driver genes.

Mutation-specific effects of cancer driver genes influence drug responses and the success of clinical trials. We reasoned that these effects could unbalance the distribution of each mutation across different cancer types, as a result, the cancer preference can be used to distinguish the effects of the causal mutation. Here, we developed a network-based framework to systematically measure cancer diversity for each driver mutation. We found that half of the driver genes harbor cancer type-specific and pancancer mutations simultaneously, suggesting that the pervasive functional heterogeneity of the mutations from even the same driver gene. We further demonstrated that the specificity of the mutations could influence patient drug responses. Moreover, we observed that diversity was generally increased in advanced tumors. Finally, we scanned potentially novel cancer driver genes based on the diversity spectrum. Diversity spectrum analysis provides a new approach to define driver mutations and optimize off-label clinical trials.

regBase: whole genome base-wise aggregation and functional prediction for human non-coding regulatory variants.

Predicting the functional or pathogenic regulatory variants in the human non-coding genome facilitates the interpretation of disease causation. While numerous prediction methods are available, their performance is inconsistent or restricted to specific tasks, which raises the demand of developing comprehensive integration for those methods. Here, we compile whole genome base-wise aggregations, regBase, that incorporate largest prediction scores. Building on different assumptions of causality, we train three composite models to score functional, pathogenic and cancer driver non-coding regulatory variants respectively. We demonstrate the superior and stable performance of our models using independent benchmarks and show great success to fine-map causal regulatory variants on specific locus or at base-wise resolution. We believe that regBase database together with three composite models will be useful in different areas of human genetic studies, such as annotation-based casual variant fine-mapping, pathogenic variant discovery as well as cancer driver mutation identification. regBase is freely available at https://github.com/mulinlab/regBase.

Understanding Human-Virus Protein-Protein Interactions Using a Human Protein Complex-Based Analysis Framework.

Computational analysis of human-virus protein-protein interaction (PPI) data is an effective way toward systems understanding the molecular mechanism of viral infection. Previous work has mainly focused on characterizing the global properties of viral targets within the entire human PPI network. In comparison, how viruses manipulate host local networks (e.g., human protein complexes) has been rarely addressed from a computational perspective. By mainly integrating information about human-virus PPIs, human protein complexes, and gene expression profiles, we performed a large-scale analysis of virally targeted complexes (VTCs) related to five common human-pathogenic viruses, including influenza A virus subtype H1N1, human immunodeficiency virus type 1, Epstein-Barr virus, human papillomavirus, and hepatitis C virus. We found that viral targets are enriched within human protein complexes. We observed in the context of VTCs that viral targets tended to have a high within-complex degree and to be scaffold and housekeeping proteins. Complexes that are essential for viral propagation were simultaneously targeted by multiple viruses. We characterized the periodic expression patterns of VTCs and provided the corresponding candidates that may be involved in the manipulation of the host cell cycle. As a potential application of the current analysis, we proposed a VTC-based antiviral drug target discovery strategy. Finally, we developed an online VTC-related platform known as VTcomplex (http://zzdlab.com/vtcomplex/index.php or http://systbio.cau.edu.cn/vtcomplex/index.php). We hope that the current analysis can provide new insights into the global landscape of human-virus PPIs at the VTC level and that the developed VTcomplex will become a vital resource for the community. IMPORTANCE Although human protein complexes have been reported to be directly related to viral infection, previous studies have not systematically investigated human-virus PPIs from the perspective of human protein complexes. To the best of our knowledge, we have presented here the most comprehensive and in-depth analysis of human-virus PPIs in the context of VTCs. Our findings confirm that human protein complexes are heavily involved in viral infection. The observed preferences of virally targeted subunits within complexes reflect the mechanisms used by viruses to manipulate host protein complexes. The identified periodic expression patterns of the VTCs and the corresponding candidates could increase our understanding of how viruses manipulate the host cell cycle. Finally, our proposed conceptual application framework of VTCs and the developed VTcomplex could provide new hints to develop antiviral drugs for the clinical treatment of viral infections.

Spectrum of somatic mutations detected by targeted next-generation sequencing and their prognostic significance in adult patients with acute lymphoblastic leukemia.

Target-specific next-generation sequencing technology was used to analyze 112 genes in adult patients with acute lymphoblastic leukemia (ALL). This sequencing mainly focused on the specific mutational hotspots. Among the 121 patients, 93 patients were B-ALL (76.9%), and 28 patients (23.1%) were T-ALL. Of the 121 patients, 110 (90.9%) harbored at least one mutation. The five most frequently mutated genes in T-ALL are NOTCH1, JAK3, FBXW7, FAT1, and NRAS. In B-ALL, FAT1, SF1, CRLF2, TET2, and PTPN1 have higher incidence of mutations. Gene mutations are different between Ph+ALL and Ph-ALL patients. B-ALL patients with PTPN11 mutation and T-ALL patients with NOTCH1 and/or FBXW7 mutations showed better survival. But B-ALL with JAK1/JAK2 mutations showed worse survival. The results suggest that gene mutations exist in adult ALL patients universally, they are related with prognosis.

Differential Coexpression Analysis Reveals Extensive Rewiring of Arabidopsis Gene Coexpression in Response to Pseudomonas syringae Infection.

Plant defense responses to pathogens involve massive transcriptional reprogramming. Recently, differential coexpression analysis has been developed to study the rewiring of gene networks through microarray data, which is becoming an important complement to traditional differential expression analysis. Using time-series microarray data of Arabidopsis thaliana infected with Pseudomonas syringae, we analyzed Arabidopsis defense responses to P. syringae through differential coexpression analysis. Overall, we found that differential coexpression was a common phenomenon of plant immunity. Genes that were frequently involved in differential coexpression tend to be related to plant immune responses. Importantly, many of those genes have similar average expression levels between normal plant growth and pathogen infection but have different coexpression partners. By integrating the Arabidopsis regulatory network into our analysis, we identified several transcription factors that may be regulators of differential coexpression during plant immune responses. We also observed extensive differential coexpression between genes within the same metabolic pathways. Several metabolic pathways, such as photosynthesis light reactions, exhibited significant changes in expression correlation between normal growth and pathogen infection. Taken together, differential coexpression analysis provides a new strategy for analyzing transcriptional data related to plant defense responses and new insights into the understanding of plant-pathogen interactions.

Identification of WD40 repeats by secondary structure-aided profile-profile alignment.

A WD40 protein typically contains four or more repeats of ~40 residues ended with the Trp-Asp dipeptide, which folds into ß-propellers with four ß strands in each repeat. They often function as scaffolds for protein-protein interactions and are involved in numerous fundamental biological processes. Despite their important functional role, the "velcro" closure of WD40 propellers and the diversity of WD40 repeats make their identification a difficult task. Here we develop a new WD40 Repeat Recognition method (WDRR), which uses predicted secondary structure information to generate candidate repeat segments, and further employs a profile-profile alignment to identify the correct WD40 repeats from candidate segments. In particular, we design a novel alignment scoring function that combines dot product and BLOSUM62, thereby achieving a great balance of sensitivity and accuracy. Taking advantage of these strategies, WDRR could effectively reduce the false positive rate and accurately identify more remote homologous WD40 repeats with precise repeat boundaries. We further use WDRR to re-annotate the Pfam families in the ß-propeller clan (CL0186) and identify a number of WD40 repeat proteins with high confidence across nine model organisms. The WDRR web server and the datasets are available at http://protein.cau.edu.cn/wdrr/.

Network-Based Comparative Analysis of Arabidopsis Immune Responses to Golovinomyces orontii and Botrytis cinerea Infections.

A comprehensive exploration of common and specific plant responses to biotrophs and necrotrophs is necessary for a better understanding of plant immunity. Here, we compared the Arabidopsis defense responses evoked by the biotrophic fungus Golovinomyces orontii and the necrotrophic fungus Botrytis cinerea through integrative network analysis. Two time-course transcriptional datasets were integrated with an Arabidopsis protein-protein interaction (PPI) network to construct a G. orontii conditional PPI sub-network (gCPIN) and a B. cinerea conditional PPI sub-network (bCPIN). We found that hubs in gCPIN and bCPIN played important roles in disease resistance. Hubs in bCPIN evolved faster than hubs in gCPIN, indicating the different selection pressures imposed on plants by different pathogens. By analyzing the common network from gCPIN and bCPIN, we identified two network components in which the genes were heavily involved in defense and development, respectively. The co-expression relationships between interacting proteins connecting the two components were different under G. orontii and B. cinerea infection conditions. Closer inspection revealed that auxin-related genes were overrepresented in the interactions connecting these two components, suggesting a critical role of auxin signaling in regulating the different co-expression relationships. Our work may provide new insights into plant defense responses against pathogens with different lifestyles.

BEAN 2.0: an integrated web resource for the identification and functional analysis of type III secreted effectors.

Gram-negative pathogenic bacteria inject type III secreted effectors (T3SEs) into host cells to sabotage their immune signaling networks. Because T3SEs constitute a meeting-point of pathogen virulence and host defense, they are of keen interest to host-pathogen interaction research community. To accelerate the identification and functional understanding of T3SEs, we present BEAN 2.0 as an integrated web resource to predict, analyse and store T3SEs. BEAN 2.0 includes three major components. First, it provides an accurate T3SE predictor based on a hybrid approach. Using independent testing data, we show that BEAN 2.0 achieves a sensitivity of 86.05% and a specificity of 100%. Second, it integrates a set of online sequence analysis tools. Users can further perform functional analysis of putative T3SEs in a seamless way, such as subcellular location prediction, functional domain scan and disorder region annotation. Third, it compiles a database covering 1215 experimentally verified T3SEs and constructs two T3SE-related networks that can be used to explore the relationships among T3SEs. Taken together, by presenting a one-stop T3SE bioinformatics resource, we hope BEAN 2.0 can promote comprehensive understanding of the function and evolution of T3SEs.

Revealing shared and distinct gene network organization in Arabidopsis immune responses by integrative analysis.

Pattern-triggered immunity (PTI) and effector-triggered immunity (ETI) are two main plant immune responses to counter pathogen invasion. Genome-wide gene network organizing principles leading to quantitative differences between PTI and ETI have remained elusive. We combined an advanced machine learning method and modular network analysis to systematically characterize the organizing principles of Arabidopsis (Arabidopsis thaliana) PTI and ETI at three network resolutions. At the single network node/edge level, we ranked genes and gene interactions based on their ability to distinguish immune response from normal growth and successfully identified many immune-related genes associated with PTI and ETI. Topological analysis revealed that the top-ranked gene interactions tend to link network modules. At the subnetwork level, we identified a subnetwork shared by PTI and ETI encompassing 1,159 genes and 1,289 interactions. This subnetwork is enriched in interactions linking network modules and is also a hotspot of attack by pathogen effectors. The subnetwork likely represents a core component in the coordination of multiple biological processes to favor defense over development. Finally, we constructed modular network models for PTI and ETI to explain the quantitative differences in the global network architecture. Our results indicate that the defense modules in ETI are organized into relatively independent structures, explaining the robustness of ETI to genetic mutations and effector attacks. Taken together, the multiscale comparisons of PTI and ETI provide a systems biology perspective on plant immunity and emphasize coordination among network modules to establish a robust immune response.

Using weakly conserved motifs hidden in secretion signals to identify type-III effectors from bacterial pathogen genomes.

BACKGROUND: As one of the most important virulence factor types in gram-negative pathogenic bacteria, type-III effectors (TTEs) play a crucial role in pathogen-host interactions by directly influencing immune signaling pathways within host cells. Based on the hypothesis that type-III secretion signals may be comprised of some weakly conserved sequence motifs, here we used profile-based amino acid pair information to develop an accurate TTE predictor. RESULTS: For a TTE or non-TTE, we first used a hidden Markov model-based sequence searching method (i.e., HHblits) to detect its weakly homologous sequences and extracted the profile-based k-spaced amino acid pair composition (HH-CKSAAP) from the N-terminal sequences. In the next step, the feature vector HH-CKSAAP was used to train a linear support vector machine model, which we designate as BEAN (Bacterial Effector ANalyzer). We compared our method with four existing TTE predictors through an independent test set, and our method revealed improved performance. Furthermore, we listed the most predictive amino acid pairs according to their weights in the established classification model. Evolutionary analysis shows that predictive amino acid pairs tend to be more conserved. Some predictive amino acid pairs also show significantly different position distributions between TTEs and non-TTEs. These analyses confirmed that some weakly conserved sequence motifs may play important roles in type-III secretion signals. Finally, we also used BEAN to scan one plant pathogen genome and showed that BEAN can be used for genome-wide TTE identification. The webserver and stand-alone version of BEAN are available at http://protein.cau.edu.cn:8080/bean/.