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Microbial communities are massively resident in the human body, yet dysbiosis has been reported to correlate with many diseases, including various cancers. Most studies focus on the gut microbiome, while the bacteria that participate in tumor microenvironments on site remain unclear. Previous studies have acquired the bacteria expression profiles from RNA-seq, whole genome sequencing, and whole exon sequencing in The Cancer Genome Atlas (TCGA). However, small-RNA sequencing data were rarely used. Using TCGA miRNA sequencing data, we evaluated bacterial abundance in 32 types of cancer. To uncover the bacteria involved in cancer, we applied an analytical process to align unmapped human reads to bacterial references and developed the BIC database for the transcriptional landscape of bacteria in cancer. BIC provides cancer-associated bacterial information, including the relative abundance of bacteria, bacterial diversity, associations with clinical relevance, the co-expression network of bacteria and human genes, and their associated biological functions. These results can complement previously published databases. Users can easily download the result plots and tables, or download the bacterial abundance matrix for further analyses. In summary, BIC can provide information on cancer microenvironments related to microbial communities. BIC is available at: http://bic.jhlab.tw/.

High blood glucose is one of the risk factors for metabolic disease and INS (insulin) is the key regulatory hormone for glucose homeostasis. Hypoinsulinemia accompanied with hyperglycemia was diagnosed in mice with pancreatic β-cells exhibiting autophagy deficiency; however, the underlying mechanism remains elusive. The role of secretory autophagy in the regulation of metabolic syndrome is gaining more attention. Our data demonstrated that increased macroautophagic/autophagic activity leads to induction of insulin secretion in β-cells both in vivo and in vitro under high-glucose conditions. Moreover, proteomic analysis of purified autophagosomes from β-cells identified a group of vesicular transport proteins participating in insulin secretion, implying that secretory autophagy regulates insulin exocytosis. RAB37, a small GTPase, regulates vesicle biogenesis, trafficking, and cargo release. We demonstrated that the active form of RAB37 increased MAP1LC3/LC3 lipidation (LC3-II) and is essential for the promotion of insulin secretion by autophagy, but these phenomena were not observed in rab37 knockout (rab37-/-) cells and mice. Unbalanced insulin and glucose concentration in the blood was improved by manipulating autophagic activity using a novel autophagy inducer niclosamide (an antihelminthic drug) in a high-fat diet (HFD)-obesity mouse model. In summary, we reveal that secretory autophagy promotes RAB37-mediated insulin secretion to maintain the homeostasis of insulin and glucose both in vitro and in vivo.

With the proliferation of genomic sequence data for biomedical research, the exploration of human genetic information by domain experts requires a comprehensive interrogation of large numbers of scientific publications in PubMed. However, a query in PubMed essentially provides search results sorted only by the date of publication. A search engine for retrieving and interpreting complex relations between biomedical concepts in scientific publications remains lacking. Here, we present pubmedKB, a web server designed to extract and visualize semantic relationships between four biomedical entity types: variants, genes, diseases, and chemicals. pubmedKB uses state-of-the-art natural language processing techniques to extract semantic relations from the large number of PubMed abstracts. Currently, over 2 million semantic relations between biomedical entity pairs are extracted from over 33 million PubMed abstracts in pubmedKB. pubmedKB has a user-friendly interface with an interactive semantic graph, enabling the user to easily query entities and explore entity relations. Supporting sentences with the highlighted snippets allow to easily navigate the publications. Combined with a new explorative approach to literature mining and an interactive interface for researchers, pubmedKB thus enables rapid, intelligent searching of the large biomedical literature to provide useful knowledge and insights. pubmedKB is available at https://www.pubmedkb.cc/.

Ectopic ATP synthase complex (eATP synthase), located on cancer cell surface, has been reported to possess catalytic activity that facilitates the generation of ATP in the extracellular environment to establish a suitable microenvironment and to be a potential target for cancer therapy. However, the mechanism of intracellular ATP synthase complex transport remains unclear. Using a combination of spatial proteomics, interaction proteomics, and transcriptomics analyses, we find ATP synthase complex is first assembled in the mitochondria and subsequently delivered to the cell surface along the microtubule via the interplay of dynamin-related protein 1 (DRP1) and kinesin family member 5B (KIF5B). We further demonstrate that the mitochondrial membrane fuses to the plasma membrane in turn to anchor ATP syntheses on the cell surface using super-resolution imaging and real-time fusion assay in live cells. Our results provide a blueprint of eATP synthase trafficking and contribute to the understanding of the dynamics of tumor progression.

Understanding the genetic basis of human complex diseases is increasingly important in the development of precision medicine. Over the last decade, genome-wide association studies (GWAS) have become a key technique for detecting associations between common diseases and single nucleotide polymorphisms (SNPs) present in a cohort of individuals. Alternatively, the polygenic risk score (PRS), which often applies results from GWAS summary statistics, is calculated for the estimation of genetic propensity to a trait at the individual level. Despite many GWAS and PRS tools being available to analyze a large volume of genotype data, most clinicians and medical researchers are often not familiar with the bioinformatics tools and lack access to a high-performance computing cluster resource. To fill this gap, we provide a publicly available web server, PGSbuilder, for the GWAS and PRS analysis of human genomes with variant annotations. The user-friendly and intuitive PGSbuilder web server is developed to facilitate the discovery of the genetic variants associated with complex traits and diseases for medical professionals with limited computational skills. For GWAS analysis, PGSbuilder provides the most renowned analysis tool PLINK 2.0 package. For PRS, PGSbuilder provides six different PRS methods including Clumping and Thresholding, Lassosum, LDPred2, GenEpi, PRS-CS, and PRSice2. Furthermore, PGSbuilder provides an intuitive user interface to examine the annotated functional effects of variants from known biomedical databases and relevant literature using advanced natural language processing approaches. In conclusion, PGSbuilder offers a reliable platform to aid researchers in advancing the public perception of genomic risk and precision medicine for human disease genetics. PGSbuilder is freely accessible athttp://pgsb.tw23.org.

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Supplementary Data from Identification of GRP75 as an Independent Favorable Prognostic Marker of Neuroblastoma by a Proteomics Analysis

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Purpose: Neuroblastoma (NB) is a heterogeneous neoplasm. Detailed biological discrimination is critical for the effective treatment of this disease. Because the tumor behavior of NB is closely associated with the histologic state of differentiation, we thus aimed to identify novel differentiation-associated markers of NB with prognostic implication.Experimental Design: A human NB cell line SH-SY5Y was used as a model system to explore potential biomarkers for the differentiation of NB by proteomic analyses. Seventy-two NB tumor tissues were subsequently investigated by immunohistochemistry to validate the correlations between the expression of a novel prognostic marker, various clinicopathologic and biological factors, and patient survival.Results: Using two-dimensional differential gel electrophoresis, we found a total of 24 spots of proteins in SH-SY5Y cells whose expression was enhanced following differentiation. Glucose-regulated protein 75 (GRP75) was unambiguously identified as one of the five proteins that were dramatically up-regulated following differentiation. Immunohistochemical analyses of 72 NB tumor tissues further revealed that positive GRP75 immunostaining is strongly correlated with differentiated histologies (P < 0.001), mass-screened tumors (P = 0.016), and early clinical stages (P < 0.001) but inversely correlated with MYCN amplification (P = 0.010). Univariate and multivariate survival analyses showed that GRP75 expression is an independent favorable prognostic factor.Conclusions: The present findings clearly showed that our proteomics-based novel experimental paradigm could be a powerful tool to uncover novel biomarkers associated with the differentiation of NB. Our data also substantiate an essential role of GRP75 in the differentiation of NB.

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Purpose: Notch signaling has been implicated to play a critical role in the tumorigenesis of neuroblastoma (NB) and can modulate calreticulin (CRT) expression that strongly correlates with tumor differentiation and favorable prognosis of NB. We thus sought to determine how Notch regulates CRT expression and affects NB tumor behavior.Experimental Design: The Notch-dependent regulation of CRT expression in cultured NB cells was analyzed by confocal microscopy and Western blotting. Notch1 protein expression in 85 NB tumors was examined by immunohistochemistry and correlated with the clinicopathologic/biological characters of NB patients. The progression of NB tumors in response to attenuated Notch signaling was examined by using a xenograft mouse model.Results: We showed that CRT is essential for the neuronal differentiation of NB cells elicited by inhibition of Notch signaling. This effect was mediated by a c-Jun-NH2-kinase–dependent pathway. Furthermore, NB tumors with elevated Notch1 protein expression were strongly correlated with advanced tumor stages, MYCN amplification, an undifferentiated histology, as well as a low CRT expression level. Most importantly, the opposing effect between Notch1 and CRT could reciprocally affect the survival of NB patients. The administration of a γ-secretase inhibitor into a xenograft mouse model of NB significantly suppressed the tumor progression.Conclusions: Our findings provide the first evidence that a c-Jun-NH2-kinase-CRT–dependent pathway is essential for the neuronal differentiation elicited by Notch signaling blockade and that Notch1 and CRT can synergistically predict the clinical outcomes of NB patients. The present data suggest that Notch signaling could be a therapeutic target for NB. Clin Cancer Res; 16(17); 4411–20. ©2010 AACR.

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Supplementary Figures S1-S4 and Supplementary Materials and Methods.

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PDF file, 84K, Addictive effects of citreoviridin (Citreo) and 26S proteosome inhibitor.

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PDF file, 82K, Supplementary materials and methods for cell surface biotinylation, preparation of purified plasma membrane proteins using affinity chromatography, two-dimensional electrophoresis, in-gel digestion, and protein identification.

Supplementary Materials, Figures 1-7, Table 1 from A Novel Sialyltransferase Inhibitor Suppresses FAK/Paxillin Signaling and Cancer Angiogenesis and Metastasis Pathways

PDF file, 110K, Distribution of respiratory chain complexes in A549 and CL1-0 cells.

PDF file, 126K, Biotinylated purification of plasma membrane proteins.

PDF file, 234K, Summary of the plausible mechanisms induced by ATP synthase inhibitor citreoviridin.

PDF file, 215K, Citreoviridin does not induce apoptosis in CL1-0 or A549 cells.

PDF file, 228K, Protein expression profiles of citreoviridin-treated cells. The protein expression profiles of citreoviridin- (Citreo, lower) treated CL1-0 cells were compared to DMSO- (upper) treated controls on a time-course from 12 to 72 h.

PDF file, 149K, Citreoviridin does not inhibit the cell proliferation of p53 null cell H1299.

XLS file, 20K, The differential expressed proteins identified by MALDI-TOF mass spectrometry.

The small nucleolar RNA host gene 1 (SNHG1) is a novel oncogenic long non-coding RNA (lncRNA) aberrantly expressed in different tumor types. We previously found highly expressed SNHG1 was associated with poor prognosis and MYCN status in neuroblastoma (NB). However, the molecular mechanisms of SNHG1 in NB are still unclear. Here, we disrupted endogenous SNHG1 in the MYCN-amplified NB cell line SK-N-BE(2)C using the CRISPR/Cas9 system and demonstrated the proliferation and colony formation ability of SNHG1-knowndown cells were suppressed. The transcriptome analysis and functional assays of SNHG1-knockdown cells revealed SNHG1 was involved in various biological processes including cell growth, migration, apoptosis, cell cycle, and reactive oxygen species (ROS). Interestingly, the expression of core regulatory circuitry (CRC) transcription factors in MYCN-amplified NB, including PHOX2B, HAND2, GATA3, ISL1, TBX1, and MYCN, were decreased in SNHG1-knockdown cells. The chromatin-immunoprecipitation sequencing (ChIP-seq) and transposase-accessible chromatin using sequencing (ATAC-seq) analyses showed that chromatin status of these CRC members was altered, which might stem from interactions between SNHG1 and HDAC1/2. These findings demonstrate that SNHG1 plays a crucial role in maintaining NB identity via chromatin regulation and reveal the function of the lncRNA SNHG1 in NB.

High blood glucose is one of the risk factors for metabolic disease and INS (insulin) is the key regulatory hormone for glucose homeostasis. Hypoinsulinemia accompanied with hyperglycemia was diagnosed in mice with pancreatic β-cells exhibiting autophagy deficiency; however, the underlying mechanism remains elusive. The role of secretory autophagy in the regulation of metabolic syndrome is gaining more attention. Our data demonstrated that increased macroautophagic/autophagic activity leads to induction of insulin secretion in β-cells both

Single-cell RNA sequencing (scRNA-seq) technology allows massively parallel characterization of thousands of cells at the transcriptome level. scRNA-seq is emerging as an important tool to investigate the cellular components and their interactions in the tumor microenvironment. scRNA-seq is also used to reveal the association between tumor microenvironmental patterns and clinical outcomes and to dissect cell-specific effects of drug treatment in complex tissues. Recent advances in scRNA-seq have driven the discovery of biomarkers in diseases and therapeutic targets. Although methods for prediction of drug response using gene expression of scRNA-seq data have been proposed, an integrated tool from scRNA-seq analysis to drug discovery is required. We present scDrug as a bioinformatics workflow that includes a one-step pipeline to generate cell clustering for scRNA-seq data and two methods to predict drug treatments. The scDrug pipeline consists of three main modules: scRNA-seq analysis for identification of tumor cell subpopulations, functional annotation of cellular subclusters, and prediction of drug responses. scDrug enables the exploration of scRNA-seq data readily and facilitates the drug repurposing process. scDrug is freely available on GitHub at https://github.com/ailabstw/scDrug.

The EGFR tyrosine kinase inhibitor gefitinib is commonly used for lung cancer patients. However, some patients eventually become resistant to gefitinib and develop progressive disease. Here, we indicate that ecto-ATP synthase, which ectopically translocated from mitochondrial inner membrane to plasma membrane, is considered as a potential therapeutic target for drug-resistant cells. Quantitative multi-omics profiling reveals that ecto-ATP synthase inhibitor mediates CK2-dependent phosphorylation of DNA topoisomerase IIα (topo IIα) at serine 1106 and subsequently increases the expression of long noncoding RNA, GAS5. Additionally, we also determine that downstream of GAS5, p53 pathway, is activated by ecto-ATP synthase inhibitor for regulation of programed cell death. Interestingly, GAS5-proteins interactomic profiling elucidates that GAS5 associates with topo IIα and subsequently enhancing the phosphorylation level of topo IIα. Taken together, our findings suggest that ecto-ATP synthase blockade is an effective therapeutic strategy via regulation of CK2/phospho-topo IIα/GAS5 network in gefitinib-resistant lung cancer cells.

Post-COVID-19 pulmonary fibrosis (PCPF) is a long-term complication that appears in some COVID-19 survivors. However, there are currently limited options for treating PCPF patients. To address this problem, we investigated COVID-19 patients’ transcriptome at single-cell resolution and combined biological network analyses to repurpose the drugs treating PCPF. We revealed a novel gene signature of PCPF. The signature is functionally associated with the viral infection and lung fibrosis. Further, the signature has good performance in diagnosing and assessing pulmonary fibrosis. Next, we applied a network-based drug repurposing method to explore novel treatments for PCPF. By quantifying the proximity between the drug targets and the signature in the interactome, we identified several potential candidates and provided a drug list ranked by their proximity. Taken together, we revealed a novel gene expression signature as a theragnostic biomarker for PCPF by integrating different computational approaches. Moreover, we showed that network-based proximity could be used as a framework to repurpose drugs for PCPF.

Background Increasing amount of long non-coding RNAs (lncRNAs) have been found involving in many biological processes and played salient roles in cancers. However, up until recently, functions of most lncRNAs in lung cancer have not been fully discovered, particularly in the co-regulated lncRNAs. Thus, this study aims to investigate roles of lncRNA modules and uncover a module-based biomarker in lung adenocarcinoma (LUAD). Results We used gene expression profiles from The Cancer Genome Atlas (TCGA) to construct the lncRNA association networks, from which the highly-associated lncRNAs are connected as modules. It was found that the expression of some modules is significantly associated with patient’s survival, including module N1 (HR = 0.62, 95% CI = 0.46–0.84, p = 0.00189); N2 (HR = 0.68, CI = 0.50–0.93, p = 0.00159); N4 (HR = 0.70, CI = 0.52–0.95, p = 0.0205) and P3 (HR = 0.68, CI = 0.50–0.92, p = 0.0123). The lncRNA signature consisting of these four prognosis-related modules, a 4-modular lncRNA signature, is associated with favourable prognosis in TCGA-LUAD (HR = 0.51, CI = 0.37–0.69, p value = 2.00e−05). Afterwards, to assess the performance of the generic modular signature as a prognostic biomarker, we computed the time-dependent area under the receiver operating characteristics (AUC) of this 4-modular lncRNA signature, which showed AUC equals 68.44% on 336th day. In terms of biological functions, these modules are correlated with several cancer hallmarks and pathways, including Myc targets, E2F targets, cell cycle, inflammation/immunity-related pathways, androgen/oestrogen response, KRAS signalling, DNA repair and epithelial-mesenchymal transition (EMT). Conclusion Taken together, we identified four novel LUAD prognosis-related lncRNA modules, and assessed the performance of the 4-modular lncRNA signature being a prognostic biomarker. Functionally speaking, these modules involve in oncogenic hallmarks as well as pathways. The results unveiled the co-regulated lncRNAs in LUAD and may provide a framework for further lncRNA studies in lung cancer.

Background Recently, non-coding RNAs are of growing interest, and more scientists attach importance to research on their functions. Long non-coding RNAs (lncRNAs) are defined as non-protein coding transcripts longer than 200 nucleotides. We already knew that lncRNAs are related to cancers and will be dysregulated in them. But most of their functions are still left to further study. A mechanism of RNA regulation, known as competing endogenous RNAs (ceRNAs), has been proposed to explain the complex relationships among mRNAs and lncRNAs by competing for binding with shared microRNAs (miRNAs). Methods We proposed an analysis framework to construct the association networks among lncRNA, mRNA, and miRNAs based on their expression patterns and decipher their network modules. Results We collected a large-scale gene expression dataset of 1,061 samples from breast invasive carcinoma (BRCA) patients, each consisted of the expression profiles of 4,359 lncRNAs, 16,517 mRNAs, and 534 miRNAs, and applied the proposed analysis approach to interrogate them. We have uncovered the underlying ceRNA modules and the key modulatory lncRNAs for different subtypes of breast cancer. Conclusions We proposed a modulatory analysis to infer the ceRNA effects among mRNAs and lncRNAs and performed functional analysis to reveal the plausible mechanisms of lncRNA modulation in the four breast cancer subtypes. Our results might provide new directions for breast cancer therapeutics and the proposed method could be readily applied to other diseases.

Background A new class of regulatory elements called super-enhancers, comprised of multiple neighboring enhancers, have recently been reported to be the key transcriptional drivers of cellular, developmental, and disease states. Results Here, we defined super-enhancer RNAs as highly expressed enhancer RNAs that are transcribed from a cluster of localized genomic regions. Using the cap analysis of gene expression sequencing data from FANTOM5, we systematically explored the enhancer and messenger RNA landscapes in hundreds of different cell types in response to various environments. Applying non-negative matrix factorization (NMF) to super-enhancer RNA profiles, we found that different cell types were well classified. In addition, through the NMF of individual time-course profiles from a single cell-type, super-enhancer RNAs were clustered into several states with progressive patterns. We further investigated the enriched biological functions of the proximal genes involved in each pattern, and found that they were associated with the corresponding developmental process. Conclusions The proposed super-enhancer RNAs can act as a good alternative, without the complicated measurement of histone modifications, for identifying important regulatory elements of cell type specification and identifying dynamic cell states.

Neuroblastoma is the most common malignant disease of infancy, and amplification of the MYCN oncogene is closely associated with poor prognosis. Recently, expression of MYCN was shown to be inversely correlated with aryl hydrocarbon receptor (AHR) expression in neuroblastoma, and overexpression of AHR downregulated MYCN expression, promoting cell differentiation. Therefore, we further investigated the potential of AHR to serve as a prognostic indicator or a therapeutic target in neuroblastoma. First, the clinical significance of AHR in neuroblastoma was examined. Positive AHR immunostaining strongly correlated with differentiated histology of neuroblastoma and predicted better survival for patients. The mouse xenograft model showed that overexpression of AHR significantly suppressed neuroblastoma tumor growth. In addition, activation of AHR by the endogenous ligand kynurenine inhibited cell proliferation and promoted cell differentiation in vitro and in vivo. kynurenine treatment also upregulated the expression of KISS1, a tumor metastasis suppressor, and attenuated metastasis in the xenograft model. Finally, analysis of KISS1 levels in neuroblastoma patient tumors using the R2: Genomics Analysis and Visualization Platform revealed that KISS1 expression positively correlated with AHR, and high KISS1 expression predicted better survival for patients. In conclusion, our results indicate that AHR is a novel prognostic biomarker for neuroblastoma, and that overexpression or activation of AHR offers a new therapeutic possibility for patients with neuroblastoma. Significance: These findings show that AHR may function as a tumor suppressor in childhood neuroblastoma, potentially influencing the aetiologic and therapeutic targeting of the disease.

Purpose: Neuroblastoma is a pediatric malignancy of the sympathetic nervous system with diverse clinical behaviors. Genomic amplification of MYCN oncogene has been shown to drive neuroblastoma pathogenesis and correlate with aggressive disease, but the survival rates for those high-risk tumors carrying no MYCN amplification remain equally dismal. The paucity of mutations and molecular heterogeneity has hindered the development of targeted therapies for most advanced neuroblastomas. We use an alternative method to identify potential drugs that target nononcogene dependencies in high-risk neuroblastoma. Experimental Design: By using a gene expression–based integrative approach, we identified prognostic signatures and potentially effective single agents and drug combinations for high-risk neuroblastoma. Results: Among these predictions, we validated in vitro efficacies of some investigational and marketed drugs, of which niclosamide, an anthelmintic drug approved by the FDA, was further investigated in vivo. We also quantified the proteomic changes during niclosamide treatment to pinpoint nucleoside diphosphate kinase 3 (NME3) downregulation as a potential mechanism for its antitumor activity. Conclusions: Our results establish a gene expression–based strategy to interrogate cancer biology and inform drug discovery and repositioning for high-risk neuroblastoma.

Summary Cancer is a complex disease that relies on both oncogenic mutations and non-mutated genes for survival, and therefore coined as oncogene and non-oncogene addictions. The need for more effective combination therapies to overcome drug resistance in oncology has been increasingly recognized, but the identification of potentially synergistic drugs at scale remains challenging. Here we propose a gene-expression-based approach, which uses the recurrent perturbation-transcript regulatory relationships inferred from a large compendium of chemical and genetic perturbation experiments across multiple cell lines, to engender a testable hypothesis for combination therapies. These transcript-level recurrences were distinct from known compound-protein target counterparts, were reproducible in external datasets, and correlated with small-molecule sensitivity. We applied these recurrent relationships to predict synergistic drug pairs for cancer and experimentally confirmed two unexpected drug combinations in vitro. Our results corroborate a gene-expression-based strategy for combinatorial drug screening as a way to target non-mutated genes in complex diseases., Graphical Abstract, Highlights • Compound signatures targeting non-oncogene addiction for combinatorial drug discovery • These signatures are reproducible and linked to cancer hallmarks and drug sensitivity • Two synergistic drug combinations are experimentally confirmed in vitro, Bioinformatics; Cancer Systems Biology; Pharmacoinformatics

Mitochondria are crucial organelles that provide energy via oxidative phosphorylation in eukaryotic cells and also have critical roles in growth, division, and the cell cycle, as well as the rapid adaptation required to meet the metabolic needs of the cell. Mitochondrial processes are highly dynamic; fusion and fission can vary with cell type, cellular context, and stress levels. Accumulating evidence demonstrates that an imbalance in mitochondrial dynamics leads to death in numerous types of human cancer cells. Therefore, modulating mitochondrial dynamics could be a therapeutic target. In this review, we provide an overview of the protein interaction networks involved in mitochondrial dynamics as effective and feasible drug targets and discuss the related potential therapeutic strategies for cancer.

The tumor microenvironment (TME), which comprises cellular and noncellular components, is involved in the complex process of cancer development. Emerging evidence suggests that mesenchymal stem cells (MSCs), one of the vital regulators of the TME, foster tumor progression through paracrine secretion. However, the comprehensive phosphosignaling pathways that are mediated by MSC-secreting factors have not yet been fully established. In this study, we attempt to dissect the MSC-triggered mechanism in lung cancer using quantitative phosphoproteomics. A total of 1958 phosphorylation sites are identified in lung cancer cells stimulated with MSC-conditioned medium. Integrative analysis of the identified phosphoproteins and predicted kinases demonstrates that MSC-conditioned medium functionally promotes the proliferation and migration of lung cancer via the ERK/phospho-c-Fos-S374 pathway. Recent studies have reported that extracellular ATP accumulates in the TME and stimulates the P2X7R on the cancer cell membrane via purinergic signaling. We observe that ectopic ATP synthase is located on the surface of MSCs and excreted extracellular ATP into the lung cancer microenvironment to trigger the ERK/phospho-c-Fos-S374 pathway, which is consistent with these previous findings. Our results suggest that ectopic ATP synthase on the surface of MSCs releases extracellular ATP into the TME, which promotes cancer progression via activation of the ERK/phospho-c-Fos-S374 pathway.

The selection of peptides presented by MHC molecules is crucial for antigen discovery. Previously, several predictors have shown impressive performance on binding affinity. However, the decisive MHC residues and their relation to the selection of binding peptides are still unrevealed. Here, we connected HLA alleles with binding motifs via our deep learning-based framework, MHCfovea. MHCfovea expanded the knowledge of MHC-I-binding motifs from 150 to 13,008 alleles. After clustering N-terminal and C-terminal sub-motifs on both observed and unobserved alleles, MHCfovea calculated the hyper-motifs and the corresponding allele signatures on the important positions to disclose the relation between binding motifs and MHC-I sequences. MHCfovea delivered 32 pairs of hyper-motifs and allele signatures (HLA-A: 13, HLA-B: 12, and HLA-C: 7). The paired hyper-motifs and allele signatures disclosed the critical polymorphic residues that determine the binding preference, which are believed to be valuable for antigen discovery and vaccine design when allele specificity is concerned., Ko-Han Lee et al. develop MHCfovea, a machine-learning method for predicting peptide-binding by MHC molecules and inferring peptide motifs and MHC allele signatures. They demonstrate that MHCfovea is capable of detecting meaningful hyper-motifs and allele signatures, making it a useful resource for the community.

Ectopic adenosine triphosphate (eATP) synthase, located on the cell surface instead of the mitochondrial inner membrane, has been discovered to be a novel target for the treatment of various types of cancer. However, the mechanism of eATP synthase trafficking toward the cell surface remains unclear. In this study, we used an integrative approach incorporating multiomics, super-resolution imaging, real-time live-cell tracing, and various functional analyses to derive a transport model of eATP synthase from the mitochondria to the plasma membrane in cancer cells. We determined that the ATP synthase complex is first assembled in the mitochondria and subsequently delivered to the cell surface through the microtubule-mediated mitochondrial transport pathway. We also found that dynamin-related protein 1 (DRP1) enhances mitochondrial fission and subsequently associates with microtubule motor protein kinesin family member 5B (KIF5B) to promote the subcellular transportation of eATP synthase. Consequently, our work provides a blueprint for eATP synthase trafficking from the mitochondria toward the cell surface.

Motivation The hourglass model is a popular evo-devo model depicting that the developmental constraints in the middle of a developmental process are higher, and hence the phenotypes are evolutionarily more conserved, than those that occur in early and late ontogeny stages. Although this model has been supported by studies analyzing developmental gene expression data, the evolutionary explanation and molecular mechanism behind this phenomenon are not fully understood yet. To approach this problem, Raff proposed a hypothesis and claimed that higher interconnectivity among elements in an organism during organogenesis resulted in the larger constraints at the mid-developmental stage. By employing stochastic network analysis and gene-set pathway analysis, we aim to demonstrate such changes of interconnectivity claimed in Raff’s hypothesis. Results We first compared the changes of network randomness among developmental processes in different species by measuring the stochasticity within the biological network in each developmental stage. By tracking the network entropy along each developmental process, we found that the network stochasticity follows an anti-hourglass trajectory, and such a pattern supports Raff’s hypothesis in dynamic changes of interconnections among biological modules during development. To understand which biological functions change during the transition of network stochasticity, we sketched out the pathway dynamics along the developmental stages and found that species may activate similar groups of biological processes across different stages. Moreover, higher interspecies correlations are found at the mid-developmental stages. Supplementary information Supplementary data are available at Bioinformatics online.