ARID1A regulates DNA repair through chromatin organization and its deficiency triggers DNA damage-mediated anti-tumor immune response.

AT-rich interaction domain protein 1A (ARID1A), a SWI/SNF chromatin remodeling complex subunit, is frequently mutated across various cancer entities. Loss of ARID1A leads to DNA repair defects. Here, we show that ARID1A plays epigenetic roles to promote both DNA double-strand breaks (DSBs) repair pathways, non-homologous end-joining (NHEJ) and homologous recombination (HR). ARID1A is accumulated at DSBs after DNA damage and regulates chromatin loops formation by recruiting RAD21 and CTCF to DSBs. Simultaneously, ARID1A facilitates transcription silencing at DSBs in transcriptionally active chromatin by recruiting HDAC1 and RSF1 to control the distribution of activating histone marks, chromatin accessibility, and eviction of RNAPII. ARID1A depletion resulted in enhanced accumulation of micronuclei, activation of cGAS-STING pathway, and an increased expression of immunomodulatory cytokines upon ionizing radiation. Furthermore, low ARID1A expression in cancer patients receiving radiotherapy was associated with higher infiltration of several immune cells. The high mutation rate of ARID1A in various cancer types highlights its clinical relevance as a promising biomarker that correlates with the level of immune regulatory cytokines and estimates the levels of tumor-infiltrating immune cells, which can predict the response to the combination of radio- and immunotherapy.

Tumor-intrinsic SIRPA promotes sensitivity to checkpoint inhibition immunotherapy in melanoma.

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD8+ T cells in a co-culture system. Mice bearing SIRPA-deficient melanoma tumors show no response to anti-PD-L1 treatment, whereas melanoma-specific SIRPA overexpression significantly enhances immunotherapy response. Mechanistically, SIRPA is regulated by its pseudogene, SIRPAP1. Our results suggest a complicated role of SIRPA in the tumor ecosystem, highlighting cell-type-dependent antagonistic effects of the same target on immunotherapy.

Proteo-genomic characterization of virus-associated liver cancers reveals potential subtypes and therapeutic targets.

Primary liver cancer is a heterogeneous disease in terms of its etiology, histology, and therapeutic response. Concurrent proteomic and genomic characterization of a large set of clinical liver cancer samples can help elucidate the molecular basis of heterogeneity and thus serve as a valuable resource for personalized liver cancer treatment. In this study, we perform proteomic profiling of ~300 proteins on 259 primary liver cancer tissues with reverse-phase protein arrays, mutational analysis using whole genome sequencing and transcriptional analysis with RNA-Seq. Patients are of Japanese ethnic background and mainly HBV or HCV positive, providing insight into this important liver cancer subtype. Unsupervised classification of tumors based on protein expression profiles reveal three proteomic subclasses R1, R2, and R3. The R1 subclass is immunologically hot and demonstrated a good prognosis. R2 contains advanced proliferative tumor with TP53 mutations, high expression of VEGF receptor 2 and the worst prognosis. R3 is enriched with CTNNB1 mutations and elevated mTOR signaling pathway activity. Twenty-two proteins, including CDK1 and CDKN2A, are identified as potential prognostic markers. The proteomic classification presented in this study can help guide therapeutic decision making for liver cancer treatment.

A targetable LIFR-NF-κB-LCN2 axis controls liver tumorigenesis and vulnerability to ferroptosis.

The growing knowledge of ferroptosis has suggested the role and therapeutic potential of ferroptosis in cancer, but has not been translated into effective therapy. Liver cancer, primarily hepatocellular carcinoma (HCC), is highly lethal with limited treatment options. LIFR is frequently downregulated in HCC. Here, by studying hepatocyte-specific and inducible Lifr-knockout mice, we show that loss of Lifr promotes liver tumorigenesis and confers resistance to drug-induced ferroptosis. Mechanistically, loss of LIFR activates NF-κB signaling through SHP1, leading to upregulation of the iron-sequestering cytokine LCN2, which depletes iron and renders insensitivity to ferroptosis inducers. Notably, an LCN2-neutralizing antibody enhances the ferroptosis-inducing and anticancer effects of sorafenib on HCC patient-derived xenograft tumors with low LIFR expression and high LCN2 expression. Thus, anti-LCN2 therapy is a promising way to improve liver cancer treatment by targeting ferroptosis.

A four-gene signature predicts survival and anti-CTLA4 immunotherapeutic responses based on immune classification of melanoma.

Cutaneous melanoma is the most malignant skin cancer. Biomarkers for stratifying patients at initial diagnosis and informing clinical decisions are highly sought after. Here we classified melanoma patients into three immune subtypes by single-sample gene-set enrichment analysis. We further identified a four-gene tumor immune-relevant (TIR) signature that was significantly associated with the overall survival of melanoma patients in The Cancer Genome Atlas cohort and in an independent validation cohort. Moreover, when applied to melanoma patients treated with the CTLA4 antibody, ipilimumab, the TIR signature could predict the response to ipilimumab and the survival. Notably, the predictive power of the TIR signature was higher than that of other biomarkers. The genes in this signature, SEL1L3, HAPLN3, BST2, and IFITM1, may be functionally involved in melanoma progression and immune response. These findings suggest that this four-gene signature has potential use in prognosis, risk assessment, and prediction of anti-CTLA4 response in melanoma patients.

Next-Generation Analytics for Omics Data.

The increasing omics data present a daunting informatics challenge. DrBioRight, a natural language-oriented and artificial intelligence-driven analytics platform, enables the broad research community to perform analysis in an intuitive, efficient, transparent, and collaborative way. The emerging next-generation analytics will maximize the utility of omics data and lead to a new paradigm for biomedical research.

Inhibition of TMPRSS2 by HAI-2 reduces prostate cancer cell invasion and metastasis.

TMPRSS2 is an important membrane-anchored serine protease involved in human prostate cancer progression and metastasis. A serine protease physiologically often comes together with a cognate inhibitor for execution of proteolytically biologic function; however, TMPRSS2's cognate inhibitor is still elusive. To identify the cognate inhibitor of TMPRSS2, in this study, we applied co-immunoprecipitation and LC/MS/MS analysis and isolated hepatocyte growth factor activator inhibitors (HAIs) to be potential inhibitor candidates for TMPRSS2. Moreover, the recombinant HAI-2 proteins exhibited a better inhibitory effect on TMPRSS2 proteolytic activity than HAI-1, and recombinant HAI-2 proteins had a high affinity to form a complex with TMPRSS2. The immunofluorescence images further showed that TMPRSS2 was co-localized to HAI-2. Both KD1 and KD2 domain of HAI-2 showed comparable inhibitory effects on TMPRSS2 proteolytic activity. In addition, HAI-2 overexpression could suppress the induction effect of TMPRSS2 on pro-HGF activation, extracellular matrix degradation and prostate cancer cell invasion. We further determined that the expression levels of TMPRSS2 were inversely correlated with HAI-2 levels during prostate cancer progression. In orthotopic xenograft animal model, TMPRSS2 overexpression promoted prostate cancer metastasis, and HAI-2 overexpression efficiently blocked TMPRSS2-induced metastasis. In summary, the results together indicate that HAI-2 can function as a cognate inhibitor for TMPRSS2 in human prostate cancer cells and may serve as a potential factor to suppress TMPRSS2-mediated malignancy.

Sawfly Genomes Reveal Evolutionary Acquisitions That Fostered the Mega-Radiation of Parasitoid and Eusocial Hymenoptera.

The tremendous diversity of Hymenoptera is commonly attributed to the evolution of parasitoidism in the last common ancestor of parasitoid sawflies (Orussidae) and wasp-waisted Hymenoptera (Apocrita). However, Apocrita and Orussidae differ dramatically in their species richness, indicating that the diversification of Apocrita was promoted by additional traits. These traits have remained elusive due to a paucity of sawfly genome sequences, in particular those of parasitoid sawflies. Here, we present comparative analyses of draft genomes of the primarily phytophagous sawfly Athalia rosae and the parasitoid sawfly Orussus abietinus. Our analyses revealed that the ancestral hymenopteran genome exhibited traits that were previously considered unique to eusocial Apocrita (e.g., low transposable element content and activity) and a wider gene repertoire than previously thought (e.g., genes for CO2 detection). Moreover, we discovered that Apocrita evolved a significantly larger array of odorant receptors than sawflies, which could be relevant to the remarkable diversification of Apocrita by enabling efficient detection and reliable identification of hosts.

Predicting Cancer Cell Line Dependencies From the Protein Expression Data of Reverse-Phase Protein Arrays.

PURPOSE: Predicting cancer dependencies from molecular data can help stratify patients and identify novel therapeutic targets. Recently available data on large-scale cancer cell line dependency allow a systematic assessment of the predictive power of diverse molecular features; however, the protein expression data have not been rigorously evaluated. By using the protein expression data generated by reverse-phase protein arrays, we aimed to assess their predictive power in identifying cancer dependencies and to develop a related analytic tool for community use. MATERIALS AND METHODS: By using a machine learning schema, we conducted an analysis of feature importance based on cancer dependency and multiomic data from the DepMap and Cancer Cell Line Encyclopedia projects. We assessed the consistency of cancer dependency data between CRISPR/Cas9 and short hairpin RNA-mediated perturbation platforms. For a fair comparison, we focused on a set of genes with robust dependency data and four available expression-related features (copy number alteration, DNA methylation, messenger RNA expression, and protein expression) and performed the same-gene predictions of the cancer dependency using different molecular features. RESULTS: For the genes surveyed, we observed that the protein expression data contained substantial predictive power for cancer dependencies, and they were the best predictive feature for the CRISPR/Cas9-based dependency data. We also developed a user-friendly protein-dependency analytic module and integrated it with The Cancer Proteome Atlas; this module allows researchers to explore and analyze our results intuitively. CONCLUSION: This study provides a systematic assessment for predicting cancer dependencies of cell lines from different expression-related features of a gene. Our results suggest that protein expression data are a highly valuable information resource for understanding tumor vulnerabilities and identifying therapeutic opportunities.

TCPA v3.0: An Integrative Platform to Explore the Pan-Cancer Analysis of Functional Proteomic Data.

Reverse-phase protein arrays represent a powerful functional proteomics approach to characterizing cell signaling pathways and understanding their effects on cancer development. Using this platform, we have characterized ∼8,000 patient samples of 32 cancer types through The Cancer Genome Atlas and built a widely used, open-access bioinformatic resource, The Cancer Proteome Atlas (TCPA). To maximize the utility of TCPA, we have developed a new module called "TCGA Pan-Cancer Analysis," which provides comprehensive protein-centric analyses that integrate protein expression data and other TCGA data across cancer types. We further demonstrate the value of this module by examining the correlations of RPPA proteins with significantly mutated genes, assessing the predictive power of somatic copy-number alterations, DNA methylation, and mRNA on protein expression, inferring the regulatory effects of miRNAs on protein expression, constructing a co-expression network of proteins and pathways, and identifying clinically relevant protein markers. This upgraded TCPA (v3.0) will provide the cancer research community with a more powerful tool for studying functional proteomics and making translational impacts.

The GFF3toolkit: QC and Merge Pipeline for Genome Annotation.

The GFF3toolkit ( https://github.com/NAL-i5K/GFF3toolkit ) supported by the i5k Workspace@NAL provides a suite of tools to handle gene annotations in GFF3 format from arthropod genome projects and their research communities. To improve GFF3 formatting of gene annotations, a quality control and merge procedure is proposed along with the GFF3toolkit. In particular, the toolkit provides functions to sort a GFF3 file, detect GFF3 format errors, merge two GFF3 files, and generate biological sequences from a GFF3 file. This chapter explains when and how to use the provided tools to obtain nonredundant arthropod gene sets in high quality.

Navigating the i5k Workspace@NAL: A Resource for Arthropod Genomes.

The i5k Workspace@NAL is a genome database tailored toward newly sequenced arthropod genomes and their research communities. With 56 arthropod genomes and counting, the i5k Workspace strives to facilitate public data access, visualization, and community curation across arthropod species. Any researcher with an arthropod genome project who would like to take advantage of the i5k Workspace facilities is encouraged to submit their data. In this chapter, we explain how to use the i5k Workspace@NAL to submit, find, and improve arthropod genomics data.

The Toxicogenome of Hyalella azteca: A Model for Sediment Ecotoxicology and Evolutionary Toxicology.

Hyalella azteca is a cryptic species complex of epibenthic amphipods of interest to ecotoxicology and evolutionary biology. It is the primary crustacean used in North America for sediment toxicity testing and an emerging model for molecular ecotoxicology. To provide molecular resources for sediment quality assessments and evolutionary studies, we sequenced, assembled, and annotated the genome of the H. azteca U.S. Lab Strain. The genome quality and completeness is comparable with other ecotoxicological model species. Through targeted investigation and use of gene expression data sets of H. azteca exposed to pesticides, metals, and other emerging contaminants, we annotated and characterized the major gene families involved in sequestration, detoxification, oxidative stress, and toxicant response. Our results revealed gene loss related to light sensing, but a large expansion in chemoreceptors, likely underlying sensory shifts necessary in their low light habitats. Gene family expansions were also noted for cytochrome P450 genes, cuticle proteins, ion transporters, and include recent gene duplications in the metal sequestration protein, metallothionein. Mapping of differentially expressed transcripts to the genome significantly increased the ability to functionally annotate toxicant responsive genes. The H. azteca genome will greatly facilitate development of genomic tools for environmental assessments and promote an understanding of how evolution shapes toxicological pathways with implications for environmental and human health.

Discovery of Organophosphate Resistance-Related Genes Associated With Well-known Resistance Mechanisms of Plutella xylostella (L.) (Lepidoptera: Plutellidae) by RNA-Seq.

Pesticide resistance poses many challenges for pest control, particularly for destructive pests such as diamondback moths ( Plutella xylostella ). Organophosphates have been used in the field since the 1950s, leading to selection for resistance-related gene variants and the development of resistance to new insecticides in the diamondback moth. Identifying actual and potential genes involved in resistance could offer solutions for control. This study established resistant diamondback moth strains from two different collections using mevinphos. Two sets of transcriptome sequencing (RNA-Seq) data were generated for pairs of mevinphos-resistant versus susceptible (wild-type) strains. One susceptible strain containing 14 giga base pairs was assembled into a reference-based assembly using published scaffold sequences as reference. Differential expression data between resistant and susceptible strains revealed 944 transcripts (803 with annotations) showing upregulation and 427 transcripts (150 with annotations) showing downregulation. Around 6.8% of the differential expression transcripts (65) could be categorized as associated with well-known resistance mechanisms such as penetration, detoxification, and behavior response; of these 65 transcripts, 38 showed upregulation, and 12 relating to penetration were upregulated when the transcripts of 19 cytochrome P450s, 2 zeta-class glutathione S-transferases, and 4 ATP-binding cassette transporters showed upregulation. In addition, 11 groups of transcripts related to olfactory perception appeared to be downregulated in trade-off situations. Quantitative polymerase chain reaction expression results were consistent with RNA-Seq data. Possible roles of these differentially expressed genes in resistance mechanisms are discussed in this study.

Integrating RNA-seq and ChIP-seq data to characterize long non-coding RNAs in Drosophila melanogaster.

BACKGROUND: Recent advances in sequencing technology have opened a new era in RNA studies. Novel types of RNAs such as long non-coding RNAs (lncRNAs) have been discovered by transcriptomic sequencing and some lncRNAs have been found to play essential roles in biological processes. However, only limited information is available for lncRNAs in Drosophila melanogaster, an important model organism. Therefore, the characterization of lncRNAs and identification of new lncRNAs in D. melanogaster is an important area of research. Moreover, there is an increasing interest in the use of ChIP-seq data (H3K4me3, H3K36me3 and Pol II) to detect signatures of active transcription for reported lncRNAs. RESULTS: We have developed a computational approach to identify new lncRNAs from two tissue-specific RNA-seq datasets using the poly(A)-enriched and the ribo-zero method, respectively. In our results, we identified 462 novel lncRNA transcripts, which we combined with 4137 previously published lncRNA transcripts into a curated dataset. We then utilized 61 RNA-seq and 32 ChIP-seq datasets to improve the annotation of the curated lncRNAs with regards to transcriptional direction, exon regions, classification, expression in the brain, possession of a poly(A) tail, and presence of conventional chromatin signatures. Furthermore, we used 30 time-course RNA-seq datasets and 32 ChIP-seq datasets to investigate whether the lncRNAs reported by RNA-seq have active transcription signatures. The results showed that more than half of the reported lncRNAs did not have chromatin signatures related to active transcription. To clarify this issue, we conducted RT-qPCR experiments and found that ~95.24% of the selected lncRNAs were truly transcribed, regardless of whether they were associated with active chromatin signatures or not. CONCLUSIONS: In this study, we discovered a large number of novel lncRNAs, which suggests that many remain to be identified in D. melanogaster. For the lncRNAs that are known, we improved their characterization by integrating a large number of sequencing datasets (93 sets in total) from multiple sources (lncRNAs, RNA-seq and ChIP-seq). The RT-qPCR experiments demonstrated that RNA-seq is a reliable platform to discover lncRNAs. This set of curated lncRNAs with improved annotations can serve as an important resource for investigating the function of lncRNAs in D. melanogaster.

Discovery of genes related to insecticide resistance in Bactrocera dorsalis by functional genomic analysis of a de novo assembled transcriptome.

Insecticide resistance has recently become a critical concern for control of many insect pest species. Genome sequencing and global quantization of gene expression through analysis of the transcriptome can provide useful information relevant to this challenging problem. The oriental fruit fly, Bactrocera dorsalis, is one of the world's most destructive agricultural pests, and recently it has been used as a target for studies of genetic mechanisms related to insecticide resistance. However, prior to this study, the molecular data available for this species was largely limited to genes identified through homology. To provide a broader pool of gene sequences of potential interest with regard to insecticide resistance, this study uses whole transcriptome analysis developed through de novo assembly of short reads generated by next-generation sequencing (NGS). The transcriptome of B. dorsalis was initially constructed using Illumina's Solexa sequencing technology. Qualified reads were assembled into contigs and potential splicing variants (isotigs). A total of 29,067 isotigs have putative homologues in the non-redundant (nr) protein database from NCBI, and 11,073 of these correspond to distinct D. melanogaster proteins in the RefSeq database. Approximately 5,546 isotigs contain coding sequences that are at least 80% complete and appear to represent B. dorsalis genes. We observed a strong correlation between the completeness of the assembled sequences and the expression intensity of the transcripts. The assembled sequences were also used to identify large numbers of genes potentially belonging to families related to insecticide resistance. A total of 90 P450-, 42 GST-and 37 COE-related genes, representing three major enzyme families involved in insecticide metabolism and resistance, were identified. In addition, 36 isotigs were discovered to contain target site sequences related to four classes of resistance genes. Identified sequence motifs were also analyzed to characterize putative polypeptide translational products and associate them with specific genes and protein functions.

De novo motif discovery facilitates identification of interactions between transcription factors in Saccharomyces cerevisiae.

MOTIVATION: Gene regulation involves complicated mechanisms such as cooperativity between a set of transcription factors (TFs). Previous studies have used target genes shared by two TFs as a clue to infer TF-TF interactions. However, this task remains challenging because the target genes with low binding affinity are frequently omitted by experimental data, especially when a single strict threshold is employed. This article aims at improving the accuracy of inferring TF-TF interactions by incorporating motif discovery as a fundamental step when detecting overlapping targets of TFs based on ChIP-chip data. RESULTS: The proposed method, simTFBS, outperforms three naïve methods that adopt fixed thresholds when inferring TF-TF interactions based on ChIP-chip data. In addition, simTFBS is compared with two advanced methods and demonstrates its advantages in predicting TF-TF interactions. By comparing simTFBS with predictions based on the set of available annotated yeast TF binding motifs, we demonstrate that the good performance of simTFBS is indeed coming from the additional motifs found by the proposed procedures. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.