Combining TIGIT blockage with MDSC inhibition hinders breast cancer bone metastasis by activating anti-tumor immunity.

Bone is the most common site of breast cancer metastasis. Bone metastasis are incurable and are associated with severe morbidity. Utilizing an immunocompetent mouse model of spontaneous breast cancer bone metastasis, we profiled the immune transcriptome of bone metastatic lesions and peripheral bone marrow at distinct metastatic stages, revealing dynamic changes during the metastatic process. We show that crosstalk between granulocytes and T cells is central to shaping an immunosuppressive microenvironment. Specifically, we identified the PD-1 and TIGIT signaling axes and the pro-inflammatory cytokine IL1b as central players in the interactions between granulocytes and T cells. Targeting these pathways in vivo resulted in attenuated bone metastasis and improved survival, by reactivating anti-tumor immunity. Analysis of patient samples revealed that TIGIT and IL1b are prominent in human bone metastasis. Our findings suggest that co-targeting immunosuppressive granulocytes and dysfunctional T cells may be a promising novel therapeutic strategy to inhibit bone metastasis.

mRNA splicing is modulated by intronic microRNAs.

Splicing of transcripts is catalyzed by the spliceosome, a mega-complex consisting of hundreds of proteins and five snRNAs, which employs direct interactions. When U1 snRNA forms high-affinity binding, namely more than eight base pairs, with the 5'SS, the result is usually a suppressing effect on the splicing activity. This likely occurs due to the inefficient unwinding of U1/5'SS base-pairing or other regulatory obstructions. Here, we show in vitro and in patient-derived cell lines that pre-microRNAs can modulate the splicing reaction by interacting with U1 snRNA. This leads to reduced binding affinity to the 5'SS, and hence promotes the inclusion of exons containing 5'SS, despite sequence-based high affinity to U1. Application of the mechanism resulted in correction of the splicing defect in the disease-causing VCAN gene from an individual with Wagner syndrome. This pre-miRNA/U1 interaction can regulate the expression of alternatively spliced exons, thus extending the scope of mechanisms regulating splicing.

Multiple Copies of microRNA Binding Sites in Long 3'UTR Variants Regulate Axonal Translation.

Rapid responses to changes within subcellular compartments of highly polarized cells, such as neuron axons, depend on local translation and post-transcriptional regulation. The mechanism by which microRNAs (miRNAs) regulate this process is not fully understood. Here, using live cell imaging and RNA sequencing analysis, we demonstrated how miRNAs can differentially control hundreds of transcripts at the subcellular level. We demonstrated that the seed match length of the miRNA target-sequence regulates both mRNA stability and protein translation rates. While longer seed matches have an increased inhibitory effect, transcriptome analysis did not reveal differences in seed match length between axonal and somata mRNAs of motor neurons. However, mRNA variants with longer 3'UTR are enriched in axons and contain multiple repeats of specific miRNA target sequences. Finally, we demonstrated that the long 3'UTR mRNA variant of the motor protein Kif5b is enriched explicitly in motor neuron axons and contains multiple sequence repeats for binding miR-129-5p. This subsequently results in the differential post-transcriptional regulation of kif5b and its synthesis in axons. Thus, we suggest that the number of miRNA binding sites at the 3'UTR of the mRNA, rather than the miRNA seed match length, regulates the axonal transcriptome.

Blood RNA Sequencing Indicates Upregulated BATF2 and LY6E and Downregulated ISG15 and MT2A Expression in Children with Autism Spectrum Disorder.

Mutations in over 100 genes are implicated in autism spectrum disorder (ASD). DNA SNPs, CNVs, and epigenomic modifications also contribute to ASD. Transcriptomics analysis of blood samples may offer clues for pathways dysregulated in ASD. To expand and validate published findings of RNA-sequencing (RNA-seq) studies, we performed RNA-seq of whole blood samples from an Israeli discovery cohort of eight children with ASD compared with nine age- and sex-matched neurotypical children. This revealed 10 genes with differential expression. Using quantitative real-time PCR, we compared RNAs from whole blood samples of 73 Israeli and American children with ASD and 26 matched neurotypical children for the 10 dysregulated genes detected by RNA-seq. This revealed higher expression levels of the pro-inflammatory transcripts BATF2 and LY6E and lower expression levels of the anti-inflammatory transcripts ISG15 and MT2A in the ASD compared to neurotypical children. BATF2 was recently reported as upregulated in blood samples of Japanese adults with ASD. Our findings support an involvement of these genes in ASD phenotypes, independent of age and ethnicity. Upregulation of BATF2 and downregulation of ISG15 and MT2A were reported to reduce cancer risk. Implications of the dysregulated genes for pro-inflammatory phenotypes, immunity, and cancer risk in ASD are discussed.

The differential activity of biological processes in tissues and cell subsets can illuminate disease-related processes and cell-type identities.

MOTIVATION: The distinct functionalities of human tissues and cell types underlie complex phenotype-genotype relationships, yet often remain elusive. Harnessing the multitude of bulk and single-cell human transcriptomes while focusing on processes can help reveal these distinct functionalities. RESULTS: The Tissue-Process Activity (TiPA) method aims to identify processes that are preferentially active or under-expressed in specific contexts, by comparing the expression levels of process genes between contexts. We tested TiPA on 1579 tissue-specific processes and bulk tissue transcriptomes, finding that it performed better than another method. Next, we used TiPA to ask whether the activity of certain processes could underlie the tissue-specific manifestation of 1233 hereditary diseases. We found that 21% of the disease-causing genes indeed participated in such processes, thereby illuminating their genotype-phenotype relationships. Lastly, we applied TiPA to single-cell transcriptomes of 108 human cell types, revealing that process activities often match cell-type identities and can thus aid annotation efforts. Hence, differential activity of processes can highlight the distinct functionality of tissues and cells in a robust and meaningful manner. AVAILABILITY AND IMPLEMENTATION: TiPA code is available in GitHub (https://github.com/moranshar/TiPA). In addition, all data are available as part of the Supplementary Material. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Predicting bloodstream infection outcome using machine learning.

Bloodstream infections (BSI) are a main cause of infectious disease morbidity and mortality worldwide. Early prediction of BSI patients at high risk of poor outcomes is important for earlier decision making and effective patient stratification. We developed electronic medical record-based machine learning models that predict patient outcomes of BSI. The area under the receiver-operating characteristics curve was 0.82 for a full featured inclusive model, and 0.81 for a compact model using only 25 features. Our models were trained using electronic medical records that include demographics, blood tests, and the medical and diagnosis history of 7889 hospitalized patients diagnosed with BSI. Among the implications of this work is implementation of the models as a basis for selective rapid microbiological identification, toward earlier administration of appropriate antibiotic therapy. Additionally, our models may help reduce the development of BSI and its associated adverse health outcomes and complications.

Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data.

In recent years, methods were proposed for assigning feature importance scores to measure the contribution of individual features. While in some cases the goal is to understand a specific model, in many cases the goal is to understand the contribution of certain properties (features) to a real-world phenomenon. Thus, a distinction has been made between feature importance scores that explain a model and scores that explain the data. When explaining the data, machine learning models are used as proxies in settings where conducting many real-world experiments is expensive or prohibited. While existing feature importance scores show great success in explaining models, we demonstrate their limitations when explaining the data, especially in the presence of correlations between features. Therefore, we develop a set of axioms to capture properties expected from a feature importance score when explaining data and prove that there exists only one score that satisfies all of them, the Marginal Contribution Feature Importance (MCI). We analyze the theoretical properties of this score function and demonstrate its merits empirically.

Processing and Analysis of RNA-seq Data from Public Resources.

Advances in next generation sequencing (NGS) technologies resulted in a broad array of large-scale gene expression studies and an unprecedented volume of whole messenger RNA (mRNA) sequencing data, or the transcriptome (also known as RNA sequencing, or RNA-seq). These include the Genotype Tissue Expression project (GTEx) and The Cancer Genome Atlas (TCGA), among others. Here we cover some of the commonly used datasets, provide an overview on how to begin the analysis pipeline, and how to explore and interpret the data provided by these publicly available resources.

Machine learning-based prediction of COVID-19 diagnosis based on symptoms.

Effective screening of SARS-CoV-2 enables quick and efficient diagnosis of COVID-19 and can mitigate the burden on healthcare systems. Prediction models that combine several features to estimate the risk of infection have been developed. These aim to assist medical staff worldwide in triaging patients, especially in the context of limited healthcare resources. We established a machine-learning approach that trained on records from 51,831 tested individuals (of whom 4769 were confirmed to have COVID-19). The test set contained data from the subsequent week (47,401 tested individuals of whom 3624 were confirmed to have COVID-19). Our model predicted COVID-19 test results with high accuracy using only eight binary features: sex, age ≥60 years, known contact with an infected individual, and the appearance of five initial clinical symptoms. Overall, based on the nationwide data publicly reported by the Israeli Ministry of Health, we developed a model that detects COVID-19 cases by simple features accessed by asking basic questions. Our framework can be used, among other considerations, to prioritize testing for COVID-19 when testing resources are limited.

Differential network analysis of multiple human tissue interactomes highlights tissue-selective processes and genetic disorder genes.

MOTIVATION: Differential network analysis, designed to highlight network changes between conditions, is an important paradigm in network biology. However, differential network analysis methods have been typically designed to compare between two conditions and were rarely applied to multiple protein interaction networks (interactomes). Importantly, large-scale benchmarks for their evaluation have been lacking. RESULTS: Here, we present a framework for assessing the ability of differential network analysis of multiple human tissue interactomes to highlight tissue-selective processes and disorders. For this, we created a benchmark of 6499 curated tissue-specific Gene Ontology biological processes. We applied five methods, including four differential network analysis methods, to construct weighted interactomes for 34 tissues. Rigorous assessment of this benchmark revealed that differential analysis methods perform well in revealing tissue-selective processes (AUCs of 0.82-0.9). Next, we applied differential network analysis to illuminate the genes underlying tissue-selective hereditary disorders. For this, we curated a dataset of 1305 tissue-specific hereditary disorders and their manifesting tissues. Focusing on subnetworks containing the top 1% differential interactions in disease-relevant tissue interactomes revealed significant enrichment for disorder-causing genes in 18.6% of the cases, with a significantly high success rate for blood, nerve, muscle and heart diseases. SUMMARY: Altogether, we offer a framework that includes expansive manually curated datasets of tissue-selective processes and disorders to be used as benchmarks or to illuminate tissue-selective processes and genes. Our results demonstrate that differential analysis of multiple human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-selective functionality and clinical impact. AVAILABILITY AND IMPLEMENTATION: Datasets are available as part of the Supplementary data. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.