Pan-cancer proteogenomics characterization of tumor immunity.

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

Enrichr-KG: bridging enrichment analysis across multiple libraries.

Gene and protein set enrichment analysis is a critical step in the analysis of data collected from omics experiments. Enrichr is a popular gene set enrichment analysis web-server search engine that contains hundreds of thousands of annotated gene sets. While Enrichr has been useful in providing enrichment analysis with many gene set libraries from different categories, integrating enrichment results across libraries and domains of knowledge can further hypothesis generation. To this end, Enrichr-KG is a knowledge graph database and a web-server application that combines selected gene set libraries from Enrichr for integrative enrichment analysis and visualization. The enrichment results are presented as subgraphs made of nodes and links that connect genes to their enriched terms. In addition, users of Enrichr-KG can add gene-gene links, as well as predicted genes to the subgraphs. This graphical representation of cross-library results with enriched and predicted genes can illuminate hidden associations between genes and annotated enriched terms from across datasets and resources. Enrichr-KG currently serves 26 gene set libraries from different categories that include transcription, pathways, ontologies, diseases/drugs, and cell types. To demonstrate the utility of Enrichr-KG we provide several case studies. Enrichr-KG is freely available at: https://maayanlab.cloud/enrichr-kg.

GeneRanger and TargetRanger: processed gene and protein expression levels across cells and tissues for target discovery.

Several atlasing efforts aim to profile human gene and protein expression across tissues, cell types and cell lines in normal physiology, development and disease. One utility of these resources is to examine the expression of a single gene across all cell types, tissues and cell lines in each atlas. However, there is currently no centralized place that integrates data from several atlases to provide this type of data in a uniform format for visualization, analysis and download, and via an application programming interface. To address this need, GeneRanger is a web server that provides access to processed data about gene and protein expression across normal human cell types, tissues and cell lines from several atlases. At the same time, TargetRanger is a related web server that takes as input RNA-seq data from profiled human cells and tissues, and then compares the uploaded input data to expression levels across the atlases to identify genes that are highly expressed in the input and lowly expressed across normal human cell types and tissues. Identified targets can be filtered by transmembrane or secreted proteins. The results from GeneRanger and TargetRanger are visualized as box and scatter plots, and as interactive tables. GeneRanger and TargetRanger are available from https://generanger.maayanlab.cloud and https://targetranger.maayanlab.cloud, respectively.

Electrochemically Switchable Multimode Strong Coupling in Plasmonic Nanocavities.

The strong-coupling interaction between quantum emitters and cavities provides the archetypical platform for fundamental quantum electrodynamics. Here we show that methylene blue (MB) molecules interact coherently with subwavelength plasmonic nanocavity modes at room temperature. Experimental results show that the strong coupling can be switched on and off reversibly when MB molecules undergo redox reactions which transform them to leuco-methylene blue molecules. In simulations we demonstrate the strong coupling between the second excited plasmonic cavity mode and resonant emitters. However, we also show that other detuned modes simultaneously couple efficiently to the molecular transitions, creating unusual cascades of mode spectral shifts and polariton formation. This is possible due to the relatively large plasmonic particle size resulting in reduced mode splittings. The results open significant potential for device applications utilizing active control of strong coupling.

SigCom LINCS: data and metadata search engine for a million gene expression signatures.

Millions of transcriptome samples were generated by the Library of Integrated Network-based Cellular Signatures (LINCS) program. When these data are processed into searchable signatures along with signatures extracted from Genotype-Tissue Expression (GTEx) and Gene Expression Omnibus (GEO), connections between drugs, genes, pathways and diseases can be illuminated. SigCom LINCS is a webserver that serves over a million gene expression signatures processed, analyzed, and visualized from LINCS, GTEx, and GEO. SigCom LINCS is built with Signature Commons, a cloud-agnostic skeleton Data Commons with a focus on serving searchable signatures. SigCom LINCS provides a rapid signature similarity search for mimickers and reversers given sets of up and down genes, a gene set, a single gene, or any search term. Additionally, users of SigCom LINCS can perform a metadata search to find and analyze subsets of signatures and find information about genes and drugs. SigCom LINCS is findable, accessible, interoperable, and reusable (FAIR) with metadata linked to standard ontologies and vocabularies. In addition, all the data and signatures within SigCom LINCS are available via a well-documented API. In summary, SigCom LINCS, available at https://maayanlab.cloud/sigcom-lincs, is a rich webserver resource for accelerating drug and target discovery in systems pharmacology.

Nanoscale Gap-Plasmon-Enhanced Superconducting Photon Detectors at Single-Photon Level.

With a growing demand for detecting light at the single-photon level in various fields, researchers are focused on optimizing the performance of superconducting single-photon detectors (SSPDs) by using multiple approaches. However, input light coupling for visible light has remained a challenge in the development of efficient SSPDs. To overcome these limitations, we developed a novel system that integrates NbN superconducting microwire photon detectors (SMPDs) with gap-plasmon resonators to improve the photon detection efficiency to 98% while preserving all detector performance features, such as polarization insensitivity. The plasmonic SMPDs exhibit a hot-belt effect that generates a nonlinear photoresponse in the visible range operated at 9 K (∼0.64Tc), resulting in a 233-fold increase in phonon-electron interaction factor (γ) compared to pristine SMPDs at resonance under CW illumination. These findings open up new opportunities for ultrasensitive single-photon detection in areas like quantum information processing, quantum optics, imaging, and sensing at visible wavelengths.

Substrate engineering of plasmonic nanocavity antenna modes.

Plasmonic nanocavities have emerged as a promising platform for next-generation spectroscopy, sensing and photonic quantum information processing technologies, benefiting from a unique confluence of nanoscale compactness and integrability, ultrafast functionality and room-temperature viability. Harnessing their unprecedented optical field confinement and enhancement properties for such diverse application domains, however, demands continued innovation in cavity design and robust strategies for engineering their plasmonic mode characteristics, with the aim of optimizing spatial and spectral matching conditions for strong light-matter interaction involving embedded quantum emitters. Adopting the canonical gold bowtie nanoantenna, we show that the complex refractive index, n + ik, of the substrate material provides additional design flexibility in tailoring the properties of plasmonic nanocavity modes, including their resonance wavelengths, hotspot locations, intracavity field polarization and radiative decay rates. In particular, we predict that highly refractive (n ≥ 4) or highly absorptive (k ≥ 4) substrates provide two complementary approaches to engineering nanocavity modes that are especially desirable for coupling two-dimensional quantum materials, featuring namely an elevated hotspot with a dominantly in-plane polarized near-field, as well as a strongly radiative character. Our study elucidates the benefits and intricacies of a largely unexplored facet of nanocavity mode manipulation, beyond the widely practiced synthetic control over the cavity topology or physical dimensions, and paves the way for plasmonic cavity quantum electrodynamics with two-dimensional excitonic matter.

DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules.

BACKGROUND: PubMed contains millions of abstracts that co-mention terms that describe drugs with other biomedical terms such as genes or diseases. Unique opportunities exist for leveraging these co-mentions by integrating them with other drug-drug similarity resources such as the Library of Integrated Network-based Cellular Signatures (LINCS) L1000 signatures to develop novel hypotheses. RESULTS: DrugShot is a web-based server application and an Appyter that enables users to enter any biomedical search term into a simple input form to receive ranked lists of drugs and other small molecules based on their relevance to the search term. To produce ranked lists of small molecules, DrugShot cross-references returned PubMed identifiers (PMIDs) with DrugRIF or AutoRIF, which are curated resources of drug-PMID associations, to produce an associated small molecule list where each small molecule is ranked according to total co-mentions with the search term from shared PubMed IDs. Additionally, using two types of drug-drug similarity matrices, lists of small molecules are predicted to be associated with the search term. Such predictions are based on literature co-mentions and signature similarity from LINCS L1000 drug-induced gene expression profiles. CONCLUSIONS: DrugShot prioritizes drugs and small molecules associated with biomedical search terms. In addition to listing known associations, DrugShot predicts additional drugs and small molecules related to any search term. Hence, DrugShot can be used to prioritize drugs and preclinical compounds for drug repurposing and suggest indications and adverse events for preclinical compounds. DrugShot is freely and openly available at: https://maayanlab.cloud/drugshot and https://appyters.maayanlab.cloud/#/DrugShot .

Transforming L1000 profiles to RNA-seq-like profiles with deep learning.

The L1000 technology, a cost-effective high-throughput transcriptomics technology, has been applied to profile a collection of human cell lines for their gene expression response to > 30,000 chemical and genetic perturbations. In total, there are currently over 3 million available L1000 profiles. Such a dataset is invaluable for the discovery of drug and target candidates and for inferring mechanisms of action for small molecules. The L1000 assay only measures the mRNA expression of 978 landmark genes while 11,350 additional genes are computationally reliably inferred. The lack of full genome coverage limits knowledge discovery for half of the human protein coding genes, and the potential for integration with other transcriptomics profiling data. Here we present a Deep Learning two-step model that transforms L1000 profiles to RNA-seq-like profiles. The input to the model are the measured 978 landmark genes while the output is a vector of 23,614 RNA-seq-like gene expression profiles. The model first transforms the landmark genes into RNA-seq-like 978 gene profiles using a modified CycleGAN model applied to unpaired data. The transformed 978 RNA-seq-like landmark genes are then extrapolated into the full genome space with a fully connected neural network model. The two-step model achieves 0.914 Pearson's correlation coefficients and 1.167 root mean square errors when tested on a published paired L1000/RNA-seq dataset produced by the LINCS and GTEx programs. The processed RNA-seq-like profiles are made available for download, signature search, and gene centric reverse search with unique case studies.

LINCS Data Portal 2.0: next generation access point for perturbation-response signatures.

The Library of Integrated Network-Based Cellular Signatures (LINCS) is an NIH Common Fund program with the goal of generating a large-scale and comprehensive catalogue of perturbation-response signatures by utilizing a diverse collection of perturbations across many model systems and assay types. The LINCS Data Portal (LDP) has been the primary access point for the compendium of LINCS data and has been widely utilized. Here, we report the first major update of LDP (http://lincsportal.ccs.miami.edu/signatures) with substantial changes in the data architecture and APIs, a completely redesigned user interface, and enhanced curated metadata annotations to support more advanced, intuitive and deeper querying, exploration and analysis capabilities. The cornerstone of this update has been the decision to reprocess all high-level LINCS datasets and make them accessible at the data point level enabling users to directly access and download any subset of signatures across the entire library independent from the originating source, project or assay. Access to the individual signatures also enables the newly implemented signature search functionality, which utilizes the iLINCS platform to identify conditions that mimic or reverse gene set queries. A newly designed query interface enables global metadata search with autosuggest across all annotations associated with perturbations, model systems, and signatures.

EnrichrBot: Twitter bot tracking tweets about human genes.

MOTIVATION: Micro-blogging with Twitter to communicate new results, discuss ideas and share techniques is becoming central. While most Twitter users are real people, the Twitter API provides the opportunity to develop Twitter bots and to analyze global trends in tweets. RESULTS: EnrichrBot is a bot that tracks and tweets information about human genes implementing six principal functions: (i) tweeting information about under-studied genes including non-coding lncRNAs, (ii) replying to requests for information about genes, (iii) responding to GWASbot, another bot that tweets Manhattan plots from genome-wide association study analysis of the UK Biobank, (iv) tweeting randomly selected gene sets from the Enrichr database for analysis with Enrichr, (v) responding to mentions of human genes in tweets with additional information about these genes and (vi) tweeting a weekly report about the most trending genes on Twitter. AVAILABILITY AND IMPLEMENTATION: https://twitter.com/botenrichr; source code: https://github.com/MaayanLab/EnrichrBot. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

eXpression2Kinases (X2K) Web: linking expression signatures to upstream cell signaling networks.

While gene expression data at the mRNA level can be globally and accurately measured, profiling the activity of cell signaling pathways is currently much more difficult. eXpression2Kinases (X2K) computationally predicts involvement of upstream cell signaling pathways, given a signature of differentially expressed genes. X2K first computes enrichment for transcription factors likely to regulate the expression of the differentially expressed genes. The next step of X2K connects these enriched transcription factors through known protein-protein interactions (PPIs) to construct a subnetwork. The final step performs kinase enrichment analysis on the members of the subnetwork. X2K Web is a new implementation of the original eXpression2Kinases algorithm with important enhancements. X2K Web includes many new transcription factor and kinase libraries, and PPI networks. For demonstration, thousands of gene expression signatures induced by kinase inhibitors, applied to six breast cancer cell lines, are provided for fetching directly into X2K Web. The results are displayed as interactive downloadable vector graphic network images and bar graphs. Benchmarking various settings via random permutations enabled the identification of an optimal set of parameters to be used as the default settings in X2K Web. X2K Web is freely available from http://X2K.cloud.

Student nurses exposed to sustainability education can challenge practice: A cohort study.

Climate change will adversely affect the health of populations and the ability of healthcare systems to deliver appropriate and timely care. Furthermore, resource scarcity requires nurses to practice in more sustainable ways. This study investigated the extent to which student nurses reported that they were able to apply their knowledge of sustainability in clinical practice following educational sessions on relevant topics. Students were exposed to scenario-based sustainability education in years 1, 2, and 3 of their 3-year program. Data were collected using a questionnaire that asked participants if they were able to implement sustainable practice. In year 2,121 students, and in year 3, 68 stated they made a change or challenged practice as a consequence of learning about sustainability. Barriers preventing them from challenging unsustainable practice were lack of confidence and resistance to change. Year-3 students were more able to influence unsustainable practice than were those in year 2. This study indicates that sustainability sessions, focused on aspects of clinical relevance, can support nurses to implement change; barriers remain that require confidence building for the students.

Resolving Ultrafast Spin-Orbit Dynamics in Heavy Many-Electron Atoms.

We use R-matrix with time-dependence theory, with spin-orbit effects included, to study krypton irradiated by two time-delayed extreme ultraviolet ultrashort pulses. The first pulse excites the atom to 4s^{2}4p^{5}5s. The second pulse then excites 4s4p^{6}5s autoionizing levels, whose population can be observed through their subsequent decay. By varying the time delay between the two pulses, we are able to control the excitation pathway to the autoionizing states. The use of cross-polarized light pulses allows us to isolate the two-photon pathway, with one photon taken from each pulse.

RNAi Targeting of Human Metapneumovirus P and N Genes Inhibits Viral Growth.

BACKGROUND/AIMS: Human metapneumovirus (hMPV) is an important human respiratory pathogen and is implicated in an array of respiratory illnesses, ranging from asymptomatic infection to severe bronchiolitis. Currently, there is no reliable vaccine or specific antiviral therapy for hMPV infection and treatment is supportive. The use of ribonucleic acid interference has the potential to change that with the targeting of essential viral genes via small interfering RNAs (siRNAs) offering the ability to directly and rapidly treat viral infections. METHOD: The human lung carcinoma epithelial cell line, A549, was transfected with siRNAs targeting the N and P genes before infecting with hMPV A2 CAN97-83. Viral growth inhibition was then measured by the viral plaque assay and nucleoprotein (N) and phosphoprotein (P) gene knockdown was determined by real-time PCR. RESULTS: In vitro prophylactic use of siRNAs targeting the 3'-abundantly expressed N and P genes of hMPV resulted in potent, sequence-specific viral inhibition. The viral inhibition was specific and not mediated by an anti-viral interferon-ß response or cell death. CONCLUSION: The findings presented here confirmed the highly potent, sequence-specific antiviral effect of siRNAs targeting the N and P gene of hMPV. These results may facilitate the development of a novel therapeutic agent for hMPV control.

Prevalence and types of high-risk human papillomaviruses in head and neck cancers from Bangladesh.

BACKGROUND: There is a dramatic rise in the incidence of Human papillomavirus (HPV) - associated head and neck squamous cell carcinoma (HNSCC) in the world, with considerable variation by geography, gender and ethnicity. Little is known about the situation in Bangladesh, where tobacco- and areca nut-related head and neck cancers (HNCs) are the most common cancers in men. We aimed to determine the prevalence of HPV in HNSCC in Bangladesh and to explore the possible value of cell cycle markers in clinical diagnostic settings. METHODS: One hundred and ninety six archival HNSCC tissue samples were analysed for the presence of HPV DNA. The DNA quality was assured, and then amplified using a nested PCR approach. The typing of HPV was performed by automated DNA sequencing. Cellular markers p53, Cyclin D1 and pRb were tested on all samples by immunohistochemistry (IHC), as well as p16 as a putative surrogate for the detection of HPV. RESULTS: HPV DNA was detected in 36/174 (~21%) samples: 36% of cancers from the oropharynx; 31% of oral cancers, and 22% from the larynx. HPV-16 was most common, being present in 33 samples, followed by HPV-33 (2 samples) and HPV-31 (1 sample). Twenty-eight out of 174 samples were positive for p16, predominantly in HPV-positive tissues (p < 0.001). No statistically significant association was observed between the cellular markers and HPV DNA positive cases. However, p16 positivity had excellent predictive value for the presence of HPV by PCR. CONCLUSION: There is a significant burden of HPV-associated HNSCC in Bangladesh, particularly in the oropharynx but also in oral and laryngeal cancers. Whilst a combination of PCR-based DNA detection and p16 IHC is useful, the latter has excellent specificity, acceptable sensitivity and good predictive value for carriage of HPV in this population and should be used for prognostic evaluation and treatment planning of all HNSCC patients in South Asia, as in the Western world.

Graph-Based Cooperative Localization Using Symmetric Measurement Equations.

Precise localization is a key requirement for the success of highly assisted or autonomous vehicles. The diminishing cost of hardware has resulted in a proliferation of the number of sensors in the environment. Cooperative localization (CL) presents itself as a feasible and effective solution for localizing the ego-vehicle and its neighboring vehicles. However, one of the major challenges to fully realize the effective use of infrastructure sensors for jointly estimating the state of a vehicle in cooperative vehicle-infrastructure localization is an effective data association. In this paper, we propose a method which implements symmetric measurement equations within factor graphs in order to overcome the data association challenge with a reduced bandwidth overhead. Simulated results demonstrate the benefits of the proposed approach in comparison with our previously proposed approach of topology factors.

Relationship between physical performance and quality of life in Charcot-Marie-Tooth disease: a pilot study.

Charcot-Marie-Tooth (CMT) is a rare inherited peripheral neuropathy in which quality of life (QoL) is reduced compared with the general population. This paper investigates the relationship between QoL and physical performance in people with CMT with the aim of identifying avenues for future research into rehabilitation strategies. Cross-sectional data was obtained from 10 participants (5 men, 5 women, age 46 ± 13 years, height 1.7 ± 0.1 m, body mass 77 ± 17 kg) with CMT (CMT1A n = 5; CMT-X n = 3; unknown genetic origin n = 2). Participants were evaluated for QoL, falls efficacy (FES), balance, mobility, muscle strength, and power. Physical component score (PCS) of the Short Form-36 (SF-36) was significantly and directly related to higher leg press power (r = 0.75, p = 0.02). Better FES scores were significantly related to faster habitual gait speed (r = -0.70, p = 0.02), left hip abduction, and seated row strength (r = -0.68, p = 0.03; r = -0.73, p = 0.03, respectively). Future research should aim to substantiate these preliminary findings in a larger cohort and investigate whether interventions targeting muscle strength and power can improve QoL and mobility outcomes in people with CMT.

RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO.

The Gene Expression Omnibus (GEO) is a major open biomedical research repository for transcriptomics and other omics datasets. It currently contains millions of gene expression samples from tens of thousands of studies collected by many biomedical research laboratories from around the world. While users of the GEO repository can search the metadata describing studies for locating relevant datasets, there are currently no methods or resources that facilitate global search of GEO at the data level. To address this shortcoming, we developed RummaGEO, a webserver application that enables gene expression signature search of a large collection of human and mouse RNA-seq studies deposited into GEO. To develop the search engine, we performed offline automatic identification of sample conditions from the uniformly aligned GEO studies available from ARCHS4. We then computed differential expression signatures to extract gene sets from these studies. In total, RummaGEO currently contains 135,264 human and 158,062 mouse gene sets extracted from 23,395 GEO studies. Next, we analyzed the contents of the RummaGEO database to identify statistical patterns and perform various global analyses. The contents of the RummaGEO database are provided as a web-server search engine with signature search, PubMed search, and metadata search functionalities. Overall, RummaGEO provides an unprecedented resource for the biomedical research community enabling hypothesis generation for many future studies. The RummaGEO search engine is available from: https://rummageo.com/.