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Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.

Genome-wide characterization of the in vivo cellular response to perturbation is fundamental to understanding how cells survive stress. Identifying the proteins and pathways perturbed by small molecules affects biology and medicine by revealing the mechanisms of drug action. We used a yeast chemogenomics platform that quantifies the requirement for each gene for resistance to a compound in vivo to profile 3250 small molecules in a systematic and unbiased manner. We identified 317 compounds that specifically perturb the function of 121 genes and characterized the mechanism of specific compounds. Global analysis revealed that the cellular response to small molecules is limited and described by a network of 45 major chemogenomic signatures. Our results provide a resource for the discovery of functional interactions among genes, chemicals, and biological processes.

Delirium is an acute change in attention and cognition occurring in ~ 65% of severe SARS-CoV-2 cases. It is also common following surgery and an indicator of brain vulnerability and risk for the development of dementia. In this work we analyzed the underlying role of metabolism in delirium-susceptibility in the postoperative setting using metabolomic profiling of cerebrospinal fluid and blood taken from the same patients prior to planned orthopaedic surgery. Distance correlation analysis and Random Forest (RF) feature selection were used to determine changes in metabolic networks. We found significant concentration differences in several amino acids, acylcarnitines and polyamines linking delirium-prone patients to known factors in Alzheimer’s disease such as monoamine oxidase B (MAOB) protein. Subsequent computational structural comparison between MAOB and angiotensin converting enzyme 2 as well as protein–protein docking analysis showed that there potentially is strong binding of SARS-CoV-2 spike protein to MAOB. The possibility that SARS-CoV-2 influences MAOB activity leading to the observed neurological and platelet-based complications of SARS-CoV-2 infection requires further investigation.

The microbiome shapes diverse facets of human biology and disease, with the importance of fungi only beginning to be appreciated. Microbial communities infiltrate diverse anatomical sites as with the respiratory tract of healthy humans and those with diseases such as cystic fibrosis, where chronic colonization and infection lead to clinical decline. Although fungi are frequently recovered from cystic fibrosis patient sputum samples and have been associated with deterioration of lung function, understanding of species and population dynamics remains in its infancy. Here, we coupled high-throughput sequencing of the ribosomal RNA internal transcribed spacer 1 (ITS1) with phenotypic and genotypic analyses of fungi from 89 sputum samples from 28 cystic fibrosis patients. Fungal communities defined by sequencing were concordant with those defined by culture-based analyses of 1,603 isolates from the same samples. Different patients harbored distinct fungal communities. There were detectable trends, however, including colonization with Candida and Aspergillus species, which was not perturbed by clinical exacerbation or treatment. We identified considerable inter- and intra-species phenotypic variation in traits important for host adaptation, including antifungal drug resistance and morphogenesis. While variation in drug resistance was largely between species, striking variation in morphogenesis emerged within Candida species. Filamentation was uncoupled from inducing cues in 28 Candida isolates recovered from six patients. The filamentous isolates were resistant to the filamentation-repressive effects of Pseudomonas aeruginosa, implicating inter-kingdom interactions as the selective force. Genome sequencing revealed that all but one of the filamentous isolates harbored mutations in the transcriptional repressor NRG1; such mutations were necessary and sufficient for the filamentous phenotype. Six independent nrg1 mutations arose in Candida isolates from different patients, providing a poignant example of parallel evolution. Together, this combined clinical-genomic approach provides a high-resolution portrait of the fungal microbiome of cystic fibrosis patient lungs and identifies a genetic basis of pathogen adaptation.

Glioblastoma (GBM) is one of the most aggressive cancers of the central nervous system. Despite current advances in non-invasive imaging and the advent of novel therapeutic modalities, patient survival remains very low. There is a critical need for the development of effective biomarkers for GBM diagnosis and therapeutic monitoring. Extracellular vesicles (EVs) produced by GBM tumors have been shown to play an important role in cellular communication and modulation of the tumor microenvironment. As GBM-derived EVs contain specific “molecular signatures” of their parental cells and are able to transmigrate across the blood–brain barrier into biofluids such as the blood and cerebrospinal fluid (CSF), they are considered as a valuable source of potential diagnostic biomarkers. Given the relatively harsh extracellular environment of blood and CSF, EVs have to endure and adapt to different conditions. The ability of EVs to adjust and function depends on their lipid bilayer, metabolic content and enzymes and transport proteins. The knowledge of EVs metabolic characteristics and adaptability is essential for their utilization as diagnostic and therapeutic tools. The main aim of this study was to determine the metabolome of small EVs or exosomes derived from different GBM cells and compare to the metabolic profile of their parental cells using NMR spectroscopy. In addition, a possible flux of metabolic processes in GBM-derived EVs was simulated using constraint-based modeling from published proteomics information. Our results showed a clear difference between the metabolic profiles of GBM cells, EVs and media. Machine learning analysis of EV metabolomics, as well as flux simulation, supports the notion of active metabolism within EVs, including enzymatic reactions and the transfer of metabolites through the EV membrane. These results are discussed in the context of novel GBM diagnostics and therapeutic monitoring.

NRC publication: Yes

Background Genome-wide screening in human and mouse cells using RNA interference and open reading frame over-expression libraries is rapidly becoming a viable experimental approach for many research labs. There are a variety of gene expression modulation libraries commercially available, however, detailed and validated protocols as well as the reagents necessary for deconvolving genome-scale gene screens using these libraries are lacking. As a solution, we designed a comprehensive platform for highly multiplexed functional genetic screens in human, mouse and yeast cells using popular, commercially available gene modulation libraries. The Gene Modulation Array Platform (GMAP) is a single microarray-based detection solution for deconvolution of loss and gain-of-function pooled screens. Results Experiments with specially constructed lentiviral-based plasmid pools containing ~78,000 shRNAs demonstrated that the GMAP is capable of deconvolving genome-wide shRNA \"dropout\" screens. Further experiments with a larger, ~90,000 shRNA pool demonstrate that equivalent results are obtained from plasmid pools and from genomic DNA derived from lentivirus infected cells. Parallel testing of large shRNA pools using GMAP and next-generation sequencing methods revealed that the two methods provide valid and complementary approaches to deconvolution of genome-wide shRNA screens. Additional experiments demonstrated that GMAP is equivalent to similar microarray-based products when used for deconvolution of open reading frame over-expression screens. Conclusion Herein, we demonstrate four major applications for the GMAP resource, including deconvolution of pooled RNAi screens in cells with at least 90,000 distinct shRNAs. We also provide detailed methodologies for pooled shRNA screen readout using GMAP and compare next-generation sequencing to GMAP (i.e. microarray) based deconvolution methods.

The application of new proteomics and genomics technologies support a view in which few drugs act solely by inhibiting a single cellular target. Indeed, drug activity is modulated by complex, often incompletely understood cellular mechanisms. Therefore, efforts to decipher mode of action through genetic perturbation such as RNAi typically yields “hits” that fall into several categories. Of particular interest to the present study, we aimed to characterize secondary activities of drugs on cells. Inhibiting a known target can result in clinically relevant synthetic phenotypes. In one scenario, drug perturbation could, for example, improperly activate a protein that normally inhibits a particular kinase. In other cases, additional, lower affinity targets can be inhibited as in the example of inhibition of c-Kit observed in Bcr-Abl−positive cells treated with Gleevec. Drug transport and metabolism also play an important role in the way any chemicals act within the cells. Finally, RNAi per se can also affect cell fitness by more general off-target effects, e.g., via the modulation of apoptosis or DNA damage repair. Regardless of the root cause of these unwanted effects, understanding the scope of a drug’s activity and polypharmacology is essential for better understanding its mechanism(s) of action, and such information can guide development of improved therapies. We describe a rapid, cost-effective approach to characterize primary and secondary effects of small-molecules by using small-scale libraries of virally integrated short hairpin RNAs. We demonstrate this principle using a “minipool” composed of shRNAs that target the genes encoding the reported protein targets of approved drugs. Among the 28 known reported drug−target pairs, we successfully identify 40% of the targets described in the literature and uncover several unanticipated drug−target interactions based on drug-induced synthetic lethality. We provide a detailed protocol for performing such screens and for analyzing the data. This cost-effective approach to mammalian knockdown screens, combined with the increasing maturation of RNAi technology will expand the accessibility of similar approaches in academic settings.

Motivation: Missing values are prevalent in high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. The three missingness patterns, including missing completely at random, missing at random, and missing not at random, describe unique dependencies between the missing and observed data. The optimal imputation method for each dataset depends on the type of data, the cause of the missingness, and the nature of relationships between the missing and observed data. The challenge is to identify the optimal imputation solution for a given dataset. Availability and implementation: ImpLiMet is freely available at https://complimet.ca/shiny/implimet/ and https://github.com/complimet/ImpLiMet.