Turbocharged molecular discovery of OLED emitters: from high-throughput quantum simulation to highly efficient TADF devices

Discovering new OLED emitters requires many experiments to synthesize candidates and test performance in devices. Large scale computer simulation can greatly speed this search process but the problem remains challenging enough that brute force application of massive computing power is not enough to successfully identify novel structures. We report a successful High Throughput Virtual Screening study that leveraged a range of methods to optimize the search process. The generation of candidate structures was constrained to contain combinatorial explosion. Simulations were tuned to the specific problem and calibrated with experimental results. Experimentalists and theorists actively collaborated such that experimental feedback was regularly utilized to update and shape the computational search. Supervised machine learning methods prioritized candidate structures prior to quantum chemistry simulation to prevent wasting compute on likely poor performers. With this combination of techniques, each multiplying the strength of the search, this effort managed to navigate an area of molecular space and identify hundreds of promising OLED candidate structures. An experimentally validated selection of this set shows emitters with external quantum efficiencies as high as 22%

Network-driven plasma proteomics expose molecular changes in the Alzheimer’s brain

Background Biological pathways that significantly contribute to sporadic Alzheimer’s disease are largely unknown and cannot be observed directly. Cognitive symptoms appear only decades after the molecular disease onset, further complicating analyses. As a consequence, molecular research is often restricted to late-stage post-mortem studies of brain tissue. However, the disease process is expected to trigger numerous cellular signaling pathways and modulate the local and systemic environment, and resulting changes in secreted signaling molecules carry information about otherwise inaccessible pathological processes. Results To access this information we probed relative levels of close to 600 secreted signaling proteins from patients’ blood samples using antibody microarrays and mapped disease-specific molecular networks. Using these networks as seeds we then employed independent genome and transcriptome data sets to corroborate potential pathogenic pathways. Conclusions We identified Growth-Differentiation Factor (GDF) signaling as a novel Alzheimer’s disease-relevant pathway supported by in vivo and in vitro follow-up experiments, demonstrating the existence of a highly informative link between cellular pathology and changes in circulatory signaling proteins

Network-Driven Plasma Proteomics Expose Molecular Changes in the Alzheimer\u27s Brain

Background: Biological pathways that significantly contribute to sporadic Alzheimer’s disease are largely unknown and cannot be observed directly. Cognitive symptoms appear only decades after the molecular disease onset, further complicating analyses. As a consequence, molecular research is often restricted to late-stage post-mortem studies of brain tissue. However, the disease process is expected to trigger numerous cellular signaling pathways and modulate the local and systemic environment, and resulting changes in secreted signaling molecules carry information about otherwise inaccessible pathological processes. Results: To access this information we probed relative levels of close to 600 secreted signaling proteins from patients’ blood samples using antibody microarrays and mapped disease-specific molecular networks. Using these networks as seeds we then employed independent genome and transcriptome data sets to corroborate potential pathogenic pathways. Conclusions: We identified Growth-Differentiation Factor (GDF) signaling as a novel Alzheimer’s disease-relevant pathway supported by in vivo and in vitro follow-up experiments, demonstrating the existence of a highly informative link between cellular pathology and changes in circulatory signaling proteins

Prenatal phenotyping: A community effort to enhance the Human Phenotype Ontology.

Technological advances in both genome sequencing and prenatal imaging are increasing our ability to accurately recognize and diagnose Mendelian conditions prenatally. Phenotype-driven early genetic diagnosis of fetal genetic disease can help to strategize treatment options and clinical preventive measures during the perinatal period, to plan in utero therapies, and to inform parental decision-making. Fetal phenotypes of genetic diseases are often unique and at present are not well understood; more comprehensive knowledge about prenatal phenotypes and computational resources have an enormous potential to improve diagnostics and translational research. The Human Phenotype Ontology (HPO) has been widely used to support diagnostics and translational research in human genetics. To better support prenatal usage, the HPO consortium conducted a series of workshops with a group of domain experts in a variety of medical specialties, diagnostic techniques, as well as diseases and phenotypes related to prenatal medicine, including perinatal pathology, musculoskeletal anomalies, neurology, medical genetics, hydrops fetalis, craniofacial malformations, cardiology, neonatal-perinatal medicine, fetal medicine, placental pathology, prenatal imaging, and bioinformatics. We expanded the representation of prenatal phenotypes in HPO by adding 95 new phenotype terms under the Abnormality of prenatal development or birth (HP:0001197) grouping term, and revised definitions, synonyms, and disease annotations for most of the 152 terms that existed before the beginning of this effort. The expansion of prenatal phenotypes in HPO will support phenotype-driven prenatal exome and genome sequencing for precision genetic diagnostics of rare diseases to support prenatal care

Effects of X-ray dose on rhizosphere studies using X-ray computed tomography

X-ray Computed Tomography (CT) is a non-destructive imaging technique originally designed for diagnostic medicine, which was adopted for rhizosphere and soil science applications in the early 1980s. X-ray CT enables researchers to simultaneously visualise and quantify the heterogeneous soil matrix of mineral grains, organic matter, air-filled pores and water-filled pores. Additionally, X-ray CT allows visualisation of plant roots in situ without the need for traditional invasive methods such as root washing. However, one routinely unreported aspect of X-ray CT is the potential effect of X-ray dose on the soil-borne microorganisms and plants in rhizosphere investigations. Here we aimed to i) highlight the need for more consistent reporting of X-ray CT parameters for dose to sample, ii) to provide an overview of previously reported impacts of X-rays on soil microorganisms and plant roots and iii) present new data investigating the response of plant roots and microbial communities to X-ray exposure. Fewer than 5% of the 126 publications included in the literature review contained sufficient information to calculate dose and only 2.4% of the publications explicitly state an estimate of dose received by each sample. We conducted a study involving rice roots growing in soil, observing no significant difference between the numbers of root tips, root volume and total root length in scanned versus unscanned samples. In parallel, a soil microbe experiment scanning samples over a total of 24 weeks observed no significant difference between the scanned and unscanned microbial biomass values. We conclude from the literature review and our own experiments that X-ray CT does not impact plant growth or soil microbial populations when employing a low level of dose (<30 Gy). However, the call for higher throughput X-ray CT means that doses that biological samples receive are likely to increase and thus should be closely monitored

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment