Search

The SARS-CoV-2 lineage B.1.1.7, designated variant of concern (VOC) 202012/01 by Public Health England1, was first identified in the UK in late summer to early autumn 20202. Whole-genome SARS-CoV-2 sequence data collected from community-based diagnostic testing for COVID-19 show an extremely rapid expansion of the B.1.1.7 lineage during autumn 2020, suggesting that it has a selective advantage. Here we show that changes in VOC frequency inferred from genetic data correspond closely to changes inferred by S gene target failures (SGTF) in community-based diagnostic PCR testing. Analysis of trends in SGTF and non-SGTF case numbers in local areas across England shows that B.1.1.7 has higher transmissibility than non-VOC lineages, even if it has a different latent period or generation time. The SGTF data indicate a transient shift in the age composition of reported cases, with cases of B.1.1.7 including a larger share of under 20-year-olds than non-VOC cases. We estimated time-varying reproduction numbers for B.1.1.7 and co-circulating lineages using SGTF and genomic data. The best-supported models did not indicate a substantial difference in VOC transmissibility among different age groups, but all analyses agreed that B.1.1.7 has a substantial transmission advantage over other lineages, with a 50% to 100% higher reproduction number.

14 pages, 8 figures, supplementary information https://doi.org/10.1038/s41588-022-01089-w.-- Data availability: Raw and aligned sequencing data are available at https://danio-code.zfin.org/dataExport/. The raw sequencing data produced for this study are available on the European Bioinformatics Institute (EBI) European Nucleotide Archive (ENA) under study numbers PRJNA824720, PRJNA821001, PRJNA821088, PRJNA821148 and PRJNA821034. Annotation tracks are available at http://genome.ucsc.edu/cgi-bin/hgTracks?db=danRer10&hubUrl=https://danio-code.zfin.org/trackhub/DANIO-CODE.hub.txt (danRer10) and http://genome.ucsc.edu/cgi-bin/hgTracks?db=danRer11&hubUrl=https://danio-code.zfin.org/trackhub/DANIO-CODE.hub.txt (danRer11). Code availability: The processing pipelines for the individual assays are available at https://gitlab.com/danio-code. The code used for the analysis is available at https://github.com/DANIO-CODE/DANIO-CODE_Data_analysis (https://doi.org/10.5281/zenodo.6424702). The script to generate the TrackHub is available at https://gitlab.com/danio-code/TrackHub, Zebrafish, a popular organism for studying embryonic development and for modeling human diseases, has so far lacked a systematic functional annotation program akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created a central repository to store and process zebrafish developmental functional genomic data. Our data coordination center (https://danio-code.zfin.org) combines a total of 1,802 sets of unpublished and re-analyzed published genomic data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements throughout development, including classes with distinct features dependent on their activity in time and space. We delineated the distinct distance topology and chromatin features between regulatory elements active during zygotic genome activation and those active during organogenesis. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predicted functional relationships between them beyond sequence similarity, thus extending the utility of zebrafish developmental genomics to mammals, We thank our main funders, the Horizon 2020 MSCA-ITN project ZENCODE-ITN by the European Commission to F.M., B.L., C.O.D., J.M.V. and B.P. (GA no: 643062), BBSRC support (DanioPeaks, P61715) to B.L., F.M. and F.C.W., Wellcome Trust (Joint-Investigator award 106955/Z/15/Z) to F.M. and B.L. and AQUA-FAANG (Horizon 2020, GA 817923) to B.L., D.B. and F.M. and BBSRC (BB/R015457/1) to F.vE and Key Special Project for Introduced Talents Team to Z.C. (GML2019ZD0401) and PrecisionTox project by the European Commission (GA no: 965406), With the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021., A study of the evolution of the SARS-CoV-2 virus in England between September 2020 and June 2021 finds that interventions capable of containing previous variants were insufficient to stop the more transmissible Alpha and Delta variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections were rising during early summer 2021 in many countries as a result of the Delta variant. We assessed reverse transcription polymerase chain reaction swab positivity in the Real-time Assessment of Community Transmission–1 (REACT-1) study in England. During June and July 2021, we observed sustained exponential growth with an average doubling time of 25 days, driven by complete replacement of the Alpha variant by Delta and by high prevalence at younger, less-vaccinated ages. Prevalence among unvaccinated people [1.21% (95% credible interval 1.03%, 1.41%)] was three times that among double-vaccinated people [0.40% (95% credible interval 0.34%, 0.48%)]. However, after adjusting for age and other variables, vaccine effectiveness for double-vaccinated people was estimated at between ~50% and ~60% during this period in England. Increased social mixing in the presence of Delta had the potential to generate sustained growth in infections, even at high levels of vaccination.

We identify amino acid variants within dominant SARS-CoV-2 T-cell epitopes by interrogating global sequence data. Several variants within nucleocapsid and ORF3a epitopes have arisen independently in multiple lineages and result in loss of recognition by epitope-specific T-cells assessed by IFN-γ and cytotoxic killing assays. Complete loss of T-cell responsiveness was seen due to Q213K in the A*01:01-restricted CD8+ ORF3a epitope FTSDYYQLY207-215, due to P13L, P13S and P13T in the B*27:05-restricted CD8+ nucleocapsid epitope QRNAPRITF9-17, and due to T362I and P365S in the A*03:01/A*11:01-restricted CD8+ nucleocapsid epitope KTFPPTEPK361-369. CD8+ T-cell lines unable to recognise variant epitopes have diverse T-cell receptor repertoires. These data demonstrate the potential for T-cell evasion and highlight the need for ongoing surveillance for variants capable of escaping T-cell as well as humoral immunity., Graphical Abstract

Zebrafish, a popular model for embryonic development and for modelling human diseases, has so far lacked a systematic functional annotation programme akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created the first central repository to store and process zebrafish developmental functional genomic data. Our Data Coordination Center (https://danio-code.zfin.org) combines a total of 1,802 sets of unpublished and reanalysed published genomics data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements in development, including novel classes with distinct features dependent on their activity in time and space. We delineated the distinction between regulatory elements active during zygotic genome activation and those active during organogenesis, identifying new aspects of how they relate to each other. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predict functional relationships between them beyond sequence similarity, extending the utility of zebrafish developmental genomics to mammals.

We report severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike ΔH69/V70 in multiple independent lineages, often occurring after acquisition of receptor binding motif replacements such as N439K and Y453F, known to increase binding affinity to the ACE2 receptor and confer antibody escape. In vitro, we show that, although ΔH69/V70 itself is not an antibody evasion mechanism, it increases infectivity associated with enhanced incorporation of cleaved spike into virions. ΔH69/V70 is able to partially rescue infectivity of spike proteins that have acquired N439K and Y453F escape mutations by increased spike incorporation. In addition, replacement of the H69 and V70 residues in the Alpha variant B.1.1.7 spike (where ΔH69/V70 occurs naturally) impairs spike incorporation and entry efficiency of the B.1.1.7 spike pseudotyped virus. Alpha variant B.1.1.7 spike mediates faster kinetics of cell-cell fusion than wild-type Wuhan-1 D614G, dependent on ΔH69/V70. Therefore, as ΔH69/V70 compensates for immune escape mutations that impair infectivity, continued surveillance for deletions with functional effects is warranted., Graphical abstract, Meng et al. report that the SARS-CoV-2 spike ΔH69/V70 has arisen multiple times. The deletion increases entry efficiency associated with increased cleaved spike in virions and can compensate for loss of infectivity. The B.1.1.7 spike requires ΔH69/V70 for efficient cell entry and cell-cell fusion activity.

Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that in regression models using “-omic” platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these “omic clocks” provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid from healthy human subjects, and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer’s and Parkinson’s disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small dataset (n = 85), and discuss techniques for drift correction, missing data imputation, and regularized regression which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of cerebrospinal fluid.

SARS-CoV-2 lineage B.1.1.7 viruses are more transmissible, may lead to greater clinical severity, and result in modest reductions in antibody neutralization. subgenomic RNA (sgRNA) is produced by discontinuous transcription of the SARS-CoV-2 genome and is a crucial step in the SARS-CoV-2 life cycle. Applying our tool (periscope) to ARTIC Network Oxford Nanopore genomic sequencing data from 4400 SARS-CoV-2 positive clinical samples, we show that normalised sgRNA expression profiles are significantly increased in B.1.1.7 infections (n=879). This increase is seen over the previous dominant circulating lineage in the UK, B.1.177 (n=943), which is independent of genomic reads, E gene cycle threshold and days since symptom onset at sampling. A noncanonical sgRNA which could represent ORF9b is found in 98.4% of B.1.1.7 SARS-CoV-2 infections compared with only 13.8% of other lineages, with a 16-fold increase in median expression. We hypothesise that this is a direct consequence of a triple nucleotide mutation in nucleocapsid (28280:GAT>CAT, D3L) creating a transcription regulatory-like sequence complementary to a region 3’ of the genomic leader. These findings provide a unique insight into the biology of B.1.1.7 and support monitoring of sgRNA profiles in sequence data to evaluate emerging potential variants of concern.One Sentence SummaryThe recently emerged and more transmissible SARS-CoV-2 lineage B.1.1.7 shows greater subgenomic RNA expression in clinical infections and enhanced expression of a noncanonical subgenomic RNA near ORF9b.

Cancer treatments can be highly toxic and frequently only a subset of the patient population will benefit from a given treatment. Tumour genetic makeup plays an important role in cancer drug sensitivity. We suspect that gene expression markers could be used as a decision aid for treatment selection or dosage tuning. Using in vitro cancer cell line dose-response and gene expression data from the Genomics of Drug Sensitivity in Cancer (GDSC) project, we build a dose-varying regression model. Unlike existing approaches, this allows us to estimate dosage-dependent associations with gene expression. We include the transcriptomic profiles as dose-invariant covariates into the regression model and assume that their effect varies smoothly over the dosage levels. A two-stage variable selection algorithm (variable screening followed by penalized regression) is used to identify genetic factors that are associated with drug response over the varying dosages. We evaluate the effectiveness of our method using simulation studies focusing on the choice of tuning parameters and cross-validation for predictive accuracy assessment. We further apply the model to data from five BRAF targeted compounds applied to different cancer cell lines under different dosage levels. We highlight the dosage-dependent dynamics of the associations between the selected genes and drug response, and we perform pathway enrichment analysis to show that the selected genes play an important role in pathways related to tumorigenesis and DNA damage response., Author summary Tumour cell lines allow scientists to test anticancer drugs in a laboratory environment. Cells are exposed to the drug in increasing concentrations, and the drug response, or amount of surviving cells, is measured. Generally, drug response is summarized via a single number such as the concentration at which 50% of the cells have died (IC50). To avoid relying on such summary measures, we adopted a functional regression approach that takes the dose-response curves as inputs, and uses them to find biomarkers of drug response. One major advantage of our approach is that it describes how the effect of a biomarker on the drug response changes with the drug dosage. This is useful for determining optimal treatment dosages and predicting drug response curves for unseen drug-cell line combinations. Our method scales to large numbers of biomarkers by using regularization and, in contrast with existing literature, selects the most informative genes by accounting for drug response at untested dosages. We demonstrate its value using data from the Genomics of Drug Sensitivity in Cancer project to identify genes whose expression is associated with drug response. We show that the selected genes recapitulate prior biological knowledge, and belong to known cancer pathways.

Global dispersal and increasing frequency of the SARS-CoV-2 spike protein variant D614G are suggestive of a selective advantage but may also be due to a random founder effect. We investigate the hypothesis for positive selection of spike D614G in the United Kingdom using more than 25,000 whole genome SARS-CoV-2 sequences. Despite the availability of a large dataset, well represented by both spike 614 variants, not all approaches showed a conclusive signal of positive selection. Population genetic analysis indicates that 614G increases in frequency relative to 614D in a manner consistent with a selective advantage. We do not find any indication that patients infected with the spike 614G variant have higher COVID-19 mortality or clinical severity, but 614G is associated with higher viral load and younger age of patients. Significant differences in growth and size of 614G phylogenetic clusters indicate a need for continued study of this variant., Graphical Abstract, Highlights • Increasing frequency of SARS-CoV-2 D614G is consistent with a selective advantage • Phylodynamic analyses do not show significantly different growth of D614G clusters • There is no association of D614G replacement with greater severity of infection • The D614G replacement is associated with higher viral loads and younger patient age, Analysis of the spread and frequency of SARS-CoV-2 D614G in the United Kingdom suggests a selective advantage for this strain that is associated with higher viral loads in younger patients but not higher COVID-19 clinical severity or mortality.

Expression quantitative trait loci (eQTL) can provide a link between disease susceptibility variants discovered by genetic association studies and biology. To date, eQTL mapping studies have been primarily conducted in healthy individuals from population-based cohorts. Genetic effects have been known to be context-specific and vary with changing environmental stimuli. We conducted a transcriptome- and genome-wide eQTL mapping study in a cohort of patients with idiopathic or heritable pulmonary arterial hypertension (PAH) using RNA sequencing (RNAseq) data from whole blood. We sought confirmation from three published population-based eQTL studies, including the GTEx Project, and followed up potentially novel eQTL not observed in the general population. In total, we identified 2314 eQTL of which 90% were cis-acting and 75% were confirmed by at least one of the published studies. While we observed a higher GWAS trait colocalization rate among confirmed eQTL, colocalisation rate of novel eQTL reported for lung-related phenotypes was twice as high as that of confirmed eQTL. Functional enrichment analysis of genes with novel eQTL in PAH highlighted immune-related processes, a suspected contributor to PAH. These potentially novel eQTL specific to or active in PAH could be useful in understanding genetic risk factors for other diseases that share common mechanisms with PAH.

Summary A SARS-CoV-2 variant carrying the Spike protein amino acid change D614G has become the most prevalent form in the global pandemic. Dynamic tracking of variant frequencies revealed a recurrent pattern of G614 increase at multiple geographic levels: national, regional and municipal. The shift occurred even in local epidemics where the original D614 form was well established prior to the introduction of the G614 variant. The consistency of this pattern was highly statistically significant, suggesting that the G614 variant may have a fitness advantage. We found that the G614 variant grows to higher titer as pseudotyped virions. In infected individuals G614 is associated with lower RT-PCR cycle thresholds, suggestive of higher upper respiratory tract viral loads, although not with increased disease severity. These findings illuminate changes important for a mechanistic understanding of the virus, and support continuing surveillance of Spike mutations to aid in the development of immunological interventions., Highlights: - A SARS-CoV-2 variant with Spike G614 has replaced D614 as the dominant pandemic form - The consistent increase of G614 at regional levels may indicate a fitness advantage - G614 is associated with lower RT PCR Ct’s, suggestive of higher viral loads in patients - The G614 variant grows to higher titers as pseudotyped virions

We have developed periscope, a tool for the detection and quantification of sub-genomic RNA (sgRNA) in SARS-CoV-2 genomic sequence data. The translation of the SARS-CoV-2 RNA genome for most open reading frames (ORFs) occurs via RNA intermediates termed “sub-genomic RNAs”. sgRNAs are produced through discontinuous transcription which relies on homology between transcription regulatory sequences (TRS-B) upstream of the ORF start codons and that of the TRS-L which is located in the 5’ UTR. TRS-L is immediately preceded by a leader sequence. This leader sequence is therefore found at the 5’ end of all sgRNA. We applied periscope to 1,155 SARS-CoV-2 genomes from Sheffield, UK and validated our findings using orthogonal datasets and in vitro cell systems. Using a simple local alignment to detect reads which contain the leader sequence we were able to identify and quantify reads arising from canonical and non-canonical sgRNA. We were able to detect all canonical sgRNAs at expected abundances, with the exception of ORF10. A number of recurrent non-canonical sgRNAs are detected. We show that the results are reproducible using technical replicates and determine the optimum number of reads for sgRNA analysis. In VeroE6 ACE2+/− cell lines, periscope can detect the changes in the kinetics of sgRNA in orthogonal sequencing datasets. Finally, variants found in genomic RNA are transmitted to sgRNAs with high fidelity in most cases. This tool can be applied to all sequenced COVID-19 samples worldwide to provide comprehensive analysis of SARS-CoV-2 sgRNA.

Ascertaining the state of coronavirus outbreaks is crucial for public health decision-making. Absent repeated representative viral test samples in the population, public health officials and researchers alike have relied on lagging indicators of infection to make inferences about the direction of the outbreak and attendant policy decisions. Recently researchers have shown that SARS-CoV-2 RNA can be detected in municipal sewage sludge with measured RNA concentrations rising and falling suggestively in the shape of an epidemic curve while providing an earlier signal of infection than hospital admissions data. The present paper presents a SARS-CoV-2 epidemic model to serve as a basis for estimating the incidence of infection, and shows mathematically how modeled transmission dynamics translate into infection indicators by incorporating probability distributions for indicator-specific time lags from infection. Hospital admissions and SARS-CoV-2 RNA in municipal sewage sludge are simultaneously modeled via maximum likelihood scaling to the underlying transmission model. The results demonstrate that both data series plausibly follow from the transmission model specified and provide a 95% confidence interval estimate of the reproductive number R0 ≈ 2.4 ±0.2. Sensitivity analysis accounting for alternative lag distributions from infection until hospitalization and sludge RNA concentration respectively suggests that the detection of viral RNA in sewage sludge leads hospital admissions by 3 to 5 days on average. The analysis suggests that stay-at-home restrictions plausibly removed 89% of the population from the risk of infection with the remaining 11% exposed to an unmitigated outbreak that infected 9.3% of the total population.HighlightsA maximum likelihood method for aligning observed lagged epidemic indicators via an underlying transmission model is derived and illustrated using observed COVID-19 hospital admissions and SARS-CoV-2 RNA concentrations measured in sewage sludge to model a local SARS-CoV-2 outbreakThe method enables direct estimation of the reproductive number R0 from the observed indicators along with the initial prevalence of SARS-CoV-2 infection in the population at riskThe analysis suggests tracking SARS-CoV-2 RNA concentration in sewage sludge provides a 3 to 5 day lead time over tracking hospital admissions, consistent with purely statistical time series analysis previously reportedThe model enables estimation of the fraction of the population compliant with government-mandated stay-at-home restrictions, the size of the exposed population, and the fraction of the population infected with SARS-CoV-2 over the outbreak

This study aims to evaluate the in vivo distribution of Gold Nanoparticles (GNPs) at different time points after intratumoral (IT) injection, exploiting their properties as contrast agents for Computed Tomography (CT). GNPs approximately 40 nm in diameter were synthesized with a surface plasmon peak at ~530 nm, capped with Bovine Serum Albumin (BSA) to improve colloidal stability, and characterized with standard methods. CT phantom imaging was performed to quantify X-ray attenuation as a function of GNP concentration and surface functionalization and to determine the appropriate particle dose for in vivo studies. Concentrated GNPs were intratumorally (IT) injected into Lewis Lung Carcinoma (LLC) solid tumors grown on the right flank of 6-week old female C57BL/6 mice. Ten days post-injection, follow up CT imaging was performed to assess the distribution and retention of the particles in the tumor. Using the CT attenuation quantification, images for each timepoint were segmented, and 3D volumes rendered, to conduct biodistribution analyses. The successful retention and permanence of the GNPs into the solid tumor after ten days suggests the significance of GNPs as a potential theranostic agent.

Background: Highly accurate next-generation sequencing (NGS) of genetic variants is key to many areas of science and medicine, such as cataloguing population genetic variation and diagnosing patients with genetic diseases. Certain genomic loci and regions can be prone to higher rates of systematic sequencing and alignment bias that pose a challenge to achieving high accuracy, resulting in false positive variant calls. Current standard practices to differentiate between loci that can and cannot be sequenced with high confidence utilise consensus between different sequencing methods as a proxy for sequencing confidence. This assumption is not accurate in cases where all sequencing pipelines have consensus on the same errors due to similar systematic biases in sequencing. Alternative methods are therefore required to identify systematic biases. Methods: We have developed a novel statistical method based on summarising sequenced reads from whole genome clinical samples and cataloguing them in "Incremental Databases" (IncDBs) that maintain individual confidentiality. Variant statistics were analysed and catalogued for each genomic position that consistently showed systematic biases with the corresponding sequencing pipeline. Results: We have demonstrated that systematic errors in NGS data are widespread, with persistent low-fraction alleles present at 1.26-2.43% of the human autosomal genome across three different Illumina-based pipelines, each consisting of at least 150 patient samples. We have identified a variety of genomic regions that are more or less prone to systematic biases, such as GC-rich regions (OR = 6.47-8.19) and the NIST high-confidence genomic regions (OR = 0.154-0.191). We have verified our predictions on a gold-standard reference genome and have shown that these systematic biases can lead to suspect variant calls at clinically important loci, including within introns and exons. Conclusions: Our results recommend increased caution to minimise the effect of systematic biases in whole genome sequencing and alignment. This study supports the utility of a statistical approach to enhance quality control of clinically sequenced samples in order to flag up variant calls made at known suspect loci for further analysis or exclusion, using anonymised summary databases from which individual patients cannot be re-identified, so that results can be shared more widely.

Carcinoma-associated fibroblasts (CAFs) are abundant stromal cells in tumor microenvironment that are critically involved in cancer progression. Contrasting reports have shown that CAFs can have either pro- or antitumorigenic roles, indicating that CAFs are functionally heterogeneous. Therefore, to precisely target the cancer-promoting CAF subsets, it is necessary to identify specific markers to define these subpopulations and understand their functions. We characterized two CAFs subsets from 28 non–small cell lung cancer (NSCLC) patient tumors that were scored and classified based on desmoplasia [mainly characterized by proliferating CAFs; high desmoplastic CAFs (HD-CAF; n = 15) and low desmoplastic CAFs (LD-CAF; n = 13)], which is an independent prognostic factor. Here, for the first time, we demonstrate that HD-CAFs and LD-CAFs show different tumor-promoting abilities. HD-CAFs showed higher rate of collagen matrix remodeling, invasion, and tumor growth compared to LD-CAFs. Transcriptomic analysis identified 13 genes that were differentially significant (fold ≥1.5; adjusted P value < .1) between HD-CAFs and LD-CAFs. The top upregulated differentially expressed gene, ST8SIA2 (11.3 fold; adjusted P value = .02), enhanced NSCLC tumor cell invasion in 3D culture compared to control when it was overexpressed in CAFs, suggesting an important role of ST8SIA2 in cancer cell invasion. We confirmed the protumorigenic role of ST8SIA2, showing that ST8SIA2 was significantly associated with the risk of relapse in three independent NSCLC clinical datasets. In summary, our studies show that functional heterogeneity in CAF plays key role in promoting cancer cell invasion in NSCLC.

PurposeLong-read, third generation, sequencing technologies have the potential to improve current state of the art diagnostic strategies. In order to determine if long-read sequencing technologies are suitable for the diagnosis of mitochondrial disorders due to mitochondrial DNA (mtDNA) variants, particularly large deletions, we compared the performance of Oxford Nanopore Technologies (ONT) MinION to current diagnostic methods.MethodsWe sequenced mtDNA from nine patients with mtDNA deletion disorders and three normal controls with both ONT MinION and Illumina MiSeq. We applied a computational pipeline to estimate the positions of mtDNA deletions in patients, and subsequently validated the breakpoints using Sanger sequencing.ResultsWe were able to detect mtDNA deletions with a MinION workflow, successfully calling the disease causing event in all cases. Sequencing coverage was in most cases significantly more (p=0.03, Wilcoxon test) uniform with MinION than with MiSeq and subsequent correction of MinION reads improved breakpoint accuracy and reduced false positives. Although heteroplasmic single nucleotide variants are detectable, the high number of false positives and false negatives precludes their use in diagnostics at this time.ConclusionThe MinION is becoming an increasingly attractive diagnostic tool due to the reducing cost, increasing accuracy, and the speed at which data can be obtained.

Availability of lung cancer models that closely mimic human tumors remains a significant gap in cancer research, as tumor cell lines and mouse models may not recapitulate the spectrum of lung cancer heterogeneity seen in patients. We aimed to establish a patient-derived tumor xenograft (PDX) resource from surgically resected non-small cell lung cancer (NSCLC). Fresh tumor tissue from surgical resection was implanted and grown in the subcutaneous pocket of non-obese severe combined immune deficient (NOD SCID) gamma mice. Subsequent passages were in NOD SCID mice. A subset of matched patient and PDX tumors and non-neoplastic lung tissues were profiled by whole exome sequencing, single nucleotide polymorphism (SNP) and methylation arrays, and phosphotyrosine (pY)-proteome by mass spectrometry. The data were compared to published NSCLC datasets of NSCLC primary and cell lines. 127 stable PDXs were established from 441 lung carcinomas representing all major histological subtypes: 52 adenocarcinomas, 62 squamous cell carcinomas, one adeno-squamous carcinoma, five sarcomatoid carcinomas, five large cell neuroendocrine carcinomas, and two small cell lung cancers. Somatic mutations, gene copy number and expression profiles, and pY-proteome landscape of 36 PDXs showed greater similarity with patient tumors than with established cell lines. Novel somatic mutations on cancer associated genes were identified but only in PDXs, likely due to selective clonal growth in the PDXs that allows detection of these low allelic frequency mutations. The results provide the strongest evidence yet that PDXs established from lung cancers closely mimic the characteristics of patient primary tumors.

Altered immune response may be a part of the pathogenesis of preeclampsia. The few epidemiologic studies that have investigated the associations between genetic variations in the complement system genes and preeclampsia risk have reached inconsistent results. The aim of this study is to determine if polymorphisms in the complement system genes could influence the risk of preeclampsia. We examined 51 SNPs in the C3, C5, C6, MASP1, MBL2 and CD55 genes and the risk of preeclampsia and its clinical subtypes in a nested case–control study of 203 preeclampsia cases and 233 controls. Both C6 and MASP1 were associated with the risk of preeclampsia. C6 (rs7444800, rs4957381) and MASP1 (rs1108450, rs3774282, rs698106) polymorphisms were associated with the risk of early-onset preeclampsia and severe preeclampsia, while MASP1 (rs1357134, rs698090) polymorphisms were associated with the risk of late-onset preeclampsia and severe preeclampsia. Our study provided novel evidence that genetic variations in complement genes C6 and MASP1were associated with preeclampsia risk, and that the risk varied by preeclampsia subtypes.

Integrin α11β1 is a stromal cell-specific receptor for fibrillar collagens and is overexpressed in carcinoma-associated fibroblasts (CAFs). We have investigated its direct role in cancer progression by generating severe combined immune deficient (SCID) mice deficient in integrin α11 (α11) expression. The growth of A549 lung adenocarcinoma cells and two patient-derived non-small cell lung carcinoma (NSCLC) xenografts in these α11 knockout (α11(-/-)) mice was significantly impeded, as compared with wild-type (α11(+/+)) SCID mice. Orthotopic implantation of a spontaneously metastatic NCI-H460SM cell line into the lungs of α11(-/-) and α11(+/+) mice showed significant reduction in the metastatic potential of these cells in the α11(-/-) mice. We identified that collagen cross-linking is associated with stromal α11 expression, and the loss of tumor stromal α11 expression was correlated with decreased collagen reorganization and stiffness. This study shows the role of integrin α11β1, a receptor for fibrillar collagen in differentiation of fibroblasts into CAFs. Furthermore, our data support an important role for α11 signaling pathway in CAFs, promoting tumor growth and metastatic potential of NSCLC cells and being closely associated with collagen cross-linking and the organization and stiffness of fibrillar collagen matrices.

Murine syngeneic tumor models are critical to novel immuno-based therapy development, but the molecular and immunologic features of these models are still not clearly defined. The translational relevance of differences between the models is not fully understood, impeding appropriate preclinical model selection for target validation, and ultimately hindering drug development. Across a panel of commonly used murine syngeneic tumor models, we showed variable responsiveness to immunotherapies. We used array comparative genomic hybridization, whole-exome sequencing, exon microarray analysis, and flow cytometry to extensively characterize these models, which revealed striking differences that may underlie these contrasting response profiles. We identified strong differential gene expression in immune-related pathways and changes in immune cell–specific genes that suggested differences in tumor immune infiltrates between models. Further investigation using flow cytometry showed differences in both the composition and magnitude of the tumor immune infiltrates, identifying models that harbor “inflamed” and “non-inflamed” tumor immune infiltrate phenotypes. We also found that immunosuppressive cell types predominated in syngeneic mouse tumor models that did not respond to immune-checkpoint blockade, whereas cytotoxic effector immune cells were enriched in responsive models. A cytotoxic cell–rich tumor immune infiltrate has been correlated with increased efficacy of immunotherapies in the clinic, and these differences could underlie the varying response profiles to immunotherapy between the syngeneic models. This characterization highlighted the importance of extensive profiling and will enable investigators to select appropriate models to interrogate the activity of immunotherapies as well as combinations with targeted therapies in vivo. Cancer Immunol Res; 5(1); 29–41. ©2016 AACR.

Personalized therapy is a major goal of modern oncology, as patient responses vary greatly even within a histologically defined cancer subtype. This is especially true in acute myeloid leukemia (AML), which exhibits striking heterogeneity in molecular segmentation. When calibrated to cell-specific data, executable network models can reveal subtle differences in signaling that help explain differences in drug response. Furthermore, they can suggest drug combinations to increase efficacy and combat acquired resistance. Here, we experimentally tested dynamic proteomic changes and phenotypic responses in diverse AML cell lines treated with pan-PIM kinase inhibitor and fms-related tyrosine kinase 3 (FLT3) inhibitor as single agents and in combination. We constructed cell-specific executable models of the signaling axis, connecting genetic aberrations in FLT3, tyrosine kinase 2 (TYK2), platelet-derived growth factor receptor alpha (PDGFRA), and fibroblast growth factor receptor 1 (FGFR1) to cell proliferation and apoptosis via the PIM and PI3K kinases. The models capture key differences in signaling that later enabled them to accurately predict the unique proteomic changes and phenotypic responses of each cell line. Furthermore, using cell-specific models, we tailored combination therapies to individual cell lines and successfully validated their efficacy experimentally. Specifically, we showed that cells mildly responsive to PIM inhibition exhibited increased sensitivity in combination with PIK3CA inhibition. We also used the model to infer the origin of PIM resistance engineered through prolonged drug treatment of MOLM16 cell lines and successfully validated experimentally our prediction that this resistance can be overcome with AKT1/2 inhibition. Cancer Res; 77(4); 827–38. ©2016 AACR.

The challenge of developing effective pharmacodynamic biomarkers for preclinical and clinical testing of FGFR signaling inhibition is significant. Assays that rely on the measurement of phospho-protein epitopes can be limited by the availability of effective antibody detection reagents. Transcript profiling enables accurate quantification of many biomarkers and provides a broader representation of pathway modulation. To identify dynamic transcript biomarkers of FGFR signaling inhibition by AZD4547, a potent inhibitor of FGF receptors 1, 2, and 3, a gene expression profiling study was performed in FGFR2-amplified, drug-sensitive tumor cell lines. Consistent with known signaling pathways activated by FGFR, we identified transcript biomarkers downstream of the RAS-MAPK and PI3K/AKT pathways. Using different tumor cell lines in vitro and xenografts in vivo, we confirmed that some of these transcript biomarkers (DUSP6, ETV5, YPEL2) were modulated downstream of oncogenic FGFR1, 2, 3, whereas others showed selective modulation only by FGFR2 signaling (EGR1). These transcripts showed consistent time-dependent modulation, corresponding to the plasma exposure of AZD4547 and inhibition of phosphorylation of the downstream signaling molecules FRS2 or ERK. Combination of FGFR and AKT inhibition in an FGFR2-mutated endometrial cancer xenograft model enhanced modulation of transcript biomarkers from the PI3K/AKT pathway and tumor growth inhibition. These biomarkers were detected on the clinically validated nanoString platform. Taken together, these data identified novel dynamic transcript biomarkers of FGFR inhibition that were validated in a number of in vivo models, and which are more robustly modulated by FGFR inhibition than some conventional downstream signaling protein biomarkers. Mol Cancer Ther; 15(11); 2802–13. ©2016 AACR.

Although cancers are considered stem cell diseases, mechanisms involving stem cell alterations are poorly understood. Squamous cell carcinoma (SQCC) is the second most common lung cancer, and its pathogenesis appears to hinge on changes in the stem cell behavior of basal cells in the bronchial airways. Basal cells are normally quiescent and differentiate into mucociliary epithelia. Smoking triggers a hyperproliferative response resulting in progressive premalignant epithelial changes ranging from squamous metaplasia to dysplasia. These changes can regress naturally, even with chronic smoking. However, for unknown reasons, dysplasias have higher progression rates than earlier stages. We used primary human tracheobronchial basal cells to investigate how copy number gains in SOX2 and PIK3CA at 3q26-28, which co-occur in dysplasia and are observed in 94% of SQCCs, may promote progression. We find that SOX2 cooperates with PI3K signaling, which is activated by smoking, to initiate the squamous injury response in basal cells. This response involves SOX9 repression, and, accordingly, SOX2 and PI3K signaling levels are high during dysplasia, while SOX9 is not expressed. By contrast, during regeneration of mucociliary epithelia, PI3K signaling is low and basal cells transiently enter a SOX2LoSOX9Hi state, with SOX9 promoting proliferation and preventing squamous differentiation. Transient reduction in SOX2 is necessary for ciliogenesis, although SOX2 expression later rises and drives mucinous differentiation, as SOX9 levels decline. Frequent coamplification of SOX2 and PIK3CA in dysplasia may, thus, promote progression by locking basal cells in a SOX2HiSOX9Lo state with active PI3K signaling, which sustains the squamous injury response while precluding normal mucociliary differentiation. Surprisingly, we find that, although later in invasive carcinoma SOX9 is generally expressed at low levels, its expression is higher in a subset of SQCCs with less squamous identity and worse clinical outcome. We propose that early pathogenesis of most SQCCs involves stabilization of the squamous injury state in stem cells through copy number gains at 3q, with the pro-proliferative activity of SOX9 possibly being exploited in a subset of SQCCs in later stages., A study of tracheobronchial basal cells reveals a mechanism by which lung squamous cell carcinoma is almost universally initiated by exploitation of a stem cell injury response involving SOX2 and PI3K., Author Summary Squamous cell carcinoma (SQCC) is a deadly and common form of lung cancer. How it develops from stem cells is poorly understood. SQCCs predominantly arise in bronchial epithelia, likely from basal cells, stem cells that normally generate mucinous and ciliated cells. Smoking, however, causes normally quiescent basal cells to proliferate and generate protective squamous epithelia. Continuous smoking eventually causes precancerous changes and, ultimately, SQCC. However, some precancerous changes regress to normal epithelia, suggesting that the natural stem cell injury response is not sustainable indefinitely. Here, we describe how the SOX2 transcription factor and PI3K signaling, which is activated by smoking, induce the squamous injury response in basal cells. We also provide evidence that regeneration of mucociliary epithelia after injury requires basal cells to enter a period of low SOX2 expression and PI3K signaling. Ninety-four percent of SQCCs have copy number gains in chromosome 3 that affect SOX2 and PIK3CA, a catalytic PI3K subunit. These gains occur at a point when precancerous tissue has an increased likelihood of progressing to SQCC. Our data suggest that SQCC is genetically initiated by events that sustain a normally self-limiting injury state, which forces stem cells to proliferate indefinitely in the presence of smoking-associated mutagens.

Cell type-specific gene expression in humans involves complex interactions between regulatory factors and DNA at enhancers and promoters. Mapping studies for expression quantitative trait loci (eQTLs), transcription factors (TFs) and chromatin markers have become widely used tools for identifying gene regulatory elements, but prediction of target genes remains a major challenge. Here, we integrate genome-wide data on TF-binding sites, chromatin markers and functional annotations to predict genes associated with human eQTLs. Using the random forest classifier, we found that genomic proximity plus five TF and chromatin features are able to predict >90% of target genes within 1 megabase of eQTLs. Despite being regularly used to map target genes, proximity is not a good indicator of eQTL targets for genes 150 kilobases away, but insulators, TF co-occurrence, open chromatin and functional similarities between TFs and genes are better indicators. Using all six features in the classifier achieved an area under the specificity and sensitivity curve of 0.91, much better compared with at most 0.75 for using any single feature. We hope this study will not only provide validation of eQTL-mapping studies, but also provide insight into the molecular mechanisms explaining how genetic variation can influence gene expression.

Purpose Although epidermal growth factor receptor (EGFR) –mutated adenocarcinomas initially have high response rates to EGFR tyrosine kinase inhibitors (TKIs), most patients eventually develop resistance. Patient-derived xenografts (PDXs) are considered preferred preclinical models to study the biology of patient tumors. EGFR-mutant PDX models may be valuable tools to study the biology of these tumors and to elucidate mechanisms of resistance to EGFR-targeted therapies. Methods Surgically resected early-stage non–small-cell lung carcinoma (NSCLC) tumors were implanted into nonobese diabetic severe combined immune deficient (NOD-SCID) mice. EGFR TKI treatment was initiated at tumor volumes of 150 μL. Gene expression analysis was performed using a microarray platform. Results Of 33 lung adenocarcinomas with EGFR activating mutations, only 6 (18%) engrafted and could be propagated beyond passage one. Engraftment was associated with upregulation of genes involved in mitotic checkpoint and cell proliferation. A differentially expressed gene set between engrafting and nonengrafting patients could identify patients harboring EGFR-mutant tumor with significantly different prognoses in The Cancer Genome Atlas Lung Adenocarcinoma datasets. The PDXs included models with variable sensitivity to first- and second-generation EGFR TKIs and the monoclonal antibody cetuximab. All EGFR-mutant NSCLC PDXs studied closely recapitulated their corresponding patient tumor phenotype and clinical course, including response pattern to EGFR TKIs. Conclusion PDX models closely recapitulate primary tumor biology and clinical outcome. They may serve as important laboratory models to investigate mechanisms of resistance to targeted therapies, and for preclinical testing of novel treatment strategies.

SummaryMany genes that affect replicative lifespan (RLS) in the budding yeast Saccharomyces cerevisiae also affect aging in other organisms such as C. elegans and M. musculus. We performed a systematic analysis of yeast RLS in a set of 4,698 viable single-gene deletion strains. Multiple functional gene clusters were identified, and full genome-to-genome comparison demonstrated a significant conservation in longevity pathways between yeast and C. elegans. Among the mechanisms of aging identified, deletion of tRNA exporter LOS1 robustly extended lifespan. Dietary restriction (DR) and inhibition of mechanistic Target of Rapamycin (mTOR) exclude Los1 from the nucleus in a Rad53-dependent manner. Moreover, lifespan extension from deletion of LOS1 is nonadditive with DR or mTOR inhibition, and results in Gcn4 transcription factor activation. Thus, the DNA damage response and mTOR converge on Los1-mediated nuclear tRNA export to regulate Gcn4 activity and aging.

Rhodococcus sp. RHA1 (RHA1) is a potent polychlorinated biphenyl-degrading soil actinomycete that catabolizes a wide range of compounds and represents a genus of considerable industrial interest. RHA1 has one of the largest bacterial genomes sequenced to date, comprising 9,702,737 bp (67% G+C) arranged in a linear chromosome and three linear plasmids. A targeted insertion methodology was developed to determine the telomeric sequences. RHA1's 9,145 predicted protein-encoding genes are exceptionally rich in oxygenases (203) and ligases (192). Many of the oxygenases occur in the numerous pathways predicted to degrade aromatic compounds ( 30 ) or steroids ( 4 ). RHA1 also contains 24 nonribosomal peptide synthase genes, six of which exceed 25 kbp, and seven polyketide synthase genes, providing evidence that rhodococci harbor an extensive secondary metabolism. Among sequenced genomes, RHA1 is most similar to those of nocardial and mycobacterial strains. The genome contains few recent gene duplications. Moreover, three different analyses indicate that RHA1 has acquired fewer genes by recent horizontal transfer than most bacteria characterized to date and far fewer than Burkholderia xenovorans LB400, whose genome size and catabolic versatility rival those of RHA1. RHA1 and LB400 thus appear to demonstrate that ecologically similar bacteria can evolve large genomes by different means. Overall, RHA1 appears to have evolved to simultaneously catabolize a diverse range of plant-derived compounds in an O 2 -rich environment. In addition to establishing RHA1 as an important model for studying actinomycete physiology, this study provides critical insights that facilitate the exploitation of these industrially important microorganisms.

Ground-based and spacecraft photometry covering phase angles from 2° to 179° has been acquired in wavelength bands from blue to near infrared. An unexpected brightness surge is seen in the B and V bands when the disk of Venus is less than 2% illuminated. This excess luminosity appears to be the result of forward scattering from droplets of H2SO4 (sulfuric acid) in the high atmosphere of Venus. The fully sunlit brightness of Venus, adjusted to a distance of one AU from the Sun and observer, was found to be V = − 4.38 , and the corresponding geometric albedo is 67%. The phase integral is 1.35 and the resulting spherical albedo is 90%. Comparison between our data and photometry obtained over the past 50 years indicates a bias in the older photoelectric results, however atmospheric abundance variations suggest that brightness changes may have occurred too.

Copyright: © 2015 Razdan A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. GlobalFilerTM Express PCR Amplification Kit is a highly discriminative STR kit developed by Life Technologies to meet the requirements of FBI recommendations [1,2], including 21 autosomal STR loci (i.e., D3S1358, vWA, D16S539, CSF1PO, TPOX, D8S1179, D21S11, D18S51, D2S441, D19S433, TH01, FGA, D22S1045, D5S818, D13S317, D7S820, SE33, D10S1248, D1S1656, D12S391, and D2S1338), 1 Y STR locus (i.e., DYS391), 1 Y-indel locus, and Amelogenin. In this study, we did population genetic analyses with the GlobalFilerTM Express Kit for Indian population. 201 samples were collected, with consent, from unrelated individuals from different geographic regions of India. The samples were anonymized prior to analysis.

Pancreatic ductal adenocarcinoma (PDAC) is the fourth most common cause of cancer death in North America. Activating KRAS mutations and Smad4 loss occur in approximately 90% and 55% of PDAC, respectively. While their roles in the early stages of PDAC development have been confirmed in genetically modified mouse models, their roles in the multistep malignant transformation of human pancreatic duct cells have not been directly demonstrated. Here, we report that Smad4 represents a barrier in KRAS-mediated malignant transformation of the near normal immortalized human pancreatic duct epithelial (HPDE) cell line model. Marked Smad4 downregulation by shRNA in KRAS (G12V) expressing HPDE cells failed to cause tumorigenic transformation. However, KRAS-mediated malignant transformation occurred in a new HPDE-TGF-β resistant (TβR) cell line that completely lacks Smad4 protein expression and is resistant to the mito-inhibitory activity of TGF-β. This transformation resulted in tumor formation and development of metastatic phenotype when the cells were implanted orthotopically into the mouse pancreas. Smad4 restoration re-established TGF-β sensitivity, markedly increased tumor latency by promoting apoptosis, and decreased metastatic potential. These results directly establish the critical combination of the KRAS oncogene and complete Smad4 inactivation in the multi-stage malignant transformation and metastatic progression of normal human HPDE cells.

Background The epidermal growth factor receptor (EGFR) kinase domain T790M (amino acid substitution at position 790 in EGFR from threonine [T] to methionine [M]) mutation in non–small-cell lung cancer (NSCLC) results in resistance to EGFR tyrosine kinase inhibitors (TKIs). We used a patient-derived tumor xenograft (PDX) model containing an EGFR exon 19 deletion/T790M mutation to assess response to the EGFR-directed antibody cetuximab. Changes in the EGFR signaling pathway and ligand expression after treatment were investigated. Methods PDX were randomized into control and treatment arms. Pharmacodynamic studies were performed at 2 and 24 hours and at 4 days after a single administration of cetuximab, erlotinib, or dacomitinib. Changes in the EGFR signaling pathway were assessed using Western blot analysis, and baseline mRNA expression of EGFR ligands using microarray analysis. Relative changes after treatment were assessed using quantitative polymerase chain reaction. Results The xenograft showed a dramatic response to cetuximab. A complete reduction of total EGFR and phosphorylated EGFR occurred after cetuximab treatment. The PDX had increased baseline levels of heparin-binding epidermal growth factor-like growth factor ( HB-EGF ) compared with other PDX models with or without EGFR mutations. Amphiregulin was significantly reduced 2 hours after treatment with cetuximab. Compared with control mice, cetuximab- and EGFR–TKI-treated mice had significantly reduced HB-EGF gene expression at 2 hours, however, by day 4 the level of HB-EGF expression was higher. The effect of cetuximab compared with EGFR TKI on HB-EGF gene expression levels differed significantly at 2 and 24 hours but not at 4 days. Conclusion We showed a dramatic tumor response with cetuximab in an exon 19 deletion/T790M EGFR mutant lung adenocarcinoma PDX model, which suggests a role for the autocrine feedback loop in the mutant EGFR signaling pathway. Further investigation using cetuximab in NSCLC with T790M mutation is warranted.

The establishment of valid lung carcinoma preclinical models for testing new cancer therapies is necessary, as existing cell lines and mouse models may not recapitulate the full spectrum of heterogeneity of patient tumors. Studies suggest lung cancer patient-derived xenograft (PDX) models recapitulate well gene copy number variation, gene expression profiles, and metabolic states of corresponding patient tumors. However, an understanding of mechanisms linking cancer-associated genome, transcriptome, and proteome alterations with driver mutations and dysregulated signal transduction networks in primary and PDX models is lacking. We report a large (>150) resource of lung cancer PDX models, derived from surgically resected tumors, and endobronchial ultrasound-guided (EBUS) and CT-guided biopsies. Tumor specimens were grown and serially passaged in the subcutaneous pocket at the flanks of NSG mice (NOD SCID gamma) at initial implant, and following passages in NOD SCID (non-obese diabetic severe combined immunodeficiency) mice. Among 127 established PDX models from 441 surgically derived tumor specimens, all major histological subtypes were included: 52 adenocarcinomas, 62 squamous cell carcinomas, 1 adeno-squamous cell carcinomas, 5 sarcomatoid carcinomas, 5 large cell neuroendocrine carcinomas, and 2 small cell lung cancers. Over 100 PDX models have been profiled by next-generation exome sequencing (SureSelect Human 50Mbp kit), and array-based assays for copy number variant (HumanOmni 2.5-Quad BeadChip), DNA methylation (Infinium HumanMethylation450 BeadChip) and mRNA (DASL HumanHT-12 v4 BeadChip) profiles. Smaller subsets of PDXs have been characterized by mass spectrometry (MS)-based comprehensive proteome and protein-phosphotyrosine characterization. Genome/transcriptome/proteome profiles of 36 non-small cell lung carcinoma (NSCLC) PDX models correlated with patient primary tumors but to a much lesser extent with established NSCLC cell lines. A number of PDX models have genetic abnormalities linked to targeted therapies including mutations in EGFR (6), PIK3CA (13), and KRAS (21), and amplifications in FGFR1 (7) and CDK4 (6). This study provides the most solid evidence as yet that PDXs established from lung cancers mimic closely the genomic and proteomic characteristics of patient primary tumors and retain driver genetic abnormalities. Citation Format: Nhu-An Pham, Dennis Wang, Jiefei Tong, Chang-Qi Zhu, Lei Li, Wen Zhang, Ruoshi Shi, Shingo Sakashita, Melania Pintilie, Michael F. Moran, Geoffrey Liu, Ming-Sound Tsao. Fidelity of genomic and proteomic features of patient-derived xenografts of lung cancers. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr B32.

The pre-clinical assessment of immuno-oncology (IO) therapies can be enabled by the use of murine syngeneic tumors established in immuno-competent mice. With the aims of selecting relevant models and of minimizing animal experimentation by reducing the number of models tested, the full characterisation of syngeneic models at the transcriptomic and genomic level is a key objective for pre-clinical scientists. Model characterisation includes global aCGH, exon array analysis and FACS profiling alongside exome sequencing. The model data is undergoing hypothesis free and driven analyses which are already generating valuable insights. Comparison of in vivo tumor samples with their in vitro equivalents has highlighted enrichment for a number of immune pathways; as has the comparison of different tumor lines. The genomic, transcriptomic and ‘proteomic’ model data are being integrated to give a functional output which will act as a ‘Syngenomic Fingerprint’ for each model. The resulting Syngenomic fingerprints will help pre-clinical scientists to refine their in vivo plans through an improved understanding of the limits and advantages as well as the clinical relevance of some of our preclinical models. It is also supporting the targeted modification of models to better match specific human cancer types. Citation Format: John E. Prime, Suzanne Mosely, Jens-Oliver Koopmann, Dennis YQ Wang, Danielle Greenawalt, James Harper, Miika J. Ahdesmaki, Rebecca Leyland, Olivia Harris, Ross Stewart, Philip Brohawn, Brandon Higgs, Bryony Langford, Athula Herath, Robert Kozarski, Jane Coates-Ulrichsen, Judith Anderton, Michelle Morrow, Richard C. A Sainson, Robert W. Wilkinson. Syngenomic fingerprint: the biomic characterization of the mouse syngeneic tumor models. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 4186.

Background Cellular development requires the precise control of gene expression states. Transcription factors are involved in this regulatory process through their combinatorial binding with DNA. Information about transcription factor binding sites can help determine which combinations of factors work together to regulate a gene, but it is unclear how far the binding data from one cell type can inform about regulation in other cell types. Results By integrating data on co-localized transcription factor binding sites in the K562 cell line with expression data across 38 distinct hematopoietic cell types, we developed regression models to describe the relationship between the expression of target genes and the transcription factors that co-localize nearby. With K562 binding sites identifying the predictors, the proportion of expression explained by the models is statistically significant only for monocytic cells (p-value< 0.001), which are closely related to K562. That is, cell type specific binding patterns are crucial for choosing the correct transcription factors for the model. Comparison of predictors obtained from binding sites in the GM12878 cell line with those from K562 shows that the amount of difference between binding patterns is directly related to the quality of the prediction. By identifying individual genes whose expression is predicted accurately by the binding sites, we are able to link transcription factors FOS, TAF1 and YY1 to a sparsely studied gene LRIG2. We also find that the activity of a transcription factor may be different depending on the cell type and the identity of other co-localized factors. Conclusion Our approach shows that gene expression can be explained by a modest number of co-localized transcription factors, however, information on cell-type specific binding is crucial for understanding combinatorial gene regulation.

Background As the primary sites of synaptic or sensory input in the nervous system, dendrites play an essential role in processing neuronal and sensory information. Moreover, the specification of class specific dendrite arborization is critically important in establishing neural connectivity and the formation of functional networks. Cytoskeletal modulation provides a key mechanism for establishing, as well as reorganizing, dendritic morphology among distinct neuronal subtypes. While previous studies have established differential roles for the small GTPases Rac and Rho in mediating dendrite morphogenesis, little is known regarding the direct regulators of these genes in mediating distinct dendritic architectures. Methodology/Principal Findings Here we demonstrate that the RhoGEF Trio is required for the specification of class specific dendritic morphology in dendritic arborization (da) sensory neurons of the Drosophila peripheral nervous system (PNS). Trio is expressed in all da neuron subclasses and loss-of-function analyses indicate that Trio functions cell-autonomously in promoting dendritic branching, field coverage, and refining dendritic outgrowth in various da neuron subtypes. Moreover, overexpression studies demonstrate that Trio acts to promote higher order dendritic branching, including the formation of dendritic filopodia, through Trio GEF1-dependent interactions with Rac1, whereas Trio GEF-2-dependent interactions with Rho1 serve to restrict dendritic extension and higher order branching in da neurons. Finally, we show that de novo dendritic branching, induced by the homeodomain transcription factor Cut, requires Trio activity suggesting these molecules may act in a pathway to mediate dendrite morphogenesis. Conclusions/Significance Collectively, our analyses implicate Trio as an important regulator of class specific da neuron dendrite morphogenesis via interactions with Rac1 and Rho1 and indicate that Trio is required as downstream effector in Cut-mediated regulation of dendrite branching and filopodia formation.

Background The epidermal growth factor receptor (EGFR) signaling pathway plays a key role in regulation of cellular growth and development. While highly studied, it is still not fully understood how the signal is orchestrated. One of the reasons for the complexity of this pathway is the extensive network of inter-connected components involved in the signaling. In the aim of identifying critical mechanisms controlling signal transduction we have performed extensive analysis of an executable model of the EGFR pathway using the stochastic pi-calculus as a modeling language. Results Our analysis, done through simulation of various perturbations, suggests that the EGFR pathway contains regions of functional redundancy in the upstream parts; in the event of low EGF stimulus or partial system failure, this redundancy helps to maintain functional robustness. Downstream parts, like the parts controlling Ras and ERK, have fewer redundancies, and more than 50% inhibition of specific reactions in those parts greatly attenuates signal response. In addition, we suggest an abstract model that captures the main control mechanisms in the pathway. Simulation of this abstract model suggests that without redundancies in the upstream modules, signal transduction through the entire pathway could be attenuated. In terms of specific control mechanisms, we have identified positive feedback loops whose role is to prolong the active state of key components (e.g., MEK-PP, Ras-GTP), and negative feedback loops that help promote signal adaptation and stabilization. Conclusions The insights gained from simulating this executable model facilitate the formulation of specific hypotheses regarding the control mechanisms of the EGFR signaling, and further substantiate the benefit to construct abstract executable models of large complex biological networks.

The BioMoby project was initiated in 2001 from within the model organism database community. It aimed to standardize methodologies to facilitate information exchange and access to analytical resources, using a consensus driven approach. Six years later, the BioMoby development community is pleased to announce the release of the 1.0 version of the interoperability framework, registry Application Programming Interface and supporting Perl and Java code-bases. Together, these provide interoperable access to over 1400 bioinformatics resources worldwide through the BioMoby platform, and this number continues to grow. Here we highlight and discuss the features of BioMoby that make it distinct from other Semantic Web Service and interoperability initiatives, and that have been instrumental to its deployment and use by a wide community of bioinformatics service providers. The standard, client software, and supporting code libraries are all freely available at http://www.biomoby.org/.

Coronary artery aneurysm associated with the Paclitaxel drug eluting stent (Taxus) is a rare complication. We describe the case of a 71-year-old female, who developed two coronary artery aneurysms in her right coronary artery associated with the insertion of a Taxus stent. Although no adverse clinical outcome resulted, this case highlights a potential problem going forward.

Cytoplasmic second messengers, Ca2+ and cAMP, regulate nerve growth cone turning responses induced by many guidance cues, but the causal relationship between these signaling pathways has been unclear. We here report that, for growth cone turning induced by a gradient of myelin-associated glycoprotein (MAG), cAMP acts by modulating MAG-induced Ca2+ signaling. Growth cone repulsion induced by MAG was accompanied by localized Ca2+ signals on the side of the growth cone facing the MAG source, due to Ca2+ release from intracellular stores. Elevating cAMP signaling activity or membrane depolarization enhanced MAG-induced Ca2+ signals and converted growth cone repulsion to attraction. Directly imposing high- or low-amplitude Ca2+ signals with an extracellular gradient of Ca2+ ionophore was sufficient to trigger either attractive or repulsive turning, respectively. Thus, distinct Ca2+ signaling, which can be modulated by cAMP, mediates the bidirectional turning responses induced by MAG.

Background: Resistance to small molecule EGFR tyrosine kinase inhibitors (TKIs) is seen in some NSCLC patients with activating EGFR mutations. We evaluated in vivo PDX models for their utility in studying EGFR-targeted drug resistance mechanisms. Methods: Surgically resected early stage NSCLC tumors were implanted into non-obese diabetic severe combined immune deficient (NOD-SCID) mice. Tumors were passaged and expanded in new mouse hosts once the humane endpoint of 1.5 cm maximum diameter was reached. EGFR TKI treatment was initiated at an average tumor volume of 150mm3. Treatments included daily oral gavage with first and second generation EGFR TKIs, and with weekly intraperitoneally administered cetuximab. Results: Of the 55 NSCLC tumors with EGFR activating mutations, only 6 engrafted (11%) and could be propagated beyond the first passage, and 4 have been studied for their responsiveness to EGFR-targeted agents. Model 148, developed from a patient who received pre-operative erlotinib, showed intrinsic pan-resistance to all EGFR-targeted therapies despite having an L858R mutation. The corresponding patient did not respond to erlotinib, relapsed after surgery and did not receive additional TKI therapy. Model 137, with an exon19 E746-A750 deletion, recapitulated the patient's response to gefitinib at relapse; this model was sensitive to first and second generation EGFR TKIs. Model 192 also has the exon19 E746-A750 deletion, however it did not recapitulate the patient's observed sensitivity to erlotinib. Selection for a MET amplified population during engraftment may be the cause for the disparate drug sensitivities. Model 164 has a double exon19 L747-T751 deletion and T790M EGFR mutation. Neither patient nor xenograft responded to erlotinib; the xenograft responded to cetuximab. Resistance developed over time to a second generation EGFR TKI; this resistant phenotype was not stable as each subsequent passage of the ‘resistant’ tumor exhibited the same initial response pattern. Two potential mechanisms for this transient sensitivity are currently being investigated: epigenetic mechanisms and intratumoural mutational heterogeneity. Conclusions: PDX models may provide important insight into biomarkers and mechanisms of resistance to targeted therapies, and provide a means to test novel treatment strategies to improve future treatment efficacies. Citation Format: Erin L. Stewart, Celine Mascaux, Shingo Shakashita, Devang Panchal, Dennis Wang, Ming Li, Nhu-An Pham, Natasha Leighl, Geoffrey Liu, Frances A. Shepherd, Ming-Sound Tsao. Modeling mechanisms of resistance of epidermal growth factor receptor (EGFR) mutations to targeted drugs through patient-derived xenografts (PDX) of non-small cell lung cancer (NSCLC). [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1184. doi:10.1158/1538-7445.AM2014-1184

The espins are actin-binding and -bundling proteins localized to parallel actin bundles. The 837-amino-acid “espin” of Sertoli cell–spermatid junctions (ectoplasmic specializations) and the 253-amino-acid “small espin” of brush border microvilli are splice isoforms that share a C-terminal 116-amino-acid actin-bundling module but contain different N termini. To investigate the roles of espin and its extended N terminus, we examined the actin-binding and -bundling properties of espin constructs and the stoichiometry and developmental accumulation of espin within the ectoplasmic specialization. An espin construct bound to F-actin with an approximately threefold higher affinity (Kd= ∼70 nM) than small espin and was ∼2.5 times more efficient at forming bundles. The increased affinity appeared to be due to an additional actin-binding site in the N terminus of espin. This additional actin-binding site bound to F-actin with a Kdof ∼1 μM, decorated actin stress fiber-like structures in transfected cells, and was mapped to a peptide between the two proline-rich peptides in the N terminus of espin. Espin was detected at ∼4–5 × 106copies per ectoplasmic specialization, or ∼1 espin per 20 actin monomers and accumulated there coincident with the formation of parallel actin bundles during spermiogenesis. These results suggest that espin is a major actin-bundling protein of the Sertoli cell–spermatid ectoplasmic specialization.

Background Neurons are one of the most structurally and functionally diverse cell types found in nature, owing in large part to their unique class specific dendritic architectures. Dendrites, being highly specialized in receiving and processing neuronal signals, play a key role in the formation of functional neural circuits. Hence, in order to understand the emergence and assembly of a complex nervous system, it is critical to understand the molecular mechanisms that direct class specific dendritogenesis. Methodology/Principal Findings We have used the Drosophila dendritic arborization (da) neurons to gain systems-level insight into dendritogenesis by a comparative study of the morphologically distinct Class-I (C-I) and Class-IV (C-IV) da neurons. We have used a combination of cell-type specific transcriptional expression profiling coupled to a targeted and systematic in vivo RNAi functional validation screen. Our comparative transcriptomic analyses have revealed a large number of differentially enriched/depleted gene-sets between C-I and C-IV neurons, including a broad range of molecular factors and biological processes such as proteolytic and metabolic pathways. Further, using this data, we have identified and validated the role of 37 transcription factors in regulating class specific dendrite development using in vivo class-specific RNAi knockdowns followed by rigorous and quantitative neurometric analysis. Conclusions/Significance This study reports the first global gene-expression profiles from purified Drosophila C-I and C-IV da neurons. We also report the first large-scale semi-automated reconstruction of over 4,900 da neurons, which were used to quantitatively validate the RNAi screen phenotypes. Overall, these analyses shed global and unbiased novel insights into the molecular differences that underlie the morphological diversity of distinct neuronal cell-types. Furthermore, our class-specific gene expression datasets should prove a valuable community resource in guiding further investigations designed to explore the molecular mechanisms underlying class specific neuronal patterning.

Background The large airways of the lungs (trachea and bronchi) are lined with a pseudostratified mucociliary epithelium, which is maintained by stem cells/progenitors within the basal cell compartment. Alterations in basal cell behavior can contribute to large airway diseases including squamous cell carcinomas (SQCCs). Basal cells have traditionally been thought of as a uniform population defined by basolateral position, cuboidal cell shape, and expression of pan-basal cell lineage markers like KRT5 and TP63. While some evidence suggests that basal cells are not all functionally equivalent, few heterogeneously expressed markers have been identified to purify and study subpopulations. In addition, few signaling pathways have been identified that regulate their cell behavior. The goals of this work were to investigate tracheal basal cell diversity and to identify new signaling pathways that regulate basal cell behavior. Methods We used flow cytometry (FACS) to profile cell surface marker expression at a single cell level in primary human tracheal basal cell cultures that maintain stem cell/progenitor activity. FACS results were validated with tissue staining, in silico comparisons with normal basal cell and lung cancer datasets, and an in vitro proliferation assay. Results We identified 105 surface markers, with 47 markers identifying potential subpopulations. These subpopulations generally fell into more (~ > 13%) or less abundant (~