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RAS mutations are the most common oncogenic drivers across human cancers, but there remains a paucity of clinically-validated pharmacological inhibitors of RAS, as druggable pockets have proven difficult to identify. Here, we identify two RAS-binding Affimer proteins, K3 and K6, that inhibit nucleotide exchange and downstream signaling pathways with distinct isoform and mutant profiles. Affimer K6 binds in the SI/SII pocket, whilst Affimer K3 is a non-covalent inhibitor of the SII region that reveals a conformer of wild-type RAS with a large, druggable SII/α3 pocket. Competitive NanoBRET between the RAS-binding Affimers and known RAS binding small-molecules demonstrates the potential to use Affimers as tools to identify pharmacophores. This work highlights the potential of using biologics with small interface surfaces to select unseen, druggable conformations in conjunction with pharmacophore identification for hard-to-drug proteins., Oncogenic RAS mutants remain difficult to target with small molecules. Here, the authors show that RAS-binding Affimer proteins inhibit RAS signaling while binding diverse regions on the RAS surface, suggesting the potential to use Affimers as tools to identify new binding pockets and pharmacophores.

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XRN1 is a highly conserved exoribonuclease which degrades uncapped RNAs in a 5′–3′ direction. Degradation of RNAs by XRN1 is important in many cellular and developmental processes and is relevant to human disease. Studies in D. melanogaster demonstrate that XRN1 can target specific RNAs, which have important consequences for developmental pathways. Osteosarcoma is a malignancy of the bone and accounts for 2% of all pediatric cancers worldwide. Five-year survival of patients has remained static since the 1970s and therefore furthering our molecular understanding of this disease is crucial. Previous work has shown a down-regulation of XRN1 in osteosarcoma cells; however, the transcripts regulated by XRN1 which might promote osteosarcoma remain elusive. Here, we confirm reduced levels of XRN1 in osteosarcoma cell lines and patient samples and identify XRN1-sensitive transcripts in human osteosarcoma cells. Using RNA-seq in XRN1-knockdown SAOS-2 cells, we show that 1178 genes are differentially regulated. Using a novel bioinformatic approach, we demonstrate that 134 transcripts show characteristics of direct post-transcriptional regulation by XRN1. Long noncoding RNAs (lncRNAs) are enriched in this group, suggesting that XRN1 normally plays an important role in controlling lncRNA expression in these cells. Among potential lncRNAs targeted by XRN1 is HOTAIR, which is known to be up-regulated in osteosarcoma and contributes to disease progression. We have also identified G-rich and GU motifs in post-transcriptionally regulated transcripts which appear to sensitize them to XRN1 degradation. Our results therefore provide significant insights into the specificity of XRN1 in human cells which are relevant to disease.

Many bacteria communicate with kin and coordinate group behaviors through a form of cell-cell signaling called acyl-homoserine lactone (AHL) quorum sensing (QS). In these systems, a signal synthase produces an AHL to which its paired receptor selectively responds. Selectivity is fundamental to cell signaling. Despite its importance, it has been challenging to determine how this selectivity is achieved and how AHL QS systems evolve and diversify. We hypothesized that we could use covariation within the protein sequences of AHL synthases and receptors to identify selectivity residues. We began by identifying about 6000 unique synthase-receptor pairs. We then used the protein sequences of these pairs to identify covariation patterns and mapped the patterns onto the LasI/R system from Pseudomonas aeruginosa PAO1. The covarying residues in both proteins cluster around the ligand-binding sites. We demonstrate that these residues are involved in system selectivity toward the cognate signal and go on to engineer the Las system to both produce and respond to an alternate AHL signal. We have thus demonstrated that covariation methods provide a powerful approach for investigating selectivity in protein-small molecule interactions and have deepened our understanding of how communication systems evolve and diversify., eLife digest Communication is vital in any community and it is no different for bacteria. Some of the microbes living in bacterial communities are closely related to one another and can help each other survive and grow. They do this by releasing chemical signals that coordinate their behaviors, including those that are damaging to the infected host. A key aspect of this coordination is knowing how many related bacteria are present in a given environment. In a process known as quorum sensing, the bacteria release a chemical signal which neighboring sibling bacteria detect and respond to. The larger the population of bacteria, the more the signal accumulates. At a certain threshold, the signal activates the genes needed to trigger a coordinated action, such as producing toxins or antibiotics. Many bacteria communicate using acylhomoserine lactone signaling systems, which involve different signals depending on the species of bacteria. But it is unclear how this diversity evolved, and how bacteria can distinguish between signals from related and unrelated bacterial cells. To understand this, Wellington Miranda et al. used computational techniques to analyze how the proteins responsible for acylhomoserine lactone signaling coevolved. The analysis identified specific parts of these proteins that determine which signal will be produced and which will trigger a bacterium into action. Wellington Miranda et al. then used these insights to engineer the bacteria Pseudomonas aeruginosa to produce and respond to a signal that is naturally made by another bacterial species. These computational methods could be used to analyze other proteins that have coevolved but do not physically interact. Within the area of quorum sensing, this approach will help to better understand the costs and benefits of signal selectivity. This may help to predict bacterial interactions and therefore behaviors during infections.

Simple Summary Many different human leukocyte antigen (HLA)-types exist across the population that each binds a specific motif of amino acids. HLA-peptide complexes are the driving force behind recognition of cancers and infected cells by cytotoxic T cells. HLA-immunopeptidomics aims to identify peptides derived from (cancer) antigens in the HLA-binding cleft with mass spectrometry (MS). Peptides eluted from HLA are analyzed by MS and translated to a protein derived amino acid sequence by specialized software. These software packages use statistical thresholds to limit false discoveries and return only the most confidently identified peptides. However, we believe, as do others, that many useful peptides can still be found in the excluded pool of peptides. This idea drove the development of specialized algorithms that utilize HLA specific motifs to retrieve additional relevant peptides. It is unknown, however, how many peptides could potentially be found in this pool. By adjusting the statistical threshold, we empirically demonstrate the vastness of valuable data beyond the traditional thresholds that await to be discovered. Abstract Immunopeptidomics is used to identify novel epitopes for (therapeutic) vaccination strategies in cancer and infectious disease. Various false discovery rates (FDRs) are applied in the field when converting liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectra to peptides. Subsequently, large efforts have recently been made to rescue peptides of lower confidence. However, it remains unclear what the overall relation is between the FDR threshold and the percentage of obtained HLA-binders. We here directly evaluated the effect of varying FDR thresholds on the resulting immunopeptidomes of HLA-eluates from human cancer cell lines and primary hepatocyte isolates using HLA-binding algorithms. Additional peptides obtained using less stringent FDR-thresholds, although generally derived from poorer spectra, still contained a high amount of HLA-binders and confirmed recently developed tools that tap into this pool of otherwise ignored peptides. Most of these peptides were identified with improved confidence when cell input was increased, supporting the validity and potential of these identifications. Altogether, our data suggest that increasing the FDR threshold for peptide identification in conjunction with data filtering by HLA-binding prediction, is a valid and highly potent method to more efficient exhaustion of immunopeptidome datasets for epitope discovery and reveals the extent of peptides to be rescued by recently developed algorithms.

Currently there is no secured ongoing funding in Australia for next generation sequencing (NGS) such as exome sequencing (ES) for adult neurological disorders. Studies have focused on paediatric populations in research or highly specialised settings, utilised standard NGS pipelines focusing only on small insertions, deletions and single nucleotide variants, and not explored impacts on management in detail. This prospective multi-site study performed ES and an extended bioinformatics repeat expansion analysis pipeline, on patients with broad phenotypes (ataxia, dementia, dystonia, spastic paraparesis, motor neuron disease, Parkinson's disease and complex/not-otherwise-specified), with symptom onset between 2 and 60 years. Genomic data analysis was phenotype-driven, using virtual gene panels, reported according to American College of Medical Genetics and Genomics guidelines. One-hundred-and-sixty patients (51% female) were included, median age 52 years (range 14-79) and median 9 years of symptoms. 34/160 (21%) patients received a genetic diagnosis. Highest diagnostic rates were in spastic paraparesis (10/25, 40%), complex/not-otherwise-specified (10/38, 26%) and ataxia (7/28, 25%) groups. Findings were considered 'possible/uncertain' in 21/160 patients. Repeat expansion detection identified an unexpected diagnosis of Huntington disease in an ataxic patient with negative ES. Impacts on management, such as more precise and tailored care, were seen in most diagnosed patients (23/34, 68%). ES and a novel bioinformatics analysis pipepline had a substantial diagnostic yield (21%) and management impacts for most diagnosed patients, in heterogeneous, complex, mainly adult-onset neurological disorders in real-world settings in Australia, providing evidence for NGS and complementary multiple, new technologies as valuable diagnostic tools.

Agrobacterium-mediated transformation of canola (Brassica napus) via hypocotyl segments has been a commonly used method for the past 30 years. While the hypocotyl-based method is well-established, it is not readily adapted to elite germplasm and the prolonged process is not ideal for a production transformation setting. We developed an Agrobacterium-mediated transformation method based on epicotyl and higher stem (internodal) segments that is efficient, rapid and amenable for high-throughput transformation and genome editing. The method has been successfully implemented in multiple canola genotypes. The method appears to be genotype-independent, with varying transformation efficiencies. Internodal segment transformation was used to generate transgenic events as well as CRISPR-Cas9-mediated frameshift gene knockouts.

Summary Expansion of pulmonary neuroendocrine cells (PNECs) is a pathological feature of many human lung diseases. Human PNECs are inherently difficult to study due to their rarity (, Graphical Abstract, Highlights • PNECs can be efficiently generated from induced pluripotent stem cells (iPSCs) • Induced PNECs express key PNEC markers and express and secrete all major neuropeptides • Induced PNECs resemble the transcriptomic profile of human primary fetal PNECs • Basal cells are able to differentiate into PNECs, Cell Biology; Stem Cells Research; Transcriptomics

Genetic sequencing, or DNA sequencing, using the Sanger technique has become widely used in the veterinary diagnostic community. This technology plays a role in verification of PCR results and is used to provide the genetic sequence data needed for phylogenetic analysis, epidemiologic studies, and forensic investigations. The Laboratory Technology Committee of the American Association of Veterinary Laboratory Diagnosticians has prepared guidelines for sample preparation, submission to sequencing facilities or instrumentation, quality assessment of nucleic acid sequence data performed, and for generating basic sequencing data and phylogenetic analysis for diagnostic applications. This guidance is aimed at assisting laboratories in providing consistent, high-quality, and reliable sequence data when using Sanger-based genetic sequencing as a component of their laboratory services.

After radiation exposure, one of the critical processes for cellular survival is the repair of DNA double strand breaks. The pathways involved in this response are complex in nature and involve many individual steps that act across different time scales, all of which combine to produce an overall behaviour. It is therefore experimentally challenging to unambiguously determine the mechanisms involved and how they interact whilst maintaining strict control of all confounding variables. In silico methods can provide further insight into results produced by focused experimental investigations through testing of the hypotheses generated. Such computational testing can asses competing hypotheses by investigating their effects across all time scales concurrently, highlighting areas where further experimental work can have the most significance. We describe the construction of a mechanistic model by combination of several hypothesised mechanisms reported in the literature and supported by experiment. Compatibility of these mechanisms was tested by fitting simulation to results reported in the literature. To avoid over-fitting, we used an approach of sequentially testing individual mechanisms within this pathway. We demonstrate that using this approach the model is capable of reproducing published protein kinetics and overall repair trends. This provides evidence supporting the feasibility of the proposed mechanisms and revealed how they interact to produce an overall behaviour. Furthermore, we show that the assumed motion of individual double strand break ends plays a crucial role in determining overall system behaviour.

Relatedness inference is an essential component of many genetic analyses and popular in consumer genetic testing. Ramstetter et al. evaluate twelve....., Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these approaches in real data has been lacking. Here, we report an assessment of 12 state-of-the-art pairwise relatedness inference methods using a data set with 2485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (92–99%) when detecting first- and second-degree relationships, but their accuracy dwindles to 76% of relative pairs. Overall, the most accurate methods are Estimation of Recent Shared Ancestry (ERSA) and approaches that compute total IBD sharing using the output from GERMLINE and Refined IBD to infer relatedness. Combining information from the most accurate methods provides little accuracy improvement, indicating that novel approaches, such as new methods that leverage relatedness signals from multiple samples, are needed to achieve a sizeable jump in performance.

Colorectal cancer is a major contributor to death and disease worldwide. The ApcMin mouse is a widely used model of intestinal neoplasia, as it carries a mutation also found in human colorectal cancers. However, the method most commonly used to quantify tumour burden in these mice is manual adenoma counting, which is time consuming and poorly suited to standardization across different laboratories. We describe a method to produce suitable photographs of the small intestine of ApcMin mice, process them with an ImageJ macro, FeatureCounter, which automatically locates image features potentially corresponding to adenomas, and a machine learning pipeline to identify and quantify them. Compared to a manual method, the specificity (or True Negative Rate, TNR) and sensitivity (or True Positive Rate, TPR) of this method in detecting adenomas are similarly high at about 80% and 87%, respectively. Importantly, total adenoma area measures derived from the automatically-called tumours were just as capable of distinguishing high-burden from low-burden mice as those established manually. Overall, our strategy is quicker, helps control experimenter bias, and yields a greater wealth of information about each tumour, thus providing a convenient route to getting consistent and reliable results from a study.

Mechanistic models are critical for our understanding of both within-host dynamics (i.e., pathogen population growth and immune system processes) and among-host dynamics (i.e., transmission). Rarely, however, have within-host models been synthesized with data to infer processes, validate hypotheses, or generate new theories. In this study we use mechanistic models and empirical, time-series data of viral titer to better understand the growth of ranaviruses within their amphibian hosts and the immune dynamics that limit viral replication. Specifically, we fit a suite of potential models to our data, where each model represents a hypothesis about the interactions between viral growth and immune defense. Through formal model comparison, we find a parsimonious model that captures key features of our time-series data: the viral titer rises and falls through time, likely due to an immune system response, and that the initial viral dosage affects both the peak viral titer and the timing of the peak. Importantly, our model makes several predictions, including the existence of long-term viral infections, that can be validated in future studies.

Background Multiple genes can contribute to disease risk. Common pathways can be identified among risk genes with gene-set analysis, which measures association between a set of pathway-related genes and the disorder of interest. Pathway-related genes, however, are often broadly defined having differential contributions to diverse pathway functions. Mathematical models of biology can predict the relative contribution of a gene to a specific function of a pathway. Our goal was to use model predictions to strengthen gene to disorder connections by identifying a specific function common among risk genes. Results We present a method (GEMB) to enrich gene-set analyses with models from mathematical biology. The method combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We use the test to examine the hypothesis that intracellular calcium ion concentrations contribute to bipolar disorder, using publicly-available summary data from the Psychiatric Genetics Consortium (n=41,653; ~9 million SNPs). Conclusions With our method, we can strengthen inferences from a P-value of 0.081 to 1.7×10−4 by moving from a general calcium signaling pathway to a specific model-predicted function. This dramatic difference demonstrates that incorporating math biology into gene-set analysis can strengthen gene to disease connections.

The ability to rapidly assemble and prototype cellular circuits is vital for biological research and its applications in biotechnology and medicine. Current methods that permit the assembly of DNA circuits in mammalian cells are laborious, slow, expensive and mostly not permissive of rapid prototyping of constructs. Here we present the Mammalian ToolKit (MTK), a Golden Gate-based cloning toolkit for fast, reproducible and versatile assembly of large DNA vectors and their implementation in mammalian models. The MTK consists of a curated library of characterized, modular parts that can be easily mixed and matched to combinatorially assemble one transcriptional unit with different characteristics, or a hierarchy of transcriptional units weaved into complex circuits. MTK renders many cell engineering operations facile, as showcased by our ability to use the toolkit to generate single-integration landing pads, to create and deliver libraries of protein variants and sgRNAs, and to iterate through Cas9-based prototype circuits. As a biological proof of concept, we used the MTK to successfully design and rapidly construct in mammalian cells a challenging multicistronic circuit encoding the Ebola virus (EBOV) replication complex. This construct provides a non-infectious biosafety level 2 (BSL2) cellular assay for exploring the transcription and replication steps of the EBOV viral life cycle in its host. Its construction also demonstrates how the MTK can enable important and time sensitive applications such as the rapid testing of pharmacological inhibitors of emerging BSL4 viruses that pose a major threat to human health.

Pseudoenzymes generally lack detectable catalytic activity despite adopting the overall protein fold of their catalytically competent counterparts, indeed ‘pseudo’ family members seem to be incorporated in all enzyme classes. The small GTPase enzymes are important signaling proteins, and recent studies have identified many new family members with noncanonical residues within the catalytic cleft, termed pseudoGTPases. To illustrate recent discoveries in the field, we use the p190RhoGAP proteins as an example. p190RhoGAP proteins (ARHGAP5 and ARHGAP35) are the most abundant GTPase activating proteins for the Rho family of small GTPases. These are key regulators of Rho signaling in processes such as cell migration, adhesion and cytokinesis. Structural biology has complemented and guided biochemical analyses for these proteins and has allowed discovery of two cryptic pseudoGTPase domains, and the re-classification of a third, previously identified, GTPase-fold domain as a pseudoGTPase. The three domains within p190RhoGAP proteins illustrate the diversity of this rapidly expanding pseudoGTPase group.

Inferring relatedness from genomic data is an essential component of genetic association studies, population genetics, forensics, and genealogy. While numerous methods exist for inferring relatedness, thorough evaluation of these methods in real data has been lacking. Here, we report an assessment of 11 state-ofthe-art relatedness inference methods using a dataset with 2,485 individuals contained in several large pedigrees that span up to six generations. We find that all methods have high accuracy (∼93%−99%) when reporting first and second degree relationships, but their accuracy dwindles to less than 60% for fifth degree relationships. However, the inferred relationships were correct to within one relatedness degree at a rate of 83%−99% across all methods and considered relationship degrees. Furthermore, most methods infer unrelated individuals correctly at a rate of ∼99%, suggesting a low rate of false positives. Overall, the most accurate methods were ERSA 2.0 and approaches that classify relationships using the IBD segments inferred by Refined IBD and IBDseq. Combining results from the most accurate methods provides little accuracy improvement, indicating that novel approaches for relatedness inference may be needed to achieve a sizeable jump in performance.

The Influenza Research Database (IRD) is a U.S. National Institute of Allergy and Infectious Diseases (NIAID)-sponsored Bioinformatics Resource Center dedicated to providing bioinformatics support for influenza virus research. IRD facilitates the research and development of vaccines, diagnostics and therapeutics against influenza virus by providing a comprehensive collection of influenza-related data integrated from various sources, a growing suite of analysis and visualization tools for data mining and hypothesis generation, personal workbench spaces for data storage and sharing, and active user community support. Here, we describe the recent improvements in IRD including the use of cloud and high performance computing resources, analysis and visualization of user-provided sequence data with associated metadata, predictions of novel variant proteins, annotations of phenotype-associated sequence markers and their predicted phenotypic effects, hemagglutinin (HA) clade classifications, an automated tool for HA subtype numbering conversion, linkouts to disease event data and the addition of host factor and antiviral drug components. All data and tools are freely available without restriction from the IRD website at https://www.fludb.org.

Prospects have never seemed better for a truly global approach to science to improve human health, with leaders of national initiatives laying out their vision of a worldwide network of related projects. An extensive literature addresses obstacles to global genomic data sharing, yet a series of public polls suggests that the scientific community may be overlooking a significant barrier: potential public resistance to data sharing across national borders. In several large United States surveys, university researchers in other countries were deemed the least acceptable group of data users, and a just-completed US survey found a marked increase in privacy and security concerns related to data access by non-US researchers. Furthermore, diminished support for sharing beyond national borders is not unique to the US, although the limited data from outside the US suggest variation across countries as well as demographic groups. Possible sources of resistance include apprehension about privacy and security protections. Strategies for building public support include making the affirmative case for global data sharing, addressing privacy, security, and other legitimate concerns, and investigating public concerns in greater depth.

Zinc-finger nucleases (ZFNs) have been used for genome engineering in a wide variety of organisms; however, it remains challenging to design effective ZFNs for many genomic sequences using publicly available zinc-finger modules. This limitation is in part because of potential finger-finger incompatibility generated on assembly of modules into zinc-finger arrays (ZFAs). Herein, we describe the validation of a new set of two-finger modules that can be used for building ZFAs via conventional assembly methods or a new strategy-finger stitching-that increases the diversity of genomic sequences targetable by ZFNs. Instead of assembling ZFAs based on units of the zinc-finger structural domain, our finger stitching method uses units that span the finger-finger interface to ensure compatibility of neighbouring recognition helices. We tested this approach by generating and characterizing eight ZFAs, and we found their DNA-binding specificities reflected the specificities of the component modules used in their construction. Four pairs of ZFNs incorporating these ZFAs generated targeted lesions in vivo, demonstrating that stitching yields ZFAs with robust recognition properties.

Dendritic cell (DC) maturation involves widespread changes in cellular function and gene expression. The regulatory role of IFNAR in the program of DC maturation remains incompletely defined. Thus, the time evolution impact of IFNAR on this process was evaluated. Changes in DC phenotype, function, and gene expression induced by poly I:C were measured in wild-type and IFNAR(-/-) DC at 9 time points over 24 h. Temporal gene expression profiles were filtered on consistency and response magnitude across replicates. The number of genes whose expression was altered by poly I:C treatment was greatly reduced in IFNAR(-/-) DC, including the majority of the downregulated gene expression program previously observed in wild-type (WT) DC. Furthermore, the number of genes upregulated was almost equal between WT and IFNAR(-/-) DC, yet the identities of those genes were distinct. Integrating these data with protein-protein interaction data revealed several novel subnetworks active during maturation, including nucleotide synthesis, metabolism, and repair. A subnetwork associated with redox activity was uniquely identified in IFNAR(-/-) DC. Overall, temporal gene expression and network analyses identified many genes regulated by the type I interferon response and revealed previously unidentified aspects of the DC maturation process.

Socioeconomic status (SES) is a fundamental contributor to health, and a key factor underlying racial disparities in disease. However, SES data are rarely included in genetic studies due in part to the difficultly of collecting these data when studies were not originally designed for that purpose. The emergence of large clinic-based biobanks linked to electronic health records (EHRs) provides research access to large patient populations with longitudinal phenotype data captured in structured fields as billing codes, procedure codes, and prescriptions. SES data however, are often not explicitly recorded in structured fields, but rather recorded in the free text of clinical notes and communications. The content and completeness of these data vary widely by practitioner. To enable gene-environment studies that consider SES as an exposure, we sought to extract SES variables from racial/ethnic minority adult patients (n=9,977) in BioVU, the Vanderbilt University Medical Center biorepository linked to de-identified EHRs. We developed several measures of SES using information available within the de-identified EHR, including broad categories of occupation, education, insurance status, and homelessness. Two hundred patients were randomly selected for manual review to develop a set of seven algorithms for extracting SES information from de-identified EHRs. The algorithms consist of 15 categories of information, with 830 unique search terms. SES data extracted from manual review of 50 randomly selected records were compared to data produced by the algorithm, resulting in positive predictive values of 80.0% (education), 85.4% (occupation), 87.5% (unemployment), 63.6% (retirement), 23.1% (uninsured), 81.8% (Medicaid), and 33.3% (homelessness), suggesting some categories of SES data are easier to extract in this EHR than others. The SES data extraction approach developed here will enable future EHR-based genetic studies to integrate SES information into statistical analyses. Ultimately, incorporation of measures of SES into genetic studies will help elucidate the impact of the social environment on disease risk and outcomes.

A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies.

Digital PCR enables the absolute quantitation of nucleic acids in a sample. The lack of scalable and practical technologies for digital PCR implementation has hampered the widespread adoption of this inherently powerful technique. Here we describe a high-throughput droplet digital PCR (ddPCR) system that enables processing of ~2 million PCR reactions using conventional TaqMan assays with a 96-well plate workflow. Three applications demonstrate that the massive partitioning afforded by our ddPCR system provides orders of magnitude more precision and sensitivity than real-time PCR. First, we show the accurate measurement of germline copy number variation. Second, for rare alleles, we show sensitive detection of mutant DNA in a 100,000-fold excess of wildtype background. Third, we demonstrate absolute quantitation of circulating fetal and maternal DNA from cell-free plasma. We anticipate this ddPCR system will allow researchers to explore complex genetic landscapes, discover and validate new disease associations, and define a new era of molecular diagnostics.

Background Social insects exhibit striking phenotypic plasticity in the form of distinct reproductive (queen) and non-reproductive (worker) castes, which are typically driven by differences in the environment during early development. Nutritional environment and nourishment during development has been shown to be broadly associated with caste determination across social insect taxa such as bees, wasps, and termites. In primitively social insects such as Polistes paper wasps, caste remains flexible throughout adulthood, but there is evidence that nourishment inequalities can bias caste development with workers receiving limited nourishment compared to queens. Dominance and vibrational signaling are behaviors that have also been linked to caste differences in paper wasps, suggesting that a combination of nourishment and social factors may drive caste determination. To better understand the molecular basis of nutritional effects on caste determination, we used RNA-sequencing to investigate the gene expression changes in response to proteinaceous nourishment deprivation in Polistes metricus larvae. Results We identified 285 nourishment-responsive transcripts, many of which are related to lipid metabolism and oxidation-reduction activity. Via comparisons to previously identified caste-related genes, we found that nourishment restriction only partially biased wasp gene expression patterns toward worker caste-like traits, which supports the notion that nourishment, in conjunction with social environment, is a determinant of developmental caste bias. In addition, we conducted cross-species comparisons of nourishment-responsive genes, and uncovered largely lineage-specific gene expression changes, suggesting few shared nourishment-responsive genes across taxa. Conclusion Overall, the results from this study highlight the complex and multifactorial nature of environmental effects on the gene expression patterns underlying plastic phenotypes. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1410-y) contains supplementary material, which is available to authorized users.

Hapi is a new dynamic programming algorithm that ignores uninformative states and state transitions in order to efficiently compute minimum-recombinant and maximum likelihood haplotypes. When applied to a dataset containing 103 families, Hapi performs 3.8 and 320 times faster than state-of-the-art algorithms. Because Hapi infers both minimum-recombinant and maximum likelihood haplotypes and applies to related individuals, the haplotypes it infers are highly accurate over extended genomic distances., National Institutes of Health (U.S.) (NIH grant 5-T90-DK070069), National Institutes of Health (U.S.) (Grant 5-P01-NS055923), National Science Foundation (U.S.) (Graduate Research Fellowship)

The ability to correlate single-cell genetic information to cellular phenotypes will provide the kind of detailed insight into human physiology and disease pathways that is not possible to infer from bulk cell analysis. Microfluidic technologies are attractive for single-cell manipulation due to precise handling and low risk of contamination. Additionally, microfluidic single-cell techniques can allow for high-throughput and detailed genetic analyses that increase accuracy and decreases reagent cost compared to bulk techniques. Incorporating these microfluidic platforms into research and clinical laboratory workflows can fill an unmet need in biology, delivering the highly accurate, highly informative data necessary to develop new therapies and monitor patient outcomes. In this perspective, we describe the current and potential future uses of microfluidics at all stages of single-cell genetic analysis, including cell enrichment and capture, single-cell compartmentalization and manipulation, and detection and analyses.

Annotating and interpreting the results of genome-wide association studies (GWAS) remains challenging. Assigning function to genetic variants as expression quantitative trait loci is an expanding and useful approach, but focuses exclusively on mRNA rather than protein levels. Many variants remain without annotation. To address this problem, we measured the steady state abundance of 441 human signaling and transcription factor proteins from 68 Yoruba HapMap lymphoblastoid cell lines to identify novel relationships between inter-individual protein levels, genetic variants, and sensitivity to chemotherapeutic agents. Proteins were measured using micro-western and reverse phase protein arrays from three independent cell line thaws to permit mixed effect modeling of protein biological replicates. We observed enrichment of protein quantitative trait loci (pQTLs) for cellular sensitivity to two commonly used chemotherapeutics: cisplatin and paclitaxel. We functionally validated the target protein of a genome-wide significant trans-pQTL for its relevance in paclitaxel-induced apoptosis. GWAS overlap results of drug-induced apoptosis and cytotoxicity for paclitaxel and cisplatin revealed unique SNPs associated with the pharmacologic traits (at p, Author Summary The central dogma of biology explains that DNA is transcribed to mRNA that is further translated into protein. Many genome-wide studies have implicated genetic variation that influences gene expression and that ultimately affect downstream complex traits including response to drugs. However, because of technical limitations, few studies have evaluated the contribution of genetic variation on protein expression and ensuing effects on downstream phenotypes. To overcome this challenge, we used a novel technology to simultaneously measure the baseline expression of 441 proteins in lymphoblastoid cell lines and compared them with publicly available genetic data. To further illustrate the utility of this approach, we compared protein-level measurements with chemotherapeutic induced apoptosis and cell-growth inhibition data. This study demonstrates the importance of using protein information to understand the functional consequences of genetic variants identified in genome-wide association studies. This protein data set will also have broad utility for understanding the relationship between other genome-wide studies of complex traits.

International audience; Respiratory infections stemming from influenza viruses and the Severe Acute Respiratory Syndrome corona virus (SARS-CoV) represent a serious public health threat as emerging pandemics. Despite efforts to identify the critical interactions of these viruses with host machinery, the key regulatory events that lead to disease pathology remain poorly targeted with therapeutics. Here we implement an integrated network interrogation approach, in which proteome and transcriptome datasets from infection of both viruses in human lung epithelial cells are utilized to predict regulatory genes involved in the host response. We take advantage of a novel ``crowd-based'' approach to identify and combine ranking metrics that isolate genes/proteins likely related to the pathogenicity of SARS-CoV and influenza virus. Subsequently, a multivariate regression model is used to compare predicted lung epithelial regulatory influences with data derived from other respiratory virus infection models. We predicted a small set of regulatory factors with conserved behavior for consideration as important components of viral pathogenesis that might also serve as therapeutic targets for intervention. Our results demonstrate the utility of integrating diverse `omic datasets to predict and prioritize regulatory features conserved across multiple pathogen infection models.

Lymphoblastoid cell lines (LCLs) are widely used in pharmacogenomic studies and the applicability of LCLs for various clinical phenotypes is emerging. Early studies have yielded promising results for LCLs as a proxy for genetic variants in treatment outcome for a number of cancers, as well as toxicity in varying tissue types including taxane-induced neuropathy. Although LCLs have demonstrated utility in the elucidation of functional mechanisms for results of clinical genotype–drug phenotype studies, there are more relevant cell-based models developing. Utility of LCLs as a resource in pharmacogenomic discovery

Physicochemical properties of DNA, such as shape, affect protein-DNA recognition. However, the properties of DNA that are most relevant for predicting the binding sites of particular transcription factors (TFs) or classes of TFs have yet to be fully understood. Here, using a model that accurately captures the melting behavior and breathing dynamics (spontaneous local openings of the double helix) of double-stranded DNA, we simulated the dynamics of known binding sites of the TF and nucleoid-associated protein Fis in Escherichia coli. Our study involves simulations of breathing dynamics, analysis of large published in vitro and genomic datasets, and targeted experimental tests of our predictions. Our simulation results and available in vitro binding data indicate a strong correlation between DNA breathing dynamics and Fis binding. Indeed, we can define an average DNA breathing profile that is characteristic of Fis binding sites. This profile is significantly enriched among the identified in vivo E. coli Fis binding sites. To test our understanding of how Fis binding is influenced by DNA breathing dynamics, we designed base-pair substitutions, mismatch, and methylation modifications of DNA regions that are known to interact (or not interact) with Fis. The goal in each case was to make the local DNA breathing dynamics either closer to or farther from the breathing profile characteristic of a strong Fis binding site. For the modified DNA segments, we found that Fis-DNA binding, as assessed by gel-shift assay, changed in accordance with our expectations. We conclude that Fis binding is associated with DNA breathing dynamics, which in turn may be regulated by various nucleotide modifications., Author Summary Cellular transcription factors (TFs) are proteins that regulate gene expression, and thereby cellular activity and fate, by binding to specific DNA segments. The physicochemical determinants of protein-DNA binding specificity are not completely understood. Here, we report that the propensity of transient opening and re-closing of the double helix, resulting from thermal fluctuations, aka “DNA breathing” or “DNA bubbles,” can be associated with binding affinity in the case of Fis, a well-studied nucleoid-associated protein in Escherichia coli. We found that a particular breathing profile is characteristic of high-affinity Fis binding sites and that DNA fragments known to bind Fis in vivo are statistically enriched for this profile. Furthermore, we used simulations of DNA breathing dynamics to guide design of gel-shift experiments aimed at testing the idea that local breathing influences Fis binding. As a result, we show that via nucleotide modifications but without modifying nucleotides that directly contact Fis, we were able to transform a low-affinity Fis binding site into a high-affinity site and vice versa. The nucleotide modifications were designed only based on DNA breathing simulations. Our study suggests that strong Fis-DNA binding depends on DNA breathing - a novel physicochemical characteristic that could be used for prediction and rational design of TF binding sites.

A fundamental problem in meta-analysis is how to systematically combine information from multiple statistical tests to rigorously evaluate a single overarching hypothesis. This problem occurs in systems biology when attempting to map genomic attributes to complex phenotypes such as behavior. Behavior and other complex phenotypes are influenced by intrinsic and environmental determinants that act on the transcriptome, but little is known about how these determinants interact at the molecular level. We developed an informatic technique that identifies statistically significant meta-associations between gene expression patterns and transcription factor combinations. Deploying this technique for brain transcriptome profiles from ca. 400 individual bees, we show that diverse determinants of behavior rely on shared combinations of transcription factors. These relationships were revealed only when we considered complex and variable regulatory rules, suggesting that these shared transcription factors are used in distinct ways by different determinants. This regulatory code would have been missed by traditional gene coexpression or cis -regulatory analytic methods. We expect that our meta-analysis tools will be useful for a broad array of problems in systems biology and other fields.

Zinc-finger nucleases (ZFNs) allow targeted gene inactivation in a wide range of model organisms. However, construction of target-specific ZFNs is technically challenging. Here, we evaluate a straightforward modular assembly-based approach for ZFN construction and gene inactivation in zebrafish. From an archive of 27 different zinc-finger modules, we assembled more than 70 different zinc-finger cassettes and evaluated their specificity using a bacterial one-hybrid assay. In parallel, we constructed ZFNs from these cassettes and tested their ability to induce lesions in zebrafish embryos. We found that the majority of zinc-finger proteins assembled from these modules have favorable specificities and nearly one-third of modular ZFNs generated lesions at their targets in the zebrafish genome. To facilitate the application of ZFNs within the zebrafish community we constructed a public database of sites in the zebrafish genome that can be targeted using this archive. Importantly, we generated new germline mutations in eight different genes, confirming that this is a viable platform for heritable gene inactivation in vertebrates. Characterization of one of these mutants, gata2a, revealed an unexpected role for this transcription factor in vascular development. This work provides a resource to allow targeted germline gene inactivation in zebrafish and highlights the benefit of a definitive reverse genetic strategy to reveal gene function.

Choline kinase-alpha expression and activity are increased in multiple human neoplasms as a result of growth factor stimulation and activation of cancer-related signaling pathways. The product of choline kinase-alpha, phosphocholine, serves as an essential metabolic reservoir for the production of phosphatidylcholine, the major phospholipid constituent of membranes and substrate for the production of lipid second messengers. Using in silico screening for small molecules that may interact with the choline kinase-alpha substrate binding domain, we identified a novel competitive inhibitor, N-(3,5-dimethylphenyl)-2-[[5-(4-ethylphenyl)-1H-1,2,4-triazol-3-yl] sulfanyl] acetamide (termed CK37) that inhibited purified recombinant human choline kinase-alpha activity, reduced the steady-state concentration of phosphocholine in transformed cells, and selectively suppressed the growth of neoplastic cells relative to normal epithelial cells. Choline kinase-alpha activity is required for the downstream production of phosphatidic acid, a promoter of several Ras signaling pathways. CK37 suppressed mitogen-activated protein kinase and phosphatidylinositol 3-kinase/AKT signaling, disrupted actin cytoskeletal organization, and reduced plasma membrane ruffling. Finally, administration of CK37 significantly decreased tumor growth in a lung tumor xenograft mouse model, suppressed tumor phosphocholine, and diminished activating phosphorylations of extracellular signal-regulated kinase and AKT in vivo. Together, these results further validate choline kinase-alpha as a molecular target for the development of agents that interrupt Ras signaling pathways, and indicate that receptor-based computational screening should facilitate the identification of new classes of choline kinase-alpha inhibitors. Brown Foundation NCCRR (1P20 RR18733) James Graham Brown Cancer Center Ky Lung Cancer Research Program United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Cancer Institute (NCI) (2R56CA116428-0509), (R01CA116428), (R56CA116428) United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Center for Research Resources (NCRR) (P20RR018733) Kansas Technology Enterprise Corporation National Science Foundation’s EPSCoR Program (EPS-0236913) State of Kansas

Summary Genomic structural variants (SVs) are abundant in humans, differing from other variation classes in extent, origin, and functional impact. Despite progress in SV characterization, the nucleotide resolution architecture of most SVs remains unknown. We constructed a map of unbalanced SVs (i.e., copy number variants) based on whole genome DNA sequencing data from 185 human genomes, integrating evidence from complementary SV discovery approaches with extensive experimental validations. Our map encompassed 22,025 deletions and 6,000 additional SVs, including insertions and tandem duplications. Most SVs (53%) were mapped to nucleotide resolution, which facilitated analyzing their origin and functional impact. We examined numerous whole and partial gene deletions with a genotyping approach and observed a depletion of gene disruptions amongst high frequency deletions. Furthermore, we observed differences in the size spectra of SVs originating from distinct formation mechanisms, and constructed a map constructed a map of SV hotspots formed by common mechanisms. Our analytical framework and SV map serves as a resource for sequencing-based association studies.

April 1, 2011, The 1000 Genomes Project aims to provide a deep characterization of human genome sequence variation as a foundation for investigating the relationship between genotype and phenotype. Here we present results of the pilot phase of the project, designed to develop and compare different strategies for genome-wide sequencing with high-throughput platforms. We undertook three projects: low-coverage whole-genome sequencing of 179 individuals from four populations; high-coverage sequencing of two mother–father–child trios; and exon-targeted sequencing of 697 individuals from seven populations. We describe the location, allele frequency and local haplotype structure of approximately 15 million single nucleotide polymorphisms, 1 million short insertions and deletions, and 20,000 structural variants, most of which were previously undescribed. We show that, because we have catalogued the vast majority of common variation, over 95% of the currently accessible variants found in any individual are present in this data set. On average, each person is found to carry approximately 250 to 300 loss-of-function variants in annotated genes and 50 to 100 variants previously implicated in inherited disorders. We demonstrate how these results can be used to inform association and functional studies. From the two trios, we directly estimate the rate of de novo germline base substitution mutations to be approximately 10[superscript −8] per base pair per generation. We explore the data with regard to signatures of natural selection, and identify a marked reduction of genetic variation in the neighbourhood of genes, due to selection at linked sites. These methods and public data will support the next phase of human genetic research., Ministry of Science and Technology of the People's Republic of China (973 program no. 2011CB809200), National Natural Science Foundation (China) (30725008), National Natural Science Foundation (China) (30890032), National Natural Science Foundation (China) (30811130531), National Natural Science Foundation (China) (30221004), China (Chinese 863 program 2006AA02Z177), China (Chinese 863 program 2006AA02Z334), China (Chinese 863 program 2006AA02A302), China (Chinese 863 program 2009AA022707), National Institutes of Health (U.S.) (Grant U54HG2750), National Institutes of Health (U.S.) (Grant U01HG5208), National Institutes of Health (U.S.) (Grant U54HG3067)

The main function of CAs (carbonic anhydrases) is to participate in the regulation of acid–base balance. Although 12 active isoenzymes of this family had already been described, analyses of genomic databases suggested that there still exists another isoenzyme, CA XV. Sequence analyses were performed to identify those species that are likely to have an active form of this enzyme. Eight species had genomic sequences encoding CA XV, in which all the amino acid residues critical for CA activity are present. However, based on the sequence data, it was apparent that CA XV has become a non-processed pseudogene in humans and chimpanzees. RT-PCR (reverse transcriptase PCR) confirmed that humans do not express CA XV. In contrast, RT-PCR and in situ hybridization performed in mice showed positive expression in the kidney, brain and testis. A prediction of the mouse CA XV structure was performed. Phylogenetic analysis showed that mouse CA XV is related to CA IV. Therefore both of these enzymes were expressed in COS-7 cells and studied in parallel experiments. The results showed that CA XV shares several properties with CA IV, i.e. it is a glycosylated glycosylphosphatidylinositol-anchored membrane protein, and it binds CA inhibitor. The catalytic activity of CA XV is low, and the correct formation of disulphide bridges is important for the activity. Both specific and non-specific chaperones increase the production of active enzyme. The results suggest that CA XV is the first member of the α-CA gene family that is expressed in several species, but not in humans and chimpanzees.