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Tumor cell lines are widely used for cancer research, but challenges regarding quality control of cell line identity, cross contamination, and tumor somatic molecular stability remain, demanding novel approaches beyond conventional short tandem repeat profiling. A total of 21 commonly used multiple myeloma cell lines obtained from public repositories were analyzed by digital multiplex ligation-dependent probe amplification (digitalMLPA) to characterize germline single-nucleotide polymorphisms, insertions/deletions, and somatic copy number aberrations (CNAs). Using generated profiles and an in-house developed analytical pipeline, blinded experiments were performed to determine capability of digitalMLPA to predict cell line identity and potential spike-in DNA contamination in 41 anonymized cell line samples. The dominant cell line was correctly identified in all cases, and cross contamination was correctly detected in 33 of 37 samples with spike-in DNA; there were no false-positive predictions. The four samples in which spike in was not detected all carried low levels of contamination (1%), whereas levels of contamination ≥5% were correctly identified in all cases. Unsupervised clustering of CNA profiles identified shared commonalities that correlated with initiating Ig heavy locus translocation events. Longitudinal CNA assessment of nine cell lines revealed changes under standard culturing conditions not detected by insertion/deletion profiling alone. Results suggest that digitalMLPA can be utilized as a high-throughput tool for advanced quality assurance for in vitro cancer research.

Lack of data provenance negatively impacts scientific reproducibility and the reliability of genomic data. The ATCC Genome Portal ( https://genomes.atcc.org ) addresses this by providing data provenance information for microbial whole-genome assemblies originating from authenticated biological materials. To date, we have sequenced 1,579 complete genomes, including 466 type strains and 1,156 novel genomes.

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.

Antibodies are indispensable tools used for a large number of applications in both foundational and translational bioscience research; however, there are drawbacks to using traditional antibodies generated in animals. These include a lack of standardization leading to problems with reproducibility, high costs of antibodies purchased from commercial sources, and ethical concerns regarding the large number of animals used to generate antibodies. To address these issues, we have developed practical methodologies and tools for generating low-cost, high-yield preparations of recombinant monoclonal antibodies and antibody fragments directed to protein epitopes from primary sequences. We describe these methods here, as well as approaches to diversify monoclonal antibodies, including customization of antibody species specificity, generation of genetically encoded small antibody fragments, and conversion of single chain antibody fragments (e.g. scFv) into full-length, bivalent antibodies. This study focuses on antibodies directed to epitopes important for mitotic cell division; however, the methods and reagents described here are applicable to antibodies and antibody fragments for use in any field.

SummaryFor many important crops, including sorghum, the use of CRISPR/Cas technology is limited not only by the delivery of the gene-modification components into a plant cell, but more critically, by the ability to regenerate a fertile plant from the engineered cell through conventional tissue culture methods. Here, we report that Wuschel2 (Wus2)-enabled sorghum (Sorghum bicolor L.) transformation increases not only the efficiency of transgenic plant recovery, but also increases the observed efficiency of CRISPR/Cas-targeted genome editing within the population of T0 plants. Using Agrobacterium-mediated transformation, we have demonstrated Wus2-induced direct somatic embryo formation and regeneration, bypassing genotype-dependent callus formation and significantly shortening the tissue culture cycle time. This method also increased regeneration capacity, resulting in higher transformation efficiency across different sorghum genotypes. Subsequently, advanced excision systems and “altruistic” transformation technology have been developed to generate high-quality morphogenic gene-free and/or selectable marker-free sorghum events. Finally, we demonstrated up to an additional 6.8-fold increase in CRISPR/Cas9-mediated gene dropout frequency using Wus2-enabled, Agrobacterium-mediated transformation (compared to the control treatment with no Wus2), across various targeted loci in different sorghum genotypes. Wus2-enabled genome editing may be applicable to other crops and increasingly popular in planta transformation strategies.

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.

Cytochrome P450 enzyme variant alleles have shown evidence that functional consequences differ between substrates. A systematic effort has not yet been made to confirm substrate-dependent activity. This review will discuss the challenges of assessing three examples (CYP2C8*3, CYP2D6*10, and CYP2C9*2) where substrate-dependent activity has been hypothesized with differing levels of evidence and their potential clinical implications. Data supports bidirectional substrate-dependent activity for CYP2C8*3. Although some data suggests CYP2D6*10 causes differences in the magnitude of effect across substrates, confirmatory studies are needed. Convincing evidence for CYP2C9*2 was lacking likely due to compensatory CYP450 metabolism or experimental variability. Confirmed substrate-dependent activity has the potential to impact clinical use of pharmacogenomics, and must be taken into consideration to ensure the goal of improving treatment through personalization is met. It is important for the pharmacogenomics community to begin thinking about this important topic and how it can be best accommodated in clinical practice.

The bacterial cell division protein FtsZ is conserved in most bacteria and essential for viability. There have been concerted efforts in developing inhibitors that target FtsZ as potential antibiotics. Key to this is an in-depth understanding of FtsZ structure at the molecular level across diverse bacterial species to ensure inhibitors have high affinity for the FtsZ target in a variety of clinically relevant pathogens. In this study, we show that FtsZ structures differ in three ways: (1) the H7 helix curvature; (2) the dimensions of the interdomain cleft; and (3) the opening/closing mechanism of the interdomain cleft, whereas no differences were observed in the dimensions of the nucleotide-binding pocket and T7 loop. Molecular dynamics simulation may suggest that there are two possible mechanisms for the process of opening and closing of the interdomain cleft on FtsZ structures. This discovery highlights significant differences between FtsZ structures at the molecular level and this knowledge is vital in assisting the design of potent FtsZ inhibitors.

Whether or not DNA variation changes genome-wide nucleotide compositions remains largely unknown. By examining 4,604,291 DNA variants between two rat strains, we observed that sequencing depth is strongly correlated with genome content as 21.41, 38.36, 44.26 and 6512.70 average reads per locus were collected for Y, X, autosomes and mitochondrial (MT) genomes; respectively (PG/G—>C rate in 5’UTRs (untranslated regions) and G/C INDELs in the 5’UTR and/or exonic regions highlight their importance for execution of gene function. Our study provides evidence that DNA variation does not jeopardize genome stability and functional conservation during evolution.

Understanding the genetic links between an animal’s genome and phenome is one of the most challenging tasks in the postgenome sequencing era. These links may be provided through the usage of alternative transcription start, termination and splicing sites in RNA, which promote the synthesis of multiple proteins from a single gene and enable a finite genome to build an infinite phenome during an animal’s lifespan. Therefore, alternative transcripts, rather than individual genes, are the minimal functional units in a genome. This chapter focuses on alternative transcriptome analysis, including platforms, strategies, methods, and applications. We will also demonstrate how these data can help us understand the genetic information flows from genome to phenome that are relevant to physical, physiological, pathological, and psychological processes.

Biofuels produced by microbial fermentation of plant feedstocks provide renewable and sustainable energy sources that have the potential to mitigate climate change and improve energy security. Engineered strains of the bacterium Z. mobilis can convert sugars extracted from plant feedstocks into next-generation biofuels like isobutanol; however, conversion by these strains remains inefficient due to key gaps in our knowledge about genes involved in metabolism and stress responses such as alcohol tolerance. Here, we develop CRISPRi as a tool to explore gene function in Z. mobilis. We characterize genes that are essential for growth, required to ferment sugar to ethanol, and involved in resistance to isobutanol. Our Z. mobilis CRISPRi system makes it straightforward to define gene function and can be applied to improve strain engineering and increase biofuel yields., Zymomonas mobilis is a promising biofuel producer due to its high alcohol tolerance and streamlined metabolism that efficiently converts sugar to ethanol. Z. mobilis genes are poorly characterized relative to those of model bacteria, hampering our ability to rationally engineer the genome with pathways capable of converting sugars from plant hydrolysates into valuable biofuels and bioproducts. Many of the unique properties that make Z. mobilis an attractive biofuel producer are controlled by essential genes; however, these genes cannot be manipulated using traditional genetic approaches (e.g., deletion or transposon insertion) because they are required for viability. CRISPR interference (CRISPRi) is a programmable gene knockdown system that can precisely control the timing and extent of gene repression, thus enabling targeting of essential genes. Here, we establish a stable, high-efficacy CRISPRi system in Z. mobilis that is capable of perturbing all genes—including essential genes. We show that Z. mobilis CRISPRi causes either strong knockdowns (>100-fold) using single guide RNA (sgRNA) spacers that perfectly match target genes or partial knockdowns using spacers with mismatches. We demonstrate the efficacy of Z. mobilis CRISPRi by targeting essential genes that are universally conserved in bacteria, are key to the efficient metabolism of Z. mobilis, or underlie alcohol tolerance. Our Z. mobilis CRISPRi system will enable comprehensive gene function discovery, opening a path to rational design of biofuel production strains with improved yields. IMPORTANCE Biofuels produced by microbial fermentation of plant feedstocks provide renewable and sustainable energy sources that have the potential to mitigate climate change and improve energy security. Engineered strains of the bacterium Z. mobilis can convert sugars extracted from plant feedstocks into next-generation biofuels like isobutanol; however, conversion by these strains remains inefficient due to key gaps in our knowledge about genes involved in metabolism and stress responses such as alcohol tolerance. Here, we develop CRISPRi as a tool to explore gene function in Z. mobilis. We characterize genes that are essential for growth, required to ferment sugar to ethanol, and involved in resistance to isobutanol. Our Z. mobilis CRISPRi system makes it straightforward to define gene function and can be applied to improve strain engineering and increase biofuel yields.

Background Sequencing of patient-derived xenograft (PDX) mouse models allows investigation of the molecular mechanisms of human tumor samples engrafted in a mouse host. Thus, both human and mouse genetic material is sequenced. Several methods have been developed to remove mouse sequencing reads from RNA-seq or exome sequencing PDX data and improve the downstream signal. However, for more recent chromatin conformation capture technologies (Hi-C), the effect of mouse reads remains undefined. Results We evaluated the effect of mouse read removal on the quality of Hi-C data using in silico created PDX Hi-C data with 10% and 30% mouse reads. Additionally, we generated 2 experimental PDX Hi-C datasets using different library preparation strategies. We evaluated 3 alignment strategies (Direct, Xenome, Combined) and 3 pipelines (Juicer, HiC-Pro, HiCExplorer) on Hi-C data quality. Conclusions Removal of mouse reads had little-to-no effect on data quality as compared with the results obtained with the Direct alignment strategy. Juicer extracted more valid chromatin interactions for Hi-C matrices, regardless of the mouse read removal strategy. However, the pipeline effect was minimal, while the library preparation strategy had the largest effect on all quality metrics. Together, our study presents comprehensive guidelines on PDX Hi-C data processing.

Immunomodulatory drugs could contribute to a functional cure for Human Immunodeficiency Virus (HIV). Interleukin-15 (IL-15) promotes expansion and activation of CD8+ T cell and natural killer (NK) cell populations. In one study, an IL-15 superagonist, N-803, suppressed Simian Immunodeficiency Virus (SIV) in non-human primates (NHPs) who had received prior SIV vaccination. However, viral suppression attenuated with continued N-803 treatment, partially returning after long treatment interruption. While there is evidence of concurrent drug tolerance, immune regulation, and viral escape, the relative contributions of these mechanisms to the observed viral dynamics have not been quantified. Here, we utilize mathematical models of N-803 treatment in SIV-infected macaques to estimate contributions of these three key mechanisms to treatment outcomes: 1) drug tolerance, 2) immune regulation, and 3) viral escape. We calibrated our model to viral and lymphocyte responses from the above-mentioned NHP study. Our models track CD8+ T cell and NK cell populations with N-803-dependent proliferation and activation, as well as viral dynamics in response to these immune cell populations. We compared mathematical models with different combinations of the three key mechanisms based on Akaike Information Criterion and important qualitative features of the NHP data. Two minimal models were capable of reproducing the observed SIV response to N-803. In both models, immune regulation strongly reduced cytotoxic cell activation to enable viral rebound. Either long-term drug tolerance or viral escape (or some combination thereof) could account for changes to viral dynamics across long breaks in N-803 treatment. Theoretical explorations with the models showed that less-frequent N-803 dosing and concurrent immune regulation blockade (e.g. PD-L1 inhibition) may improve N-803 efficacy. However, N-803 may need to be combined with other immune therapies to countermand viral escape from the CD8+ T cell response. Our mechanistic model will inform such therapy design and guide future studies., Author summary Immune therapy may be a critical component in the functional cure for Human Immunodeficiency Virus (HIV). N-803 is an immunotherapeutic drug that activates antigen-specific CD8+ T cells of the immune system. These CD8+ T cells eliminate HIV-infected cells in order to limit the spread of infection in the body. In one study, N-803 reduced plasma viremia in macaques that were infected with Simian Immunodeficiency Virus, an analog of HIV. Here, we used mathematical models to analyze the data from this study to better understand the effects of N-803 therapy on the immune system. Our models indicated that inhibitory signals may be reversing the stimulatory effect of N-803. Results also suggested the possibilities that tolerance to N-803 could build up within the CD8+ T cells themselves and that the treatment may be selecting for virus strains that are not targeted by CD8+ T cells. Our models predict that N-803 therapy may be made more effective if the time between doses is increased or if inhibitory signals are blocked by an additional drug. Also, N-803 may need to be combined with other immune therapies to target virus that would otherwise evade CD8+ T cells.

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.

The antigenic diversity of influenza A viruses (IAV) circulating in swine challenges the development of effective vaccines, increasing zoonotic threat and pandemic potential. High-throughput sequencing technologies can quantify IAV genetic diversity, but there are no accurate approaches to adequately describe antigenic phenotypes. This study evaluated an ensemble of nonlinear regression models to estimate virus phenotype from genotype. Regression models were trained with a phenotypic data set of pairwise hemagglutination inhibition (HI) assays, using genetic sequence identity and pairwise amino acid mutations as predictor features. The model identified amino acid identity, ranked the relative importance of mutations in the hemagglutinin (HA) protein, and demonstrated good prediction accuracy. Four previously untested IAV strains were selected to experimentally validate model predictions by HI assays. Errors between predicted and measured distances of uncharacterized strains were 0.35, 0.61, 1.69, and 0.13 antigenic units. These empirically trained regression models can be used to estimate antigenic distances between different strains of IAV in swine by using sequence data. By ranking the importance of mutations in the HA, we provide criteria for identifying antigenically advanced IAV strains that may not be controlled by existing vaccines and can inform strain updates to vaccines to better control this pathogen. IMPORTANCE Influenza A viruses (IAV) in swine constitute a major economic burden to an important global agricultural sector, impact food security, and are a public health threat. Despite significant improvement in surveillance for IAV in swine over the past 10 years, sequence data have not been integrated into a systematic vaccine strain selection process for predicting antigenic phenotype and identifying determinants of antigenic drift. To overcome this, we developed nonlinear regression models that predict antigenic phenotype from genetic sequence data by training the model on hemagglutination inhibition assay results. We used these models to predict antigenic phenotype for previously uncharacterized IAV, ranked the importance of genetic features for antigenic phenotype, and experimentally validated our predictions. Our model predicted virus antigenic characteristics from genetic sequence data and provides a rapid and accurate method linking genetic sequence data to antigenic characteristics. This approach also provides support for public health by identifying viruses that are antigenically advanced from strains used as pandemic preparedness candidate vaccine viruses.

Zymomonas mobilis is a promising biofuel producer due to its high alcohol tolerance and streamlined metabolism that efficiently converts sugar to ethanol. Z. mobilis genes are poorly characterized relative to model bacteria, hampering our ability to rationally engineer the genome with pathways capable of converting sugars from plant hydrolysates into valuable biofuels and bioproducts. Many of the unique properties that make Z. mobilis an attractive biofuel producer are controlled by essential genes; however, these genes cannot be manipulated using traditional genetic approaches (e.g., deletion or transposon insertion) because they are required for viability. CRISPR interference (CRISPRi) is a programmable gene knockdown system that can precisely control the timing and extent of gene repression, thus enabling targeting of essential genes. Here, we establish a stable, high-efficacy CRISPRi system in Z. mobilis that is capable of perturbing all genes—including essentials. We show that Z. mobilis CRISPRi causes either strong knockdowns (>100-fold) using single guide RNA (sgRNA) spacers that perfectly match target genes, or partial knockdowns using spacers with mismatches. We demonstrate the efficacy of Z. mobilis CRISPRi by targeting essential genes that are universally conserved in bacteria, key to the efficient metabolism of Z. mobilis, or underlie alcohol tolerance. Our Z. mobilis CRISPRi system will enable comprehensive gene function discovery, opening a path to rational design of biofuel production strains with improved yields.IMPORTANCEBiofuels produced by microbial fermentation of plant feedstocks provide renewable and sustainable energy sources that have the potential to mitigate climate change and improve energy security. Engineered strains of the bacterium Z. mobilis can convert sugars extracted from plant feedstocks into next generation biofuels such as isobutanol; however, conversion by these strains remains inefficient due to key gaps in our knowledge about genes involved in metabolism and stress responses such as alcohol tolerance. Here, we develop CRISPRi as a tool to characterize gene function in Z. mobilis. We identify genes that are essential for growth, required to ferment sugar to ethanol, and involved in resistance to alcohol. Our Z. mobilis CRISPRi system makes it straightforward to define gene function and can be applied to improve strain engineering and increase biofuel yields.

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

Background Gene-set analyses measure the association between a disease of interest and a “set" of genes related to a biological pathway. These analyses often incorporate gene network properties to account for differential contributions of each gene. We extend this concept further—defining gene contributions based on biophysical properties—by leveraging mathematical models of biology to predict the effects of genetic perturbations on a particular downstream function. Results We present a method that combines gene weights from model predictions and gene ranks from genome-wide association studies into a weighted gene-set test. We demonstrate in simulation how such a method can improve statistical power. To this effect, we identify a gene set, weighted by model-predicted contributions to intracellular calcium ion concentration, that is significantly related to bipolar disorder in a small dataset (P = 0.04; n = 544). We reproduce this finding using publicly available summary data from the Psychiatric Genomics Consortium (P = 1.7 × 10−4; n = 41,653). By contrast, an approach using a general calcium signaling pathway did not detect a significant association with bipolar disorder (P = 0.08). The weighted gene-set approach based on intracellular calcium ion concentration did not detect a significant relationship with schizophrenia (P = 0.09; n = 65,967) or major depression disorder (P = 0.30; n = 500,199). Conclusions Together, these findings show how incorporating math biology into gene-set analyses might help to identify biological functions that underlie certain polygenic disorders.

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.

Alternative polyadenylation is an essential RNA processing event that contributes significantly to regulation of transcriptome diversity and functional dynamics in both animals and plants. Here we review newly developed next generation sequencing methods for genome-wide profiling of alternative polyadenylation (APA) sites, bioinformatics pipelines for data processing and both wet and dry laboratory approaches for APA validation. The library construction methods LITE-Seq (Low-Input 3'-Terminal sequencing) and PAC-seq (PolyA Click sequencing) tag polyA+ cDNA, while BAT-seq (BArcoded, three-prime specific sequencing) and PAPERCLIP (Poly(A) binding Protein-mediated mRNA 3′End Retrieval by CrossLinking ImmunoPrecipitation) enrich polyA+ RNA. Interestingly, only WTTS-seq (Whole Transcriptome Termini Site sequencing) targets both polyA+ RNA and polyA+ cDNA. Varieties of bioinformatics pipelines are well established to pursue read quality control, mapping, clustering, characterization and pathway analysis. The RHAPA (RNase H alternative polyadenylation assay) and 3'RACE-seq (3' rapid amplification of cDNA end sequencing) methods directly validate APA sites, while WTSS-seq (whole transcriptome start site sequencing), RNA-seq (RNA sequencing) and public APA databases can serve as indirect validation methods. We hope that these tools, pipelines and resources trigger huge waves of interest in the research community to investigate APA events underlying physiological, pathological and psychological changes and thus understand the information transfer events from genome to phenome relevant to economically important traits in both animals and plants.

Conformation capture technologies measure frequencies of interactions between chromatin regions. However, understanding gene-regulation require knowledge of detailed spatial structures of heterogeneous chromatin in cells. Here we describe the nC-SAC (n-Constrained-Self Avoiding Chromatin) method that transforms experimental interaction frequencies into 3D ensembles of chromatin chains. nC-SAC first distinguishes specific from non-specific interaction frequencies, then generates 3D chromatin ensembles using identified specific interactions as spatial constraints. Application to α-globin locus shows that these constraints (∼20%) drive the formation of ∼99% all experimentally captured interactions, in which ∼30% additional to the imposed constraints is found to be specific. Many novel specific spatial contacts not captured by experiments are also predicted. A subset, of which independent ChIA-PET data are available, is validated to be RNAPII-, CTCF-, and RAD21-mediated. Their positioning in the architectural context of imposed specific interactions from nC-SAC is highly important. Our results also suggest the presence of a many-body structural unit involving α-globin gene, its enhancers, and POL3RK gene for regulating the expression of α-globin in silent cells.

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.

© 2019 American Chemical Society. The disturbing increase in the number of bacterial pathogens that are resistant to multiple, or sometimes all, current antibiotics highlights the desperate need to pursue the discovery and development of novel classes of antibacterials. The wealth of knowledge available about the bacterial cell division machinery has aided target-driven approaches to identify new inhibitor compounds. The main division target being pursued is the highly conserved and essential protein FtsZ. Despite very active research on FtsZ inhibitors for several years, this protein is not yet targeted by any commercial antibiotic. Here, we discuss the suitability of FtsZ as an antibacterial target for drug development and review progress achieved in this area. We use hindsight to highlight the gaps that have slowed progress in FtsZ inhibitor development and to suggest guidelines for concluding that FtsZ is actually the target of these molecules, a key missing link in several studies. In moving forward, a multidisciplinary, communicative, and collaborative process, with sharing of research expertise, is critical if we are to succeed.

Following radiation induced DNA damage, several repair pathways are activated to help preserve genome integrity. Double Strand Breaks (DSBs), which are highly toxic, have specified repair pathways to address them. The main repair pathways used to resolve DSBs are Non-Homologous End Joining (NHEJ) and Homologous Recombination (HR). Cell cycle phase determines the availability of HR, but the repair choice between pathways in the G2 phases where both HR and NHEJ can operate is not clearly understood. This study compares several in silico models of repair choice to experimental data published in the literature, each model representing a different possible scenario describing how repair choice takes place. Competitive only scenarios, where initial protein recruitment determines repair choice, are unable to fit the literature data. In contrast, the scenario which uses a more entwined relationship between NHEJ and HR, incorporating protein co-localisation and RNF138-dependent removal of the Ku/DNA-PK complex, is better able to predict levels of repair similar to the experimental data. Furthermore, this study concludes that co-localisation of the Mre11-Rad50-Nbs1 (MRN) complexes, with initial NHEJ proteins must be modeled to accurately depict repair choice.

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.

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.

The experimental objective was to characterize the impact of insoluble corn-based fiber, xylanase, and an arabinoxylan-oligosaccharide on ileal digesta and mucosa microbiome of pigs. Three replicates of 20 gilts were blocked by initial body weight, individually-housed, and assigned to 1 of 4 dietary treatments: a low-fiber control (LF), a 30% corn bran high-fiber control (HF), HF+100 mg/kg xylanase (HF+XY), and HF+50 mg/kg arabinoxylan oligosaccharide (HF+AX). Gilts were fed their respective treatments for 46 days. On day 46, pigs were euthanized and ileal digesta and mucosa were collected. The V4 region of the 16S rRNA was amplified and sequenced, generating a total of 2,413,572 and 1,739,013 high-quality sequences from the digesta and mucosa, respectively. Sequences were classified into 1,538 mucosa and 2,495 digesta operational taxonomic units (OTU). Hidden-state predictions of 25 enzymes were made using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States 2 (PICRUST2). Compared to LF, HF increased Erysipelotrichaceae_UCG-002, and Turicibacter in the digesta, Lachnospiraceae_unclassified in the mucosa, and decreased Actinobacillus in both (QQFaecalibacterium by nearly 6 log2-fold change, compared to HF. Relative to HF, HF+XY increased genera Bifidobacterium, and Lactobacillus, and decreased Streptococcus and Turicibacter in digesta (QBifidobacterium and decreased Escherichia-Shigella in the mucosa (QQ

Activation induced deaminase is the single B cell specific factor mediating class switch recombination and somatic hypermutation. Numerous studies have shown that AID preferentially targets Ig substrates and also attacks non-Ig substrates to create DNA damage that contributes to lymphomagenesis. AID targeting to Ig loci is linked to transcription but the mechanism governing this process has been obscure. Here we discuss research that illustrates the connection between AID targeting to DNA substrates and transcription processes to reveal rules governing the specificity of AID attack. These observations are woven together to provide a integrated view of AID function and a surprising linkage with global regulation of gene expression.

Decades of work have aimed to genetically reprogram T cells for therapeutic purposes1,2 using recombinant viral vectors, which do not target transgenes to specific genomic sites3,4. The need for viral vectors has slowed down research and clinical use as their manufacturing and testing is lengthy and expensive. Genome editing brought the promise of specific and efficient insertion of large transgenes into target cells using homology-directed repair5,6. Here we developed a CRISPR-Cas9 genome-targeting system that does not require viral vectors, allowing rapid and efficient insertion of large DNA sequences (greater than onekilobase) at specific sites in the genomes of primary human T cells, while preserving cell viability and function. This permits individual or multiplexed modification of endogenous genes. First, we applied this strategy to correct a pathogenic IL2RA mutation in cells from patients with monogenic autoimmune disease, and demonstrate improved signalling function. Second, we replaced the endogenous T cell receptor (TCR) locus with a new TCR that redirected T cells to a cancer antigen. The resulting TCR-engineered T cells specifically recognized tumour antigens and mounted productive anti-tumour cell responses in vitro and in vivo. Together, these studies provide preclinical evidence that non-viral genome targeting can enable rapid and flexible experimental manipulation and therapeutic engineering of primary human immune cells.

The study of epigenetics, or chemical modifications to the genome that may alter gene expression, is a growing area of interest for social scientists. Anthropologists and human biologists are interested in epigenetics specifically, as it provides a potential link between the environment and the genome, as well as a new layer of complexity for the study of human biological variation. In pace with the rapid increase in interest in epigenetic research, the range of methods has greatly expanded over the past decade. The primary objective of this article is to provide an overview of the current methods for assaying DNA methylation, the most commonly studied epigenetic modification. We will address considerations for all steps required to plan and conduct an analysis of DNA methylation, from appropriate sample collection, to the most commonly used methods for laboratory analyses of locus-specific and genome-wide approaches, and recommendations for statistical analyses. Key challenges in the study of DNA methylation are also discussed, including tissue specificity, the stability of measures, timing of sample collection, statistical considerations, batch effects, and challenges related to analysis and interpretation of data. Our hope is that this review serves as a primer for anthropologists and human biologists interested in incorporating epigenetic data into their research programs. Am. J. Hum. Biol. 27:295–303, 2015. © 2015 Wiley Periodicals, Inc.

An area of great interest in evolutionary genomics is whether convergently evolved traits are the result of convergent molecular mechanisms. The presence of queen and worker castes in insect societies is a spectacular example of convergent evolution and phenotypic plasticity. Multiple insect lineages have evolved environmentally induced alternative castes. Given multiple origins of eusociality in Hymenoptera (bees, ants, and wasps), it has been proposed that insect castes evolved from common genetic "toolkits" consisting of deeply conserved genes. Here, we combine data from previously published studies on fire ants and honey bees with new data for Polistes metricus paper wasps to assess the toolkit idea by presenting the first comparative transcriptome-wide analysis of caste determination among three major hymenopteran social lineages. Overall, we found few shared caste differentially expressed transcripts across the three social lineages. However, there is substantially more overlap at the levels of pathways and biological functions. Thus, there are shared elements but not on the level of specific genes. Instead, the toolkit appears to be relatively "loose," that is, different lineages show convergent molecular evolution involving similar metabolic pathways and molecular functions but not the exact same genes. Additionally, our paper wasp data do not support a complementary hypothesis that "novel" taxonomically restricted genes are related to caste differences.

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.

Antibodies are a highly successful class of biological drugs, with over 50 such molecules approved for therapeutic use and hundreds more currently in clinical development. Improvements in technology for the discovery and optimization of high-potency antibodies have greatly increased the chances for finding binding molecules with desired biological properties; however, achieving drug-like properties at the same time is an additional requirement that is receiving increased attention. In this work, we attempt to quantify the historical limits of acceptability for multiple biophysical metrics of “developability.” Amino acid sequences from 137 antibodies in advanced clinical stages, including 48 approved for therapeutic use, were collected and used to construct isotype-matched IgG1 antibodies, which were then expressed in mammalian cells. The resulting material for each source antibody was evaluated in a dozen biophysical property assays. The distributions of the observed metrics are used to empirically define boundaries of drug-like behavior that can represent practical guidelines for future antibody drug candidates.

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.

The global architecture of the cell nucleus and the spatial organization of chromatin play important roles in gene expression and nuclear function. Single-cell imaging and chromosome conformation capture-based techniques provide a wealth of information on the spatial organization of chromosomes. However, a mechanistic model that can account for all observed scaling behaviors governing long-range chromatin interactions is missing. Here we describe a model called constrained self-avoiding chromatin (C-SAC) for studying spatial structures of chromosomes, as the available space is a key determinant of chromosome folding. We studied large ensembles of model chromatin chains with appropriate fiber diameter, persistence length and excluded volume under spatial confinement. We show that the equilibrium ensemble of randomly folded chromosomes in the confined nuclear volume gives rise to the experimentally observed higher-order architecture of human chromosomes, including average scaling properties of mean-square spatial distance, end-to-end distance, contact probability and their chromosome-to-chromosome variabilities. Our results indicate that the overall structure of a human chromosome is dictated by the spatial confinement of the nuclear space, which may undergo significant tissue- and developmental stage-specific size changes.

A fundamental goal in the biological sciences is the definition of groups of organisms based on evolutionary history and the naming of those groups. For influenza A viruses (IAVs) in swine, understanding the hemagglutinin (HA) genetic lineage of a circulating strain aids in vaccine antigen selection and allows for inferences about vaccine efficacy. Previous reporting of H1 virus HA in swine relied on colloquial names, frequently with incriminating and stigmatizing geographic toponyms, making comparisons between studies challenging. To overcome this, we developed an adaptable nomenclature using measurable criteria for historical and contemporary evolutionary patterns of H1 global swine IAVs. We also developed a web-accessible tool that classifies viruses according to this nomenclature. This classification system will aid agricultural production and pandemic preparedness through the identification of important changes in swine IAVs and provides terminology enabling discussion of swine IAVs in a common context among animal and human health initiatives., The H1 subtype of influenza A viruses (IAVs) has been circulating in swine since the 1918 human influenza pandemic. Over time, and aided by further introductions from nonswine hosts, swine H1 viruses have diversified into three genetic lineages. Due to limited global data, these H1 lineages were named based on colloquial context, leading to a proliferation of inconsistent regional naming conventions. In this study, we propose rigorous phylogenetic criteria to establish a globally consistent nomenclature of swine H1 virus hemagglutinin (HA) evolution. These criteria applied to a data set of 7,070 H1 HA sequences led to 28 distinct clades as the basis for the nomenclature. We developed and implemented a web-accessible annotation tool that can assign these biologically informative categories to new sequence data. The annotation tool assigned the combined data set of 7,070 H1 sequences to the correct clade more than 99% of the time. Our analyses indicated that 87% of the swine H1 viruses from 2010 to the present had HAs that belonged to 7 contemporary cocirculating clades. Our nomenclature and web-accessible classification tool provide an accurate method for researchers, diagnosticians, and health officials to assign clade designations to HA sequences. The tool can be updated readily to track evolving nomenclature as new clades emerge, ensuring continued relevance. A common global nomenclature facilitates comparisons of IAVs infecting humans and pigs, within and between regions, and can provide insight into the diversity of swine H1 influenza virus and its impact on vaccine strain selection, diagnostic reagents, and test performance, thereby simplifying communication of such data. IMPORTANCE A fundamental goal in the biological sciences is the definition of groups of organisms based on evolutionary history and the naming of those groups. For influenza A viruses (IAVs) in swine, understanding the hemagglutinin (HA) genetic lineage of a circulating strain aids in vaccine antigen selection and allows for inferences about vaccine efficacy. Previous reporting of H1 virus HA in swine relied on colloquial names, frequently with incriminating and stigmatizing geographic toponyms, making comparisons between studies challenging. To overcome this, we developed an adaptable nomenclature using measurable criteria for historical and contemporary evolutionary patterns of H1 global swine IAVs. We also developed a web-accessible tool that classifies viruses according to this nomenclature. This classification system will aid agricultural production and pandemic preparedness through the identification of important changes in swine IAVs and provides terminology enabling discussion of swine IAVs in a common context among animal and human health initiatives.

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.

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.

Many species of Proteobacteria communicate by using LuxI-LuxR–type quorum-sensing systems that produce and detect acyl-homoserine lactone (acyl-HSL) signals. Most of the known signals are straight-chain fatty acyl-HSLs, and evidence indicates that LuxI homologs prefer fatty acid-acyl carrier protein (ACP) over fatty acyl-CoA as the acyl substrate for signal synthesis. Two related LuxI homologs, RpaI and BtaI from Rhodopseudomonas palustris and photosynthetic stem-nodulating bradyrhizobia, direct production of the aryl-HSLs p -coumaroyl-HSL and cinnamoyl-HSL, respectively. Here we report that BjaI from the soybean symbiont Bradyrhizobium japonicum USDA110 is closely related to RpaI and BtaI and catalyzes the synthesis of isovaleryl-HSL (IV-HSL), a branched-chain fatty acyl-HSL. We show that IV-HSL induces expression of bjaI , and in this way IV-HSL functions like many other acyl-HSL quorum-sensing signals. Purified histidine-tagged BjaI was an IV-HSL synthase, which was active with isovaleryl-CoA but not detectably so with isovaleryl-ACP. This suggests that the RpaI-BtaI-BjaI subfamily of acyl-HSL synthases may use CoA- rather than ACP-linked substrates for acyl-HSL synthesis. The bjaI -linked bjaR 1 gene is involved in the response to IV-HSL, and BjaR 1 is sensitive to IV-HSL at concentrations as low as 10 pM. Low but sufficient levels of IV-HSL (about 5 nM) accumulate in B. japonicum culture fluid. The low levels of IV-HSL synthesis have likely contributed to the fact that the quorum-sensing signal from this bacterium has not been described elsewhere.

Peptides of the innate immune system provide intriguing templates for designing novel antiviral molecules. θ-defensins are nonhuman primate peptides with broad-spectrum antiviral activities. The activity of these compounds is mediated through interference with viral fusion, and this activity is based upon key structural features. However, two major limitations to their clinical use hampered their development as potential antivirals, namely difficult multi-step synthesis for their production with low final yield of desired product (~5%), and unfavorable pharmacokinetics (rapid enzymatic degradation and/or renal clearance). Recently we designed and screened two sub-libraries of new peptide-based entry inhibitors mimicking the structure of humanized θ-defensins, designated as Hapivirins (HpVs) and Diprovirins (DpVs). Although the new peptides are smaller (13-residues) and structurally more simple than retrocyclins, several retained their ability to protect cells from infection by HIV-1 and HSV-2. The most active compound, DpV16, was chosen for a second round of modifications based on (1) its potent antiviral activity (2) its ease of synthesis, and (3) the low cost of production. Subsequently, we created a library of a second generation DpV-analogues with enhanced properties. Collectively, our findings to date suggest that simplified θ-defensins are suitable candidates for further modifications to obtain analogues with clinically favorable pharmacokinetics that may be produced in large quantities using a standard chemical approach. Considering their small size, they could be used either topically (topical microbicides) and/or for systemic applications (entry inhibitors).

Throughout the last century, possible effects of exposure to toxicants, nutrients or drugs were examined primarily by studies of groups or populations. Individual variation in responses was acknowledged but could not be analyzed due to lack of information or tools to analyze individual genetic make-ups and lifestyle factors such as diet and activity. The Human Genome, Haplotype Map, 1000Genomes, and Human Variome Projects are identifying and cataloging the variation found within humans. Advances in DNA sequencing technologies will soon permit the characterization of individual genomes in clinical and basic research studies, thus allowing associations to be made between an individual genotype and the response to a particular exposure. Such knowledge and tools have generated a significant challenge for scientists: to design and conduct research studies that account for individual genetic variation. However, before these studies are done in humans, they will be performed in various in vivo and in vitro models. The advantages and disadvantages of some of the model test systems that are being used or developed in relation to individual genetic make-up and responses to xenobiotics are discussed.

Cancer is a heterogeneous disease, and patient-level genetic assessments can guide therapy choice and impact prognosis. However, little is known about the impact of genetic variability within a tumor, intratumoral heterogeneity (ITH), on disease progression or outcome. Current approaches using bulk tumor specimens can suggest the presence of ITH, but only single-cell genetic methods have the resolution to describe the underlying clonal structures themselves. Current techniques tend to be labor and resource intensive and challenging to characterize with respect to sources of biological and technical variability. We have developed a platform using a microfluidic self-digitization chip to partition cells in stationary volumes for cell imaging and allele-specific PCR. Genotyping data from only confirmed single-cell volumes is obtained and subject to a variety of relevant quality control assessments such as allele dropout, false positive, and false negative rates. We demonstrate single-cell genotyping of the NPM1 type A mutation, an important prognostic indicator in acute myeloid leukemia, on single cells of the cell line OCI-AML3, describing a more complex zygosity distribution than would be predicted via bulk analysis.