Biomark (United States)

Abstract The rapid development of synthetic catalysts, including organometallic and coordination complexes, organometallic‐based artificial metalloenzymes, and nanozymes, for biomedical applications in academia and the private sector has outpaced the ability of health and environmental regulatory agencies to establish guidelines for human exposure. This challenge extends across the chemical and biomedical industries and has driven the emergence of the safe and sustainable‐by‐Design (SSbD) concept and related regulatory frameworks. Addressing SSbD is particularly complex for emerging fields such as catalytic therapies using synthetic catalysts. Nevertheless, advancing toward this goal should become a priority for researchers in the coming years. In this work, we review the state‐of‐the‐art synthetic catalysts for catalytic therapies and analyze the materials and elements used in their design in relation to their abundance, potential toxicity, and strategies to integrate the SSbD framework into this field.

BackgroundCell therapy has been successful for treating some hematological malignancies, largely through autologous chimeric antigen receptor (CAR) T-cell therapies. However, the complexity of CAR T-cell therapies makes them costly and time-consuming to develop. One of the major challenges is heterogeneity in CAR-T products due to underlying variability across the entire development process and the lack of correlative biomarkers in existing in vitro models that can predict treatment efficacy and performance.In this study, we describe a novel flow cytometric approach using optical barcodes, called laser particles (LPs), to identify, track and characterize activated T-cells over several time points (Time-lapse flow cytometry). This method enables kinetic phenotyping, identification of cells defined by dynamic changes in one or more markers over time. We also demonstrate how connecting these kinetic phenotypes to single-cell function data can be used in a supervised machine learning (ML) approach to identify biomarkers predictive of future cell function. These biomarkers can then be leveraged by cell therapy developers to better understand the heterogeneity of their samples and improve the correlation of in vitro models to in vivo performance.MethodsT cells from healthy donors were barcoded and incubated with an anti-CD3/CD28 activator analyzed by flow cytometry at 24, 32, and 48 hours of culture. The flow cytometry panel consisted of memory (CD45RA, CCR7, CD4, CD8) and activation markers (CD25, PD-1, CD69). After 48h, cells were stimulated with PMA/Ionomycin for 4 hours and stained intracellularly for IL-2, TNFa, and IFNy and acquired. Data for each cell at each time point were integrated using optical barcodes and analyzed with manual gating and supervised ML (decision tree algorithm).ResultsUsing time-lapse flow cytometry, we identified novel kinetic phenotypes of T cells based on marker expression kinetics during activation (figure 1). With our ML approach, we predicted cells that secrete cytokine with an AUC of >0.85. We found that the main contributors to predicting this functional outcome were CCR7 and CD25 at 24 hrs as well as PD1 at 48 hr. This is likely the first ever demonstration of kinetic measurements of individual immune cells over time at scale.ConclusionsIn the future, researchers may be able to enrich for specific cells of interest based on their initial or kinetic phenotypes and their corresponding likelihood to exert specific functions at a later time point. 147 Figure 1

Looking to nature for inspiration has led to many diverse technological advances. The spiral valve intestine of sharks has provided the opportunity to observe the efficiency of different valve systems. It is supposed that the spiral intestine present in sharks, skates and rays slows the transit rate of digesta through the gut and provides increased surface area for the absorption of nutrients. In this investigation, we use a novel technique-creating three-dimensional reconstructions from CT scans of spiral intestines-to describe the morphology of the spiral intestine of at least one species from 22 different shark families. We discuss the morphological data in an evolutionary, dietary and functional context. The evolutionary analyses suggest that the columnar morphology is the ancestral form of the spiral intestine. Dietary analyses reveal no correlation between diet type and spiral intestine morphology. Flow rate was slowed significantly more when the two funnel-shaped spiral intestines were subjected to flow in the posterior to anterior direction, indicating their success at producing unidirectional flow, similar to a Tesla valve. These data are available to generate additional three-dimensional morphometrics, create computational models of the intestine, as well as to further explore the function of the gastrointestinal tract of sharks in structural and physiological contexts.

Abstract Quantifying geomorphic conditions that impact riverine ecosystems is critical in river management due to degraded riverine habitat, changing flow and thermal conditions, and increasing anthropogenic pressure. Geomorphic complexity at different scales directly impacts habitat heterogeneity and affects aquatic biodiversity resilience. Here we showed that the combination of continuous spatial survey at high resolution, topobathymetric light detection and ranging (LiDAR), and continuous wavelet analysis can help identify and characterize that complexity. We used a continuous wavelet analysis on 1‐m resolution topobathymetry in three rivers in the Salmon River Basin, Idaho (USA), to identify different scales of topographic variability and the potential effects of this variability on salmonid redd site selection. On each river, wavelet scales characterized the topographic variability by portraying repeating patterns in the longitudinal profile. We found three major representative spatial wavelet scales of topographic variability in each river: a small wavelet scale associated with local morphology such as pools and riffles, a mid‐wavelet scale that identified larger channel unit features, and a large wavelet scale related to valley‐scale controls. The small wavelet scale was used to identify pools and riffles along the entire lengths of each river as well as areas with differing riffle‐pool development. Areas along the rivers with high local topographic variability (high wavelet power) at all wavelet scales contained the largest features (i.e., deepest or longest pools) in the systems. By comparing the wavelet power for each wavelet scale to Chinook salmon redd locations, we found that higher small‐scale wavelet power, which is related to pool‐riffle topography, is important for redd site selection. The continuous wavelet methodology objectively identified scales of topographic variability present in these rivers, performed efficient channel‐unit identification, and provided geomorphic assessment without laborious field surveys.

Abstract Background Altica (Coleoptera: Chrysomelidae) is a highly diverse and taxonomically challenging flea beetle genus that has been used as a model system in which to address questions related to host plant specialization, reproductive isolation, and ecological speciation. To further evolutionary studies in this important group, here we present a high-quality draft genome of a representative specialist, Altica viridicyanea, the first Alticinae genome and the fourth chrysomelid genome reported thus far. Results The genome is 864.8 Mb and consists of 4,490 scaffolds with a N50 size of 557 kb, which covered 98.6% complete and 0.4% partial insect Benchmarking Universal Single-Copy Orthologs. Repetitive sequences accounted for 62.9% of the assembly, and a total of 17,730 protein-coding gene models and 2,462 non-coding RNA models were predicted. To provide insight into host plant specialization of this monophagous species, we examined the key gene families involved in chemosensation, detoxification of plant secondary chemistry, and plant cell wall-degradation. Conclusions The high-quality genome assembled in this work provides an important resource for further studies on host plant adaptation and functionally affiliated genes. Moreover, this work also opens the way for comparative genomics studies among closely related Altica species, which may provide insight into the molecular evolutionary processes that occur during ecological speciation.

Abstract Stream biophysical processes are commonly studied using multi‐dimensional numerical modelling that quantifies flow hydraulics from which parameters such as habitat suitability, stream carrying capacity, and bed mobility are derived. These analyses would benefit from accurate high‐resolution stream bathymetries spanning tens of kilometres of channel, especially in small streams or where navigation is difficult. Traditional ground‐based survey methods are limited by survey time, dense vegetation and stream access, and are usually only feasible for short reaches. Conversely, airborne topobathymetric LiDAR surveys may overcome these limitations, although limited research is available on how errors in LiDAR‐derived digital elevation models (DEMs) might propagate through flow models. This study investigated the performance of LiDAR‐derived topobathymetry in support of multi‐dimensional flow modelling and ecohydraulics calculations in two gravel‐bedded reaches (approximately 200 m long), one morphologically complex and one morphologically simple, and at the segment scale (32 km‐long stream segment) along a 15 m‐wide river in central Idaho, USA. We compared metre and sub‐metre‐resolution DEMs generated from RTK‐GPS ground and Experimental Advanced Airborne Research LiDAR‐B (EAARL‐B) surveys and water depths, velocities, shear stresses, habitat suitability, and bed mobility modelled with two‐dimensional (2D) hydraulic models supported by LiDAR and ground‐surveyed DEMs. Residual statistics, bias (B), and standard deviation (SD) of the residuals between depth and velocity predicted from the model supported by LiDAR and ground‐survey topobathymetries were up to −0.04 (B) and 0.09 m (SD) for depth and −0.09 (B) and 0.20 m s−1 (SD) for velocity. The accuracy (B = 0.05 m), precision (SD = 0.09 m), and point density (1 point m−2) of the LiDAR topobathymetric survey (regardless of reach complexity) were sufficient to support 2D hydrodynamic modelling and derivative stream habitat and process analyses, because these statistics were comparable to those of model calibration with B = 0 m and SD = 0.04 m for water surface elevation and B = 0.05 m s−1 and SD = 0.22 m s−1 for velocity in our investigation. © 2020 John Wiley & Sons, Ltd.

Preview this conference paper: Higher Diamondoids Fingerprints in Petroleum: Why is Each Oil Family Unique?, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/2214-4609/2019/P158-1.gif

BackgroundDNA methylation regulates gene expression, through the inhibition/activation of gene transcription of methylated/unmethylated genes. Hence, DNA methylation profiling can capture pivotal features of gene expression in cancer tissues from patients at the time of diagnosis. In this work, we analyzed a breast cancer case series, to identify DNA methylation determinants of metastatic versus non-metastatic tumors.MethodsCpG-island methylation was evaluated on a 56-gene cancer-specific biomarker microarray in metastatic versus non-metastatic breast cancers in a multi-institutional case series of 123 breast cancer patients. Global statistical modeling and unsupervised hierarchical clustering were applied to identify a multi-gene binary classifier with high sensitivity and specificity. Network analysis was utilized to quantify the connectivity of the identified genes.ResultsSeven genes (BRCA1, DAPK1, MSH2, CDKN2A, PGR, PRKCDBP, RANKL) were found informative for prognosis of metastatic diffusion and were used to calculate classifier accuracy versus the entire data-set. Individual-gene performances showed sensitivities of 63–79 %, 53–84 % specificities, positive predictive values of 59–83 % and negative predictive values of 63–80 %. When modelled together, these seven genes reached a sensitivity of 93 %, 100 % specificity, a positive predictive value of 100 % and a negative predictive value of 93 %, with high statistical power. Unsupervised hierarchical clustering independently confirmed these findings, in close agreement with the accuracy measurements. Network analyses indicated tight interrelationship between the identified genes, suggesting this to be a functionally-coordinated module, linked to breast cancer progression.ConclusionsOur findings identify CpG-island methylation profiles with deep impact on clinical outcome, paving the way for use as novel prognostic assays in clinical settings.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-015-1412-9) contains supplementary material, which is available to authorized users.

Cerebrospinal fluid (CSF) tau is a common biomarker for Alzheimer disease (AD). Measurements of tau have historically been performed using immunoassays. Given the molecular diversity of tau in CSF, the selectivity of these immunoassays has often been questioned. Therefore, we aimed to develop an analytically sensitive and selective immunoaffinity liquid chromatography-tandem mass spectrometry (LC-MS/MS) (IA-MS) assay. IA-MS sample analysis involved the addition of an internal standard, immunoaffinity purification of tau using a tau monoclonal antibody coupled to magnetic beads, trypsin digestion, and quantification of a surrogate tau peptide by LC-MS/MS using a Waters Trizaic nanoTile ultraperformance LC microfluidic device. Further characterization of tau peptides was performed by full-scan MS using a Thermo Orbitrap LC-MS. CSF samples from a cohort of age-matched controls and patients with AD were analyzed by the IA-MS method as well as a commercially available immunoassay. The IA-MS assay had intra- and interassay imprecision values of 3.2% to 8.1% CV and 7.8% to 18.9% C, respectively, a mean recovery of 106%, and a limit of quantification of 0.25 pmol/L and was able to quantify tau concentrations in all human specimens tested. The IA-MS assay showed a correlation of R(2) = 0.950 against a total-tau immunoassay. In patients with AD, tau was increased approximately 2-fold. Combining immunoaffinity enrichment with microflow LC-MS/MS analysis is an effective approach for the development of a highly selective assay to measure total tau and, potentially, other posttranslationally modified forms of tau in CSF.

We developed PolyA-seq, a strand-specific and quantitative method for high-throughput sequencing of 3′ ends of polyadenylated transcripts, and used it to globally map polyadenylation (polyA) sites in 24 matched tissues in human, rhesus, dog, mouse, and rat. We show that PolyA-seq is as accurate as existing RNA sequencing (RNA-seq) approaches for digital gene expression (DGE), enabling simultaneous mapping of polyA sites and quantitative measurement of their usage. In human, we confirmed 158,533 known sites and discovered 280,857 novel sites (FDR < 2.5%). On average 10% of novel human sites were also detected in matched tissues in other species. Most novel sites represent uncharacterized alternative polyA events and extensions of known transcripts in human and mouse, but primarily delineate novel transcripts in the other three species. A total of 69.1% of known human genes that we detected have multiple polyA sites in their 3′UTRs, with 49.3% having three or more. We also detected polyadenylation of noncoding and antisense transcripts, including constitutive and tissue-specific primary microRNAs. The canonical polyA signal was strongly enriched and positionally conserved in all species. In general, usage of polyA sites is more similar within the same tissues across different species than within a species. These quantitative maps of polyA usage in evolutionarily and functionally related samples constitute a resource for understanding the regulatory mechanisms underlying alternative polyadenylation.