Biology subjects

Ralph, P.

Publications and source records attributed to Ralph, P..

7 recordsLinked to original sources

bioRxiv2025

Cell-Based Potency Assay for Anti-CD3-Anti-CD19 Diabody

A cytotoxicity assay was developed to measure the potency of GP101, a single chain diabody. GP101 is comprised of two linked Fv domains, with one that binds CD3 (expressed on T cells), and the other that binds CD19 (expressed on B cells). GP101 directs CD3 positive T lymphocytes to CD19 positive B lymphocytes. This immunotherapy redirects CD3+ T cells to CD19-expressing B-cell malignancies, enabling T-cell-mediated tumor cell lysis. We developed a GMP cell-based potency assay to satisfy the FDA requirements. The potency assay uses a cytotoxic T cell line, and a B cell lymphoma cell line as the target. The target lymphoma B cell line is prelabeled with a fluorescent dye, and upon T cell mediated killing, the fluorescence dye is released and detected using a fluorometer. The emitted fluorescence is proportional to the dose of GP101. Greater than 90% of the target B cells were killed within two hours exposure in vitro with the lowest amount of detectable killing at 60pg/mL GP101. The assay is suitable for measuring purified GP101, or GP101 expressed by cells transduced by GP101 plasmid or AAV preparations, and bioactivity in animal or human blood. This novel assay met GMP/GLP compliance, allowing a quantifiable and reproducible measure of efficacy, ensuring batch-to-batch consistency, and met safety and effectiveness regulatory requirements. This potency assay may be applicable for testing other CD3-CD19 T cell engagers and suitable for developing other diabody mediated potency assays with appropriate antigens.

cancer biology↗

bioRxiv2022

Diverse RNA viruses associated with diatom, eustigmatophyte, dinoflagellate and rhodophyte microalgae cultures

Unicellular microalgae are of immense ecological importance with growing commercial potential in industries such as renewable energy, food and pharmacology. Viral infections can have a profound impact on the growth and evolution of their hosts. However, very little is known of the diversity within, and effect of, unicellular microalgal RNA viruses. In addition, identifying RNA viruses in these organisms that could have originated more than a billion years ago constitutes a robust data set to dissect molecular events and address fundamental questions on virus evolution. We assessed the diversity of RNA viruses in eight microalgal cultures including representatives from the diatom, eustigmatophyte, dinoflagellate, red algae and euglenid groups. Using metatranscriptomic sequencing combined with bioinformatic approaches optimised to detect highly divergent RNA viruses, we identified ten RNA virus sequences, with nine constituting new viral species. Most of the newly identified RNA viruses belonged to the double-stranded Totiviridae, Endornaviridae and Partitiviridae, greatly expanding the reported host range for these families. Two new species belonging to the single-stranded RNA viral clade Marnaviridae, commonly associated with microalgal hosts, were also identified. This study highlights that a great diversity of RNA viruses likely exists undetected within the unicellular microalgae. It also highlights the necessity for RNA viral characterisation and to investigate the effects of viral infections on microalgal physiology, biology and growth, considering their environmental and industrial roles. ImportanceIn comparison to animals or plants, our knowledge of the diversity of RNA viruses infecting microbial algae - the microalgae - is minimal. Yet describing the RNA viruses infecting these organisms is of primary importance at both the ecological and economical levels because of the fundamental roles these organisms play in aquatic environments and their growing value across a range of industrial fields. Using metatranscriptomic sequencing we aimed to reveal the RNA viruses present in cultures of eight microalgae species belonging to the diatom, dinoflagellate, eustigmatophyte, rhodophyte and euglena major clades of algae. This work identified ten new divergent RNA virus species, belonging to RNA virus families as diverse as the double-stranded Totiviridae, Endornaviridae, Partitiviridae and the single-stranded Marnaviridae. By expanding the known diversity of RNA viruses infecting unicellular eukaryotes, this study contributes to a better understanding of the early evolution of the virosphere and will inform the use of microalgae in industrial applications.

microbiology↗

bioRxiv2020

A derived ZW chromosome system in Amborella trichopoda, the sister species to all other extant flowering plants

Sex determination is poorly understood in plants. Amborella trichopoda is a well-known plant model for evo-devo studies, which is also dioecious (has male and female individuals), with an unknown sex determination mechanism. A. trichopoda is a "sex switcher", which points to possible environmental factors that act on sex, but populations grown from seed under greenhouse conditions exhibit a 50:50 sex ratio, which indicates the operation of genetic factors. Here, we use a new method (SDpop) to identify sex-linked genes from genotyping data of male and female individuals sampled in the field, and find that A. trichopoda has a ZW sex-chromosome system. The sex-linked genes map to a 4 Mb sex-determining region on chromosome 9. The low extent of ZW divergence suggests these sex chromosomes are of recent origin, which is consistent with dioecy being derived character in the A. trichopoda lineage. Our work has uncovered clearly formed sex chromosomes in a species in which both genetic and environmental factors can influence sex. One Sentence SummaryAmborella trichopoda, a dioecious species in which both genetics and the environment influence sex, possesses a pair of quite recently evolved ZW chromosomes.

plant biology↗

bioRxiv2020

Overexpression of key sterol pathway enzymes in two model marine diatoms alters sterol profiles in Phaeodactylum tricornutum

Sterols are a class of triterpenoid molecules with diverse functional roles in eukaryotic cells, including intracellular signaling and regulation of cell membrane fluidity. Diatoms are a dominant eukaryotic phytoplankton group that produce a wide diversity of sterol compounds. The enzymes 3-hydroxy-3-methyl glutaryl CoA reductase (HMGR) and squalene epoxidase (SQE) have been reported to be rate-limiting steps in sterol biosynthesis in other model eukaryotes; however, the extent to which these enzymes regulate triterpenoid production in diatoms is not known. To probe the role of these two metabolic nodes in the regulation of sterol metabolic flux in diatoms, we independently over-expressed two versions of the native HMGR and a conventional, heterologous SQE gene in the diatoms Thalassiosira pseudonana and Phaeodactylum tricornutum. Overexpression of these key enzymes resulted in significant differential accumulation of downstream sterol pathway intermediates in P. tricornutum. HMGR-mVenus overexpression resulted in the accumulation of squalene, cycloartenol, and obtusifoliol, while cycloartenol and obtusifoliol accumulated in response to heterologous NoSQE-mVenus overexpression. In addition, accumulation of the end-point sterol 24-methylenecholesta-5,24(24)-dien-3{beta}-ol was observed in all P. tricornutum overexpression lines, and campesterol increased 3-fold in P. tricornutum lines expressing NoSQE-mVenus. Minor differences in end-point sterol composition were also found in T. pseudonana, but no accumulation of sterol pathway intermediates was observed. Despite the successful manipulation of pathway intermediates and individual sterols in P. tricornutum, total sterol levels did not change significantly in transformed lines, suggesting the existence of tight pathway regulation to maintain total sterol content.

molecular biology↗

bioRxiv2018

Demographic inference in a spatially-explicit ecological model from genomic data: a proof of concept for the Mojave Desert Tortoise

In this paper, we study the general problem of extracting information from spatially explicit genomic data to inform inference of ecologically and geographically realistic population models. We describe methods and apply them to simulations motivated by the demography of the Mojave desert tortoise (Gopherus agassizii). The tortoise is an example of a long-lived, threatened species for which we have an excellent understanding of range, habitat preference, and certain aspects of demography, but inadequate information on other life history components that are important for conservation management. We use an individual-based model on a discretized geographic landscape with overlapping generations and age and sex-specific dispersal, fecundity, and mortality to develop and test a method that uses genomic data to infer demographic parameters. We do this by seeking parameters that best match a set of spatial statistics of genomes, which we introduce and discuss. We find that for inferring only overall population density and mean migration distance, a simple statistical learning method performs well using simulated training data, inferring parameters to within 10% accuracy. In the process, we introduce spatial analogues of common population genetics statistics, and discuss how and why they are expected to contain signal about the geography of population dynamics that are key for ecological modeling generally and conservation of endangered taxa.

genomics↗

bioRxiv2018

Efficient pedigree recording for fast population genetics simulation

In this paper we describe how to efficiently record the entire genetic history of a population in forwards-time, individual-based population genetics simulations with arbitrary breeding models, population structure and demography. This approach dramatically reduces the computational burden of tracking individual genomes by allowing us to simulate only those loci that may affect reproduction (those having non-neutral variants). The genetic history of the population is recorded as a succinct tree sequence as introduced in the software package msprime, on which neutral mutations can be quickly placed afterwards. Recording the results of each breeding event requires storage that grows linearly with time, but there is a great deal of redundancy in this information. We solve this storage problem by providing an algorithm to quickly simplify a tree sequence by removing this irrelevant history for a given set of genomes. By periodically simplifying the history with respect to the extant population, we show that the total storage space required is modest and overall large efficiency gains can be made over classical forward-time simulations. We implement a general-purpose framework for recording and simplifying genealogical data, which can be used to make simulations of any population model more efficient. We modify two popular forwards-time simulation frameworks to use this new approach and observe efficiency gains in large, whole-genome simulations of one to two orders of magnitude. In addition to speed, our method for recording pedigrees has several advantages: (1) All marginal genealogies of the simulated individuals are recorded, rather than just genotypes. (2) A population of N individuals with M polymorphic sites can be stored in O(N log N + M) space, making it feasible to store a simulations entire final generation as well as its history. (3) A simulation can easily be initialized with a more efficient coalescent simulation of deep history. The software for recording and processing tree sequences is named tskit.\n\nAuthor SummarySexually reproducing organisms are related to the others in their species by the complex web of parent-offspring relationships that constitute the pedigree. In this paper, we describe a way to record all of these relationships, as well as how genetic material is passed down through the pedigree, during a forwards-time population genetic simulation. To make effective use of this information, we describe both efficient storage methods for this embellished pedigree as well as a way to remove all information that is irrelevant to the genetic history of a given set of individuals, which dramatically reduces the required amount of storage space. Storing this information allows us to produce whole-genome sequence from simulations of large populations in which we have not explicitly recorded new genomic mutations; we find that this results in computational run times of up to 50 times faster than simulations forced to explicitly carry along that information.

bioinformatics↗

bioRxiv2017

Inferring Continuous and Discrete Population Genetic Structure Across Space

A classic problem in population genetics is the characterization of discrete population structure in the presence of continuous patterns of genetic differentiation. Especially when sampling is discontinuous, the use of clustering or assignment methods may incorrectly ascribe differentiation due to continuous processes (e.g., geographic isolation by distance) to discrete processes, such as geographic, ecological, or reproductive barriers between populations. This reflects a shortcoming of current methods for inferring and visualizing population structure when applied to genetic data deriving from geographically distributed populations. Here, we present a statistical framework for the simultaneous inference of continuous and discrete patterns of population structure. The method estimates ancestry proportions for each sample from a set of two-dimensional population layers, and, within each layer, estimates a rate at which relatedness decays with distance. This thereby explicitly addresses the \"clines versus clusters\" problem in modeling population genetic variation. The method produces useful descriptions of structure in genetic relatedness in situations where separated, geographically distributed populations interact, as after a range expansion or secondary contact. We demonstrate the utility of this approach using simulations and by applying it to empirical datasets of poplars and black bears in North America.\n\nAuthor summaryOne of the first steps in the analysis of genetic data, and a principal mission of biology, is to describe and categorize natural variation. A continuous pattern of differentiation (isolation by distance), where individuals found closer together in space are, on average, more genetically similar than individuals sampled farther apart, can confound attempts to categorize natural variation into groups. This is because current statistical methods for assigning individuals to discrete clusters cannot accommodate spatial patterns, and so are forced to use clusters to describe what is in fact continuous variation. As isolation by distance is common in nature, this is a substantial shortcoming of existing methods. In this study, we introduce a new statistical method for categorizing natural genetic variation - one that describes variation as a combination of continuous and discrete patterns. We demonstrate that this method works well and can capture patterns in population genomic data without resorting to splitting populations where they can be described by continuous patterns of variation.

evolutionary biology↗