Biology subjects

Haase, S. B.

Publications and source records attributed to Haase, S. B..

12 recordsLinked to original sources

bioRxiv2026

A G2 Checkpoint Arrests Cryptococcus neoformans Cell Division in response to Hypoxia

Saturated cultures of the pathogenic yeast, Cryptococcus neoformans, arrest as unbudded cells in the G2 phase of the cell cycle. As cells divided and cultures saturated, we found that oxygen levels in the culture medium dropped nearly tenfold. When saturation-arrested cultures were re-oxygenated without adding fresh growth medium, cells immediately formed a bud and then underwent mitosis. Thus, the arrest is due to low oxygen concentration rather than nutrient depletion. Because the G2 arrest was associated with unbudded cells, we asked whether C. neoformans cells have a morphogenesis checkpoint that blocks mitosis until cells can form a bud. Inhibition of budding by treatment with Latrunculin A also led to G2 arrest, and we determined that this arrest is dependent on the CDK inhibitory kinase, Swe1. This finding suggests that C. neoformans possesses a morphogenesis checkpoint analogous to that in the distantly related Saccharomyces cerevisiae. We also demonstrated that Swe1 is required to enforce the hypoxia-induced G2 arrest. We propose that hypoxia inhibits budding in C. neoformans, which in turn triggers a morphogenesis checkpoint to arrest cells in G2 even when nutrients are plentiful.

cell biology↗

bioRxiv2022

AutoGater: A Weakly Supervised Neural Network Model to Gate Cells in Flow Cytometric Analyses

Flow cytometry is a useful and efficient method for the rapid characterization of a cell population based on the optical and fluorescence properties of individual cells. Ideally, the cell population would consist of only healthy viable cells as dead cells can confound the analysis. Thus, separating out healthy cells from dying and dead cells, and any potential debris, is an important first step in analysis of flow cytometry data. While gating of debris can be conducted using measured optical properties, identifying dead and dying cells often requires utilizing fluorescent stains (e.g. Sytox, a nucleic acid stain that stains cells with compromised cell membranes) to identify cells that should be excluded from downstream analyses. These stains prolong the experimental preparation process and use a flow cytometers fluorescence channels that could otherwise be used to measure additional fluorescent markers within the cells (e.g. reporter proteins). Here we outline a stain-free method for identifying viable cells for downstream processing by gating cells that are dying or dead. AutoGater is a weakly supervised deep learning model that can separate healthy populations from unhealthy and dead populations using only light-scatter channels. In addition, AutoGater harmonizes different measurements of dead cells such as Sytox and CFUs.

bioinformatics↗

bioRxiv2022

The Parasite Intraerythrocytic Cycle and Human Circadian Cycle are Coupled During Malaria Infection

During infections with malaria parasites P. vivax, patients exhibit rhythmic fevers every 48 hours. These fever cycles correspond with the time parasites take to traverse the Intraerythrocytic Cycle (IEC) and may be guided by a parasite-intrinsic clock. Different species of Plasmodia have cycle times that are multiples of 24 hours, suggesting they may be coordinated with the host circadian clock. We utilized an ex vivo culture of whole blood from patients infected with P. vivax to examine the dynamics of the host circadian transcriptome and the parasite IEC transcriptome. Transcriptome dynamics revealed that the phases of the host circadian cycle and the parasite IEC were correlated across multiple patients, suggesting that the cycles are coupled. In mouse model systems, host-parasite cycle coupling appears to provide a selective advantage for the parasite. Thus, understanding how host and parasite cycles are coupled in humans could enable anti-malarial therapies that disrupt this coupling.

systems biology↗

bioRxiv2022

Computational Prediction of Synthetic Circuit Function Across Growth Conditions

A challenge in the design and construction of synthetic genetic circuits is that they will operate within biological systems that have noisy and changing parameter regimes that are largely unmeasurable. The outcome is that these circuits do not operate within design specifications or have a narrow operational envelope in which they can function. This behavior is often observed as a lack of reproducibility in function from day to day or lab to lab. Moreover, this narrow range of operating conditions does not promote reproducible circuit function in deployments where environmental conditions for the chassis are changing, as environmental changes can affect the parameter space in which the circuit is operating. Here we describe a computational method for assessing the robustness of circuit function across broad parameter regions. Previously designed circuits are assessed by this computational method and then circuit performance is measured across multiple growth conditions in budding yeast. The computational predictions are correlated with experimental findings, suggesting that the approach has predictive value for assessing the robustness of a circuit design.

synthetic biology↗

bioRxiv2022

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

Computational tools addressing various components of design-build-test-learn loops (DBTL) for the construction of synthetic genetic networks exist, but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called DART (Design Assemble Round Trip). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection, and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions.

synthetic biology↗

bioRxiv2022

Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of S. cerevisiae by Gander, et al. Our experimental campaign used a novel high throughput experimentation framework developed under DARPAs Synergistic Discovery and Design (SD2) program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report, and make some remarks about lessons learned.

synthetic biology↗

bioRxiv2022

Learning transcriptome dynamics for discovery of optimal genetic reporters of novel compounds

A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery of analyte-responsive promoters. This provides a set of biomarkers that act as a proxy for the transcriptional state referred to as cell state. We construct low-dimensional models of gene expression dynamics and rank genes by their ability to capture the perturbation-specific cell state using a novel observability analysis. Using this ranking, we extract 15 analyte-responsive promoters for the organophosphate malathion in the underutilized host organism Pseudomonas fluorescens SBW25. We develop synthetic genetic reporters from each analyte-responsive promoter and characterize their response to malathion. Furthermore, we enhance malathion reporting through the aggregation of the response of individual reporters with a synthetic consortium approach, and we exemplify the librarys ability to be useful outside the lab by detecting malathion in the environment. The library of living malathion sensors can be optimized for use in environmental diagnostics while the developed machine learning tool can be applied to discover perturbation-inducible gene expression systems in the compendium of host organisms.

synthetic biology↗

bioRxiv2022

Experimental Guidance for Discovering Genetic Networks through Iterative Hypothesis Reduction on Time Series

Large programs of dynamic gene expression, like cell cyles and circadian rhythms, are controlled by a relatively small "core" network of transcription factors and post-translational modifiers, working in concerted mutual regulation. Recent work suggests that system-independent, quantitative features of the dynamics of gene expression can be used to identify core regulators. We introduce an approach of iterative network hypothesis reduction from time-series data in which increasingly complex features of the dynamic expression of individual, pairs, and entire collections of genes are used to infer functional network models that can produce the observed transcriptional program. The culmination of our work is a computational pipeline, Iterative Network Hypothesis Reduction from Temporal Dynamics (Inherent Dynamics Pipeline), that provides a priority listing of targets for genetic perturbation to experimentally infer network structure. We demonstrate the capability of this integrated computational pipeline on synthetic and yeast cell-cycle data. Author SummaryIn this work we discuss a method for identifying promising experimental targets for genetic network inference by leveraging different features of time series gene expression data along a chained set of previously published software tools. We aim to locate small networks that control oscillations in the genome-wide expression profile in biological functions such as the circadian rhythm and the cell cycle. We infer the most promising targets for further experimentation, emphasizing that modeling and experimentation are an*Corresponding author: breschine.cummins@montana.edu essential feedback loop for confident predictions of core network structure. Our major offering is the reduction of experimental time and expense by providing targeted guidance from computational methods for the inference of oscillating core networks, particularly in novel organisms.

systems biology↗

bioRxiv2021

Impact of sickle cell trait hemoglobin on the intraerythrocytic transcriptional program of Plasmodium falciparum

Sickle-trait hemoglobin (HbAS) confers near-complete protection from severe, life-threatening falciparum malaria in African children. Despite this clear protection, the molecular mechanisms by which HbAS confers these protective phenotypes remain incompletely understood. As a forward genetic screen for aberrant parasite transcriptional responses associated with parasite neutralization in HbAS red blood cells (RBCs), we performed comparative transcriptomic analyses of Plasmodium falciparum in normal (HbAA) and HbAS erythrocytes during both in vitro cultivation of reference parasite strains and naturally-occurring P. falciparum infections in Malian children with HbAA or HbAS. During in vitro cultivation, parasites matured normally in HbAS RBCs, and the temporal expression was largely unperturbed of the highly ordered transcriptional program that underlies the parasites maturation throughout the intraerythrocytic development cycle (IDC). However, differential expression analysis identified hundreds of transcripts aberrantly expressed in HbAS, largely occurring late in the IDC. Surprisingly, transcripts encoding members of the Maurers clefts were overexpressed in HbAS despite impaired parasite protein export in these RBCs, while parasites in HbAS RBCs underexpressed transcripts associated with the endoplasmic reticulum and those encoding serine repeat antigen proteases that promote parasite egress. Analyses of P. falciparum transcriptomes from 32 children with uncomplicated malaria identified stage-specific differential expression: among infections composed of ring-stage parasites, only cyclophilin 19B was underexpressed in children with HbAS, while trophozoite-stage infections identified a range of differentially-expressed transcripts, including downregulation in HbAS of several transcripts associated with severe malaria in collateral studies. Collectively, our comparative transcriptomic screen in vitro and in vivo indicates that P. falciparum adapts to HbAS by altering its protein chaperone and folding machinery, oxidative stress response, and protein export machinery. Because HbAS consistently protects from severe P. falciparum, modulation of these responses may offer avenues by which to neutralize P. falciparum parasites. ImportanceSickle-trait hemoglobin (HbAS) confers near-complete protection from severe, life-threatening malaria, yet the molecular mechanisms that underlie HbAS protection from severe malaria remain incompletely understood. Here, we use RNA-seq to measure the impact of HbAS on the blood stage transcriptome of Plasmodium falciparum in in vitro time series experiments and in vivo samples from natural infections. Our in vitro time series data reveal that, during its blood stage, P. falciparums gene expression in HbAS is impacted primarily through alterations in the abundance of gene products as opposed to variations in the timing of gene expression. Collectively, our in vitro and in vivo data indicate that P. falciparum adapts to HbAS by altering its protein chaperone and folding machinery, oxidative stress response, and protein export machinery. Due to the persistent association of HbAS and protection from severe disease, these processes that are modified in HbAS may offer strategies to neutralize P. falciparum.

genetics↗

bioRxiv2020

Conservation of dynamic characteristics of transcriptional regulatory elements in periodic biological processes

Cell and circadian cycles control a large fraction of cell and organismal physiology by regulating large periodic transcriptional programs that encompass anywhere from 15-80% of the genome. The gene-regulatory networks (GRNs) controlling these programs were largely identified by genetics and chromosome mapping approaches in model systems, yet it is unlikely that we have identified all of the core GRN components. Moreover, large periodic transcriptional programs controlling a variety of processes certainly exist in important non-model organisms where genetic approaches to identifying networks are expensive, time-consuming or intractable. Ideally, the core network components could be identified using data-driven approaches on the transcriptome dynamics data already available. Previous work used dynamic gene expression features to identify sets of genes with periodic behavior; our work goes further to distinguish genes by role: core versus their non-regulatory outputs. Here we present a quantitative approach that can identify nodes of GRNs controlling cell or circadian cycles across taxa. There are practical applications of the approach for network biologists, but our findings reveal something unexpected--that there are quantifiable and fundamental shared features of these unrelated GRNs controlling disparate periodic phenotypes. Author summaryCircadian rhythms, cellular division, and the developmental cycles of a multitude of living creatures, including those responsible for infectious diseases, are among the many dynamic phenomena in the natural world that are known to be the eventual output of gene regulatory networks. Identifying the small number of specialized genes that control these dynamic behaviors is of fundamental importance to our understanding of life, and our treatment of disease, but is difficult because of the sheer size of the genomes. We show that the core genes in organisms separated by millions of years of evolution have remarkable similarities that can be used to identify them.

genomics↗

bioRxiv2017

The Cell-Cycle Transcriptional Network Generates and Transmits a Pulse of Transcription Once Each Cell Cycle

Multiple studies have suggested the critical roles of cyclin-dependent kinases (CDKs) as well as a transcription factor (TF) network in generating the robust cell-cycle transcriptional program. However, the precise mechanisms by which these components function together in the gene regulatory network remain unclear. Here we show that the TF network can generate and transmit a \"pulse\" of transcription independently of CDK oscillations. The premature firing of the transcriptional pulse is prevented by early G1 inhibitors, including transcriptional corepressors and the E3 ubiquitin ligase complex APCCdh1. We demonstrate that G1 cyclin-CDKs facilitate the activation and accumulation of TF proteins in S/G2/M phases through inhibiting G1 transcriptional corepressors (Whi5 and Stb1) and APCCdh1, thereby promoting the initiation and propagation of the pulse by the TF network. These findings suggest a unique oscillatory mechanism in which global phase-specific transcription emerges from a pulse-generating network that fires once-and-only-once at the start of the cycle.

systems biology↗

bioRxiv2017

Reconciling Conflicting Models for Global Control of Cell-Cycle Transcription

How the program of periodic cell-cycle transcription is controlled has been debated for several years. Models have ranged from control by a CDK-APC/C oscillator, by a transcription factor (TF) network, or by coupled CDK-APC/C and TF networks. In contrast to current models, a recent study concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator with little input from the TF network. This conclusion was largely based on an assumption that substantial drops in transcript levels of network TFs would render them unable to regulate their targets. By combining quantitative modeling and an unbiased analysis of the RNA-seq data, we demonstrate that the data from this recent study are completely consistent with previous reports indicating a critical role of a TF network. Moreover, we report substantial transcript dynamics in cells arrested with intermediate levels of B-cyclins, further supporting the model in which oscillating CDK activity is not required to produce phase-specific transcription.

cell biology↗