Biology subjects

O'Meally, D.

Publications and source records attributed to O'Meally, D..

8 recordsLinked to original sources

bioRxiv2026

Multiomic State-Transitions Reveal Post-Treatment Transcriptome Desynchronization in Acute Myeloid Leukemia

Temporal dynamics of the peripheral blood transcriptome are crucial for understanding leukemia evolution and response to therapy because they can reveal how gene expression programs drive abnormal cell states, disease heterogeneity, and treatment resistance. Using a mathematical model of state-transitions, we studied the temporal dynamics of peripheral blood messenger RNA (mRNA) and microRNA (miRNA) transcriptomes in a mouse model of acute myeloid leukemia (AML). In our state-transition model, mRNA and miRNA transcriptomes are represented as a particle undergoing Brownian motion in a two-dimensional multiomic potential landscape. Following chemotherapy, we observed a temporal desynchronization between mRNA and miRNA transcriptomic responses corresponding to an asymmetric shift in the landscape. Specifically, mRNA trajectories responded almost immediately post-treatment, whereas miRNA responses were delayed by approximately two weeks. Clustering analysis identified that the temporal delay is driven by a prominent cluster of miRNAs from the imprinted Dlk1-Dio3 region. Although previously implicated in acute promyelocytic leukemia, lymphomas, and metabolic dysregulation, this provides the first evidence linking the Dlk1-Dio3 locus to AML chemotherapy response and treatment-induced transcriptomic desynchronization. This framework offers an innovative dynamics-based strategy to identify biological drivers of therapeutic response and novel therapeutic targets across hematological malignancies. Key PointsO_LITreatment can induce desynchronization between messenger RNA and microRNA transcriptome dynamics in a murine model of AML C_LIO_LIMicroRNAs from the imprinted Dlk1-Dio3 locus are identified as key contributors to desynchronization and may serve as therapeutic targets C_LI

cancer biology↗

bioRxiv2025

Overcoming treatment resistance mediated by the bone marrow vascular niche in acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematologic malignancy originating in the bone marrow, frequently progressing to extramedullary sites. Despite advances in molecularly targeted therapies and hematopoietic stem cell transplantation, clinical outcomes remain suboptimal. Tyrosine kinase inhibitors (TKIs) confer benefit in a subset of AML patients harboring FLT3-ITD mutations, yet relapse and resistance are common. These failures are driven by both intrinsic properties of leukemic stem cells (LSCs)--a quiescent, self-renewing population--and extrinsic cues from the tumor microenvironment. We previously demonstrated that arteriolar endothelial cells (ECs) produce miR-126, which is transferred to LSCs, promoting quiescence, treatment resistance, and niche retention. During disease progression, TNF- secreted by expanding blasts suppresses EC miR-126 production, enabling LSCs and their progeny to proliferate. Following TKI administration, blast reduction lowers TNF- levels, restoring EC miR-126 production and enabling LSCs to re-enter quiescence--thereby escaping therapy and facilitating relapse. To investigate this dynamic, we developed an agent-based computational model of the AML bone marrow microenvironment, parameterized with in vitro and in vivo data. The model captures vascular niche remodeling and the feedback between leukemic populations and endothelial signaling. Simulations reveal that LSC protection mediated by miR-126 can be overcome by combining TKIs with miRisten, a miR-126 inhibitor. When administered on a defined schedule, this combination disrupts the protective niche and enhances LSC eradication. These findings underscore the therapeutic potential of targeting microenvironmental feedback to overcome resistance and prevent AML relapse.

cancer biology↗

bioRxiv2025

Longitudinal single cell RNA-sequencing reveals evolution of micro- and macro-states in chronic myeloid leukemia

Single cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cancer, yet identifying meaningful disease states from single cell data remains challenging. Here, we systematically explore the chronic myeloid leukemia (CML) specific information content encoded in single cell versus bulk transcriptomics to resolve this paradox and clarify how discrete disease-defining states emerge from inherently noisy single cell data. We demonstrate that, while CML single cell transcriptomes exist along continuous transcriptional microstates, clinically relevant leukemia phenotypes clearly manifest only at the pseudobulk (macrostate) level. By leveraging state-transition theory, we reveal how robust disease phenotype state-transitions are governed by cell type specific contributions. Our results establish a theoretical framework explaining why discrete disease phenotypes remain hidden at the single cell scale but emerge clearly at the aggregated macrostate level, enabling previously inaccessible biological insights into leukemia evolution. By resolving how single-cell variation aggregates into macroscopic disease states, our framework provides new insight into CML progression and offers a broadly applicable strategy for exploring disease dynamics across cancers and other complex conditions.

systems biology↗

bioRxiv2025

Modeling cerebral development in vitro with L-MYC-immortalized human neural stem cell-derived organoids

A promising advance for ex vivo studies of human brain development and formulation of therapeutic strategies has been the adoption of brain organoids that, to a greater extent than monolayer or spheroid cultures, recapitulate to varying extents the patterns of tissue development and cell differentiation of human brain. Previously, such studies been hampered by limited access to relevant human tissue, inadequate human in vitro models, and the necessity of using rodent models that imperfectly reproduce human brain physiology. Here we present a novel organoid-based research platform utilizing L-MYC-immortalized human fetal neural stem cells (LMNSC01) grown in a physiological 4% oxygen environment. We visualized developmental processes in LMNSC01 brain organoids for over 120 days in vitro by immunofluorescence and NanoString gene expression profiling. Gene expression patterns revealed by NanoString profiling were quantitatively compared to those occurring during normal brain development (BrainSpan database) using the Singscore method. We observe similar developmental patterns in LMNSC01 organoids and developing cortex for genes characterizing neurons, astrocytes, and oligodendrocytes, and multiple pathways including those involved in apoptosis, neuronal cytoskeleton, neurotransmission, and metabolism. Notable properties of this LMNSC01 platform are its initiation with immortalized authentic human neural stem cells, growth in a physiological oxygen environment, the consistency of the organoids produced, and favorable comparison of their gene expression patterns with those reported for normal cortical development. SUMMARYEx vivo studies of human brain development has been advanced by adoption of organoids recapitulating to varying extents in utero patterns of tissue development and cell differentiation. We here present an organoid-based human cortical development platform employing immortalized fetal neural stem cells (LMNSC01) grown in a physiological (4% oxygen) environment. Characterizing LMNSC01 organoids for over 120 days in vitro by immunofluorescence and expression profiling (using NanoString), and then comparing these profiles to those of normal cortical development (BrainSpan database), revealed similar developmental patterns for neurons, astrocytes and oligodendrocytes. Notable properties of this platform are its initiation with immortalized authentic human NSCs, growth at physiological oxygen concentration, and subsequent favorable comparison of their gene expression patterns with those observed during cortical development.

neuroscience↗

bioRxiv2024

A genome assembly and annotation for the Australian alpine skink Bassiana duperreyi using long-read technologies

The eastern three-lined skink (Bassiana duperreyi) inhabits the Australian high country in the southwest of the continent including Tasmania. It is an oviparous species that is distinctive because it undergoes sex reversal (from XX genotypic females to phenotypic males) at low incubation temperatures. We present a chromosome-scale genome assembly of a Bassiana duperreyi XY male individual, constructed using a combination of PacBio HiFi and ONT long reads scaffolded using Illumina HiC data. The genome assembly length is 1.57 Gb with a scaffold N50 of 222 Mbp, N90 of 26 Mbp, 200 gaps and 43.10% GC content. Most (95%) of the assembly is scaffolded into 6 macrochromosomes, 8 microchromosomes and the X chromosome, corresponding to the karyotype. Fragmented Y chromosome scaffolds (n=11 > 1 Mbp) were identified using Y-specific contigs generated by genome subtraction. We identified two novel alpha-satellite repeats of 187 bp and 199 bp in the putative centromeres that did not form higher order repeats. The genome assembly exceeds the standard recommended by the Earth Biogenome Project; 0.02% false expansions, 99.63% kmer completeness, 94.66% complete single copy BUSCO genes and an average 98.42% of transcriptome data mappable to the genome assembly. The mitochondrial genome (17,506 bp) and the model rDNA repeat unit (15,154 bp) were assembled. The B. duperreyi genome assembly has one of the highest completeness levels for a skink and will provide a resource for research focused on sex determination and thermolabile sex reversal, as an oviparous foundation species for studies of the evolution of viviparity, and for other comparative genomics studies of the Scincidae. Species TaxonomyEukaryota; Animalia; Chordata; Reptilia; Squamata; Scincidae; Lygosominae; Eugongylini; Bassiana (=Acritoscincus); Bassiana duperreyi (Gray, 1838) (NCBI: txid316450).

genomics↗

bioRxiv2023

State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid Leukemia (CML)

Chronic myeloid leukemia (CML) is initiated and maintained by BCR::ABL which is clinically targeted using tyrosine kinase inhibitors (TKIs). TKIs can induce long-term remission but are also not curative. Thus, CML is an ideal system to test our hypothesis that transcriptome-based state-transition models accurately predict cancer evolution and treatment response. We collected time-sequential blood samples from tetracycline-off (Tet-Off) BCR::ABL-inducible transgenic mice and wild-type controls. From the transcriptome, we constructed a CML statespace and a three-well leukemogenic potential landscape. The potentials stable critical points defined observable disease states. Early states were characterized by anti-CML genes opposing leukemia; late states were characterized by pro-CML genes. Genes with expression patterns shaped similarly to the potential landscape were identified as drivers of disease transition. Re-introduction of tetracycline to silence the BCR::ABL gene returned diseased mice transcriptomes to a near healthy state, without reaching it, suggesting parts of the transition are irreversible. TKI only reverted the transcriptome to an intermediate disease state, without approaching a state of health; disease relapse occurred soon after treatment. Using only the earliest time-point as initial conditions, our state-transition models accurately predicted both disease progression and treatment response, supporting this as a potentially valuable approach to time clinical intervention even before phenotypic changes become detectable.

cancer biology↗

bioRxiv2021

State-Transition Analysis of Time-Sequential microRNA Expression Predicts Development of Acute Myeloid Leukemia

MicroRNAs (miRNAs) are small non-coding RNA molecules involved in post-transcriptional regulation of gene expression and have been shown to hold prognostic value in a variety of settings, including acute myeloid leukemia (AML). However, the temporal dynamics of miRNA expression profiles as it relates to AML initiation and progression is poorly understood. Using serial samples from a mouse model of AML, we show that the miRNA transcriptome undergoes state-transition during AML initiation and progression. The AML state-transition was visualized and modeled by constructing an AML state-space from singular value decomposition of the time-series miRNA sequencing data. Within the AML state-space, we identified critical points of AML development characterized by unique differentially expressed miRNAs compared to healthy controls at critical points of leukemogenesis (early, transition, and late). Interestingly, we observed that changes in miRNA expression during leukemogenesis followed two patterns: 1) a monotonic pattern with continuously increasing or decreasing expression; and 2) a non-monotonic pattern with a local maximum or minimum at the transition critical point which was the "point of no-return" from health to AML. We validated the AML state-space and dynamics in an independent cohort of mice and demonstrated the state-transition model accurately predicted time to AML. Of note, we show that the miRNA-derived state-transition model produced a state-space and critical points that were strikingly similar, but not identical to that produced by the coding (i.e., messenger [m]RNA-based) transcriptome. This indicates that while both miRNA and mRNA expression may provide similar information, they also capture independent features of AML state-transition. SignificanceWe show that the microRNA transcriptome undergoes a global state transition during the initiation and progression of acute myeloid leukemia, and accurately predicts time to disease development.

cancer biology↗

bioRxiv2017

Transcriptome dynamics describe and predict state transition from health to leukemia

Temporal dynamics of gene expression are informative of changes associated with disease development and evolution. Given the complexity of high-dimensional temporal datasets, an analytical framework guided by a robust theory is needed to interpret time-sequential changes and to predict system dynamics. Herein, we use acute myeloid leukemia as a proof-of-principle to model gene expression dynamics in a transcriptome state-space constructed based on time-sequential RNA-sequencing data. We describe the construction of a state-transition model to identify state-transition critical points which accurately predicts leukemia development. We show an analytical approach based on state-transition critical points identified step-wise transcriptomic perturbations driving leukemia progression. Furthermore, the gene(s) trajectory and geometry of the transcriptome state-space provides biologically-relevant gene expression signals that are not synchronized in time, and allows quantification of gene(s) contribution to leukemia development. Therefore, our state-transition model can synthesize information, identify critical points to guide interpretation of transcriptome trajectories and predict disease development.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=193 HEIGHT=200 SRC=\"FIGDIR/small/238923v2_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (55K):\norg.highwire.dtl.DTLVardef@12eeaccorg.highwire.dtl.DTLVardef@1392af9org.highwire.dtl.DTLVardef@34786dorg.highwire.dtl.DTLVardef@ce78bb_HPS_FORMAT_FIGEXP M_FIG C_FIG In briefThe theory of state-transition is applied to acute myeloid leukemia (AML) to model transcriptome dynamics and trajectories in a state-space, and is used to identify critical points corresponding to critical transcriptomic perturbations that predict leukemia development.\n\nHighlightsO_LILeukemia transcriptome dynamics are modeled as movement in transcriptome state-space\nC_LIO_LIState-transition model and critical points accurately predicts leukemia development\nC_LIO_LICritical point-based approach identifies step-wise transcriptome events in leukemia\nC_LIO_LIState-based geometric analysis provides quantification of leukemogenic contribution\nC_LI

cancer biology↗