Biology subjects

George, H.

Publications and source records attributed to George, H..

6 recordsLinked to original sources

bioRxiv2026

Multiscale biological interactions define clinical trajectories in acute myeloid leukemia

Cancer is characterized by complex interactions across genetic, cellular, and microenvironmental scales. However, a quantitative understanding of how these interactions shape clinical trajectories remains limited. Here, we present a multi-scale single-cell dataset from 184 treatment-naive acute myeloid leukemia (AML) patients spanning all major genetic subtypes, together with an analytical framework to dissect interactions across biological scales. We show that distinct clinical outcomes are encoded by specific cross-scale, cross-compartment interactions present at diagnosis: response to induction therapy is governed by interactions between genetic alterations and leukemic differentiation state; relapse following chemotherapy is associated with non-genetic programs linked to metabolism; and relapse after allogeneic stem cell transplantation is driven by interactions between the immune microenvironment and residual healthy hematopoiesis. Together, our study provides a framework to resolve intra- and inter-patient heterogeneity in cancer and supports a model in which clinical trajectories in AML emerge from defined interactions across biological scales.

cancer biology↗

bioRxiv2025

Deep Learning-Based Drug Repurposing Using Knowledge Graph Embeddings and GraphRAG

Drug repurposing has become a crucial strategy to accelerate drug discovery and reduce development costs. Conventional drug development is time consuming and expensive; it often takes more than a decade and billions of dollars to bring a new drug into the market. To address these challenges, this work puts forward a deep learning-based strategy for drug repurposing using knowledge graph embeddings and Graph-based Retrieval-Augmented Generation. The system leverages pretrained TransE model embeddings on the Drug Repurposing Knowledge Graph (DRKG) that includes over 97,000 biomedical entities and 4.4 million relationships. In the proposed framework, knowledge graph embeddings have been combined with LLMs to provide explainable predictions of drug repurposing. The system processes natural language queries about diseases, retrieves relevant drug candidates by deep learning based scoring, and generates interpretable explanations of drug disease relationships. This research combines PyTorch based embedding operations with NetworkX graph analysis and OpenAIs GPT models for an efficient, accurate, and explainable solution to automated drug repurposing, thus helping researchers and clinicians identify novel therapeutic applications for existing drugs.

bioengineering↗

bioRxiv2024

How smart was T. rex? Testing claims of exceptional cognition in dinosaurs and the application of neuron count estimates in palaeontological research

Recent years have seen increasing scientific interest in whether neuron counts can act as correlates of diverse biological phenomena. Lately, Herculano-Houzel (2023) argued that fossil endocasts and comparative neurological data from extant sauropsids allow to reconstruct telencephalic neuron counts in Mesozoic dinosaurs and pterosaurs, which might act as proxies for behaviors and life history traits in these animals. According to this analysis, large theropods such as Tyrannosaurus rex were long-lived, exceptionally intelligent animals equipped with "macaque- or baboon-like cognition" whereas sauropods as well as most ornithischian dinosaurs would have displayed significantly smaller brains and an ectothermic physiology. Besides challenging established views on Mesozoic dinosaur biology, these claims raise questions on whether neuron count estimates could benefit research on fossil animals in general. Here, we address these findings by revisiting Herculano-Houzels (2023) work, identifying several crucial shortcomings regarding analysis and interpretation. We present revised estimates of encephalization and telencephalic neuron counts in dinosaurs, which we derive from phylogenetically informed modeling and an amended dataset of endocranial measurements. For large-bodied theropods in particular, we recover significantly lower neuron counts than previously proposed. Furthermore, we review the suitability of neurological variables such as neuron numbers and relative brain size to predict cognitive complexity, metabolic rate and life history traits in dinosaurs, coming to the conclusion that they are flawed proxies of these biological phenomena. Instead of relying on such neurological estimates when reconstructing Mesozoic dinosaur biology, we argue that integrative studies are needed to approach this complex subject.

zoology↗

bioRxiv2023

Rapid Adeno-Associated Virus Genome Quantification with Amplification-Free CRISPR-Cas12a

Efficient manufacturing of recombinant Adeno-Associated Viral (rAAV) vectors to meet rising clinical demand remains a major hurdle. One of the most significant challenges is the generation of large amounts of empty capsids without the therapeutic genome. There is no standardized analytical method to accurately quantify the viral genes, and subsequently the empty-to-full ratio, making the manufacturing challenges even more complex. We propose the use of CRISPR diagnostics (CRISPR-Dx) as a robust and rapid approach to determine AAV genome titers. We designed and developed the CRISPR-AAV Evaluation (CRAAVE) assay to maximize sensitivity, minimize time-to-result, and provide a potentially universal design for quantifying multiple transgene constructs encapsidated within different AAV serotypes. We also demonstrate an on-chip CRAAVE assay with lyophilized reagents to minimize end user assay input. The CRAAVE assay was able to detect AAV titers as low as 7e7 vg/mL with high precision (<3% error) in quantifying unknown AAV titers when compared with conventional quantitative PCR (qPCR) method. The assay only requires 30 minutes of assay time, shortening the analytical workflow drastically. Our results suggest CRISPR-Dx could be a promising tool for efficient rAAV genome titer quantification and has the potential to revolutionize biomanufacturing process analytical technology (PAT). Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=183 HEIGHT=200 SRC="FIGDIR/small/567134v1_ufig1.gif" ALT="Figure 1"> View larger version (24K): org.highwire.dtl.DTLVardef@1bfce18org.highwire.dtl.DTLVardef@17393c9org.highwire.dtl.DTLVardef@eb7c1forg.highwire.dtl.DTLVardef@2203cd_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2019

Growth factor-mediated coupling between lineage size and cell fate choice underlies robustness of mammalian development

Precise control and maintenance of the size of cell populations is fundamental for organismal development and homeostasis. The three cell types that comprise the mammalian blastocyst-stage embryo are generated in precise proportions and over a short time, suggesting a size control mechanism ensures a reproducible outcome. Guided by experimental observations, we developed a minimal mathematical model that shows growth factor signaling is sufficient to guarantee this robustness. The model anticipates, without additional parameter fitting, the response of the embryo to perturbations in its lineage composition. We experimentally added lineage-restricted cells to the epiblast both in vivo and in silico, which resulted in a shift of the fate of progenitors away from the supernumerary cell type, while eliminating cells using laser ablation biased the specification of progenitors towards the targeted cell type. Finally, we show that FGF4 couples cell fate decisions to lineage composition through changes in local concentration of the growth factor. Our results provide a basis for the regulative abilities of the mammalian embryo and reveal how, in a self-organizing system, individual cell fate decisions are coordinated at the population level to robustly generate tissues in the right proportions.

developmental biology↗

bioRxiv2019

Extensive Mammalian Germline Genome Engineering

Xenotransplantation, specifically the use of porcine organs for human transplantation, has long been sought after as an alternative for patients suffering from organ failure. However, clinical application of this approach has been impeded by two main hurdles: 1) risk of transmission of porcine endogenous retroviruses (PERVs) and 2) molecular incompatibilities between donor pigs and humans which culminate in rejection of the graft. We previously demonstrated that all 25 copies of the PERV elements in the pig genome could be inactivated and live pigs successfully generated. In this study, we improved the scale of porcine germline editing from targeting a single repetitive locus with CRISPR to engineering 13 different genes using multiple genome engineering methods. we engineered the pig genome at 42 alleles using CRISPR-Cas9 and transposon and produced PERVKO{middle dot}3KO{middle dot}9TG pigs which carry PERV inactivation, xeno-antigen KO and 9 effective human transgenes. The engineered pigs exhibit normal physiology, fertility, and germline transmission of the edited alleles. In vitro assays demonstrated that these pigs gain significant resistance to human humoral and cell mediated damage, and coagulation dysregulations, similar to that of allotransplantation. Successful creation of PERVKO{middle dot}3KO{middle dot}9TG pigs represents a significant step forward towards safe and effective porcine xenotransplantation, which also represents a synthetic biology accomplishment of engineering novel functions in a living organism. One Sentence SummaryExtensive genome engineering is applied to modify pigs for safe and immune compatible organs for human transplantation

synthetic biology↗