Biology subjects

Orlando, G.

Publications and source records attributed to Orlando, G..

11 recordsLinked to original sources

bioRxiv2025

Medin drives Aβ40 to adopt Aβ42-like fibril polymorphs in vitro

Medin, a vascular amyloid derived from MFG-E8, is the most prevalent form of localized human amyloid and co-localizes with A{beta} in Alzheimers disease and, in particular, cerebral amyloid angiopathy (CAA). While it was shown that medin can promote A{beta} aggregation, it remains unclear whether this amyloid-amyloid interaction affects the structure of the resulting fibrils. Here, we investigate how medin modulates A{beta}40 fibril assembly in vitro using cryo-electron microscopy, aggregation kinetics, and immunogold electron microscopy. We show that medin accelerates A{beta}40 aggregation, co-assembles into hybrid fibrils, and modulates fibril morphology. Cryo-EM analysis reveals two fibril populations: one corresponding to a previously described in vitro A{beta}40 morphology, and a second, previously unobserved polymorph with A{beta}42-like features, including a structured N-terminus and a compact hydrophobic C-terminal core. The presence of a peripheral, unresolved cryo-EM density near the fibril surface suggests that the new polymorph is stabilised through heterotypic interactions, yet the atomic details remain unresolved, likely due to substantial structural heterogeneity. Rather than representing a limitation, this highlights how not all determinants critical for fibril assembly are necessarily ordered or resolvable in the final fibril structure, reflecting the inherent dynamic and heterogeneous nature of amyloid interactions. Our findings provide structural evidence that heterotypic co-aggregation can redirect A{beta}40 into distinct conformational states and suggest that dynamic or transient interactions contribute to fibril polymorphism beyond what can be fully captured in static structural models.

biochemistry↗

bioRxiv2025

Dose Modeling for Mitochondrial Transplantation in Barrier-Limited Tissues

Mitochondrial transplantation is an emerging regenerative therapy aimed at preventing ischemia- reperfusion injury (IRI) by restoring oxidative metabolism in vulnerable tissues. While early clinical studies have demonstrated feasibility in pediatric myocardium and safety in adult stroke patients, dosing strategies remain undefined for barrier-limited organs where autoregulation, restricted perfusion, and anatomical constraints limit parenchymal uptake. We developed a physiologically grounded computational model to estimate route-specific input dose requirements for brain, spinal cord, and kidney. The model integrates organ-level parameters including tissue mass, regional perfusion or cerebrospinal flow, permeability-surface area product, and extraction efficiency. A benchmark dose of 2 x 106 mitochondria per gram, derived from validated cardiac protocols, was applied across delivery scenarios. Sensitivity analysis explored the effects of extraction fraction and tissue mass. Route-adjusted input requirements diverged quantitatively from cardiac-derived estimates, with up to 2-fold increases in central nervous system (CNS) targets and approximately 20% higher dosing required for the kidney. This framework provides a quantitative foundation for translational dose planning in IRI and related conditions.

pharmacology and toxicology↗

bioRxiv2025

Scalable 3D Bioprinting of Human Islets in a Pancreatic Decellularized Extracellular Matrix-Enriched Bioink for Beta-Cell Replacement Therapy

Allogeneic cell transplantation such as beta-cell replacement for treatment of type 1 diabetes (T1D) is constrained by poor graft survival and functionality, immune rejection, and the lack of scalable biomanufacturing processes. Here, we engineered functional human islet constructs that replicate the physiomimetic human pancreatic microenvironment by employing a clinically-scalable 3D bioprinting system. To support human islet viability and function, we developed alginate-based bioinks incorporating human pancreatic decellularized extracellular matrix (dECM). These bioink formulations were optimized for shear-thinning properties for extrusion of human islets, as well as selective permeability that supports nutrient and therapeutic molecule exchange. Extrusion-based printing parameters were refined to minimize shear stress-induced damage to human islets. The resulting bioprinted pancreatic constructs demonstrated robust structural integrity, high human islet viability (>85%), and long-term glucose-stimulated insulin secretion (GSIS) over a 21-days in vitro culture period, even at a high islet packing density (10,000 islet equivalent/mL) while free islet controls displayed a significant functional decline. The higher performance of bioprinted islets maybe attributable to the supportive 3D dECM-rich microenvironment mitigating culture-induced stress by recapitulating the islet pancreatic niche. This scalable 3D dECM-alginate bioprinted platform represents a new advanced functional material for advancing clinically translatable bio-artificial pancreas therapies for T1D.

bioengineering↗

bioRxiv2024

In-silico identification of archaeal DNA-binding proteins

The rapid advancement of next-generation sequencing technologies has generated an immense volume of genetic data. However, this data is unevenly distributed, with well-studied organisms being disproportionately represented, while other organisms, such as from archaea, remain significantly underexplored. The study of archaea is particularly challenging due to the extreme environments they inhabit and the difficulties associated with culturing them in the laboratory. Despite these challenges, archaea likely represent a crucial evolutionary link between eukaryotic and prokaryotic organisms, and their investigation could shed light on the early stages of life on Earth. Yet, a significant portion of archaeal proteins are annotated with limited or inaccurate information. Among the various classes of archaeal proteins, DNA-binding proteins are of particular importance. While they represent a large portion of every known proteome, their identification in archaea is complicated by the substantial evolutionary divergence between archaeal and the other better studied organisms. To address the challenges of identifying DNA-binding proteins in archaea, we developed Xenusia, a neural network-based tool capable of screening entire archaeal proteomes to identify DNA-binding proteins. Xenusia has proven effective across diverse datasets, including metagenomics data, successfully identifying novel DNA-binding proteins, with experimental validation of its predictions. Xenusia is available as a PyPI package, with source code accessible at https://github.com/grogdrinker/xenusia, and as a Google Colab web server application at https://colab.research.google.com/drive/1c4eb4sEz8OsBqHL62XDFrqmwa7CxImww?usp=sharing.

bioinformatics↗

bioRxiv2024

De novo design of high-affinity single-domain antibodies

Antibody-based therapeutics have become indispensable in modern medicine, but traditional methods of antibody discovery often present with limitations in developability, cross-reactivity, and ethical concerns. While deep learning and generative approaches have shown promise in the design of high affinity protein binders, de novo antibody design remains challenging. Here, we present EvolveX, a structure-based computational pipeline for designing antibody fragments. EvolveX utilizes ModelX and empirical force field FoldX to optimize complementarity- determining regions (CDRs) and TANGO for aggregation analysis. We demonstrate the ability of EvolveX to redesign a single-domain VHH antibody fragment targeting mouse Vsig4 to address two challenges: enhancing stability and affinity for the original target and redesigning it for high affinity to the human ortholog. The redesigned variants of VHH fragments specific to mouse Vsig4 showed improved physicochemical properties, while retaining binding affinities comparable to the original version. Notably, EvolveX improved the binding affinity of VHHs to human Vsig4 by over 1000-fold, transforming low-affinity binders into nanomolar-affinity molecules. Structural analyses by X-ray crystallography and NMR confirmed the accuracy of the designs, which display optimized interactions with the antigen. NGS and re-modelling analysis further demonstrated the efficiency of FoldX-based design pipeline. Collectively, our study highlights EvolveXs potential to overcome current limitations in antibody design, offering a powerful tool for the development of next-generation therapeutics with enhanced specificity, stability, and efficacy.

bioinformatics↗

bioRxiv2023

Chromothripsis orchestrates leukemic transformation in blast phase MPN through targetable amplification of DYRK1A

Chromothripsis, the process of catastrophic shattering and haphazard repair of chromosomes, is a common event in cancer. Whether chromothripsis might constitute an actionable molecular event amenable to therapeutic targeting remains an open question. We describe recurrent chromothripsis of chromosome 21 in a subset of patients in blast phase of a myeloproliferative neoplasm (BP-MPN), which alongside other structural variants leads to amplification of a region of chromosome 21 in [~]25% of patients ( chr21amp). We report that chr21amp BP-MPN has a particularly aggressive and treatment-resistant phenotype. The chr21amp event is highly clonal and present throughout the hematopoietic hierarchy. DYRK1A, a serine threonine kinase and transcription factor, is the only gene in the 2.7Mb minimally amplified region which showed both increased expression and chromatin accessibility compared to non-chr21amp BP-MPN controls. We demonstrate that DYRK1A is a central node at the nexus of multiple cellular functions critical for BP-MPN development, including DNA repair, STAT signalling and BCL2 overexpression. DYRK1A is essential for BP-MPN cell proliferation in vitro and in vivo, and DYRK1A inhibition synergises with BCL2 targeting to induce BP-MPN cell apoptosis. Collectively, these findings define the chr21amp event as a prognostic biomarker in BP-MPN and link chromothripsis to a druggable target.

cancer biology↗

bioRxiv2023

Data-driven probabilistic definition of the low energy conformational states of protein residues

Protein dynamics and related conformational changes are essential for their function but difficult to characterise and interpret. Amino acids in a protein behave according to their local energy landscape, which is determined by their local structural context and environmental conditions. The lowest energy state for a given residue can correspond to sharply defined conformations, e.g., in a stable helix, or can cover a wide range of conformations, e.g., in intrinsically disordered regions. A good definition of such low energy states is therefore important to describe the behavior of a residue and how it changes with its environment. We propose a data-driven probabilistic definition of six low energy conformational states typically accessible for amino acid residues in proteins. This definition is based on solution NMR information of 1,322 proteins through a combined analysis of structure ensembles with interpreted chemical shifts. We further introduce a conformational state variability parameter that captures, based on an ensemble of protein structures from molecular dynamics or other methods, how often a residue moves between these conformational states. The approach enables a different perspective on the local conformational behavior of proteins that is complementary to their static interpretation from single structure models.

bioinformatics↗

bioRxiv2023

Integrating physics in deep learning algorithms: A force field as a PyTorch module

Deep learning algorithms applied to structural biology often struggle to converge to meaningful solutions when limited data is available, since they are required to learn complex physical rules from examples. State-of-the-art force-fields, however, cannot interface with deep learning algorithms due to their implementation. We present MadraX, a forcefield implemented as a differentiable PyTorch module, able to interact with deep learning algorithms in an end-to-end fashion. MadraX is available at madrax.readthedocs.io

bioinformatics↗

bioRxiv2022

Predicting regulators of epithelial cell state through regularized regression analysis of single cell multiomic sequencing

Chronic disease processes are marked by cell-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We developed a regularized regression approach, RENIN, (Regulatory Network Inference) to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generated a single nucleus multiomic dataset from seven adult human kidney biopsies and applied RENIN to study drivers of a failed injury response associated with kidney disease. We demonstrate that RENIN is highly effective tool at predicting key cis- and trans-regulatory elements.

genomics↗

bioRxiv2020

Prediction of disordered regions in proteins with recurrent Neural Networks and protein dynamics

The role of intrinsically disordered protein regions (IDRs) in cellular processes has become increasingly evident over the last years. These IDRs continue to challenge structural biology experiments because they lack a well-defined conformation, and bioinformatics approaches that accurately delineate disordered protein regions remain essential for their identification and further investigation. Typically, these predictors use only the protein amino acid sequence, without taking into account likely emergent properties that are sequence context dependent, such as protein backbone dynamics. The DisoMine method predicts protein disorder with recurrent neural networks not directly from the amino acid sequence, but instead from more generic predictions of key biophysical properties, here protein dynamics, secondary structure and early folding. The tool is fast and requires only a single sequence, making it applicable for large-scale screening, including poorly studied and orphan proteins. DisoMine compares well to 10 state of the art predictors, also if these use evolutionary information. DisoMine is freely available through an interactive webserver at http://bio2byte.com/disomine/

bioinformatics↗

bioRxiv2020

Membrane estrogen receptor (GPER) and follicle-stimulating hormone receptor heteromeric complexes promote human ovarian follicle survival

Classically, follicle stimulating hormone receptor (FSHR) driven cAMP-mediated signaling boosts human ovarian follicle growth and would be essential for oocyte maturation. However, contradicting in vitro suggest a different view on physiological and clinical significance of FSHR-mediated cAMP signaling. We found that the G protein coupled estrogen receptor (GPER) heteromerizes with FSHR, reprogramming cAMP/death signals into proliferative stimuli fundamental for sustaining oocyte survival. In human granulosa cells, survival signals are effectively delivered upon equal expression levels of both receptors, while they are missing at high FSHR:GPER ratio, which negatively impacts follicle maturation and strongly correlates with FSH responsiveness of patients undergoing controlled ovarian stimulation. Consistent with high FSHR expression levels during follicular selection, cell viability is dramatically reduced in FSHR overexpressing cells due to preferential coupling to the Gs protein/cAMP pathway. In contrast, FSHR/GPER heteromer formation resulted in FSH-triggered anti-apoptotic/proliferative signaling delivered via the G{beta}{gamma} dimer while heteromer impairment or GPER-associated Gs inhibitory protein complexes resulted in cell death. GPER-depleted granulosa cells have an amplified FSH-dependent decrease in cell viability and steroidogenesis, consistent with the requirement of estrogen signaling for successful oocyte growth. Therefore, our findings indicate how oocyte maturation depends on the capability of GPER to shape FSHR selective signals, indicating hormone receptor heteromers may be a marker of cell proliferation. One Sentence SummaryFSHR/GPER heteromers block cAMP-dependent selection of ovarian follicles and target tumor growth and poor FSH-response in women.

physiology↗