Biology subjects

Orosz, A.

Publications and source records attributed to Orosz, A..

6 recordsLinked to original sources

bioRxiv2023

TMPyP binding evokes a complex, tunable nanomechanical response in DNA

TMPyP is a porphyrin capable of DNA binding and used in photodynamic therapy and G-quadruplex stabilization. Despite its broad applications, the effect of TMPyP on DNA nanomechanics is unknown. Here we investigated, by manipulating {lambda}-phage DNA with optical tweezers combined with microfluidics, how TMPyP influences DNA nanomechanics across a wide range of TMPyP concentration (5-5120 nM), mechanical force (0-100 pN), NaCl concentration (0.01-1 M) and pulling rate (0.2-20 m/s). Complex responses were recorded, for the analysis of which we introduced a simple mathematical model. TMPyP binding leads to the lengthening and softening of dsDNA. dsDNA stability, measured as the force of DNAs overstretch transition, increased at low (<10 nM) TMPyP concentrations, then decreased progressively upon increasing TMPyP concentration. The cooperativity of the overstretch transition decreased, due most likely to mechanical roadblocks of ssDNA-bound TMPyP. TMPyP binding increased ssDNAs contour length. The addition of NaCl at high (1 M) concentration competed with many of the nanomechanical changes evoked by TMPyP. Because the largest amplitude of the nanomechanical changes are induced by TMPyP in the pharmacologically relevant nanomolar concentration range, this porphyrin derivative may be used to tune DNAs structure and properties, hence control the myriad of biomolecular processes associated with DNA.

biophysics↗

bioRxiv2023

Absolute number of three populations of interneurons and GABAergic synapses in the human hippocampus

The human hippocampus, essential for learning and memory, is implicated in numerous neurological and psychiatric disorders, each linked to specific neuronal subpopulations. Advancing our understanding of hippocampal function requires computational models grounded in precise quantitative neuronal data. While extensive data exist on the neuronal composition and synaptic architecture of the rodent hippocampus, analogous quantitative data for the human hippocampus remain very limited. Given the critical role of local GABAergic interneurons in modulating hippocampal functions, we employed unbiased stereological techniques to estimate the density and total number of three major GABAergic cell types in the human hippocampus: parvalbumin (PV)-expressing, somatostatin (SOM)-positive, and calretinin (CR)-positive interneurons. Our findings reveal an estimated 49,400 PV-positive, 141,500 SOM-positive, and 250,600 CR-positive interneurons per hippocampal hemisphere. Notably, the higher proportion of CR-positive, primarily interneuron-selective, cells in humans, compared to rodents, may enhance local interneuron regulation. Additionally, using 3-dimensional electron microscopy, we estimated approximately 25 billion GABAergic synapses per hippocampal hemisphere, with PV-positive boutons comprising around 3.5 billion synapses, or 14% of the total GABAergic synapses. These findings contribute crucial quantitative insights for modeling human hippocampal circuits and understanding its complex regulatory dynamics.

neuroscience↗

bioRxiv2023

Synaptic and dendritic architecture of two types of hippocampal somatostatin interneurons

GABAergic inhibitory neurons fundamentally shape the activity and plasticity of cortical circuits. A major subset of these neurons contains somatostatin (SOM); these cells play crucial roles in neuroplasticity, learning and memory in many brain areas including the hippocampus, and are implicated in several neuropsychiatric diseases and neurodegenerative disorders. Two main types of SOM-containing cells in area CA1 of the hippocampus are oriens-lacunosum-moleculare (OLM) cells and hippocampo-septal (HS) cells. These cell types show many similarities in their soma-dendritic architecture, but they have different axonal targets, display different activity patterns in vivo and are thought to have distinct network functions. However, a complete understanding of the functional roles of these interneurons requires a precise description of their intrinsic computational properties and their synaptic interactions. In the current study we generated, analyzed and make available several key datasets that enable a quantitative comparison of various anatomical and physiological properties of OLM and HS cells in mouse. The dataset includes detailed scanning electron microscopy-based 3-dimensional reconstructions of OLM and HS cells along with their excitatory and inhibitory synaptic inputs. Combining this core dataset with other anatomical data, patch-clamp electrophysiology and compartmental modeling, we examined the precise morphological structure, inputs, outputs, and basic physiological properties of these cells. Our results highlight key differences between OLM and HS cells, particularly regarding the density and distribution of their synaptic inputs and mitochondria. For instance, we estimated that an OLM cell receives about 8400, whereas an HS cell about 15600 synaptic inputs, about 16% of which are GABAergic. Our data and models provide insight into the possible basis of the different functionality of OLM and HS cell types and supply essential information for more detailed functional models of these neurons and the hippocampal network.

neuroscience↗

bioRxiv2022

Set1 regulates telomere function via H3K4 methylation-dependent and independent pathways and calibrates the abundance of telomere maintenance factors

Set1 is an H3K4 methyltransferase which comprises the catalytic subunit of the COMPASS complex and has been implicated in transcription, DNA repair, cell cycle control, and numerous other genomic functions. Set1 also promotes proper telomere maintenance, as cells lacking Set1 have short telomeres and disrupted subtelomeric gene repression; however, the precise role for Set1 in these processes has not been fully defined. In this study, we have tested mutants of Set1 and the COMPASS complex which differentially alter H3K4 methylation status and attempt to separate catalytic and non-catalytic functions of Set1. Our data reveal that Set1-dependent subtelomeric gene repression relies on its catalytic activity towards H3K4, whereas telomere length is regulated by Set1 catalytic activity but likely independent of the H3K4 substrate. Furthermore, we uncover a role for Set1 in calibrating the abundance of critical telomere maintenance proteins, including components of the telomerase holoenzyme and members of the telomere capping CST (Cdc13-Stn1-Ten1) complex, through both transcriptional and post-transcriptional pathways. Altogether, our data provide new insights into the H3K4 methylation-dependent and independent roles for Set1 in telomere maintenance in yeast and shed light on possible roles for Set1-related methyltransferases in other systems.

molecular biology↗

bioRxiv2021

Absolute quantitation of serum antibody reactivity using the Richards growth model for antigen microspot titration

In spite of its pivotal role in the characterization of humoral immunity, there is no accepted method for the absolute quantitation of antigen specific serum antibodies. We devised a novel method to quantify polyclonal antibody reactivity, which exploits protein microspot assays and employs a novel analytical approach. Microarrays with a density series of disease specific antigen were treated with different serum dilutions and developed for IgG and IgA binding. By fitting binding data of both dilution series to a product of two generalized logistic functions, we obtained estimates of antibody reactivity of two immunoglobulin classes simultaneously. These estimates are the antigen concentrations required for reaching the inflection point of thermodynamic activity coefficient of antibodies and the limiting activity coefficient of antigen. By providing universal chemical units, this approach may improve standardization of serological testing, the quality control of antibodies and the quantitative mapping of antibody-antigen interaction space.

immunology↗

bioRxiv2020

Socio-Ecological Network Structures from Process Graphs

We propose a process graph (P-graph) approach to develop ecosystem networks from knowledge of the properties of the component species. Originally developed as a process engineering tool for designing industrial plants, the P-graph framework has key advantages over conventional ecological network analysis (ENA) techniques. A P-graph is a bipartite graph consisting of two types of nodes, which we propose to represent components of an ecosystem. Compartments within ecosystems (e.g., organism species) are represented by one class of nodes, while the roles or functions that they play relative to other compartments are represented by a second class of nodes. This bipartite graph representation enables a powerful, unambiguous representation of relationships among ecosystem compartments, which can come in tangible (e.g., mass flow in predation) or intangible form (e.g., symbiosis). For example, within a P-graph, the distinct roles of bees as pollinators for some plants and as prey for some animals can be explicitly represented, which would not otherwise be possible using conventional ENA. After a discussion of the mapping of ecosystems into P-graph, we also discuss how this framework can be used to guide understanding of complex networks that exist in nature. Two component algorithms of P-graph, namely maximal structure generation (MSG) and solution structure generation (SSG), are shown to be particularly useful for ENA. This method can be used to determine the (a) effects of loss of specific ecosystem compartments due to extinction, (b) potential efficacy of ecosystem reconstruction efforts, and (c) maximum sustainable exploitation of human ecosystem services by humans. We illustrate the use of P-graph for the analysis of ecosystem compartment loss using a small-scale stylized case study, and further propose a new criticality index that can be easily derived from SSG results. Author summaryIn this study, we propose the novel application of the process graph (P-graph) methodology to the analysis of ecological networks. P-graph was originally developed for engineering design problems; in our work, we show how its five axioms and two algorithms - maximal structure generation (MSG) and solution structure generation (SSG) can be adapted to the problem of understanding complex interactions in ecosystems. The methodology allows multiple types of interactions among ecosystem components to be handled simultaneously based on representation as a bipartite graph. Complete network structures can be deduced from knowledge of local interactions of components using MSG. Finally, all structurally feasible networks of viable ecosystems can be identified with SSG. We illustrate the features of the P-graph methodology with a stylized illustrative example.

ecology↗