Biology subjects

Silva, G. A.

Publications and source records attributed to Silva, G. A..

8 recordsLinked to original sources

bioRxiv2026

Revision of Paraglomeromycetes unveils a new order (Pervertustales), a new family (Innosporaceae), and a new genus (Opikia) in Glomeromycota

Currently, the fungal class Paraglomeromycetes (phylum Glomeromycota) consists of one order, Paraglomerales, with two families: Paraglomeraceae, comprising the genera Paraglomus with ten species and Innospora with one species, and the monogeneric and monospecific Pervetustaceae represented by Pervetustus with Pe. simplex. However, our morphological analyses of these species and an undescribed specimen morphologically resembling other Paraglomeromycetes species, as well as analyses of sequences of these fungi and environmental sequences, indicated that the current taxonomic composition of this class should be updated. We did so here by creating in Paraglomeromycetes Pervetustales ord. nov. with Pervetustus and Opikia catenata gen. nov. and sp. nov., as well as transferring Innospora with In. majewskii to Innosporaceae fam. nov. These novelties were introduced primarily based on strong genetic divergences of the analyzed taxa, as many morphological and histochemical features of their spores and mycorrhizal structures overlap. Only Op. catenata possesses the unique trait of occasionally producing spores in chains. This mode of sporulation has also not been recognized in any other Glomeromycota species. Unlike species of Pervetustales and Innosporaceae, which produce single-walled spores, the subcellular spore structure of Paraglomus species contains one or two walls.

microbiology↗

bioRxiv2026

Revision of Archaeosporomycetes with two old and two new fungal orders: Archaeosporales, Geosiphonales, Polonosporales, and Ambisporales

Currently, the fungal class Archaeosporomycetes consists of one order, Archaeosporales with four families: Archaeosporaceae, Ambisporaceae, Geosiphonaceae, and Polonosporaceae. In the present study, the objective was to re-analyze the phylogeny and morphology of the Archaeosporomycetes from order to genus level. The different ecological strategies and, consequently, distinct evolutionary patterns of these taxa, as well as their morphological characters and other data updated here, suggest the need to divide Archaeosporales into four orders: (i) the type order Archaeosporales, (ii) Ambisporales ord. nov., both with four genera, (iii) Geosiphonales and (iv) Polonosporales ord. nov., both with single families and genera. Remarkably, the order Geosiphonales was described in the past, but was not considered in the Archaeosporomycetes until now. Phylogenetically, the four main clades (orders here proposed) of Archaeosporomycetes are well supported, with bootstrap values higher than 95% in all analyses, except Ambisporales/Ambisporaceae for RAxML-NG FBP analysis in the SSU tree (75%). Ecologically, this class includes three orders of arbuscular mycorrhizal fungi (AMF) forming symbiotic associations with plants, while Geosiphonales form an endocytobiosis with the cyanobacterium Nostoc. Morphologically, there are at least two AMF orders with spore bimorphism, which has not (yet) been described for Polonosporales. The only known species of Polonosporales, Polonospora polonica, forms spores directly on the neck of sporiferous saccules and the spores can morphologically be differentiated from all other taxa in Archaeosporomycetes by the formation of three permanent, rather thick spore walls, of which two form de novo during spore formation. The outer spore wall of Archaeosporales and Ambisporales are semi-permanent, evanescent or even short-lived, or show multiple fissures during aging, when it is more resistant. Ambisporales can easily be differentiated from Archaeosporales for instance by larger spores of the acaulosporoid morph and thicker spore walls. Our phylogenetic analyses suggested that Archaeosporales can be divided into two families: Antiquisporaceae that was described to form intraradical hyphae, vesicles and spores, staining darkly in Trypan blue, and Archaeosporaceae whose hyphae generally do not or only faintly stain in this reagent, and vesicles and intraradical spores have been rarely, if ever reported.

microbiology↗

bioRxiv2022

A Computational Model for Storing Memories in the Synaptic Structures of the Brain

Spike-timing dependent plasticity (STDP) is widely accepted as a mechanism through which the brain can learn information from different stimuli(1, 2). Basing synaptic changes on the timing between presynaptic and postsynaptic spikes enhances contributing edges within a network(3, 4). While STDP rules control the evolution of networks, most research focuses on spiking rates or specific activation paths when evaluating learned information(5-7). However, since STDP augments structural weights, synapses may also contain embedded information. While imaging studies demonstrate physical changes to synapses due to STDP, these changes have not been interrogated based on their embedding capacity of a stimulus(8-12). Here, we show that networks with biological features and STDP rules can embed information on their stimulus into their synaptic weights. We use a k-nearest neighbor algorithm on the synaptic weights of thousands of independent networks to identify their stimulus with high accuracy based on local neighborhoods, demonstrating that the network structure can store stimulus information. While spike rates and timings remain useful, structural embed-dings represent a new way to integrate information within a biological network. Our results demonstrate that there may be value in observing these changes directly. Beyond computational applications for monitoring these structural changes, this analysis may also inform investigation into neuroscience. Research is underway on the potential of astrocytes to integrate synapses in the brain and communicate that information elsewhere(13-15). In addition, observations of these synaptic embeddings may lead to novel therapies for memory disorders that are difficult to explain with current paradigms, such as transient epileptic amnesia. Significance StatementLearning in the brain is often achieved via spike-timing dependent plasticity changing the structure of synapses to augment the strength between neurons. Typically, these changes contribute to other behaviors in the network, such as spiking rates or spike timings. However, observing these changes themselves may be fruitful for interrogating the learning capability of networks in the brain. Using a computational model, we demonstrate that the synaptic weights contain an embedding of the stimulus after a certain amount of recurrent activity occurs. It is possible that networks in the brain embed information in a similar way and that external readers, such as astrocytes, can interrogate, integrate, and transport this synaptic weight information to process stimuli.

neuroscience↗

bioRxiv2021

All-optical electrophysiology in hiPSC-derived neurons with synthetic voltage sensors

Voltage imaging and "all-optical electrophysiology" in human induced pluripotent stem cell (hiPSC)-derived neurons have opened unprecedented opportunities for high-throughput phenotyping of activity in neurons possessing unique genetic backgrounds of individual patients. While prior all-optical electrophysiology studies relied on genetically encoded voltage indicators, viral transduction of human neurons with large or multiple expression vectors can impact cell function and often lead to massive cell death. Here, we demonstrate an alternative protocol using a synthetic voltage sensor and genetically encoded optogenetic actuator that generate robust and reproducible results. We demonstrate the functionality of this method by measuring spontaneous and evoked activity in three independent hiPSC-derived neuronal cell lines with distinct genetic backgrounds.

neuroscience↗

bioRxiv2020

Understanding the Human Brain using Brain Organoids and a Structure-Function Theory

A basic neurobiology-clinical trial paradigm motivates our use of constrained mathematical models and analysis of personalized human-derived brain organoids toward predicting clinical outcomes and safely developing new therapeutics. Physical constraints imposed on the brain can guide the analyses an interpretation of experimental data and the construction of mathematical models that attempt to make sense of how the brain works and how cognitive functions emerge. Development of these mathematical models for human-derived brain organoids offer an opportunity for testing new hypotheses about the human brain. When it comes to testing ideas about the brain that require a careful balance between experimental accessibility, manipulation, and complexity, in order to connect neurobiological details with higher level cognitive properties and clinical considerations, we argue that fundamental structure-function constraints applied to models of brain organoids offer a path forward. Moreover, we show these constraints appear in canonical and novel math models of neural activity and learning, and we make the case that constraint-based modeling and use of representations can bridge to machine learning for powerful mutual benefit.

neuroscience↗

bioRxiv2020

Computing temporal sequences associated with dynamic patterns on the C. elegans connectome

Understanding how the structural connectivity of a network constrains the dynamics it is able to support is a very active and open area of research. We simulated the plausible dynamics resulting from the known C. elegans connectome using a recent model and theoretical analysis that computes the dynamics of neurobiological networks by focusing on how local interactions among connected neurons give rise to the global dynamics in an emergent way, independent of the biophysical or molecular details of the cells themselves. We studied the dynamics which resulted from stimulating a chemosensory neuron (ASEL) in a known feeding circuit, both in isolation and embedded in the full connectome. We show that contralateral motor neuron activations in ventral (VB) and dorsal (DB) classes of motor neurons emerged from the simulations, which are qualitatively similar to rhythmic motor neuron firing pattern associated with locomotion of the worm. One interpretation of these results is that there is an inherent - and we propose - purposeful structural wiring to the C. elegans connectome that has evolved to serve specific behavioral functions. To study network signaling pathways responsible for the dynamics we developed an analytic framework that constructs Temporal Sequences (TSeq), time-ordered walks of signals on graphs. We found that only 5% of TSeq are preserved between the isolated feeding network relative to its embedded counterpart. The remaining 95% of signaling pathways computed in the isolated network are not present in the embedded network. This suggests a cautionary note for computational studies of isolated neurobiological circuits and networks.

neuroscience↗

bioRxiv2020

Super-selective reconstruction of causal and direct connectivity with application to in-vitro iPSC neuronal networks

Despite advancements in the development of cell-based in-vitro neuronal network models, the lack of appropriate computational tools limits their analyses. Methods aimed at deciphering the effective connections between neurons from extracellular spike recordings would increase utility of in-vitro local neural circuits, especially for studies of human neural development and disease based on induced pluripotent stem cells (hiPSC). Current techniques allow statistical inference of functional couplings in the network but are fundamentally unable to correctly identify indirect and apparent connections between neurons, generating redundant maps with limited ability to model the causal dynamics of the network. In this paper, we describe a novel mathematically rigorous, model-free method to map effective - direct and causal - connectivity of neuronal networks from multi-electrode array data. The inference algorithm uses a combination of statistical and deterministic indicators which, first, enables identification of all existing functional links in the network and then, reconstructs the directed and causal connection diagram via a super-selective rule enabling highly accurate classification of direct, indirect and apparent links. Our method can be generally applied to the functional characterization of any in-vitro neuronal networks. Here, we show that, given its accuracy, it can offer important insights into the functional development of in-vitro iPSC-derived neuronal cultures.

neuroscience↗

bioRxiv2020

Human-derived NLS enhance the gene transfer efficiency of chitosan

Nuclear import is considered one of the major limitations for non-viral gene delivery systems and the incorporation of nuclear localization signals (NLS) that mediate nuclear intake can be used as a strategy to enhance internalization of exogenous DNA. In this work, human-derived endogenous NLS peptides based on Insulin Growth Factor Binding Proteins (IGFBP), namely IGFBP-3 and IGFBP-5, were tested for their ability to improve nuclear translocation of genetic material by non-viral vectors. Several strategies were tested to determine their effect on chitosan mediated transfection efficiency: co-administration with polyplexes, co-complexation at the time of polyplex formation, and covalent ligation to chitosan. Our results show that co-complexation and covalent ligation of human-derived NLS peptides to chitosan polyplexes yields a 2-fold increase in transfection efficiency. These results indicate that the integration of IGFBP-NLS peptides into polyplexes has potential as a strategy to enhance the efficiency of non-viral vectors.

bioengineering↗