510 research outputs found
Genome editing of candidate genes related to disease resistance to Piscirickettsia salmonis in Atlantic salmon (Salmo salar)
Salmon Rickettsial Syndrome (SRS), caused by the bacterium Piscirickettsia salmonis, is one of the most severe infectious diseases threatening the Chilean Atlantic salmon industry. Among the leading causes of mortality and morbidity, SRS significantly affect the seawater production stage, where biomass losses account for a major economic impact.
One potential avenue to tackle SRS is the improvement of host resistance using selective breeding. To accomplish this, insight into the genetic basis of host response, identifying specific genes and pathways involved in this response, and comprehending the potential function these genes have in infection overcome, is valuable.
Consequently, this study aims to identify functional genes and pathways that contribute to genetic host resistance to SRS and investigate the effect of CRISPR/Cas9 knockout on these genes during P.salmonis infection.
Candidate genes were identified from a previous in vivo large-scale infection study of 2,265 Atlantic salmon smolts injected with P.salmonis and genotyped. These data were used to estimate SRS resistance breeding values. Head-kidney and liver samples for RNA-Seq were obtained from 48 individuals at pre-infection, 3 and 9 days post-infection, and tests of differential expression between pre- and post-infection, and between high and low resistance breeding values were performed. From the thousands of differentially expressed genes, enrichment of several KEGG pathways related to immune response such as bacterial internalisation, intracellular trafficking, apoptosis, and inflammasome was observed in both tissues in fish relatively more resistant to infection. A literature review of the biological function of genes in these pathways highlighted the most suitable candidates for functional studies.
Subsequently, five genes related to SRS resistance were successfully edited using a CRISPR/Cas9 Ribonucleoprotein (RNP) transfection to knockout these genes in an Atlantic salmon cell line (SHK-1). An in vitro infection challenge model of the knockout and control cell lines with P.salmonis was performed to elucidate the impact on cytopathic damage, cell viability and bacterial load during infection. These findings suggest a promising avenue of research into the genetic architecture of host resistance to SRS
Scene-selectivity in CA1/subicular complex: Multivoxel pattern analysis at 7T
Prior univariate functional magnetic resonance imaging (fMRI) studies in humans suggest that the anteromedial subicular complex of the hippocampus is a hub for scene-based cognition. However, it is possible that univariate approaches were not sufficiently sensitive to detect scene-related activity in other subfields that have been implicated in spatial processing (e.g., CA1). Further, as connectivity-based functional gradients in the hippocampus do not respect classical subfield boundary definitions, category selectivity may be distributed across anatomical subfields. Region-of-interest approaches, therefore, may limit our ability to observe category selectivity across discrete subfield boundaries. To address these issues, we applied searchlight multivariate pattern analysis to 7T fMRI data of healthy adults who undertook a simultaneous visual odd-one-out discrimination task for scene and non-scene (including face) visual stimuli, hypothesising that scene classification would be possible in multiple hippocampal regions within, but not constrained to, anteromedial subicular complex and CA1. Indeed, we found that the scene-selective searchlight map overlapped not only with anteromedial subicular complex (distal subiculum, pre/para subiculum), but also inferior CA1, alongside posteromedial (including retrosplenial) and parahippocampal cortices. Probabilistic overlap maps revealed gradients of scene category selectivity, with the strongest overlap located in the medial hippocampus, converging with searchlight findings. This was contrasted with gradients of face category selectivity, which had stronger overlap in more lateral hippocampus, supporting ideas of parallel processing streams for these two categories. Our work helps to map the scene, in contrast to, face processing networks within, and connected to, the human hippocampus
Complexity Science in Human Change
This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience
Interrogating the interconnected biological networks in liver diseases reveals the core components of a perturbed homeostatic system.
This thesis explores the interplay between genetics, environment, and immunological regulation in metabolic associated fatty liver disease (MAFLD), metabolic steatohepatitis (MeSH), and hepatocellular carcinoma (HCC), focusing on liver response to dietary exposome and bone marrow hematopoietic stem and progenitor cells (HSPCs) activity. Using weighted gene co-expression network analysis (WGCNA), we assessed mRNA expression in murine models and human datasets, identifying conserved metabolic and immunological programs and discrepancies in immune responses. The heightened immune response in certain mouse models, reflective of bone marrow HSPCs response, is found protective and consistent with human data, emphasizing the crucial role of immune system-tumorigenesis interplay.
Investigating regulatory factors, we spotlight bile acids’ significance. Maintaining a robust immune response is linked to reduced liver tumor burden, with HSPC dietary response as a potential regulatory factor. While cholesterol homeostasis disruptions alone don’t stimulate HSPCs, when combined with disrupted bile acid homeostasis, they significantly impact HSPCs. Rescuing bile acid synthesis dampens HSPC activity, underscoring bile acids' regulatory role.
Our findings provide valuable insights into the intricate regulatory networks governing liver disease, presenting potential new avenues for research, including exploring bile acid metabolism’s direct regulation of bone marrow HSPCs, assessing the long-term impact of HSPC stimulation, and investigating liver cholesterol homeostasis’s effect on immunotherapy response. This research suggests exploration of minimal therapeutics targeting sensitive targets and context-driven interpretation in animal model extrapolation. Overall, our experimental approach shows potential in aiding the development of effective treatments for liver diseases, paving the way for future studies in this field
Higher-order interactions in single-cell gene expression: towards a cybergenetic semantics of cell state
Finding and understanding patterns in gene expression guides our understanding of living organisms, their development, and diseases, but is a challenging and high-dimensional problem as there are many molecules involved. One way to learn about the structure of a gene regulatory network is by studying the interdependencies among its constituents in transcriptomic data sets. These interdependencies could be arbitrarily complex, but almost all current models of gene regulation contain pairwise interactions only, despite experimental evidence existing for higher-order regulation that cannot be decomposed into pairwise mechanisms. I set out to capture these higher-order dependencies in single-cell RNA-seq data using two different approaches. First, I fitted maximum entropy (or Ising) models to expression data by training restricted Boltzmann machines (RBMs). On simulated data, RBMs faithfully reproduced both pairwise and third-order interactions. I then trained RBMs on 37 genes from a scRNA-seq data set of 70k astrocytes from an embryonic mouse. While pairwise and third-order interactions were revealed, the estimates contained a strong omitted variable bias, and there was no statistically sound and tractable way to quantify the uncertainty in the estimates. As a result I next adopted a model-free approach. Estimating model-free interactions (MFIs) in single-cell gene expression data required a quasi-causal graph of conditional dependencies among the genes, which I inferred with an MCMC graph-optimisation algorithm on an initial estimate found by the Peter-Clark algorithm. As the estimates are model-free, MFIs can be interpreted either as mechanistic relationships between the genes, or as substructures in the cell population. On simulated data, MFIs revealed synergy and higher-order mechanisms in various logical and causal dynamics more accurately than any correlation- or information-based quantities. I then estimated MFIs among 1,000 genes, at up to seventh-order, in 20k neurons and 20k astrocytes from two different mouse brain scRNA-seq data sets: one developmental, and one adolescent. I found strong evidence for up to fifth-order interactions, and the MFIs mostly disambiguated direct from indirect regulation by preferentially coupling causally connected genes, whereas correlations persisted across causal chains. Validating the predicted interactions against the Pathway Commons database, gene ontology annotations, and semantic similarity, I found that pairwise MFIs contained different but a similar amount of mechanistic information relative to networks based on correlation. Furthermore, third-order interactions provided evidence of combinatorial regulation by transcription factors and immediate early genes.
I then switched focus from mechanism to population structure. Each significant MFI can be assigned a set of single cells that most influence its value. Hierarchical clustering of the MFIs by cell assignment revealed substructures in the cell population corresponding to diverse cell states. This offered a new, purely data-driven view on cell states because the inferred states are not required to localise in gene expression space. Across the four data sets, I found 69 significant and biologically interpretable cell states, where only 9 could be obtained by standard approaches. I identified immature neurons among developing astrocytes and radial glial cells, D1 and D2 medium spiny neurons, D1 MSN subtypes, and cell-cycle related states present across four data sets. I further found evidence for states defined by genes associated to neuropeptide signalling, neuronal activity, myelin metabolism, and genomic imprinting. MFIs thus provide a new, statistically sound method to detect substructure in single-cell gene expression data, identifying cell types, subtypes, or states that can be delocalised in gene expression space and whose hierarchical structure provides a new view on the semantics of cell state. The estimation of the quasi-causal graph, the MFIs, and inference of the associated states is implemented as a publicly available Nextflow pipeline called Stator
Pathogenesis Study of Glioma: From Glioma Stem Cells, Genomic Tags, to Rodent Models
Despite the past two decades of research progress, glioma still remains the most challenging of all primary central nervous system (CNS) tumors. The complexity of its pathogenesis makes the disease hard to deal with, especially glioblastoma multiforme (GBM, WHO grade IV), the most aggressive brain tumor entity. This Special Issue collect studies that focus on the pathogenesis of glioma, such as its cell origin, the role of GSCs, genomic alterations, animal models, etc. We have collected 10 high-quality papers, and we welcome all researchers to read
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
LIPIcs, Volume 277, GIScience 2023, Complete Volume
LIPIcs, Volume 277, GIScience 2023, Complete Volum
- …