Biology subjects

Scoglio, C. M.

Publications and source records attributed to Scoglio, C. M..

7 recordsLinked to original sources

bioRxiv2023

CLIPB4 is a central node in the protease network that regulates humoral immunity in Anopheles gambiae mosquitoes

Insect humoral immune responses are regulated in part by protease cascades, whose components circulate as zymogens in the hemolymph. In mosquitoes, these cascades consist of clip domain serine proteases (cSPs) and/or their non-catalytic homologs (cSPHs), which form a complex network, whose molecular make-up is not fully understood. Using a systems biology approach, based on a co-expression network of gene family members that function in melanization and co-immunoprecipitation using the serine protease inhibitor (SRPN)2, a key negative regulator of the melanization response in mosquitoes, we identify the cSP CLIPB4 from the African malaria mosquito Anopheles gambiae as a central node in this protease network. CLIPB4 is tightly co-expressed with SRPN2 and forms protein complexes with SRPN2 in the hemolymph of immune-challenged female mosquitoes. Genetic and biochemical approaches validate our network analysis and show that CLIPB4 is required for melanization and antibacterial immunity, acting as a prophenoloxidase (proPO)-activating protease, which is inhibited by SRPN2. In addition, we provide novel insight into the structural organization of the cSP network in An. gambiae, by demonstrating that CLIPB4 is able to activate proCLIPB8, a cSP upstream of the proPO-activating protease CLIPB9. These data provide the first evidence that, in mosquitoes, cSPs provide branching points in immune protease networks and deliver positive reinforcement in proPO activation cascades.

immunology↗

bioRxiv2022

A global Anopheles gambiae gene co-expression network constructed from hundreds of experimental conditions with missing values

Gene co-expression networks can be used to determine gene regulation and attribute gene function to biological processes. Different high throughput technologies, including one and two-channel microarrays and RNA-sequencing, allow evaluating thousands of gene expression data simultaneously, but these methodologies provide results that cannot be directly compared. Thus, it is complex to analyze coexpression relations between genes, especially when there are missing values arising for experimental reasons. Networks are a helpful tool for studying gene co-expression, where nodes represent genes and edges represent co-expression of pairs of genes. In this paper, we propose a method for constructing a gene co-expression network for the Anopheles gambiae transcriptome from 257 unique studies obtained with different methodologies and experimental designs. We introduce the sliding threshold approach to select node pairs with high Pearson correlation coefficients. The robustness of the method was verified by comparing edge weight distributions under random removal of conditions. The properties of the constructed network are studied in this paper, including node degree distribution, coreness, and community structure. The network core is largely comprised of genes that encode components of the mitochondrial respiratory chain and the ribosome, while different communities are enriched for genes involved in distinct biological processes. This suggests that the overall network structure is driven to maximize the integration of essential cellular functions, possibly allowing the flexibility to add novel functions.

bioinformatics↗

bioRxiv2020

Towards resilient beef cattle production systems: Impact of truck contamination and information sharing on foot-and-mouth disease spreading

As cattle movement data in the United States are scarce due to the absence of mandatory traceability programs, previous epidemic models for U.S. cattle production systems heavily rely on contact rates estimated based on expert opinions and survey data. These models are often based on static networks and ignore the sequence of movement, possibly overestimating the epidemic sizes. In this research, we adapt and employ an agent-based model that simulates beef cattle production and transportation in southwest Kansas to analyze the between-premises transmission of a highly contagious disease, the foot-and-mouth disease. First, we assess the impact of truck contamination on the disease transmission with the truck agent following an independent clean-infected-clean cycle. Second, we add an information-sharing functionality such that producers/packers can trace back and forward their trade records to inform their trade partners during outbreaks. Scenario analysis results show that including indirect contact routes between premises via truck movements can significantly increase the amplitude of disease spread, compared with equivalent scenarios that only consider animal movement. Mitigation strategies informed by information sharing can dramatically improve the system resilience against epidemics, highlighting the benefit of promoting information sharing in the cattle industry. In addition, we identify salient characteristics that must be considered when designing an information-sharing strategy, including the number of days to trace back and forward in the trade records and the role of different cattle supply chain stakeholders. Sensitivity analysis results show that epidemic sizes are sensitive to variations in parameters of fomite survival time and indirect contact transmission probability and future studies can focus on a more accurate estimation of these parameters.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pathology↗

bioRxiv2019

Risk assessment of Ebola virus disease spreading in Uganda using a multilayer temporal network

Network-based modelling of infectious diseases apply compartmental models on a contact network, which makes the epidemic process crucially dependent on the network structure. For highly contagious diseases such as Ebola virus disease (EVD), the inter-personal contact plays the most vital role in the human to human transmission. Therefore, for accurate representation of the EVD spreading, the contact network needs to resemble the reality. Prior research work has mainly focused on static networks (only permanent contacts) or activity driven networks (only temporal contacts) for Ebola spreading. A comprehensive network for EVD spreading should include both these network structures, as there are always some permanent contacts together with temporal contacts. Therefore, we propose a multilayer temporal network for Uganda, which is at risk of Ebola outbreak from the neighboring Democratic Republic of Congo (DRC) epidemic. The network has a permanent layer representing permanent contacts among individuals within family level, and a data driven temporal network for human movements motivated by cattle trade, fish trade, or general communications. We propose a Gillespie algorithm with the susceptible-infected-recovered (SIR) compartmental model to simulate the evolution of the EVD spreading as well as to evaluate the risk throughout our network. As an example, we applied our method to a multilayer network consisting of 23 districts along different movement routes in Uganda starting from bordering districts of DRC to Kampala. Simulation results shows that some regions are at higher risk of infection, suggesting some focal points for Ebola preparedness and providing direction to inform interventions in the field. Simulation results also shows that decreasing physical contacts as well as increasing preventive measures result in a reduction of chances to develop an outbreak. Overall, the main contribution of this paper lies in the novel method for risk assessment, the accuracy of which can be increased by increasing the amount and the accuracy of the data used to build the network and the model.

systems biology↗

bioRxiv2019

Understanding the survival of Zika virus in a vector interconnected sexual contact network

The recent outbreaks of the insect-vectored Zika virus have demonstrated its potential to be sexually transmitted, which complicates modeling and our understanding of disease dynamics. Autochthonous outbreaks in the US mainland may be a consequence of both modes of transmission, which affect the outbreak size, duration, and virus persistence. We propose a novel individual-based interconnected network model that incorporates both insect-vectored and sexual transmission of this pathogen. This model interconnects a homogeneous mosquito vector population with a heterogeneous human host contact network. The model incorporates the seasonal variation of mosquito abundance and characterizes host dynamics based on age group and gender in order to produce realistic projections. We use a sexual contact network which is generated on the basis of real world sexual behavior data. Our findings suggest that for a high relative transmissibility of asymptomatic hosts, Zika virus shows a high probability of sustaining in the human population for up to 3 months without the presence of mosquito vectors. Zika outbreaks are strongly affected by the large proportion of asymptomatic individuals and their relative transmissibility. The outbreak size is also affected by the time of the year when the pathogen is introduced. Although sexual transmission has a relatively low contribution in determining the epidemic size, it plays a role in sustaining the epidemic and creating potential endemic scenarios.

epidemiology↗

bioRxiv2018

A spatio-temporal individual-based network framework for West Nile virus in the USA: spreading pattern of West Nile virus

West Nile virus (WNV)--a mosquito-borne arbovirus-- entered the USA through New York City in 1999 and spread to the contiguous USA within three years while transitioning from epidemic outbreaks to endemic transmission. The virus is transmitted by vector competent mosquitoes and maintained in the avian populations. WNV spatial distribution is mainly determined by the movement of residential and migratory avian populations. We developed an individual-level heterogeneous network framework across the USA with the goal of understanding the long-range spatial distribution of WNV. To this end, we proposed three distance dispersal kernels model: 1) exponential--short-range dispersal, 2) power-law--long-range dispersal in all directions, and 3) power-law biased by flyway direction--long-range dispersal only along established migratory routes. To select the appropriate dispersal kernel we used the human case data and adopted a model selection framework based on approximate Bayesian computation with sequential Monte Carlo sampling (ABC-SMC). From estimated parameters, we find that the power-law biased by flyway direction kernel is the best kernel to fit WNV human case data, supporting the hypothesis of long-range WNV transmission is mainly along the migratory bird flyways. Through extensive simulation from 2014 to 2016, we proposed and tested hypothetical mitigation strategies and found that mosquito population reduction in the infected states and neighboring states is potentially cost-effective.\n\nAuthor summaryThe underlying pattern of West Nile virus (WNV) geographic spread across the United States is not completely clear, which is a necessary step for continental or state level mitigation strategies to reduce WNV transmission. We report a network model that explains the geographic spread of WNV in the United States. West Nile virus is a mosquito-borne pathogen that infects many avian species with different movement ranges. From our research, we found that migration patterns and routes play an essential role in the WNV spatial distribution. The virus spreads in all directions at short distances because of local birds and short-distance migratory birds. However, the virus also disperses long distances along the avian migratory routes. Our model is designed to be flexible and therefore can be used to explore spreading patterns of other infectious diseases in other geographic locations.

epidemiology↗

bioRxiv2018

Individual-based network model for Rift Valley Fever in Kabale District, Uganda, to guide mitigation measures: A One Health Model

Rift Valley fever (RVF) is a zoonotic disease which causes significant morbidity and mortality among ungulate livestock and humans in endemic regions. In the major RVF epizootic regions of East Africa, the causative agent of the disease, Rift Valley fever virus (RVFV), is primarily transmitted by multiple mosquito species in Aedes, Culex, and Mansonia genera during both epizootic and enzootic periods in a complex transmission cycle largely driven by the environment. However, recent RVFV activity in Uganda demonstrated that RVFV could also spread into new regions through livestock movements, and underscored the need to develop effective mitigation strategies to reduce transmission and prevent spread among cattle operations. We simulated RVFV transmission among cattle in different sub counties of Kabale District in Uganda using real world livestock data in a network-based model. This model considered livestock as spatially explicit factors in different sub-counties subjected to specific vector mosquito and environmental factors, and was configured to investigate and quantitatively evaluate the relative impacts of mosquito control, livestock movement regulations, and diversity in cattle populations on the spread of the RVF epizootic. We concluded that cattle movement should be restricted during periods of high vector mosquito abundance to control the epizootic spreading among sub-counties. On the other hand we found that mosquito control would only be sufficient to control the epizootic when mosquito abundance was low. Importantly, simulation results also showed that cattle populations with a higher diversity with regard to indigenous combined with exotic breeds led to reduced numbers of infected cattle compared to more homogenous cattle populations.

epidemiology↗