Biology subjects

Golovko, G.

Publications and source records attributed to Golovko, G..

11 recordsLinked to original sources

bioRxiv2025

Mosquito host background influences microbiome-ZIKV interactions in field and laboratory-reared Aedes aegypti

The mosquito microbiota represents an intricate assemblage of microorganisms, comprising bacteria, fungi, viruses, and protozoa. Factors modulating microbiome abundance and composition include host genetic background, environmental parameters, and pathogen exposure. Conversely, the microbiome profoundly influences pathogen infection of the mosquito host and thus harbours considerable potential to impact the transmission of vector-borne diseases. As such, there is a growing interest in using the microbiome in novel vector-control strategies, including exploiting the natural ability of some microbes to interfere with infection of the vectors by pathogens. However, before novel microbiome-based vector control approaches can move towards translation, a more complete understanding of the interactions between mosquitoes, their microbiome, and the pathogens they transmit, is required to better appreciate how variation in the microbiome of field mosquitoes affects these interactions. To examine the impact of the host background and the associated diversity of microbiomes within distinct hosts, but without artificially manipulating the microbiome, we exposed several laboratory-reared and field-collected Aedes aegypti mosquito lines to Zika virus (ZIKV) and correlated their microbial load and composition to pathogen exposure and viral infection success. We observed significant differences in ZIKV exposure outcomes between the different mosquito lines and their associated microbiomes, and found that ZIKV alteration of the microbiomes was distinct in different lines. We also identified microbial taxa correlating with either ZIKV infection or a lack of infection. In summary, our study provides novel insights into the variability of pathogen interactions within the mosquito holobiont. A more complete understanding of which factors influence the tripartite interactions between Aedes mosquitoes, their microbiome, and arboviral pathogens, will be critical for the development of microbial-based interventions aimed at reducing vector-borne disease burden. Author summary.The mosquito microbiome composition differs within an individual across its development, as well as between individual mosquitoes at the same developmental stage, and between spatially or genomically different mosquito populations. The microbiome is highly relevant for the ability of mosquitoes to transmit pathogens. Furthermore, certain microbes have been shown to influence pathogen infection of the mosquito, while conversely, infection with a pathogen can alter the mosquito microbiome. However, we have a poor understanding how universally conserved these pathogen-related effects observed in a specific host-microbiome combination are in different mosquito populations with their respective microbiomes. To address this, we infected different mosquito lines, either reared in the laboratory or caught in the field and examined the microbiomes after exposure to Zika virus (ZIKV) compared to unchallenged microbiomes. We also examined how the virus infection progressed in different mosquito lines and correlations with further microbiome changes. The observed microbiome responses differed between host lines, potentially due to either different microbiomes associated with the respective hosts. Alternatively, the host may respond differently to the viral infection, which subsequently alters the microbiome in a distinct manner, or a combination of host and microbiome effects may occur. As microbes are being evaluated for novel approaches to control mosquito-borne disease, our findings are highly relevant to contribute to a more complete understanding of host-microbe interactions which will be critical to develop these approaches. Variation of the microbiome of different mosquito lines need to be considered in experimental designs and when interpreting results from specific studies. It is especially relevant for deployment of interventions in the field where microbial variability is known to be higher and where variation is observed between mosquito populations.

microbiology↗

bioRxiv2023

Interkingdom interactions shape the fungal microbiome of mosquitoes

The mosquito microbiome is an important modulator of vector competence and vectoral capacity. Unlike the extensively studied bacterial microbiome, fungal communities in the mosquito microbiome (mycobiome) remain largely unexplored. To work towards getting an improved understanding of the fungi associated with mosquitoes, we sequenced the mycobiome of three field-collected and laboratory-reared mosquito species (Aedes albopictus, Aedes aegypti, and Culex quinquefasciatus). Our analysis showed both environment and host species were contributing to the diversity of the fungal microbiome of mosquitoes. When comparing species, Ae. albopictus possessed a higher number of diverse fungal taxa than Cx. quinquefasciatus, while strikingly less than 1% of reads from Ae. aegypti samples were fungal. Fungal reads from Ae. aegypti were <1% even after inhibiting host amplification using a PNA blocker, indicating that this species lacked a significant fungal microbiome that was amplified using this sequencing approach. Using a mono-association mosquito infection model, we confirmed that mosquito-derived fungal isolates colonize and for Aedes mosquitoes, support growth and development at comparable rates to their bacterial counterparts. Strikingly, native bacterial taxa isolated from mosquitoes impeded the colonization of symbiotic fungi in Ae. aegypti suggesting interkingdom interactions shape fungal microbiome communities. Collectively, this study adds to our understanding of the fungal microbiome of different mosquito species, that these fungal microbes support growth and development, and highlights that microbial interactions underpin fungal colonization of these medically relevent species.

microbiology↗

bioRxiv2021

Multidimensional Boolean Patterns in Multi-omics Data

MotivationVirtually all biological systems are governed by a set of complex relations between their components. Identification of relations within biological systems involves a rigorous search for patterns among variables/parameters. Two-dimensional (involving two variables) patterns are identified using correlation, covariation, and mutual information approaches. However, these approaches are not suited to identify more complicated multidimensional relations, which simultaneously include 3, 4, and more variables. ResultsWe present a novel pattern-specific method to quantify the strength and estimate the statistical significance of multidimensional Boolean patterns in multiomics data. In contrast with dimensionality reduction and AI solutions, patterns identified by the proposed approach may provide a better background for meaningful mechanistic interpretation of the biological processes. Our preliminary analysis suggests that multidimensional patterns may dominate the landscape of multi-omics data, which is not surprising because complex interactions between components of biological systems are unlikely to be reduced to simple pairwise interactions.

bioinformatics↗

bioRxiv2020

Endothelial exosome plays functional role during rickettsial infection

Spotted fever group rickettsioses (SFRs) are devastating human infections. Vascular endothelial cells (ECs) are the primary targets of infection. Edema resulting from EC barrier dysfunction occurs in the brain and lungs in most cases of lethal SFR, but the underlying mechanisms remain unclear. The aim of the study is to explore the potential role of Rickettsia (R)-infected, EC-derived exosomes (Exos) during infection. Using size-exclusion chromatography (SEC), we purified Exos from conditioned, filtered, bacteria-free media collected from R-infected human umbilical vein ECs (HUVECs) (R-ECExos) and plasma of R-infected mice (R-plsExos). We observed that rickettsial infection increases the release of heterogeneous plsExos, but endothelial exosomal size, morphology, and production were not significantly altered following infection. Compared to normal plsExos and ECExos, both R-plsExos and R-ECExos induced dysfunction of recipient normal brain microvascular Ecs (BMECs). The effect of R-plsExos on mouse recipient BMEC barrier function is dose-dependent. The effect of R-ECExos on human recipient BMEC barrier function is dependent on exosomal RNA cargo. Next-generation sequencing analysis and stem-loop quantitative reverse transcription PCR (RT-qPCR) validation revealed that R infection triggered the selective enrichment of endothelial exosomal mir-23a and mir-30b, which target the endothelial barrier. To our knowledge, this is the first report on the functional role of extracellular vesicles following infection by obligately intracellular bacteria. ImportanceSpotted fever group rickettsioses are devastating human infections. Vascular endothelial cells are the primary targets of infection. Edema resulting from endothelial barrier dysfunction occurs in the brain and lungs in most cases of lethal rickettsioses, but the underlying mechanisms remain unclear. The aim of the study is to explore the potential role of Rickettsia-infected, endothelial cell-derived exosomes during infection. We observed that rickettsial infection increases the release of heterogeneous plasma Exos, but endothelial exosomal size, morphology, and production were not significantly altered following infection. Rickettsia-infected, endothelial cell-derived exosomes induced dysfunction of recipient normal brain microvascular endothelial cells. The effect is dependent on exosomal RNA cargo. Next-generation sequencing analysis revealed that rickettsial infection triggered the selective enrichment of endothelial exosomal mir-23a and mir-30b, which target the endothelial barrier. To our knowledge, this is the first report on the functional role of extracellular vesicles following infection by obligately intracellular bacteria.

microbiology↗

bioRxiv2020

Microbial interactions in the mosquito gut determine Serratia colonization and blood feeding propensity.

How microbe-microbe interactions dictate microbial complexity in the mosquito gut is unclear. Previously we found that Serratia, a gut symbiont that alters vector competence and is being considered for vector control, poorly colonized Aedes aegypti yet was abundant in Culex quinquefasciatus reared under identical conditions. To investigate the incompatibility between Serratia and Ae. aegypti, we characterized two distinct strains of Serratia marcescens from Cx. quinquefasciatus and examined their ability to infect Ae. aegypti. Both Serratia strains poorly infected Ae. aegypti, but when microbiome homeostasis was disrupted, the prevalence and titers of Serratia were similar to the infection in its native host. Examination of multiple genetically diverse Ae. aegypti lines found microbial interference to S. marcescens was commonplace, however one line of Ae. aegypti was susceptible to infection. Microbiome analysis of resistant and susceptible lines indicated an inverse correlation between Enterobacteriaceae bacteria and Serratia, and experimental co-infections in a gnotobiotic system recapitulated the interference phenotype. Furthermore, we observed an effect on host behaviour; Serratia exposure to Ae. aegypti disrupted their feeding behaviour, and this phenotype was also reliant on interactions with their native microbiota. Our work highlights the complexity of host-microbe interactions and provides evidence that microbial interactions influence mosquito behaviour.

microbiology↗

bioRxiv2019

Identification of Complex Multidimensional Patterns in Microbial Communities

MotivationIdentification of complex relationships within members of microbial communities is key to understand and guide microbial transplantation and provide personalized anti-microbial and probiotic treatments. Since members of a given microbial community can be simultaneously involved in multiple relations that altogether will determine their abundance, not all significant relations between organisms are expected to be manifested as visually uninterrupted patterns and be detected using traditional correlation nor mutual information coefficient based approaches. ResultsThis manuscript proposes a pattern specific way to quantify the strength and estimate the statistical significance of two-dimensional co-presence, co-exclusion, and one-way relations patterns between abundance profiles of two organisms which can be extended to three or more dimensional patterns. Presented approach can also be extended by including a variety of physical (pH, temperature, oxygen concentration) and biochemical (antimicrobial susceptibility, nutrient and metabolite concentration) variables into the search for multidimensional patterns. The presented approach has been tested using 2,380 microbiome samples from the Human Microbiome Project resulting in body-site specific networks of statistically significant 2D patterns. We also were able to demonstrate the presence of several 3D patterns in the Human Microbiome Project data. AvailabilityC++ source code for two and three-dimensional patterns, as well as executable files for the Pickle pipeline, are in the attached supplementary materials. Contactgegolovk@utmb.edu

bioinformatics↗

bioRxiv2018

Novel Wolbachia strains in Anopheles malaria vectors from Sub-Saharan Africa

Anopheles (An.) mosquitoes contain bacteria that can influence Plasmodium parasites. Wolbachia, a common insect endosymbiont, has historically been considered absent from Anopheles but has recently been found in An. gambiae populations. Here, we assessed a range of Anopheles species from five malaria-endemic countries for Wolbachia and Plasmodium infection. Strikingly, we found Wolbachia infections in An. coluzzii, An. gambiae s.s, An. arabiensis, An. moucheti and An. species A increasing the number of Anopheles species known to be naturally infected by this endosymbiont. Molecular analysis suggests the presence of phylogenetically diverse novel strains, while qPCR and 16S rRNA sequencing indicates that Wolbachia is the dominant member of the microbiota in An. moucheti and An. species A. We found no evidence of Wolbachia/Asaia co-infections, and presence of these endosymbionts did not have significant effects on malaria prevalence. We discuss the importance of novel Wolbachia strains in Anopheles and potential implications for disease control.

microbiology↗

bioRxiv2018

Microbiome interaction networks and community structure from lab-reared and field-collected Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus mosquito vectors.

Microbial interactions are an underappreciated force in shaping insect microbiome communities. Although pairwise patterns of symbiont interactions have been identified, we have a poor understanding regarding the scale and the nature of co-occurrence and co-exclusion interactions within the microbiome. To characterize these patterns in mosquitoes, we sequenced the bacterial microbiome of Aedes aegypti, Ae. albopictus, and Culex quinquefasciatus caught in the field or reared in the laboratory and used these data to generate interaction networks. For collections, we used traps that attracted host-seeking or ovipositing female mosquitoes to determine how physiological state affects the microbiome under field conditions. Interestingly, we saw few differences in species richness or microbiome community structure in mosquitoes caught in either trap. Co-occurrence and co-exclusion analysis identified 116 pairwise interactions substantially increasing the list of bacterial interactions observed in mosquitoes. Networks generated from the microbiome of Ae. aegypti often included highly interconnected hub bacteria. There were several instances where co-occurring bacteria co-excluded a third taxa, suggesting the existence of tripartite relationships. Several associations were observed in multiple species or in field and laboratory-reared mosquitoes indicating these associations are robust and not influenced by environmental or host factors. To demonstrate that microbial interactions can influence colonization of the host, we administered symbionts to Ae. aegypti larvae that either possessed or lacked their resident microbiota. We found that the presence of resident microbiota can inhibit colonization of particular bacterial taxa. Our results highlight that microbial interactions in mosquitoes are complex and influence microbiome composition.

microbiology↗

bioRxiv2018

Using High Throughput DNA Sequencing to Evaluate the Accuracy of Serial Dilution Based Tests of Microbial Activities in Oil Pipelines

Microbial activities have detrimental effects on industrial infrastructure. If not controlled, microbial presence can result in corrosion, biofilm formation, and product degradation. Serial dilution tests are routinely used for evaluating presence and abundance of microorganisms by diluting samples and culturing microbes in specific media designed to support microorganisms with particular properties, such as sulfate reduction.\n\nA high-throughput sequencing approach was used to evaluate changes in microbial composition during four standard serial dilution tests. Analysis of 159 isolates revealed significant differences in the microbial compositions of sequential serial dilution titers and identified several cases where: (a) bacteria known to have a detrimental metabolic function (such as acid production) were lost in the serial dilution medium designed to test for this function; (b) bacteria virtually absent in the original sample became dominant in the serial dilution medium. These observations raise concerns regarding the accuracy and overall usefulness of serial dilution tests.

microbiology↗

bioRxiv2018

Detection of Multidimensional Co-Exclusion Patterns in Microbial Communities

MotivationIdentification of complex relationships among members of microbial communities is key to understand and control the microbiota. Co-exclusion is arguably one of the most important patterns reflecting microorganisms intolerance to each others presence. Knowing these relations opens an opportunity to manipulate microbiotas, personalize anti-microbial and probiotic treatments as well as guide microbiota transplantation. The co-exclusion pattern however, cannot be appropriately described by a linear function nor its strength be estimated using covariance or (negative) Pearson and Spearman correlation coefficients. This manuscript proposes a way to quantify the strength and evaluate the statistical significance of co-exclusion patterns between two, three or more variables describing a microbiota and allows one to extend analysis beyond microorganism abundance by including other microbiome associated measurements such as, pH, temperature etc., as well as estimate the expected numbers of false positive co-exclusion patterns in a co-exclusion network.\n\nResultsThe implemented computational pipeline (CoEx) tested against 2,380 microbial profiles (samples) from The Human Microbiome Project resulted in body-site specific pairwise co-exclusion patterns.\n\nAvailabilityC++ source code for calculation of the score and p-value for 2, 3, and 4 dimensional co-exclusion patterns as well as source code and executable files for the CoEx pipeline are available at https://scsb.utmb.edu/labgroups/fofanov/co-exclusion_in_microbial_communities.asp\n\nContactlealbayr@utmb.edu\n\nSupplementary informationSupplementary data are available at biorxiv online.

genomics↗

bioRxiv2018

Broom: Application for non-redundant storage of High Throughput Sequencing data

MotivationThe data generation capabilities of High Throughput Sequencing (HTS) instruments have exponentially increased over the last few years, while the cost of sequencing has dramatically decreased allowing this technology to become widely used in biomedical studies. For small labs and individual researchers, however, storage and transfer of large amounts of HTS data present a significant challenge. The recent trends in increased sequencing quality and genome coverage can be used to reconsider HTS data storage strategies.\n\nResultsWe present Broom, a stand-alone application designed to select and store only high-quality sequencing reads at extremely high compression rates. Written in C++, the application accepts single and paired-end reads in FASTQ and FASTA formats and decompresses data in FASTA format.\n\nAvailabilityC++ code available at https://scsb.utmb.edu/labgroups/fofanov/broom.asp\n\nContactlealbayr@utmb.edu

bioinformatics↗