Periodic Reporting for period 1 - TICKITS (SFTS virus-tick cell interactions in a vector system.)
Periodo di rendicontazione: 2021-06-01 al 2023-05-31
Ticks serve as vectors for arboviruses in various regions worldwide. The rising prevalence and emergence of tick-borne arbovirus infections among human populations emphasize the importance of implementing vector control strategies. However, our understanding of the cellular-level interactions between arboviruses and ticks remains limited, with a significant gap in knowledge regarding negative strand RNA arboviruses. Studying tick-arbovirus interactions presents significant challenges due to the lack of fundamental genomic data for non-model arthropods, such as Rhipicephalus microplus ticks, the natural vector of the emergent Dabie bandavirus (Bunyavirales; Phenuiviridae) previously known as severe fever with thrombocytopenia syndrome virus.
To overcome these limitations, the TICKITS (TICK cell Interactions with SFTSV) project aims to employ a system virology approach to provide an unprecedented understanding of SFTSV interactions with vector tick cells. The findings will be of high relevance for tick-borne bunyaviruses. The novel and innovative aspect of our project is the combination of vector tick cells with cutting edge transcriptomics and proteomics techniques to study virus-host interactions in this non-model organism. First, it will provide (1) novel insights on gene and proteome regulation during SFTSV infection of vector tick cells, highlighting both pro- and anti-viral factors, (2) allow us to identify the specific viral-host protein interactions which may be involved in re-wiring cellular pathways during infection and (3) take advantage of the silencing technology to assess the function of a selected set of genes during SFTSV infection of tick cells (for example immune genes or viral protein interactors).
This integrative perspective of the host response will generate valuable outcome and new knowledge on tick-borne virus infection. The generation of novel genomic and proteome data will be reinforced by the first description of intracellular tick-bunyavirus protein interactions. Though my research is of fundamental nature, these findings could be translated into proof-of-concept work on manipulation of cellular pathway of the tick vector to control viral infections in the future.
To overcome these limitations, the TICKITS (TICK cell Interactions with SFTSV) project aims to employ a system virology approach to provide an unprecedented understanding of SFTSV interactions with vector tick cells. The findings will be of high relevance for tick-borne bunyaviruses. The novel and innovative aspect of our project is the combination of vector tick cells with cutting edge transcriptomics and proteomics techniques to study virus-host interactions in this non-model organism. First, it will provide (1) novel insights on gene and proteome regulation during SFTSV infection of vector tick cells, highlighting both pro- and anti-viral factors, (2) allow us to identify the specific viral-host protein interactions which may be involved in re-wiring cellular pathways during infection and (3) take advantage of the silencing technology to assess the function of a selected set of genes during SFTSV infection of tick cells (for example immune genes or viral protein interactors).
This integrative perspective of the host response will generate valuable outcome and new knowledge on tick-borne virus infection. The generation of novel genomic and proteome data will be reinforced by the first description of intracellular tick-bunyavirus protein interactions. Though my research is of fundamental nature, these findings could be translated into proof-of-concept work on manipulation of cellular pathway of the tick vector to control viral infections in the future.
The project had 3 scientific objectives: 1) Perform “Proteomics informed by Transcriptomics” (PIT) on infected cells to identify genes and proteins differentially regulated in cells during SFTSV infection; 2) Identify SFTSV N and L protein-host protein complexes in vivo by affinity purification mass spectrometry (AP/MS) analysis; 3) Validate and assess candidate factors to characterize their impact on viral infection in cells, with an emphasis on potential immune factors/pathways.
For objective 1, I developed a methodology to enable RNA sequencing of tick cells by depleting ribosomal RNA, allowing us to collect unprecedented datasets encompassing coding and non-coding tick RNAs, as well as viral RNA. Once this methodology was established, it was applied to the proteomics informed by transcriptomics (PIT) method. Briefly, R.microplus cells were infected (MOI 1) with Dabie bandavirus, RNA and protein were extracted and analyzed by sequencing and mass-spectrometry. By comparing the data between day 3 and day 6 post-infection, we were able to establish the infectious landscape within tick cells. However, due to the large size of the R. microplus genome and the extensive dataset collected, data processing is still underway to enrich our current understanding of the tick transcriptome and proteome.
Regarding objective 2, we encountered insufficient viral protein expression through plasmid transfection. As an alternative approach, we decided to utilize the available N antibody and conducted an affinity purification-mass spectrometry experiment on infected tick cells. Using the de novo proteome generated through objective 1, I successfully identified tick interactors of Dabie bandavirus nucleocapsid protein for the first time.
For objective 3, I selected a subset of identified targets from either objective 1 or objective 2, with a particular emphasis on proteins associated with the antiviral immune response. To induce gene silencing, I developed a transfection methodology utilizing magnetic nanobeads for the delivery of dsRNA into tick cells. Following the infection of these knock downed cells, we identified important viral restriction factors crucial for tick cell infection. As an example, NMD (Non-Sense Mediated Decay) pathway was identified as anti-viral for the first time in arthropod vector. Further studies will help us to understand this anti-viral mechanism.
The results from the 3 objectives will be published as one high impact publication (currently being drafted, and interest for the publication was expressed by PLoS Biology editorial board). Due to the nature of the project, and the large size of the tick cells genome, we had to extend the time allocated to the data analysis. Consequently, this delayed the progress of the papers; however, the first scientific output will be submitted for publication in the prestigious open access journal PLoS Biology. The remaining papers which will relate to different data analysis on the generated datasets (identification of transposable elements, and non-coding RNAs important for the infection) will be written and submitted in 2024.
For objective 1, I developed a methodology to enable RNA sequencing of tick cells by depleting ribosomal RNA, allowing us to collect unprecedented datasets encompassing coding and non-coding tick RNAs, as well as viral RNA. Once this methodology was established, it was applied to the proteomics informed by transcriptomics (PIT) method. Briefly, R.microplus cells were infected (MOI 1) with Dabie bandavirus, RNA and protein were extracted and analyzed by sequencing and mass-spectrometry. By comparing the data between day 3 and day 6 post-infection, we were able to establish the infectious landscape within tick cells. However, due to the large size of the R. microplus genome and the extensive dataset collected, data processing is still underway to enrich our current understanding of the tick transcriptome and proteome.
Regarding objective 2, we encountered insufficient viral protein expression through plasmid transfection. As an alternative approach, we decided to utilize the available N antibody and conducted an affinity purification-mass spectrometry experiment on infected tick cells. Using the de novo proteome generated through objective 1, I successfully identified tick interactors of Dabie bandavirus nucleocapsid protein for the first time.
For objective 3, I selected a subset of identified targets from either objective 1 or objective 2, with a particular emphasis on proteins associated with the antiviral immune response. To induce gene silencing, I developed a transfection methodology utilizing magnetic nanobeads for the delivery of dsRNA into tick cells. Following the infection of these knock downed cells, we identified important viral restriction factors crucial for tick cell infection. As an example, NMD (Non-Sense Mediated Decay) pathway was identified as anti-viral for the first time in arthropod vector. Further studies will help us to understand this anti-viral mechanism.
The results from the 3 objectives will be published as one high impact publication (currently being drafted, and interest for the publication was expressed by PLoS Biology editorial board). Due to the nature of the project, and the large size of the tick cells genome, we had to extend the time allocated to the data analysis. Consequently, this delayed the progress of the papers; however, the first scientific output will be submitted for publication in the prestigious open access journal PLoS Biology. The remaining papers which will relate to different data analysis on the generated datasets (identification of transposable elements, and non-coding RNAs important for the infection) will be written and submitted in 2024.
Viral infection studies in tick cells pose significant challenges due to the inherent limitations associated with tick genome annotations, which are often of low quality. To overcome this hurdle, the TICKITS project employed a technically and conceptually innovative approach that involved the generation of de novo proteome and transcriptome data. This cutting-edge methodology enabled the investigation of interactions between Severe Fever with Thrombocytopenia Syndrome Virus (SFTSV) and non-model, vector tick cells. The integration of proteomics and transcriptomics through the Proteomics informed by Transcriptomics (P.I.T) method provided valuable insights into the regulation of genes and proteomes during Bunyavirus infection in non-model tick cells.
By employing the P.I.T method, we obtained a novel dataset that facilitated the identification of tick interactors associated with the Nucleocapsid of SFTSV (N protein), a crucial protein involved in virus replication. This discovery represents the first characterization of essential factors linked to Bunyavirus replication in tick cells. Furthermore, the comprehensive coverage and high resolution of our dataset empowered us to delve deeper into the virus-host relationship. Specifically, we aim to characterize the activity of transposons during infection and will conduct the initial characterization of long non-coding RNAs in tick cells infected with SFTSV.
Collectively, our data generated a new layer of 'omic information, leading to an unparalleled characterization of bunyavirus interactions with vector cells. The robustness of our findings was reinforced by biological validation, resulting in a robust dataset that describes how SFTSV exploits cellular pathways to replicate and infect tick cells. This invaluable resource not only enhances our understanding of tick-virus interactions but also extends our knowledge of tick biology.
By employing the P.I.T method, we obtained a novel dataset that facilitated the identification of tick interactors associated with the Nucleocapsid of SFTSV (N protein), a crucial protein involved in virus replication. This discovery represents the first characterization of essential factors linked to Bunyavirus replication in tick cells. Furthermore, the comprehensive coverage and high resolution of our dataset empowered us to delve deeper into the virus-host relationship. Specifically, we aim to characterize the activity of transposons during infection and will conduct the initial characterization of long non-coding RNAs in tick cells infected with SFTSV.
Collectively, our data generated a new layer of 'omic information, leading to an unparalleled characterization of bunyavirus interactions with vector cells. The robustness of our findings was reinforced by biological validation, resulting in a robust dataset that describes how SFTSV exploits cellular pathways to replicate and infect tick cells. This invaluable resource not only enhances our understanding of tick-virus interactions but also extends our knowledge of tick biology.