Biology subjects

Ghorbani, A.

Publications and source records attributed to Ghorbani, A..

5 recordsLinked to original sources

bioRxiv2025

A two-step mechanism for RIG-I activation by influenza virus mini viral RNAs

Influenza A virus (IAV) non-canonical replication products can be bound by host pathogen sensors, such as retinoic acid-inducible gene I (RIG-I). However, innate immune activation is infrequent in cell culture infection, in particular by adapted strains. Moreover, it is not understood why non-canonical IAV RNAs activate RIG-I in a sequence- or RNA structure-dependent manner. We therefore hypothesized that multiple errors need to occur before influenza virus RNA synthesis activates innate immune signaling. To test this idea, we investigated whether RIG-I activation is stimulated by the non-canonical or aberrant transcription of mini viral RNAs (mvRNA), a <125 nt long RNA that is overexpressed in pandemic and highly pathogenic IAV infections. Using mvRNA sequences identified in tissue culture and ferret infections, we find that mvRNAs can cause non-canonical transcription termination through a truncated 5' polyadenylation signal or a 5' transient RNA structure that interrupts polyadenylation. The resulting capped complementary RNAs (ccRNA) can stimulate the release of a template mvRNA in vitro. Finally, we find that both mvRNA and ccRNA sequences can be bound by RIG-I in cell culture and that blocking mvRNA transcription with baloxavir reduces IFN promoter activity. Overall, our findings indicate that sequential rounds of non-canonical or aberrant viral replication and transcription are needed before mvRNAs trigger innate immune signaling in a sequence-dependent manner.

microbiology↗

bioRxiv2022

A Systems Biology Approach to Pathogenesis of Gastric Cancer: Gene Network Modeling and Pathway Analysis

IntroductionGastric cancer (GC) ranks among the most common malignancies worldwide. In our previous study, we found overexpressed genes in GC clinical samples. The goal of the current study was to find critical genes and key pathways involved in the pathogenesis of GC. MethodsGene interactions were analyzed using STRING, and Cytoscape was used to visualize the molecular interaction network. CytoHubba was used for drawing the PPI network and identifying hub proteins. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) at STRING were used for the enrichment analysis of the hub genes. Cluster analysis of the network was done using CytoCluster. MEME Suite was used for promoter analysis of the hub genes using Tomtom and GoMo tools. Results and DiscussionOur results showed that the most affected processes in GC are the metabolic processes. The OXPHOS pathway was also considerably enriched in our analyses. These results showed the significant role of mitochondria in GC pathogenesis. Although many investigations have focused on the mitochondrial role in the pathogenesis of various cancers, the characteristics of respiratory and metabolic changes in GC have not been fully elucidated. Our results also showed that most of the affected pathways in GC were the pathways also involved in neurodegenerative diseases. Also, promoter analysis showed that negative regulation of signal transduction might play an important role in GC pathogenesis. The results of this study might open up new insights into GC pathogenesis. The identified genes might be novel diagnostic or prognostic biomarkers or potential therapeutic targets for GC. Nonetheless, these results were obtained by bioinformatics analysis and require further clinical validation.

systems biology↗

bioRxiv2020

Assessment of two DNA extraction kits for profiling poultry respiratory microbiota from multiple sample types

Characterization of poultry microbiota is becoming increasingly important due to the growing need for microbiome-based interventions to improve poultry health and production performance. However, the lack of standardized protocols for sampling, sample processing, DNA extraction, sequencing, and bioinformatic analysis can hinder data comparison between studies. Here, we investigated how the DNA extraction process affects microbial community compositions and diversity metrics in different chicken respiratory sample types including choanal and tracheal swabs, nasal cavity and tracheal washes, and lower respiratory lavage. We did a side-by-side comparison of the performances of Qiagen DNeasy blood and tissue (BT) and ZymoBIOMICS DNA Miniprep (ZB) kits. In general, samples extracted with the BT kit yielded higher concentrations of total DNA while those extracted with the ZB kit contained higher numbers of bacterial 16S rRNA gene copies per unit volume. Therefore, the samples were normalized to equal amounts of 16S rRNA gene copies prior to sequencing. For each sample type, all predominant taxa detected in samples extracted with one kit were present in replicate samples extracted with the other kit and did not show significant differences at the class level. Furthermore, between-kit differences in alpha and beta diversity metrics were statistically indistinguishable. Therefore, both kits perform similarly with regard to 16S rRNA gene-based poultry microbiome analysis.

microbiology↗

bioRxiv2020

Respiratory and gut microbiota in commercial turkey flocks with disparate weight gain trajectories display differential compositional dynamics

Host-associated communities of bacteria (microbiota) substantially contribute to the overall poultry health and performance. Gut microbiota are known to play roles in resistance to pathogen infection and optimal weight gain in turkey flocks. However, knowledge of turkey respiratory microbiota and its link to gut microbiota is lacking. This study presents a 16S rRNA gene-based census of the turkey respiratory microbiota (nasal cavity and trachea) alongside gut microbiota (cecum and ileum) in two identical commercial Hybrid Converter turkey flocks raised in parallel under typical field commercial conditions. The flocks were housed in adjacent barns during the brood stage and in geographically separated farms during the grow-out stage. Several bacterial taxa that were acquired in the respiratory tract (RT) at the beginning of the brood stage persisted throughout the flock cycle, primarily Staphylococcus. Late-emerging predominant taxa in RT included Deinococcus and Corynebacterium. Tracheal and nasal microbiota of turkeys were identifiably distinct from one another and from gut microbiota. Nevertheless, gut and RT microbiota changed in parallel over time and appeared to share many taxa. During the brood stage, the two flocks generally acquired similar gut and RT microbiota, and their average body weights were comparable. Separating the flocks during the grow-out stage resulted in divergent microbial profiles and body weight gain trajectories. Lower weight gain corresponded with emergence of Deinococcus and Ornithobacterium in RT, and Fusobacterium and Parasutterella in gut. This study provides an overview of turkey microbiota under field conditions and suggests several hypotheses concerning the respiratory microbiome. IMPORTANCETurkey meat is an important source of animal protein, and the industry around its production contributes significantly to the agricultural economy. The nonpathogenic symbionts present in the gut of turkeys are known to impact bird health and flock performance. However, the respiratory microbiota in turkeys are entirely unexplored. This study has elucidated the microbiota of respiratory tracts of turkeys from two commercial flocks raised in parallel throughout a normal flock cycle. Further, the study suggests that bacteria originating in the gut or in poultry house environments may influence respiratory communities and consequently induce poor performance, either directly or indirectly. Future attempts to develop microbiome-based interventions for turkey health should delimit the contributions of respiratory microbiota and aim to limit disturbances to those communities.

microbiology↗

bioRxiv2019

Deep Learning Interpretation of Echocardiograms

Echocardiography uses ultrasound technology to capture high temporal and spatial resolution images of the heart and surrounding structures and is the most common imaging modality in cardiovascular medicine. Using convolutional neural networks on a large new dataset, we show that deep learning applied to echocardiography can identify local cardiac structures, estimate cardiac function, and predict systemic phenotypes that modify cardiovascular risk but not readily identifiable to human interpretation. Our deep learning model, EchoNet, accurately identified the presence of pacemaker leads (AUC = 0.89), enlarged left atrium (AUC = 0.85), normal left ventricular wall thickness (AUC = 0.75), left ventricular end systolic and diastolic volumes(R2 = 0.73 and R2 = 0.68), and ejection fraction (R2 = 0.48) as well as predicted systemic phenotypes of age (R2 = 0.46), sex (AUC = 0.88), weight (R2 = 0.56), and height (R2 = 0.33). Interpretation analysis validates that EchoNet shows appropriate attention to key cardiac structures when performing human-explainable tasks and highlight hypothesis-generating regions of interest when predicting systemic phenotypes difficult for human interpretation. Machine learning on echocardiography images can streamline repetitive tasks in the clinical workflow, standardize interpretation in areas with insufficient qualified cardiologists, and more consistently produce echocardiographic measurements.

bioengineering↗