Biology subjects

Ahsan, R.

Publications and source records attributed to Ahsan, R..

5 recordsLinked to original sources

bioRxiv2025

The structure of a thermostable phage's Portal Vertex and Neck Complex illuminates its maturation process

Viruses assemble from component parts inside their host cells, but the mechanisms coordinating this complex process are not completely understood. In tailed bacteriophages, the genome is packaged into its capsid shell through the portal complex. The portal complex then closes to retain DNA and connects to the tail, which is required for host recognition and infection. The trigger to stop pumping DNA and assemble the mature virus has been a longstanding conundrum in the field. We determined the structure of the portal, the proteins that connect it to the tail, and portal vertex in the hyperthermophilic phage Oshimavirus using cryo-Electron Microscopy (cryo-EM). We find highly intertwined loop structures, like in a wicker basket, stabilizing the portal vertex against high temperatures. Moreover, we observe that the portal protrudes from the capsid in mature virions. We propose that portal is repositioned by packaged DNA, forming a pressure-sensitive switch that terminates genome packaging and triggers tail attachment in headful phages.

microbiology↗

bioRxiv2024

Protists as mediators of complex microbial and viral associations

Microbial eukaryotes (aka protists) are known for their important roles in nutrient cycling across different ecosystems. However, the composition and function of protist-associated microbiomes remains largely elusive. Here, we employ cultivation-independent single-cell isolation and genome-resolved metagenomics to provide detailed insights into underexplored microbiomes and viromes of over 100 currently uncultivable ciliates and amoebae isolated from diverse environments. Our findings reveal unique microbiome compositions and hint at an intricate network of complex interactions and associations with bacterial symbionts and viruses. We observed stark differences between ciliates and amoebae in terms of microbiome and virome compositions, highlighting the specificity of protist-microbe interactions. Over 115 of the recovered microbial genomes were affiliated with known endosymbionts of eukaryotes, including diverse members of the Holosporales, Rickettsiales, Legionellales, Chlamydiae, Dependentiae, and more than 250 were affiliated with possible host-associated bacteria of the phylum Patescibacteria. We also identified more than 80 giant viruses belonging to diverse viral lineages, of which some were actively expressing genes in single cell transcriptomes, suggesting a possible association with the sampled protists. We also revealed a wide range of other viruses that were predicted to infect eukaryotes or host-associated bacteria. Our results provide further evidence that protists serve as mediators of complex microbial and viral associations, playing a critical role in ecological networks. The frequent co-occurrence of giant viruses and diverse microbial symbionts in our samples suggests multipartite associations, particularly among amoebae. Our study provides a preliminary assessment of the microbial diversity associated with lesser-known protist lineages and paves the way for a deeper understanding of protist ecology and their roles in environmental and human health.

microbiology↗

bioRxiv2024

Genome content in the non-model ciliate Chilodonella uncinata: insights into nuclear architecture, gene-sized chromosomes among the total DNA in their somatic macronuclei during their development

Ciliates are a model lineage for studies of genome architecture given their unusual genome structures. All ciliates have both somatic macronuclei (MAC) and germline micronuclei (MIC), both of which develop from a zygotic nucleus following sex (i.e., conjugation). Nuclear developmental stages are not as well explored among non-model ciliate genera, including Chilodonella uncinata (Class-Phyllopharyngea), the focus of our work. Here, we characterize nuclear architecture and genome dynamics in C. uncinata by combining DAPI (4',6-diamidino-2-phenylindole) staining and fluorescence in situ hybridization (FISH) techniques with confocal microscopy. We developed a telomere probe for staining alongside DAPI, which allows for the identification of fragmented somatic chromosomes among the total DNA in the nuclei. We quantify both total DNA and telomere-bound signals to explore changes in DNA content and chromosome maturation across Chilodonellas nuclear life cycle. Specifically, we find that MAC developmental stages in the ciliate C. uncinata are different than the data reported from other ciliate species. These data provide insights into nuclear dynamics during nuclear development and enrich our understanding of genome evolution in non-model ciliates.

developmental biology↗

bioRxiv2024

Phylogeny and species delimitation of ciliates in the genus Spirostomum (Class, Heterotrichea) using single-cell transcriptomes

Ciliates are single-celled microbial eukaryotes that diverged from other eukaryotic lineages over a billion years ago. The extensive evolutionary timespan of ciliate has led to enormous genetic and phenotypic changes, contributing significantly to their high level of diversity. Recent analyses based on molecular data have revealed numerous cases of cryptic species complexes in different ciliate lineages, demonstrating the need for a robust approach to delimit species boundaries and elucidate phylogenetic relationships. Heterotrich ciliate species of the genus Spirostomum are abundant in freshwater and brackish environments and are commonly used as biological indicators for assessing water quality. However, some Spirostomum species are difficult to identify due to a lack of distinguishable morphological characteristics, and the existence of cryptic species in this genus remains largely unexplored. Previous phylogenetic studies have focused on only a few loci, namely the ribosomal RNA genes, alpha-tubulin, and mitochondrial CO1. In this study, we obtained single-cell transcriptome of 25 Spirostomum species populations (representing six morphospecies) sampled from South Korea and the USA, and used concatenation- and coalescent-based methods for species tree inference and delimitation. Phylogenomic analysis of 37 Spirostomum populations and 265 protein-coding genes provided a robustious insight into the evolutionary relationships among Spirostomum species and confirmed that species with moniliform and compact macronucleus each form a distinct monophyletic lineage. Furthermore, the multispecies coalescent (MSC) model suggests that there are at least nine cryptic species in the Spirostomum genus, three in S. minus, two in S. ambiguum, S. subtilis, and S. teres each. Overall, our fine sampling of closely related Spirostomum populations and wide scRNA-seq allowed us to demonstrate the hidden crypticity of species within the genus Spirostomum, and to resolve and provide much stronger support than hitherto to the phylogeny of this important ciliate genus.

evolutionary biology↗

bioRxiv2018

The First Implication of Image Processing Techniques on Influenza A Virus Sub-Typing Based on HA/NA Protein Sequences, using Convolutional Deep Neural Network

Increase in influenza A virus host range throughout its evolution has given rise to major concerns worldwide. Although the increasing host range mechanism of the virus is largely unknown; persistent genetic mutations have been blamed as a key factor in the re-organization of the host response and the host range. To uncover the underlying core bases of the two important antigenic proteins of influenza virus (HA and NA), functional data mining and image processing analysis of over 8000 protein sequences of different HA and NA subtypes were performed. Each amino acid sequence in HA or NA proteins sat as a feature or variable and two polynomial datasets were created and subjected into conventional prediction models. The average accuracies of these predictive models for HA subtype classifications ranged from 38.9% for SVM to 87.2% for Decision Tree models. NA subtype classification with conventional prediction models varied from 81.3% to 99.87% for SVM and KNN models, respectively. Then amino acid sequence datasets were converted to binary images; subtypes feature sat as target variable, and target label determined by image processing convolution neural network. The performances of Image processing models (convolutional neural network) on binary images for both HA and NA datasets reached to 100%; and the application of Gabor2 filter decreased the time for the predicting model to reach the best performance for HA subtype; while it increased the epochs time for NA subtype classification.\n\nFor the first time ever, converting influenza virus HA and NA amino acid sequences into the binary image datasets and their classifications by convolution neural network increased the prediction accuracies and performances to the highest possible point. The finding of this paper paves new avenues for virus classification based on antigenic HA and NA amino acid profiles, and easily classifying and predicting the possible future emerging strains of pandemic influenza.

bioinformatics↗