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Palaniappan, S.

Publications and source records attributed to Palaniappan, S..

2 recordsLinked to original sources

A novel clonal and axenic isolation pipeline for the human gut protist Blastocystis reveals within-subtype strain coexistence

Blastocystis is the most prevalent protist in the human gut microbiome, but its study is impeded by limitations in existing cultivation methods that hinder routine axenic and clonal isolation. These barriers have left open the question of whether multiple strains of a protist species can coexist within a single host, as observed within the bacterial gut microbiota. To address this, we introduce egg-based monophasic (EBM) medium, a novel optically clear liquid medium that supports exponential growth of Blastocystis from low inoculum densities in standard tissue-culture plates and flasks, a capability not supported by existing media formulations tested here. Using EBM medium, we established a modular axenization and clonal isolation pipeline and recovered axenic and clonally isolated cultures representing Blastocystis subtypes 1-4 from human and non-human primate hosts. To investigate within-host strain diversity, we sequenced a subtype 1 culture before and after clonal isolation. The non-clonal culture yielded a 21 Mb assembly with gene copy distribution and allelic polymorphism patterns consistent with multiple strains, whereas its clonal derivative yielded a 15 Mb assembly with patterns consistent with a single strain. These results indicate that the original culture contained multiple closely related strains co-colonizing one host. We identified the same genomic signature in published xenic cultures established from single human donors, supporting the presence of strain-level diversity within Blastocystis subtypes. Our pipeline provides a scalable platform for Blastocystis cultivation, imaging, and clonal isolation, enabling investigation of strain-level diversity that may be obscured in non-clonal cultures.

microbiology↗

Evolution of research in biomedical sciences - a network-based characterization based on PubMed

The rapid growth of scientific publications every year makes it infeasible to keep pace with and survey manually, even for a specific field. Keeping up with literature and gaining a birds-eye view in a timely manner is crucial to the pursuit of scientific discovery and innovation. To help gain a clearer understanding of the state and progress of science and the nature of discovery, one can encode key information from these publications and represent them as a network. Observations on the structural evolution of these graphs can offer valuable insights on the dynamics at play. This work describes the construction and analyses the temporal evolution of a knowledge network of keywords (specifically focusing on genes/proteins, diseases and chemicals) from publications in the biomedical sciences domain. We compare and contrast the representations and evolution of these keyword networks types and find significant differences in the network growth, largely corresponding to our intuition. Furthermore, we focus on the formation and evolution of new links, which we argue corresponds to new scientific discoveries. Our findings suggest that these links are progressively formed in short network distance, leading to clusters of extensively studied keywords. This strategy, however, seems to impede ground-breaking innovation, which could be beneficial for research progress.

bioinformatics↗