Biology subjects

Alekseyenko, A. V.

Publications and source records attributed to Alekseyenko, A. V..

3 recordsLinked to original sources

bioRxiv2023

SpaceANOVA: Spatial co-occurrence analysis of cell types in multiplex imaging data using point process and functional ANOVA

MotivationMultiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or tumor microenvironment (TME). Exploring the potential variations in the spatial co-occurrence or co-localization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. ResultsWe present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process (PPP) and functional analysis of variance (FANOVA). Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered in such a context due to the complex nature of the data-collection procedure. We demonstrate the superior statistical power and robustness of the method in comparison to existing approaches through realistic simulation studies. Furthermore, we apply the method to three real datasets on different diseases collected using different imaging platforms. In particular, one of these datasets reveals novel insights into the spatial characteristics of various types of precursor lesions associated with colorectal cancer. AvailabilityThe associated R package can be found here, https://github.com/sealx017/SpaceANOVA. Contactsealso@musc.edu Supplementary informationThe supplementary material is attached.

bioinformatics↗

bioRxiv2022

Lupus disease flares are concordant with immune responses to blooms of lipoglycan-expressing Ruminococcus blautia gnavus strains arising from unstable gut microbiota communities

Whereas genetic susceptibility for Systemic Lupus Erythematosus has been well explored, the precipitants for clinical disease flares remain a mystery. To investigate for dynamic-relationships between gut-microbial communities and Lupus disease activity, we performed taxonomic surveys of fecal 16S rRNA gene amplicon-libraries from Lupus patients and healthy volunteers, obtained at serial timepoints over many months to several years. Individual Lupus patients commonly displayed imbalances in alpha and beta microbiota-diversity, which were uniquely different from healthy individuals as well as from other Lupus patients. Moreover, multivariate analysis of sequential Lupus libraries documented community-wide ecological microbiota instability overtime, most pronounced in patients with Lupus Nephritis LN), a severe form associated with worse prognosis. Lupus gut communities displayed transient spikes of pathogenic bacterial species, with by far the most prevalent being blooms of Ruminococcus blautia gnavus (RG), occurring in nearly half of LN patients often concordant with disease activity flares. RG strains isolated during disease flares, but not those isolated from healthy individuals or patients with inflammatory bowel disease, commonly expressed a novel, highly immunogenic cell wall-associated lipoglycan with conserved structural features that include a diacyl glycerol anchor. Cross-reactive antigenic determinants on these lipoglycans were recognized by murine monoclonal antibodies, and by spontaneously arising Lupus serum IgG antibodies with peak serum antibody responses also concordant with RG blooms. As SLE is frequently characterized by remitting-relapsing disease, despite appropriate treatment, we speculate that gut blooms of pathogenic bacteria, that impair gut barrier function and stoke systemic inflammation, directly contribute to immunopathogenesis.

immunology↗

bioRxiv2020

Phylogenetic Novelty Scores: a New Approach for Weighting Genetic Sequences

BackgroundMany important applications in bioinformatics, including sequence alignment and protein family profiling, employ sequence weighting schemes to mitigate the effects of non-independence of homologous sequences and under- or over-representation of certain taxa in a dataset. These schemes aim to assign high weights to sequences that are novel compared to the others in the same dataset, and low weights to sequences that are over-represented. ResultsWe formalise this principle by rigorously defining the evolutionary novelty of a sequence within an alignment. This results in new sequence weights that we call phylogenetic novelty scores. These scores have various desirable properties, and we showcase their use by considering, as an example application, the inference of character frequencies at an alignment column -- important, for example, in protein family profiling. We give computationally efficient algorithms for calculating our scores and, using simulations, show that they improve the accuracy of character frequency estimation compared to existing sequence weighting schemes. ConclusionsOur phylogenetic novelty scores can be useful when an evolutionarily meaningful system for adjusting for uneven taxon sampling is desired. They have numerous possible applications, including estimation of evolutionary conservation scores and sequence logos, identification of targets in conservation biology, and improving and measuring sequence alignment accuracy.

bioinformatics↗