Biology subjects

Alekseyenko, A.

Publications and source records attributed to Alekseyenko, A..

6 recordsLinked to original sources

bioRxiv2026

Differences in T-cell counts and neighborhood patterns in human colorectal adenomas and sessile serrated lesions

BackgroundT-cell responses influence recurrence and survival in colorectal cancer. T-cell subset distributions vary by molecular phenotype and location, shaping cytotoxic or immune-cold tumor immune microenvironments (TiMEs). However, the T-cell contexture and their spatial proximities within preinvasive lesions are not well characterized. MethodsWe analyzed sessile serrated lesions (SSLs), tubulovillous/villous adenomas (TVs), and tubular adenomas (TAs) from three completed studies (N=120). Whole-slide multiplex immunofluorescence was used to quantify eight T-cell subsets (CD4, CD8, Th1, Th17, Treg, Tc1, Tc17, TcTreg). Counts were compared by histology using a generalized linear mixed model with a negative binomial distribution, including an offset for total cell counts and adjusting for age, sex, anatomic location, and lesion size. Nearest-neighbor (NN) analyses assessed proximities of T-cell pairs across lesion types. ResultsTAs and SSLs had higher CD4 and CD8 T-cell counts compared with TVs (q<0.05). SSLs had lower Th17 counts than TAs (q<0.05) and compared with TVs leaned toward fewer Tregs (q=0.07). NN analysis showed that TVs, compared with SSLs and TAs, had increased Treg clustering. In contrast, TA versus SSL comparisons revealed predominant CD4 clustering with Th17, Th1, and CD8 subsets. ConclusionTVs are characterized by lower T-cell infiltration and a greater tendency for regulatory T-cell clustering, consistent with an immune-cold TiME relative to TAs. SSLs and TAs were both more immune-infiltrated than TVs, but SSLs appeared less inflamed and less dominated by regulatory subsets. In contrast, CD4dominant clustering in TAs suggested stronger helper coordination. Preinvasive lesions therefore demonstrate immune and spatial heterogeneity by lesion types.

pathology↗

bioRxiv2023

Quantitative model of aging-related muscle degeneration: a Drosophila study

Changes in the composition and functionality of somatic muscles is a universal hallmark of aging that is displayed by a wide range of species. In humans, complications arising from muscle decline due to sarcopenia aggravate morbidity and mortality rates. The genetics of aging-related deterioration of muscle tissue is not well understood, which prompted us to characterize aging-related muscle degeneration in Drosophila melanogaster (fruit fly), a leading model organism in experimental genetics. Adult flies demonstrate spontaneous degeneration of muscle fibers in all types of somatic muscles, which correlates with functional, chronological, and populational aging. Morphological data imply that individual muscle fibers die by necrosis. Using quantitative analysis, we demonstrate that muscle degeneration in aging flies has a genetic component. Chronic neuronal overstimulation of muscles promotes fiber degeneration rates, suggesting a role for the nervous system in muscle aging. From the other hand, muscles decoupled from neuronal stimulation retain a basal level of spontaneous degeneration, suggesting the presence of intrinsic factors. Based on our characterization, Drosophila can be adopted for systematic screening and validation of genetic factors linked to aging-related muscle loss.

pathology↗

bioRxiv2018

Wd*-test: Robust Distance-Based Multivariate Analysis of Variance

BackgroundCommunity-wide analyses provide an essential means for evaluation of the effect of interventions or design variables on the composition of the microbiome. Applications of these analyses are omnipresent in microbiome literature, yet some of their statistical properties have not been tested for robustness towards common features of microbiome data. Recently, it has been reported that PERMANOVA can yield wrong results in the presence of heteroscedasticity and unbalanced sample sizes.\n\nFindingsWe develop a method for multivariate analysis of variance, [Formula], based on Welch MANOVA that is robust to heteroscedasticity in the data. We do so by extending a previously reported method that does the same for two-level independent factor variables. Our approach can accommodate multi-level factors, stratification, and multiple post hoc testing scenarios. An R language implementation of the method is available at https://github.com/alekseyenko/WdStar.\n\nConclusionOur method resolves potential for confounding of location and dispersion effects in multivariate analyses by explicitly accounting for the differences in multivariate dispersion in the data tested. The methods based on [Formula] have general applicability in microbiome and other omics data analyses.

bioinformatics↗

bioRxiv2018

A SIMPLE APPROXIMATION TO THE BIAS IN THE GENETIC EFFECT ESTIMATES WHEN MULTIPLE DISEASE STATES SHARE A CLINICAL DIAGNOSIS

Case-control genome-wide association (CC-GWAS) studies might provide valuable clues to the underlying pathophysiologic mechanisms of complex diseases, such as neurodegenerative disease, cancer. A commonly overlooked complication is that multiple distinct disease states might present with the same set of symptoms and hence share a clinical diagnosis. These disease states can only be distinguished in a biomarker evaluation that might not be feasible on the whole set of cases in the large number of samples that are typically needed for CC-GWAS. Instead, the biomarkers are measured on a subset of cases. Or an external reliability study estimates frequencies of the disease states of interest within the clinically diagnosed set of cases. These frequencies often vary by the genetic and/or non-genetic variables. We derive a simple approximation that relates the genetic effect estimates obtained in a logistic regression model with the clinical diagnosis as an outcome variable to the estimates in the relationship to the true disease state of interest. We performed simulation studies to assess accuracy of the approximation that weve derived. We next applied the derived approximation to the analysis of the genetic basis of innate immune system of Alzheimers disease.

genetics↗

bioRxiv2018

A Simple Approximation To The Bias Of Gene-Environment Interactions In Case/Control Studies With Silent Disease

One of the most important research areas in case-control Genome-Wide Association Studies is to determine how the effect of a genotype varies across the environment or to measure the gene-environment interaction (GxE). We consider the scenario when some of the \"healthy\" controls actually have the disease and when the frequency of these latent cases varies by the environmental variable of interest. In this scenario, performing logistic regression of clinically defined case status on the genetic variant, environmental variable, and their interaction will result in biased estimates of GxE interaction. Here, we derive a general theoretical approximation to the bias in the estimates of the GxE interaction and show, through extensive simulation, that this approximation is accurate in finite samples. Moreover, we apply this approximation to evaluate the bias in the effect estimates of the genetic variants related to mitochondrial proteins a large-scale Prostate Cancer study.

genetics↗

bioRxiv2018

A simple approximation to bias in gene-environment interaction estimates when a case might not be the case

Case-control genetic association studies are often used to examine the role of the genetic basis in complex diseases, such as cancer and neurodegenerative diseases. The role of the genetic basis might vary by non-genetic (environmental) measures, what is traditionally defined as gene-environment interactions (GxE). A commonly overlooked complication is that the set of clinically diagnosed cases might be contaminated by a subset with a nuisance pathologic state that presents with the same symptoms as the pathologic state of interest. The genetic basis of the pathologic state of interest might differ from that of the nuisance pathologic state. Often frequencies of the pathologically defined states within the clinically diagnosed set of cases vary by the environment. We derive a simple and general approximation to bias in GxE parameter estimates when presence of the nuisance pathologic state is ignored. We then perform extensive simulation studies to show that ignoring presence of the nuisance pathologic state can result in substantial bias in GxE estimates and that the approximation we derived is reasonably accurate in finite samples. We demonstrate the applicability of the proposed approximation in a study of Alzheimers disease.

genetics↗