Biology subjects

Do, K.-A.

Publications and source records attributed to Do, K.-A..

5 recordsLinked to original sources

bioRxiv2025

Oral microbial signatures of head and neck cancer patients highlight diverse longitudinal patterns of oral mucositis severity

BackgroundOral mucositis is a painful complication commonly observed in head and neck cancer patients receiving cancer treatment. Emerging evidence suggests that changes in the oral microbiome can contribute to oral mucositis development, making microbial signatures potential targets for therapeutic inter-ventions. This study aimed to: (1) characterize longitudinal microbial patterns of oral mucositis severity among head and neck cancer patients; (2) determine clinically relevant patient clusters based on oral mucositis severity trajectories; and (3) identify microbial signatures specific to these clusters. ResultsWe derived a calibrated oral mucositis score by applying non-negative sparse principal component analysis to seven oral mucositis related symptom ratings, using longitudinal microbiome data from 140 head and neck cancer patients. Functional data analysis and hierarchical clustering identified three distinct patient clusters with differing microbial trajectories of oral mucositis progression. One cluster exhibited patients with a rapid increase in oral mucositis severity following treatment initiation, while the other clusters displayed more gradual increase. Demographic comparisons revealed significant differences in age and weight distributions between clusters, with older, lighter patients more common in clusters experiencing more gradual oral mucositis progression. Partial least squares knockoff analysis identified cluster-specific microbial signatures: notably, Prevotella spp. positively associated with calibrated oral mucositis score across all clusters, while Alloprevotella (Alloprevotella0302) was significantly enriched only in patients experiencing rapid oral mucositis progression. Conversely, genera associated with oral health, including Haemophilus, Rothia, and Actinomyces, were negatively correlated with calibrated oral mucositis score. ConclusionsDistinct trajectories of oral mucositis scores in head and neck cancer patients are linked to specific oral microbial profiles and demographic factors. The identification of cluster-specific microbial profiles highlights the potential for microbiome-targeted interventions to manage oral mucositis severity. While most taxa were cluster-specific, Prevotella consistently ranked among the top taxa positively associated with the calibirated oral mucositis score across clusters, suggesting it may not differentiate between patient groups but rather reflects overall disease severity.

bioinformatics↗

bioRxiv2025

AUC-PR is a More Informative Metric for Assessing the Biological Relevance of In Silico Cellular Perturbation Prediction Models

In silico perturbation models, computational methods which can predict cellular responses to perturbations, present an opportunity to reduce the need for costly and time-intensive in vitro experiments. Many recently proposed models predict high-dimensional cellular responses, such as gene or protein expression to perturbations such as gene knockout or drugs. However, evaluating in silico performance has largely relied on metrics such as R2, which assess overall prediction accuracy but fail to capture biologically significant outcomes like the identification of differentially expressed genes. In this study, we present a novel evaluation framework that introduces the AUC-PR metric to assess the precision and recall of DE gene predictions. By applying this framework to both single-cell and pseudo-bulked datasets, we systematically benchmark simple and advanced computational models. Our results highlight a significant discrepancy between R2 and AUC-PR, with models achieving high R2 values but struggling to identify Differentially expressed genes accurately, as reflected in their low AUC-PR values. This finding underscores the limitations of traditional evaluation metrics and the importance of biologically relevant assessments. Our framework provides a more comprehensive understanding of model capabilities, advancing the application of computational approaches in cellular perturbation research.

bioinformatics↗

bioRxiv2024

A novel method to guide biomarker combinations to optimize the sensitivity

Logistic regression has demonstrated its utility in classifying binary labeled datasets through the maximum likelihood approach. However, in numerous biological and clinical contexts, the aim is often to determine coefficients that yield the highest sensitivity at the pre-specified specificity or vice versa. Therefore, the application of logistic regression is limited in such settings. To this end, we have developed an improved regression framework, SMAGS, for binary classification that, for a given specificity, finds the linear decision rule that yields the maximum sensitivity. Furthermore, we employed the method for feature selection to find the features that are satisfying the sensitivity maximization goal. We compared our method with normal logistic regression by applying it to real clinical data as well as synthetic data. In the real application data (colorectal cancer dataset), we found 14% improvement of sensitivity at 98.5% specificity. Availability and implementationSoftware is made available in Python (https://github.com/smahmoodghasemi/SMAGS)

bioinformatics↗

bioRxiv2021

Diet-derived metabolites and mucus link the gut microbiome to fever after cytotoxic cancer treatment

Not all cancer patients with severe neutropenia develop fever, and the fecal microbiome may play a role. In neutropenic hematopoietic cell transplant patients (n=119), 63 (53%) developed a subsequent fever and had increased fecal Akkermansia muciniphila, a mucus-degrading bacteria (p=0.006, corrected for multiple comparisons). In mouse models, two therapies, irradiation and melphalan, similarly expanded A. muciniphila. Dietary restriction of unirradiated mice also expanded A. muciniphila and thinned the colonic mucus layer. Azithromycin treatment depleted A. muciniphila and preserved colonic mucus. Dietary restriction raised colonic luminal pH and reduced acetate, propionate, and butyrate. Culturing A. muciniphila with lower pH and increased propionate prevented utilization of mucin. Treating irradiated mice with azithromycin or propionate preserved the mucus layer, lessened hypothermia, and reduced inflammatory cytokines in the colon. These results suggest that diet, metabolites and colonic mucus link the microbiome to neutropenic fever, and could guide future microbiome-based preventive strategies.

microbiology↗

bioRxiv2021

Performance Determinants of Unsupervised Clustering Methods for Microbiome Data

BackgroundIn microbiome data analysis, unsupervised clustering is often used to identify naturally occurring clusters, which can then be assessed for associations with characteristics of interest. In this work, we systematically compared beta diversity and clustering methods commonly used in microbiome analyses. We applied these to four published datasets where highly distinct microbiome profiles could be seen between sample groups. ResultsAlthough no single method outperformed the others consistently, we did identify key scenarios where certain methods can underperform. Specifically, the Bray Curtis metric resulted in poor clustering in a dataset where high-abundance OTUs were relatively rare. In contrast, the unweighted UniFrac metric clustered poorly when used on a dataset with a high prevalence of low-abundance OTUs. To test our proposition, we systematically modified properties of the poorly performing datasets and found that this approach resulted in improved Bray Curtis and unweighted UniFrac performance. Based on these observations, we rationally combined the Bray Curtis metric and the unweighted UniFrac metrics and found that this new beta diversity metric showed high performance across all datasets. We also evaluated our findings by examining a clinical dataset where clusters are less separated. ConclusionsOur systematic evaluation of clustering performance in these five datasets demonstrates that there is no existing clustering method that universally performs best across all datasets. We propose a combined metric of Bray Curtis and unweighted UniFrac that capitalizes on the complementary strengths of the two metrics.

microbiology↗