Biology subjects

Warren, H.

Publications and source records attributed to Warren, H..

4 recordsLinked to original sources

bioRxiv2026

The impact of intermittent palatable food consumption on microbiota structure in male and female rats

The gut microbiome plays a vital role in metabolism, behavior, and overall health, with diet being a key factor shaping its composition. This study examines the impact of intermittent palatable food (PF) consumption on microbiota structure in male and female rats, focusing on feeding preferences and sex differences. Rats were characterized as high preferring (HP) or low preferring (LP) based on PF intake, and microbial analyses were conducted across different gastrointestinal regions, including the colon, feces, cecum, and cecal contents. However, microbiota composition varied with significant differences in Firmicutes, Bacteroidetes, and Actinobacteria abundances. Sex-based differences were evident particularly in fecal and cecal samples, where Proteobacteria and Actinobacteria populations varied between males and females within the same feeding groups. Our findings support the notion that dietary habits and microbiota composition may form a feedback loop, reinforcing food preferences through gut-brain axis signaling. While alpha diversity remained unchanged, beta diversity analysis indicated subtle, but significant differences in microbial community structures based on sex and feeding behavior. This research provides novel insights into the interplay between diet, gut microbiota, and behavior, while emphasizing the importance of considering sex as a variable in microbiome studies. Understanding these relationships may inform dietary interventions aimed at optimizing microbiota composition to improve metabolic and mental health outcomes linked to diet-induced microbiota shifts. ImportanceThe gut microbiome plays a critical role in various bodily functions, from the brains protective mechanisms to dietary behaviors and food choices. In this study, we sought to broaden the understanding of how various levels of high fat and high carbohydrate diet consumption alter gut microbiota, and ultimately, shape food preference behaviors. We assessed preference behaviors by categorizing rats into high-preference and low-preference groups based on their consumption of high calorie, palatable food, then analyzed their gut bacterial composition, comparing diet preference groups and examining whether sex differences were reflected in the results. The findings from this research hold significant implications for understanding the complex interplay between diet, microbiota, and behavior. Understanding the bidirectional relationship between feeding behavior and microbiota may provide novel insights into the mechanisms underlying eating disorders and metabolic dysregulation.

microbiology↗

bioRxiv2025

A multimodal human-computer interaction dataset for neurocognitive user state evaluation

We introduce the Simulated Environment for Neurocognitive State Evaluation (SENSE-42), a multimodal dataset collected during user interactions with desktop computers. It is designed for studying spontaneous fluctuations in the neurocognitive state related to the tonic alertness of computer users, with recordings from 42 participants over 2-hour sessions. Within a simulated desktop environment, participants performed real-world routine tasks, including application switching, file management, typing, and web browsing. High-resolution data were recorded across physiological (electroencephalography, electrocardiography, respiration) and subjective modalities of alertness. At five-minute intervals, alertness state was reported using seven questions, addressing sleepiness (Karolinska Sleepiness Scale), mental and temporal demand, perceived performance, effort and frustration (NASA Task Load Index), as well as attentiveness. Behavioural data included keyboard, mouse and webcam inputs. Demographic information, experience metrics, habits, and preferences of computer usage were collected. In addition, individual differences in sleep quality were evaluated using the Pittsburgh Sleep Quality Index and the Epworth Sleepiness Scale. The SENSE-42 dataset can contribute to future research in user state monitoring, behavioural analysis and physiological computing.

neuroscience↗

bioRxiv2018

The role of glycaemic, lipid, blood pressure and obesity risk factors as mediators of the effect of height on Coronary Artery Disease and Type 2 Diabetes Mellitus: A Mendelian Randomisation Study.

We investigated by Mendelian Randomisation (MR) analyses the causal effect of adult height on coronary artery disease (CAD) (23,755 cases, 425,339 controls) and type 2 diabetes (T2D) (29,427 cases, 416,908 controls) in UK Biobank. We then examined whether such effects are mediated via known cardiometabolic risk factors including body mass index (BMI), glycaemic traits, lipid levels and blood pressure. We further cross-checked our findings by two-sample MR and Multivariate MR analyses with publicly available summary statistics data. One standard deviation (SD) higher genetically determined height (~6.5 cm) was causally associated with a 14% decrease in CAD risk (OR= 0.86, 95% CI= 0.790.94). This causal association remained significant after performing sensitivity analyses relaxing pleiotropy assumptions. The causal effect of height on CAD risk appeared to be independent of risk factors such as lipid levels, blood pressure and BMI; association was reduced by only 1-3% after adjustment for potential mediators. We observed no direct causal effect of height on T2D risk, once its effect on BMI was taken into account (OR= 0.99, 95% CI= 0.96-1.02), suggesting that BMI acts as a mediator.

genetics↗

bioRxiv2017

Characterization of Glycosylphosphatidylinositol Biosynthesis Defects by Clinical Features, Flow Cytometry, and Automated Image Analysis

BackgroundGlycosylphosphatidylinositol Biosynthesis Defects (GPIBDs) cause a group of phenotypically overlapping recessive syndromes with intellectual disability, for which pathogenic mutations have been described in 16 genes of the corresponding molecular pathway. An elevated serum activity of alkaline phosphatase (AP), a GPI-linked enzyme, has been used to assign GPIBDs to the phenotypic series of Hyperphosphatasia with Mental Retardation Syndrome (HPMRS) and to distinguish them from another subset of GPIBDs, termed Multiple Congenital Anomalies Hypotonia Seizures syndrome (MCAHS). However, the increasing number of individuals with a GPIBD shows that hyperphosphatasia is a variable feature that is not ideal for a clinical classification.\n\nMethodsWe studied the discriminatory power of multiple GPI-linked substrates that were assessed by flow cytometry in blood cells and fibroblasts of 39 and 14 individuals with a GPIBD, respectively. On the phenotypic level, we evaluated the frequency of occurrence of clinical symptoms and analyzed the performance of computer-assisted image analysis of the facial gestalt in 91 individuals.\n\nResultsWe found that certain malformations such as Morbus Hirschsprung and Diaphragmatic defects are more likely to be associated with particular gene defects (PIGV, PGAP3, PIGN). However, especially at the severe end of the clinical spectrum of HPMRS, there is a high phenotypic overlap with MCAHS. Elevation of AP has also been documented in some of the individuals with MCAHS, namely those with PIGA mutations. Although the impairment of GPI-linked substrates is supposed to play the key role in the pathophysiology of GPIBDs, we could not observe gene-specific profiles for flow cytometric markers or a correlation between their cell surface levels and the severity of the phenotype. In contrast, it was facial recognition software that achieved the highest accuracy in predicting the disease-causing gene in a GPIBD.\n\nConclusionsDue to the overlapping clinical spectrum of both, HPMRS and MCAHS, in the majority of affected individuals, the elevation of AP and the reduced surface levels of GPI-linked markers in both groups, a common classification as GPIBDs is recommended. The effectiveness of computer-assisted gestalt analysis for the correct gene inference in a GPIBD and probably beyond is remarkable and illustrates how the information contained in human faces is pivotal in the delineation of genetic entities.

genetics↗