Biology subjects

Bowman, S.

Publications and source records attributed to Bowman, S..

4 recordsLinked to original sources

bioRxiv2026

β-Hydroxybutyrate elicits divergent metabolic responses between MCF-7 and T47D ER+ breast cancer cells under glucose restriction

BackgroundThe ketogenic diet is being explored as an adjuvant intervention in breast cancer because it lowers circulating glucose and elevates ketone bodies such as {beta}-hydroxybutyrate (BHB), but how individual ER+ breast cancer subtypes adapt to these conditions remains poorly characterized. We examined metabolic responses to BHB supplementation under glucose restriction in two ER+ breast cancer cell lines, asking whether metabolic adaptation patterns differ between models. MethodsMCF-7 and T47D cells were cultured under high glucose, glucose-restricted (5% of standard), or glucose-restricted with 10 mM BHB conditions and profiled by comprehensive two-dimensional gas chromatography-mass spectrometry (GCxGC-MS). Pairwise Welchs t-tests with Benjamini-Hochberg false discovery rate (FDR) correction were applied to identify treatment-responsive metabolites. Targeted assays quantified intracellular glycine, SHMT1 protein, and total branched-chain amino acid (BCAA) concentrations across a BHB dose range (2.5-15 mM). Patient tumor transcriptomic data from TCGA (n=1,084) and paired tumor-normal samples from GSE58135 (n=20) were analyzed for genes involved in one-carbon, ketone body, and BCAA metabolism. ResultsMCF-7 and T47D cells exhibited markedly divergent metabolic responses to BHB. In MCF-7 cells, BHB supplementation produced a broad pattern-level metabolic shift: 75% of detected metabolites trended upward when BHB was added to glucose-restricted cultures (C vs. B comparison), with 1,4-butanediol reaching nominal significance (FC=2.35, p=0.016) and a 4.1-fold trend increase in lactic acid (p=0.11), although no individual metabolite survived FDR correction. T47D cells showed essentially no metabolic response to BHB at the global level. Targeted assays detected an elevation in glycine at 5 mM BHB in both cell lines that did not follow a monotonic dose response and was not accompanied by changes in SHMT1 protein expression. Total BCAA levels were elevated by BHB in T47D cells but remained unchanged in MCF-7 cells. In paired patient samples, OXCT1 (log2FC = -1.41), SHMT1 (log2FC = -1.31), and ACAT1 (log2FC = -1.07) were significantly downregulated in ER+ tumors relative to matched normal tissue (adjusted p < 0.001 for all three). ConclusionsER+ breast cancer cell lines show heterogeneous metabolic responses to BHB supplementation under glucose restriction. The broad pattern of metabolite elevation in MCF-7 but not T47D cells suggests that capacity to utilize ketone bodies as metabolic substrate varies between ER+ models. The downregulation of OXCT1, ACAT1, and SHMT1 in ER+ tumors compared to normal tissue identifies these enzymes as candidate biomarkers that may help stratify which patients are likely to benefit from ketogenic interventions. Findings related to individual metabolites should be regarded as exploratory and require validation in larger, adequately powered cohorts.

cancer biology↗

bioRxiv2022

Artificial neural network language models align neurally and behaviorally with humans even after a developmentally realistic amount of training

Artificial neural networks have emerged as computationally plausible models of human language processing. A major criticism of these models is that the amount of training data they receive far exceeds that of humans during language learning. Here, we use two complementary approaches to ask how the models ability to capture human fMRI responses to sentences is affected by the amount of training data. First, we evaluate GPT-2 models trained on 1 million, 10 million, 100 million, or 1 billion words against an fMRI benchmark. We consider the 100-million-word model to be developmentally plausible in terms of the amount of training data given that this amount is similar to what children are estimated to be exposed to during the first 10 years of life. Second, we test the performance of a GPT-2 model trained on a 9-billion-token dataset to reach state-of-the-art next-word prediction performance on the human benchmark at different stages during training. Across both approaches, we find that (i) the models trained on a developmentally plausible amount of data already achieve near-maximal performance in capturing fMRI responses to sentences. Further, (ii) lower perplexity--a measure of next-word prediction performance--is associated with stronger alignment with human data, suggesting that models that have received enough training to achieve sufficiently high next-word prediction performance also acquire representations of sentences that are predictive of human fMRI responses. In tandem, these findings establish that although some training is necessary for the models predictive ability, a developmentally realistic amount of training ([~]100 million words) may suffice.

neuroscience↗

bioRxiv2022

Hydrogen and dark oxygen drive microbial productivity in diverse groundwater ecosystems

Groundwater ecosystems are globally wide-spread yet still poorly understood. We investigated the age, aqueous geochemistry, and microbiology of 138 groundwater samples from 87 monitoring wells (<250m depth) located in 14 aquifers in the Canadian Prairie. Geochemistry and microbial ecology were tightly linked revealing large-scale aerobic and anaerobic hydrogen, methane, nitrogen, and sulfur cycling carried out by diverse microbial communities. Older groundwaters contained on average more cells (up to 1.4x107/mL) than younger ground-waters. Organic carbon-rich strata featured some of the highest abundances, challenging current estimates of global groundwater population sizes. Substantial concentrations of dissolved oxygen (n=57; 0.52{+/-}0.12 mg/L [mean{+/-}SE]; 0.39 mg/L [median]) in older groundwaters could support aerobic lifestyles in subsurface ecosystems at an unprecedented scale. Metagenomics, oxygen isotope analyses and mixing models indicated that microbial "dark oxygen" contributed to the dissolved oxygen pool in subsurface ecosystems commonly assumed to be anoxic.

microbiology↗

bioRxiv2019

Variations in terrestrial arthropod DNA metabarcoding methods recovers robust beta diversity but variable richness and site indicators based on exact sequence variants

Terrestrial arthropod fauna have been suggested as a key indicator of ecological integrity in forest systems. Because phenotypic identification is expert-limited, a shift towards DNA metabarcoding could improve scalability and democratize the use of forest floor arthropods for biomonitoring applications. The objective of this study was to establish the level of field sampling and DNA extraction replication needed for soil arthropod biodiversity assessments. Processing individually collected field samples recovered significantly higher richness (539-596 ESVs) than pooling the same number of field samples (126-154 ESVs), and we found no significant richness differences when using 1 or 3 pooled DNA extractions. Variations in the number of individual or composite samples or DNA extractions resulted in similar sample clustering based on community dissimilarities. Though our ability to identify taxa to species rank was limited, we were able to use arthropod COI metabarcodes from forest soil to assess richness, distinguish among sites, and recover site indicators based on unnamed exact sequence variants. Our results highlight the need to continue DNA barcoding of local taxa during COI metabarcoding studies to help build reference databases. All together, these sampling considerations support the use of soil arthropod COI metabarcoding as a scalable method for biomonitoring.

ecology↗