Fouladi, F. | bioRxiv Science

bioRxiv2023

Calorie Restriction Outperforms Bariatric Surgery in a Murine Model of Obesity and Triple-Negative Breast Cancer

Obesity promotes triple-negative breast cancer (TNBC), and effective interventions are urgently needed to break the obesity-TNBC link. Epidemiologic studies indicate that bariatric surgery reduces TNBC risk, while evidence is limited or conflicted for weight loss via low-fat diet (LFD) or calorie restriction (CR). Using a murine model of obesity- driven TNBC, we compared the antitumor effects of vertical sleeve gastrectomy (VSG) with LFD, chronic CR, and intermittent CR. Each intervention generated weight and fat loss and suppressed tumor growth relative to obese mice (greatest suppression with CR). VSG and CR regimens exerted both similar and unique effects, as assessed using multi-omic approaches, in reversing obesity-associated transcriptional, epigenetic, secretome, and microbiota changes and restoring antitumor immunity. Thus, in a murine model of TNBC, bariatric surgery and CR each reverse obesity-driven tumor growth via shared and distinct antitumor mechanisms, and CR is superior to VSG in reversing obesitys procancer effects.

cancer biology↗

bioRxiv2021

HashSeq: A Simple, Scalable, and Conservative De Novo Variant Caller for 16S rRNA Gene Datasets

16S rRNA gene sequencing is a common and cost-effective technique for characterization of microbial communities. Recent bioinformatics methods enable high-resolution detection of sequence variants of only one nucleotide difference. In this manuscript, we utilize a very fast HashMap-based approach to detect sequence variants in six publicly available 16S rRNA gene datasets. We then use the normal distribution combined with LOESS regression to estimate background error rates as a function of sequencing depth for individual clusters of sequences. This method is computationally efficient and produces inference that yields sets of variants that are conservative and well supported by reference databases. We argue that this approach to inference is fast, simple, scalable to large datasets, and provides a high-resolution set of sequence variants which are less likely to be the result of sequencing error.

bioinformatics↗