Biology subjects

Swamidass, S. J.

Publications and source records attributed to Swamidass, S. J..

6 recordsLinked to original sources

bioRxiv2022

The asthma gut microbiota influences lung inflammation in gnotobiotic mice

The composition of the gut microbiota in early childhood is linked to asthma risk but the role of the gut microbiota in older patients with established asthma is less clear. Here, we used a cohort of 38 school-aged children (19 with asthma) and 57 adults (17 with asthma) to develop a model that aids in the design of mechanistic experiments in gnotobiotic mice. These experiments show that enterotoxigenic Bacteroides fragilis (ETBF) is associated with increased gut permeability, oxidative stress, and markers of Th17-mediated inflammation in the lungs of mice following ovalbumin sensitization and challenge (OSC). Further, ETBF is enriched in a human population with asthma compared to healthy controls. Our results provide evidence that ETBF has the potential to alter the phenotype of airway inflammation in a subset of patients with asthma outside of early childhood which suggests that therapies targeting the gut microbiota may be helpful tools for asthma control.

microbiology↗

bioRxiv2020

DiffNets: Self-supervised deep learning to identify the mechanistic basis for biochemical differences between protein variants

Understanding the structural determinants of a proteins biochemical properties, such as activity and stability, is a major challenge in biology and medicine. Comparing computer simulations of protein variants with different biochemical properties is an increasingly powerful means to drive progress. However, success often hinges on dimensionality reduction algorithms for simplifying the complex ensemble of structures each variant adopts. Unfortunately, common algorithms rely on potentially misleading assumptions about what structural features are important, such as emphasizing larger geometric changes over smaller ones. Here we present DiffNets, self-supervised autoencoders that avoid such assumptions, and automatically identify the relevant features, by requiring that the low-dimensional representations they learn are sufficient to predict the biochemical differences between protein variants. For example, DiffNets automatically identify subtle structural signatures that predict the relative stabilities of {beta}-lactamase variants and duty ratios of myosin isoforms. DiffNets should also be applicable to understanding other perturbations, such as ligand binding.

biophysics↗

bioRxiv2018

Deep Learning Global Glomerulosclerosis in Transplant Kidney Frozen Sections

Transplantable kidneys are in very limited supply. Accurate viability assessment prior to transplantation could minimize organ discard. Rapid and accurate evaluation of intra-operative donor kidney biopsies is essential for determining which kidneys are eligible for transplantation. The criteria for accepting or rejecting donor kidneys relies heavily on pathologist determination of the percent of glomeruli (determined from a frozen section) that are normal and sclerotic. This percentage is a critical measurement that correlates with transplant outcome. Inter- and intra-observer variability in donor biopsy evaluation is, however, significant. An automated method for determination of percent global glomerulosclerosis could prove useful in decreasing evaluation variability, increasing throughput, and easing the burden on pathologists. Here, we describe the development of a deep learning model that identifies and classifies non-sclerosed and sclerosed glomeruli in whole-slide images of donor kidney frozen section biopsies. This model extends a convolutional neural network (CNN) pre-trained on a large database of digital images. The extended model, when trained on just 48 whole slide images, exhibits slide-level evaluation performance on par with expert renal pathologists. The model substantially outperforms a model trained on image patches of isolated glomeruli. Encouragingly, the models performance is robust to slide preparation artifacts associated with frozen section preparation. As the first model reported that identifies and classifies normal and sclerotic glomeruli in frozen kidney sections, and thus the first model reported in the literature relevant to kidney transplantation, it may become an essential part of donor kidney biopsy evaluation in the clinical setting.

bioinformatics↗

bioRxiv2018

Standard operating procedure for somatic variant refinement of tumor sequencing data

PurposeManual review of aligned sequencing reads is required to develop a high-quality list of somatic variants from massively parallel sequencing data (MPS). Despite widespread use in analyzing MPS data, there has been little attempt to describe methods for manual review, resulting in high inter- and intra-lab variability in somatic variant detection and characterization of tumors.\n\nMethodsOpen source software was used to develop an optimal method for manual review setup. We also developed a systemic approach to visually inspect each variant during manual review.\n\nResultsWe present a standard operating procedures for somatic variant refinement for use by manual reviewers. The approach is enhanced through representative examples of 4 different manual review categories that indicate a reviewers confidence in the somatic variant call and 19 annotation tags that contextualize commonly observed sequencing patterns during manual review. Representative examples provide detailed instructions on how to classify variants during manual review to rectify lack of confidence in automated somatic variant detection.\n\nConclusionStandardization of somatic variant refinement through systematization of manual review will improve the consistency and reproducibility of identifying true somatic variants after automated variant calling.

genetics↗

bioRxiv2017

Evolution and Functional Information

\"Functional Information\"--estimated from the mutual information of protein sequence alignments--has been proposed as a reliable way of estimating the number of proteins with a specified function and the consequent difficulty of evolving a new function. The fantastic rarity of functional proteins computed by this approach emboldens some to argue that evolution is impossible. Random searches, it seems, would have no hope of finding new functions. Here, we use simulations to demonstrate that sequence alignments are a poor estimate functional information. The mutual information of sequence alignments fantastically underestimates of the true number of functional proteins, because it also is strongly influenced by a familys history, mutational bias, and selection. Regardless, even if functional information could be reliably calculated, it tells us nothing about the difficulty of evolving new functions, because it does not estimate the distance between a new function and existing functions. The pervasive observation of multifunctional proteins suggests that functions are actually ver close to one another and abundant. Multifunctional proteins would be impossible if the FI argument against evolution were true.

evolutionary biology↗

bioRxiv2017

A Sign of Disparity: Racial/Ethnic Composition of Treatment Centers is an Independent Risk Factor in SPRINT Trial

The racial composition of treatment centers in the SPRINT trial is an independent risk factor for myocardial infarction (MI) and stroke. Independent of individual race or ethnicity, patients face a 39% increase in relative risk of MI or stroke when associated to treatment centers with a high proportion of African Americans. The magnitude of this effect is comparable to smoking. This suggests the strong influence of social determinants on health outcomes among hypertensive patients.

clinical trials↗