Biology subjects

Kavvas, E.

Publications and source records attributed to Kavvas, E..

4 recordsLinked to original sources

bioRxiv2020

Laboratory evolution of multiple E. coli strains reveals unifying principles of adaptation but diversity in driving genotypes

Fitness landscapes are a central concept in evolutionary biology and have been thoroughly detailed in terms of genotypes. However, our understanding of the selected metabolic and gene expression adaptations, and their dependence on genetic background, remains limited. Here, we reveal multi-scale adaptation principles in the E. coli species by taking multi-omics measurements of six different strains throughout their adaptive evolution to glucose minimal media. Statistics and matrix factorization is applied to yield four key results. First, analysis of the metabolic and physiological data shows evolutionary convergence in growth rate, glucose uptake rate, glycolytic ATP and NADH production but divergence in NADPH production strategies. Second, factorization-based analysis of the transcriptome revealed six conserved transcriptomic adaptations describing increased expression of ribosome and amino acid biosynthetic genes and decreased expression of stress response and structural genes. Third, correlation analysis identifies five tradeoffs underlying the transcriptomic profiles. Fourth, statistical tests leveraging ALE design identify four mutation-flux correlates and eight mutation-transcriptomic correlates that link mutations to systems level adaptation principles. Our total results reveal the dominant metabolic and regulatory constraints governing E. coli growth adaptation that either distinguish strains or are conserved principles.

systems biology↗

bioRxiv2020

Matrix factorization recovers consistent regulatory signals from disparate datasets

The availability of gene expression data has dramatically increased in recent years. This data deluge could result in detailed inference of underlying regulatory networks, but the diversity of experimental platforms and protocols introduces critical biases that could hinder scalable analysis of existing data. Here, we show that the underlying structure of the E. coli transcriptome, as determined by Independent Component Analysis (ICA), is conserved across multiple independent datasets, including both RNA-seq and microarray datasets. We also show that echoes of this structure remain in the proteome, accelerating biological discovery through multi-omics analysis. We subsequently combined five transcriptomics datasets into a large compendium containing over 800 expression profiles and discovered that its underlying ICA-based structure was still comparable to that of the individual datasets. ICA thus enables deep analysis of disparate data to uncover new insights that were not visible in the individual datasets.

bioinformatics↗

bioRxiv2018

The Staphylococcus aureus Two-Component System AgrAC Displays Four Distinct Genomic Arrangements That Delineate Genomic Virulence Factor Signatures

Two-component systems (TCSs) consist of a histidine kinase and a response regulator. Here, we evaluated the conservation of the AgrAC TCS among 149 completely sequenced S. aureus strains. It is composed of four genes: agrBDCA. We found that: i) AgrAC system (agr) was found in all but one of the 149 strains; ii) The agr positive strains were further classified into four agr types based on AgrD protein sequences, iii) the four agr types not only specified the chromosomal arrangement of the agr genes but also the sequence divergence of AgrC histidine kinase protein, which confers signal specificity, iv) the sequence divergence was reflected in distinct structural properties especially in the transmembrane region and second extracellular binding domain, and v) there was a strong correlation between the agr type and the virulence genomic profile of the organism. Taken together, these results demonstrate that bioinformatic analysis of the agr locus leads to a classification system that correlates with the presence of virulence factors and protein structural properties.

microbiology↗

bioRxiv2017

ssbio: A Python Framework for Structural Systems Biology

SummaryWorking with protein structures at the genome-scale has been challenging in a variety of ways. Here, we present ssbio, a Python package that provides a framework to easily work with structural information in the context of genome-scale network reconstructions, which can contain thousands of individual proteins. The ssbio package provides an automated pipeline to construct high quality genome-scale models with protein structures (GEM-PROs), wrappers to popular third-party programs to compute associated protein properties, and methods to visualize and annotate structures directly in Jupyter notebooks, thus lowering the barrier of linking 3D structural data with established systems workflows.\n\nAvailability and Implementationssbio is implemented in Python and available to download under the MIT license at http://github.com/SBRG/ssbio. Documentation and Jupyter notebook tutorials are available at http://ssbio.readthedocs.io/en/latest/. Interactive notebooks can be launched using Binder at https://mybinder.org/v2/gh/SBRG/ssbio/master?filepath=Binder.ipynb.\n\nContactnmih@ucsd.edu\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

systems biology↗