Biology subjects

Varadarajan, A. R.

Publications and source records attributed to Varadarajan, A. R..

5 recordsLinked to original sources

bioRxiv2023

Quiescence-inducing 3D-engineered matrix uncovers mechanosensitive and drug protective FHL2-p21 signaling axis

Resected tumors frequently relapse with distant metastasis, despite systemic treatment. Cellular dormancy has been identified as an important mechanism underlying such drug resistance enabling late relapse. Nonetheless, hurdles associated with detection and isolation of disseminated cancer cells (DCCs) in disease-free patients urge the need for in vitro models of dormant cells suited for drug discovery. Here, we explore dormancy-inducing 3D-engineered matrices, which generate mechanical confinement and induce growth arrest and survival against chemotherapy in cancer cells. We characterized the dormant phenotype of solitary cells by P-ERKlow:P-p38high dormancy signaling ratio, along with Ki67-expression. As underlying mechanism, we identified stiffness-dependent nuclear localization of the four-and-a-half LIM domains 2 (FHL2) protein, leading to p53-independent high p21Cip1/Waf1 nuclear expression, validated in murine and human tissue. Suggestive of a resistance-causing role, cells in the dormancy-inducing matrix became sensitive against chemotherapy upon FHL2 downregulation. Thus, our biomaterial-based approach will enable systematic screens for novel compounds suited to eradicate potentially relapsing, dormant cancer cells. TeaserUsing semi-synthetic bioengineered hydrogels, we reveal a mechanosensitive and drug protective mechanism of dormant cancer cells in tissues

bioengineering↗

bioRxiv2020

An integrated model system to gain mechanistic insights into biofilm formation and antimicrobial resistance development in Pseudomonas aeruginosa MPAO1

Pseudomonas aeruginosa MPAO1 is the parental strain of the widely utilized transposon mutant collection for this important clinical pathogen. Here, we validate a model system to identify genes involved in biofilm growth and antibiotic resistance. Our model employs a genomics-driven workflow to assemble the complete MPAO1 genome, identify unique and conserved genes by comparative genomics with the PAO1 reference strain and missed genes by proteogenomics. Among over 200 unique MPAO1 genes, we identified six general essential genes that were overlooked when mapping public Tn-seq datasets against PAO1, including an antitoxin. Genomic data were integrated with phenotypic data from an experimental workflow using a user-friendly, soft lithography-based microfluidic flow chamber for biofilm growth. Experiments conducted across three laboratories delivered reproducible data on P. aeruginosa biofilms and validated both known and novel genes involved in biofilm growth and antibiotic resistance identified in screens of the mutant collection. Differential protein expression data from planktonic cells versus biofilm confirmed upregulation of candidates known to affect biofilm formation, of structural and secreted proteins of type six secretion systems, and provided proteogenomic evidence for some missed MPAO1 genes. This integrated, broadly applicable model promises to improve the mechanistic understanding of biofilm formation, antimicrobial tolerance and resistance evolution.

systems biology↗

bioRxiv2019

A proteogenomic resource enabling integrated analysis of Listeria genotype-proteotype-phenotype relationships

Listeria monocytogenes is an opportunistic foodborne pathogen responsible for listeriosis, a potentially fatal foodborne disease. Many different Listeria strains and serotypes exist, but a proteogenomic resource that bridges the gap in our molecular understanding of the relationships between the Listeria genotypes and phenotypes via proteotypes is still missing. Here we devised a next-generation proteogenomics strategy that enables the community to rapidly proteotype Listeria strains and relate this information back to the genotype. Based on sequencing and de novo assembly of the two most commonly used Listeria model strains, EGD-e and ScottA, we established two comprehensive Listeria proteogenomic databases. A genome comparison established core- and strain-specific genes potentially responsible for virulence differences. Next, we established a DIA/SWATH-based proteotyping strategy, including a new and robust sample preparation workflow, that enables the reproducible, sensitive, and relative quantitative measurement of Listeria proteotypes. This reusable and publically available DIA/SWATH library covers 70% of open reading frames of Listeria and represents the most extensive spectral library for Listeria proteotype analysis to date. We used these two new resources to investigate the Listeria proteotype in states mimicking the upper gastrointestinal passage. Exposure of Listeria to bile salts at 37 {degrees}C, which simulates conditions encountered in the duodenum, showed significant proteotype perturbations including an increase of FlaA, the structural protein of flagella. Given that Listeria is known to lose its flagella above 30 {degrees}C, this was an unexpected finding. The formation of flagella, which might have implications on infectivity, was validated by parallel reaction monitoring and light and scanning electron microscopy. flaA transcript levels were not significantly different with and without exposure to bile salts at 37 {degrees}C, suggesting regulation at the post-transcriptional level. Together, these analyses provide a comprehensive proteogenomic resource and toolbox for the Listeria community enabling the analysis of Listeria genotype-proteotype-phenotype relationships.

systems biology↗

bioRxiv2017

An integrative strategy to identify the entire protein coding potential of prokaryotic genomes by proteogenomics

Accurate annotation of all protein-coding sequences (CDSs) is an essential prerequisite to fully exploit the rapidly growing repertoire of completely sequenced prokaryotic genomes. However, large discrepancies among the number of CDSs annotated by different resources, missed functional short open reading frames (sORFs), and overprediction of spurious ORFs represent serious limitations.\n\nOur strategy towards accurate and complete genome annotation consolidates CDSs from multiple reference annotation resources, ab initio gene prediction algorithms and in silico ORFs in an integrated proteogenomics database (iPtgxDB) that covers the entire protein-coding potential of a prokaryotic genome. By extending the PeptideClassifier concept of unambiguous peptides for prokaryotes, close to 95% of the identifiable peptides imply one distinct protein, largely simplifying downstream analysis. Searching a comprehensive Bartonella henselae proteomics dataset against such an iPtgxDB allowed us to unambiguously identify novel ORFs uniquely predicted by each resource, including lipoproteins, differentially expressed and membrane-localized proteins, novel start sites and wrongly annotated pseudogenes. Most novelties were confirmed by targeted, parallel reaction monitoring mass spectrometry, including unique ORFs and variants identified in a re-sequenced laboratory strain that are not present in its reference genome. We demonstrate the general applicability of our strategy for genomes with varying GC content and distinct taxonomic origin, and release iPtgxDBs for B. henselae, Bradyrhozibium diazoefficiens and Escherichia coli as well as the software to generate such proteogenomics search databases for any prokaryote.

genomics↗

bioRxiv2017

Genome-wide protein phylogenies for four African cichlid species

BackgroundThe thousands of species of closely related cichlid fishes in the great lakes of East Africa are a powerful model for understanding speciation and the genetic basis of trait variation. Recently, the genomes of five species of African cichlids representing five distinct lineages were sequenced and used to predict protein products at a genome-wide level. Here we characterize the evolutionary relationship of each cichlid protein to previously sequenced animal species.\n\nResultsWe used the Treefam database, a set of preexisting protein phylogenies built using 109 previously sequenced genomes, to identify Treefam families for each protein annotated from four cichlid species: Metriaclima zebra, Astatotilapia burtoni, Pundamilia nyererei and Neolamporologus brichardi. For each of these Treefam families, we built new protein phylogenies containing each of the cichlid protein hits. Using these new phylogenies we identified the evolutionary relationship of each cichlid protein to its nearest human and zebrafish protein. This data is available either through download or through a webserver we have implemented.\n\nConclusionThese phylogenies will be useful for any cichlid researchers trying to predict biological and protein function for a given cichlid gene, understanding the evolutionary history of a given cichlid gene, identifying recently duplicated cichlid genes, or performing genome-wide analysis in cichlids that relies on using databases generated from other species.

genomics↗