A CRISPR-enhanced metagenomic NGS test to improve pandemic preparedness.

The lack of preparedness for detecting and responding to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pathogen (i.e., COVID-19) has caused enormous harm to public health and the economy. Testing strategies deployed on a population scale at day zero, i.e., the time of the first reported case, would be of significant value. Next-generation sequencing (NGS) has such capabilities; however, it has limited detection sensitivity for low-copy-number pathogens. Here, we leverage the CRISPR-Cas9 system to effectively remove abundant sequences not contributing to pathogen detection and show that NGS detection sensitivity of SARS-CoV-2 approaches that of RT-qPCR. The resulting sequence data can also be used for variant strain typing, co-infection detection, and individual human host response assessment, all in a single molecular and analysis workflow. This NGS work flow is pathogen agnostic and, therefore, has the potential to transform how large-scale pandemic response and focused clinical infectious disease testing are pursued in the future.

LuxR Solos from Environmental Fluorescent Pseudomonads.

LuxR solos are related to quorum sensing (QS) LuxR family regulators; however, they lack a cognate LuxI family protein. LuxR solos are widespread and almost exclusively found in proteobacteria. In this study, we investigated the distribution and conservation of LuxR solos in the fluorescent pseudomonads group. Our analysis of more than 600 genomes revealed that the majority of fluorescent Pseudomonas spp. carry one or more LuxR solos, occurring considerably more frequently than complete LuxI/LuxR archetypical QS systems. Based on the adjacent gene context and conservation of the primary structure, nine subgroups of LuxR solos have been identified that are likely to be involved in the establishment of communication networks. Modeling analysis revealed that the majority of subgroups shows some substitutions at the invariant amino acids of the ligand-binding pocket of QS LuxRs, raising the possibility of binding to non-acyl-homoserine lactone (AHL) ligands. Several mutants and gene expression studies on some LuxR solos belonging to different subgroups were performed in order to shed light on their response. The commonality of LuxR solos among fluorescent pseudomonads is an indication of their important role in cell-cell signaling.IMPORTANCE Cell-cell communication in bacteria is being extensively studied in simple settings and uses chemical signals and cognate regulators/receptors. Many Gram-negative proteobacteria use acyl-homoserine lactones (AHLs) synthesized by LuxI family proteins and cognate LuxR-type receptors to regulate their quorum sensing (QS) target loci. AHL-QS circuits are the best studied QS systems; however, many proteobacterial genomes also contain one or more LuxR solos, which are QS-related LuxR proteins which are unpaired to a cognate LuxI. A few LuxR solos have been implicated in intraspecies, interspecies, and interkingdom signaling. Here, we report that LuxR solo homologs occur considerably more frequently than complete LuxI/LuxR QS systems within the Pseudomonas fluorescens group of species and that they are characterized by different genomic organizations and primary structures and can be subdivided into several subgroups. The P. fluorescens group consists of more than 50 species, many of which are found in plant-associated environments. The role of LuxR solos in cell-cell signaling in fluorescent pseudomonads is discussed.

Pangenome Analytics Reveal Two-Component Systems as Conserved Targets in ESKAPEE Pathogens.

The two-component system (TCS) helps bacteria sense and respond to environmental stimuli through histidine kinases and response regulators. TCSs are the largest family of multistep signal transduction processes, and they are involved in many important cellular processes such as antibiotic resistance, pathogenicity, quorum sensing, osmotic stress, and biofilms. Here, we perform the first comprehensive study to highlight the role of TCSs as potential drug targets against ESKAPEE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and Escherichia coli) pathogens through annotation, mapping, pangenomic status, gene orientation, and sequence variation analysis. The distribution of the TCSs is group specific with regard to Gram-positive and Gram-negative bacteria, except for KdpDE. The TCSs among ESKAPEE pathogens form closed pangenomes, except for Pseudomonas aeruginosa Furthermore, their conserved nature due to closed pangenomes might make them good drug targets. Fitness score analysis suggests that any mutation in some TCSs such as BaeSR, ArcBA, EvgSA, and AtoSC, etc., might be lethal to the cell. Taken together, the results of this pangenomic assessment of TCSs reveal a range of strategies deployed by the ESKAPEE pathogens to manifest pathogenicity and antibiotic resistance. This study further suggests that the conserved features of TCSs might make them an attractive group of potential targets with which to address antibiotic resistance.IMPORTANCE The ESKAPEE pathogens are the leading cause of health care-associated infections worldwide. Two-component systems (TCSs) can be used as effective targets against pathogenic bacteria since they are ubiquitous and manage various vital functions such as antibiotic resistance, virulence, biofilms, quorum sensing, and pH balance, among others. This study provides a comprehensive overview of the pangenomic status of the TCSs among ESKAPEE pathogens. The annotation and pangenomic analysis of TCSs show that they are significantly distributed and conserved among the pathogens, as most of them form closed pangenomes. Furthermore, our analysis also reveals that the removal of the TCSs significantly affects the fitness of the cell. Hence, they may be used as promising drug targets against bacteria.

Elucidation of Regulatory Modes for Five Two-Component Systems in Escherichia coli Reveals Novel Relationships.

Escherichia coli uses two-component systems (TCSs) to respond to environmental signals. TCSs affect gene expression and are parts of E. coli's global transcriptional regulatory network (TRN). Here, we identified the regulons of five TCSs in E. coli MG1655: BaeSR and CpxAR, which were stimulated by ethanol stress; KdpDE and PhoRB, induced by limiting potassium and phosphate, respectively; and ZraSR, stimulated by zinc. We analyzed RNA-seq data using independent component analysis (ICA). ChIP-exo data were used to validate condition-specific target gene binding sites. Based on these data, we do the following: (i) identify the target genes for each TCS; (ii) show how the target genes are transcribed in response to stimulus; and (iii) reveal novel relationships between TCSs, which indicate noncognate inducers for various response regulators, such as BaeR to iron starvation, CpxR to phosphate limitation, and PhoB and ZraR to cell envelope stress. Our understanding of the TRN in E. coli is thus notably expanded.IMPORTANCE E. coli is a common commensal microbe found in the human gut microenvironment; however, some strains cause diseases like diarrhea, urinary tract infections, and meningitis. E. coli's two-component systems (TCSs) modulate target gene expression, especially related to virulence, pathogenesis, and antimicrobial peptides, in response to environmental stimuli. Thus, it is of utmost importance to understand the transcriptional regulation of TCSs to infer bacterial environmental adaptation and disease pathogenicity. Utilizing a combinatorial approach integrating RNA sequencing (RNA-seq), independent component analysis, chromatin immunoprecipitation coupled with exonuclease treatment (ChIP-exo), and data mining, we suggest five different modes of TCS transcriptional regulation. Our data further highlight noncognate inducers of TCSs, which emphasizes the cross-regulatory nature of TCSs in E. coli and suggests that TCSs may have a role beyond their cognate functionalities. In summary, these results can lead to an understanding of the metabolic capabilities of bacteria and correctly predict complex phenotype under diverse conditions, especially when further incorporated with genome-scale metabolic models.

The Bitome: digitized genomic features reveal fundamental genome organization.

A genome contains the information underlying an organism's form and function. Yet, we lack formal framework to represent and study this information. Here, we introduce the Bitome, a matrix composed of binary digits (bits) representing the genomic positions of genomic features. We form a Bitome for the genome of Escherichia coli K-12 MG1655. We find that: (i) genomic features are encoded unevenly, both spatially and categorically; (ii) coding and intergenic features are recapitulated at high resolution; (iii) adaptive mutations are skewed towards genomic positions with fewer features; and (iv) the Bitome enhances prediction of adaptively mutated and essential genes. The Bitome is a formal representation of a genome and may be used to study its fundamental organizational properties.

Metabolic and genetic basis for auxotrophies in Gram-negative species.

Auxotrophies constrain the interactions of bacteria with their environment, but are often difficult to identify. Here, we develop an algorithm (AuxoFind) using genome-scale metabolic reconstruction to predict auxotrophies and apply it to a series of available genome sequences of over 1,300 Gram-negative strains. We identify 54 auxotrophs, along with the corresponding metabolic and genetic basis, using a pangenome approach, and highlight auxotrophies conferring a fitness advantage in vivo. We show that the metabolic basis of auxotrophy is species-dependent and varies with 1) pathway structure, 2) enzyme promiscuity, and 3) network redundancy. Various levels of complexity constitute the genetic basis, including 1) deleterious single-nucleotide polymorphisms (SNPs), in-frame indels, and deletions; 2) single/multigene deletion; and 3) movement of mobile genetic elements (including prophages) combined with genomic rearrangements. Fourteen out of 19 predictions agree with experimental evidence, with the remaining cases highlighting shortcomings of sequencing, assembly, annotation, and reconstruction that prevent predictions of auxotrophies. We thus develop a framework to identify the metabolic and genetic basis for auxotrophies in Gram-negatives.

The Escherichia coli transcriptome mostly consists of independently regulated modules.

Underlying cellular responses is a transcriptional regulatory network (TRN) that modulates gene expression. A useful description of the TRN would decompose the transcriptome into targeted effects of individual transcriptional regulators. Here, we apply unsupervised machine learning to a diverse compendium of over 250 high-quality Escherichia coli RNA-seq datasets to identify 92 statistically independent signals that modulate the expression of specific gene sets. We show that 61 of these transcriptomic signals represent the effects of currently characterized transcriptional regulators. Condition-specific activation of signals is validated by exposure of E. coli to new environmental conditions. The resulting decomposition of the transcriptome provides: a mechanistic, systems-level, network-based explanation of responses to environmental and genetic perturbations; a guide to gene and regulator function discovery; and a basis for characterizing transcriptomic differences in multiple strains. Taken together, our results show that signal summation describes the composition of a model prokaryotic transcriptome.

Pseudogene repair driven by selection pressure applied in experimental evolution.

Pseudogenes represent open reading frames that have been damaged by mutations, rendering the gene product non-functional. Pseudogenes are found in many genomes and are not always eliminated, even if they are potentially 'wasteful'. This raises a fundamental question about their prevalence. Here we report pseudogene efeU repair that restores the iron uptake system of Escherichia coli under a designed selection pressure during adaptive laboratory evolution.

Metabolic re-wiring of isogenic breast epithelial cell lines following epithelial to mesenchymal transition.

Epithelial to mesenchymal transition (EMT) has implications in tumor progression and metastasis. Metabolic alterations have been described in cancer development but studies focused on the metabolic re-wiring that takes place during EMT are still limited. We performed metabolomics profiling of a breast epithelial cell line and its EMT derived mesenchymal phenotype to create genome-scale metabolic models descriptive of both cell lines. Glycolysis and OXPHOS were higher in the epithelial phenotype while amino acid anaplerosis and fatty acid oxidation fueled the mesenchymal phenotype. Through comparative bioinformatics analysis, PPAR-γ1, PPAR- γ2 and AP-1 were found to be the most influential transcription factors associated with metabolic re-wiring. In silico gene essentiality analysis predicts that the LAT1 neutral amino acid transporter is essential for mesenchymal cell survival. Our results define metabolic traits that distinguish an EMT derived mesenchymal cell line from its epithelial progenitor and may have implications in cancer progression and metastasis. Furthermore, the tools presented here can aid in identifying critical metabolic nodes that may serve as therapeutic targets aiming to prevent EMT and inhibit metastatic dissemination.

EGFR Signal-Network Reconstruction Demonstrates Metabolic Crosstalk in EMT.

Epithelial to mesenchymal transition (EMT) is an important event during development and cancer metastasis. There is limited understanding of the metabolic alterations that give rise to and take place during EMT. Dysregulation of signalling pathways that impact metabolism, including epidermal growth factor receptor (EGFR), are however a hallmark of EMT and metastasis. In this study, we report the investigation into EGFR signalling and metabolic crosstalk of EMT through constraint-based modelling and analysis of the breast epithelial EMT cell model D492 and its mesenchymal counterpart D492M. We built an EGFR signalling network for EMT based on stoichiometric coefficients and constrained the network with gene expression data to build epithelial (EGFR_E) and mesenchymal (EGFR_M) networks. Metabolic alterations arising from differential expression of EGFR genes was derived from a literature review of AKT regulated metabolic genes. Signaling flux differences between EGFR_E and EGFR_M models subsequently allowed metabolism in D492 and D492M cells to be assessed. Higher flux within AKT pathway in the D492 cells compared to D492M suggested higher glycolytic activity in D492 that we confirmed experimentally through measurements of glucose uptake and lactate secretion rates. The signaling genes from the AKT, RAS/MAPK and CaM pathways were predicted to revert D492M to D492 phenotype. Follow-up analysis of EGFR signaling metabolic crosstalk in three additional breast epithelial cell lines highlighted variability in in vitro cell models of EMT. This study shows that the metabolic phenotype may be predicted by in silico analyses of gene expression data of EGFR signaling genes, but this phenomenon is cell-specific and does not follow a simple trend.

ComQXPA quorum sensing systems may not be unique to Bacillus subtilis: a census in prokaryotic genomes.

The comQXPA locus of Bacillus subtilis encodes a quorum sensing (QS) system typical of Gram positive bacteria. It encodes four proteins, the ComQ isoprenyl transferase, the ComX pre-peptide signal, the ComP histidine kinase, and the ComA response regulator. These are encoded by four adjacent genes all situated on the same chromosome strand. Here we present results of a comprehensive census of comQXPA-like gene arrangements in 2620 complete and 6970 draft prokaryotic genomes (sequenced by the end of 2013). After manually checking the data for false-positive and false-negative hits, we found 39 novel com-like predictions. The census data show that in addition to B. subtilis and close relatives, 20 comQXPA-like loci are predicted to occur outside the B. subtilis clade. These include some species of Clostridiales order, but none outside the phylum Firmicutes. Characteristic gene-overlap patterns were observed in comQXPA loci, which were different for the B. subtilis-like and non-B. subtilis-like clades. Pronounced sequence variability associated with the ComX peptide in B. subtilis clade is evident also in the non-B. subtilis clade suggesting grossly similar evolutionary constraints in the underlying quorum sensing systems.

The organization of the quorum sensing luxI/R family genes in Burkholderia.

Members of the Burkholderia genus of Proteobacteria are capable of living freely in the environment and can also colonize human, animal and plant hosts. Certain members are considered to be clinically important from both medical and veterinary perspectives and furthermore may be important modulators of the rhizosphere. Quorum sensing via N-acyl homoserine lactone signals (AHL QS) is present in almost all Burkholderia species and is thought to play important roles in lifestyle changes such as colonization and niche invasion. Here we present a census of AHL QS genes retrieved from public databases and indicate that the local arrangement (topology) of QS genes, their location within chromosomes and their gene neighborhoods show characteristic patterns that differ between the known Burkholderia clades. In sequence phylogenies, AHL QS genes seem to cluster according to the local gene topology rather than according to the species, which suggests that the basic topology types were present prior to the appearance of current Burkholderia species. The data are available at http://net.icgeb.org/burkholderia/.

Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

Classifying the topology of AHL-driven quorum sensing circuits in proteobacterial genomes.

Virulence and adaptability of many Gram-negative bacterial species are associated with an N-acylhomoserine lactone (AHL) gene regulation mechanism called quorum sensing (QS). The arrangement of quorum sensing genes is variable throughout bacterial genomes, although there are unifying themes that are common among the various topological arrangements. A bioinformatics survey of 1,403 complete bacterial genomes revealed characteristic gene topologies in 152 genomes that could be classified into 16 topological groups. We developed a concise notation for the patterns and show that the sequences of LuxR regulators and LuxI autoinducer synthase proteins cluster according to the topological patterns. The annotated topologies are deposited online with links to sequences and genome annotations at http://bacteria.itk.ppke.hu/QStopologies/.