Genome-scale Approaches Research Articles

H-NS is a bacterial transposon capture protein

The histone-like nucleoid structuring (H-NS) protein is a DNA binding factor, found in gammaproteobacteria, with functional equivalents in diverse microbes. Universally, such proteins are understood to silence transcription of horizontally acquired genes. Here, we identify transposon capture as a major overlooked function of H-NS. Using genome-scale approaches, we show that H-NS bound regions are transposition “hotspots”. Since H-NS often interacts with pathogenicity islands, such targeting creates clinically relevant phenotypic diversity. For example, in Acinetobacter baumannii, we identify altered motility, biofilm formation, and interactions with the human immune system. Transposon capture is mediated by the DNA bridging activity of H-NS and, if absent, more ubiquitous transposition results. Consequently, transcribed and essential genes are disrupted. Hence, H-NS directs transposition to favour evolutionary outcomes useful for the host cell.

Genome-scale analysis of essential gene knockout mutants to identify an antibiotic target process.

We describe a genome-scale approach to identify the essential biological process targeted by a new antibiotic. The procedure is based on the identification of essential genes whose inactivation sensitizes a Gram-negative bacterium (Acinetobacter baylyi) to a drug and employs recently developed transposon mutant screening and single-mutant validation procedures. The approach, based on measuring the rates of loss of newly generated knockout mutants in the presence of antibiotic, provides an alternative to traditional procedures for studying essential functions using conditional expression or activity alleles. As a proof of principle study, we evaluated whether mutations enhancing sensitivity to the β-lactam antibiotic meropenem corresponded to the known essential target process of the antibiotic (septal peptidoglycan synthesis). We found that indeed mutations inactivating most genes needed for peptidoglycan synthesis and cell division strongly sensitized cells to meropenem. Additional classes of sensitizing mutations in essential genes were also identified, including those that inactivated capsule synthesis, DNA replication, or envelope stress response regulation. The essential capsule synthesis mutants appeared to enhance meropenem sensitivity by depleting a precursor needed for both capsule and peptidoglycan synthesis. The replication mutants may sensitize cells by impairing division. Nonessential gene mutations sensitizing cells to meropenem were also identified in the screen and largely corresponded to functions subordinately associated with the essential target process, such as in peptidoglycan recycling. Overall, these results help validate a new approach to identify the essential process targeted by an antibiotic and define the larger functional network determining sensitivity to it.

The rise of the sourdough: Genome-scale metabolic modeling-based approach to design sourdough starter communities with tailored-made properties

Sourdough starters harbor microbial consortia that benefit the final product's aroma and volume. The complex nature of these spontaneously developed communities raises challenges in predicting the fermentation phenotypes. Herein, we demonstrated for the first time in this field the potential of genome-scale metabolic modeling (GEMs) in the study of sourdough microbial communities. Broad in-silico modeling of microbial growth was applied on communities composed of yeast (Saccharomyces cerevisiae) and different Lactic Acid Bacteria (LAB) species, which mainly predominate in sourdough starters. Simulations of model-represented communities associated specific bacterial compositions with sourdough phenotypes. Based on ranking the phenotypic performances of different combinations, Pediococcus spp. – Lb. sakei group members were predicted to have an optimal effect considering the increase in S. cerevisiae growth abilities and overall CO2 secretion rates. Flux Balance Analysis (FBA) revealed mutual relationships between the Pediococcus spp. – Lb. sakei group members and S. cerevisiae through bidirectional nutrient dependencies, and further underlined that these bacteria compete with the yeast over nutrients to a lesser extent than the rest LAB species. Volatile compounds (VOCs) production was further modeled, identifying species-specific and community-related VOCs production profiles. The in-silico models' predictions were validated by experimentally building synthetic sourdough communities and assessing the fermentation phenotypes. The Pediococcus spp. – Lb. sakei group was indeed associated with increased yeast cell counts and fermentation rates, demonstrating a 25 % increase in the average leavening rates during the first 10 fermentation hours compared to communities with a lower representation of these group members. Overall, these results provide a possible novel strategy towards the de-novo design of sourdough starter communities with tailored-made characterizations, including a shortened leavening period.

Convergent reductive evolution and host adaptation in Mycoavidus bacterial endosymbionts of Mortierellaceae fungi

Intimate associations between fungi and intracellular bacterial endosymbionts are becoming increasingly well understood. Phylogenetic analyses demonstrate that bacterial endosymbionts of Mucoromycota fungi are related either to free-living Burkholderia or Mollicutes species. The so-called Burkholderia-related endosymbionts or BRE comprise Mycoavidus, Mycetohabitans and Candidatus Glomeribacter gigasporarum. These endosymbionts are marked by genome contraction thought to be associated with intracellular selection. However, the conclusions drawn thus far are based on a very small subset of endosymbiont genomes, and the mechanisms leading to genome streamlining are not well understood. The purpose of this study was to better understand how intracellular existence shapes Mycoavidus and BRE functionally at the genome level. To this end we generated and analyzed 14 novel draft genomes for Mycoavidus living within the hyphae of Mortierellomycotina fungi. We found that our novel Mycoavidus genomes were significantly reduced compared to free-living Burkholderiales relatives. Using a genome-scale phylogenetic approach including the novel and available existing genomes of Mycoavidus, we show that the genus is an assemblage composed of two independently derived lineages including three well supported clades of Mycoavidus. Using a comparative genomic approach, we shed light on the functional implications of genome reduction, documenting shared and unique gene loss patterns between the three Mycoavidus clades. We found that many endosymbiont isolates demonstrate patterns of vertical transmission and host-specificity, but others are present in phylogenetically disparate hosts. We discuss how reductive evolution and host specificity reflect convergent adaptation to the intrahyphal selective landscape, and commonalities of eukaryotic endosymbiont genome evolution.

Genome-scale approach to reconstructing the phylogenetic tree of psyllids (superfamily Psylloidea) with account of systematic bias

Psyllids (class Insecta: order Hemiptera: superfamily Psylloidea) are a taxonomically and phylogenetically challenging clade. Recent studies have largely advanced the phylogeny of this group, yet the family-level relationships among Aphalaridae, Carsidaridae, and others remain unresolved. Genome-scale phylogenetic analysis is known to provide a finer resolution for problems like that. However, such phylogenomics also introduces new problems: incorrect trees with high confidence yielded due to systematic error (bias). Here we addressed these issues using hundreds of single-copy orthologous (SCO) genes in psyllid transcriptomes and genomes. Our analyses revealed conflicts between the nucleotide-based and amino-acid-based phylogenetic trees. While the nucleotide-based phylogeny strongly supported the (Aphalaridae + Carsidaridae) + Others relationship, the amino-acid-based one recovered Aphalaridae + (Carsidaridae + Others) with 100% support. Further inspection revealed significant compositional heterogeneity in nucleotide sequences for 67% of SCO genes, but not in the corresponding translated amino acid sequences. We then used different strategies to combat this compositional bias, and found that using the RY-coding strategy (coding the standard nucleotides as purines and pyrimidines) the nucleotide-based phylogeny became consistent with the amino-acid-based one. We further applied RY-coding to a published concatenated nucleotide dataset and recovered the Aphalaridae monophyly (which is refuted by the original literature on non-recoded sequences) at the base of psyllid tree. Moreover, it was found that variations in evolutionary rate could lead to errors in nucleotide-based phylogeny. The fast-evolving Heteropsylla cubana (Psyllidae: Ciriacreminae) was incorrectly placed within the subfamily Psyllinae. This bias can be avoided by using data removal or RY-coding strategies. Together, our results strongly support the family relationship of Aphalaridae + (Carsidaridae + Others), and show that the amino-acid-based concatenation analysis is more robust than nucleotide-based one. Future phylogenomic analysis of psyllid nucleotide sequences should take into account methods such as the RY-coding scheme to address potential systematic biases arising from composition and rate heterogeneities.

Identification of Novel Immunosuppressive Drug Targets for Liver Transplantation Using Genome-Scale Metabolic Modeling

Abstract The global database on donation and transplantation reported 129,681 worldwide organ transplants in 2020, with kidneys and livers being the most commonly transplanted organs. Rejection of the transplanted organ by the host's immune system is a common complication and a major reason for graft failure and patient mortality. To address these limitations, we developed genome-scale metabolic models of liver and CD4+ T cells using the pipeline for MetAbolic Drug Repurposing IDentification-MADRID and integrating our data analysis results from RNA-seq, proteomics, and microarray human sample datasets (step 1). Drug targets obtained from the ConnectivityMap database are mapped to metabolic genes in the model where systematic knockouts are performed on each gene which leads to identifying differential fluxes (step 2). Our human-cell-specific models identified novel immunosuppressive drug targets by integrating differentially expressed genes in post-liver transplanted non-tolerant versus healthy control individuals and differential fluxes (step 3). We identified 193, 217, and 196 potential metabolic gene targets for T-helper 1, 2, and 17, respectively, against patients with a non-tolerant phenotype after liver transplantation. This genome-scale metabolic modeling approach for liver transplantation can assist in personalized treatment by using any individual’s sample data at step 1 and 2. It can also save time and resources in preclinical drug development studies by predicting more precise drug targets and drug repurposing based on patients’ omics data. By using this approach, we aim to improve the success rates of liver transplantations and reduce the complications associated with organ rejection. The work was supported by University of Nebraska collaboration initiatives.

Microbial active functional modules derived from network analysis and metabolic interactions decipher the complex microbiome assembly in mangrove sediments

BackgroundThe metabolic interactions of microbes significantly affect the assembly of microbial communities that play important roles in biogeochemical processes. However, most interspecies interactions between microorganisms in natural communities remain unknown, leading to a poor understanding of community assembly mechanisms.ResultsHere, we used a genome-scale metabolic modeling-based approach to explore the potential interactions among bacteria and archaea in mangrove sediments. More than half of the assembled microbial species (backsim 340) combined about 3000 pairwise metabolic interaction relationship with high potential. The examples of predicted interactions are consistent with the implications of studies based on microbial enrichment/culture, indicating the feasibility of our strategy for extracting diverse potential interactions from complex interspecies networks. Moreover, a substantial number of previously unknown microbial metabolic interactions were also predicted. We proposed a concept of microbial active functional module (mAFM), defined as a consortium constituted by a group of microbes possessing relatively high metabolic interactions via which they can actively realize certain dominant functions in element transformations. Based on the metabolic interactions and the transcript distribution of microorganisms, five mAFMs distributed in different layers of the sediments were identified. The whole group of mAFMs covered most of the principal pathways in the cycle of carbon, nitrogen, and sulfur, while each module possessed divergently dominant functions. According to this distinction, we inferred that the mAFMs participated in the element cycles via their intra-cycle and the inter-exchange among them and the sediments.ConclusionsThe results of this study greatly expanded interaction potential of microbes in mangrove sediments, which could provide supports for prospective mutualistic system construction and microbial enrichment culture. Furthermore, the mAFMs can help to extract valuable microbial metabolic interactions from the whole community and to profile the functioning of the microbial community that promote biogeochemical cycling in mangrove sediments.F-NFn2HGRi3jBYiToka5XdVideo

IFITM proteins assist cellular uptake of diverse linked chemotypes.

The search for cell-permeable drugs has conventionally focused on low-molecular weight (MW), nonpolar, rigid chemical structures. However, emerging therapeutic strategies break traditional drug design rules by employing flexibly linked chemical entities composed of more than one ligand. Using complementary genome-scale chemical-genetic approaches we identified an endogenous chemical uptake pathway involving interferon-induced transmembrane proteins (IFITMs) that modulates the cell permeability of a prototypical biopic inhibitor of MTOR (RapaLink-1, MW: 1784 g/mol). We devised additional linked inhibitors targeting BCR-ABL1 (DasatiLink-1, MW: 1518 g/mol) and EIF4A1 (BisRoc-1, MW: 1466 g/mol), uptake of which was facilitated by IFITMs. We also found that IFITMs moderately assisted some proteolysis-targeting chimeras and examined the physicochemical requirements for involvement of this uptake pathway.

Genome-Scale Metabolic Modeling Reveals Sequential Dysregulation of Glutathione Metabolism in Livers from Patients with Alcoholic Hepatitis.

Alcoholic hepatitis (AH) is the most severe form of alcoholic liver disease for which there is no efficacious treatment aiding most patients. AH manifests differently in individuals, with some patients showing debilitating symptoms more so than others. Previous studies showed significant metabolic dysregulation associated with AH. Therefore, we sought to analyze how the activity of metabolic pathways differed in the liver of patients with varying degrees of AH severity. We utilized a genome-scale metabolic modeling approach that allowed for integration of a generic human cellular metabolic model with specific RNA-seq data corresponding to healthy and multiple liver disease states to predict the metabolic fluxes within each disease state. Additionally, we performed a systems-level analysis of the transcriptomic data and predicted metabolic flux data to identify the regulatory and functional differences in liver metabolism with increasing severity of AH. Our results provide unique insights into the sequential dysregulation of the solute transport mechanisms underlying the glutathione metabolic pathway with increasing AH disease severity. We propose targeting of the solute transporters in the glutathione pathway to mimic the flux activity of the healthy liver state as a potential therapeutic intervention for AH.

Metabolic modeling of host\u2013microbe interactions for therapeutics in colorectal cancer

The onset of colorectal cancer (CRC) is often attributed to gut bacterial dysbiosis, and thus gut microbiota are highly relevant in devising treatment strategies. Certain gut microbes, like Enterococcus spp., exhibit remarkable anti-neoplastic and probiotic properties, which can aid in silver nanoparticle (AgNPs) induced reactive oxygen species (ROS)-based CRC treatment. However, the effects of AgNPs on gut microbial metabolism have not been reported thus far. In this study, a detailed systems-level understanding of ROS metabolism in Enterococcus durans (E. durans), a representative gut microbe, was gained using constraint-based modeling, wherein, the critical association between ROS and folate metabolism was established. Experimental studies involving low AgNP concentration treatment of E. durans cultures confirmed these modeling predictions (an increased extracellular folate concentration by 52%, at the 9th h of microbial growth, was observed). Besides, the computational studies established various metabolic pathways involving amino acids, energy metabolites, nucleotides, and SCFAs as the key players in elevating folate levels on ROS exposure. The anti-cancer potential of E. durans was also studied through MTT analysis of HCT 116 cells treated with microbial culture (AgNP treated) supernatant. A decrease in cell viability by 19% implicated the role of microbial metabolites (primarily folate) in causing cell death. The genome-scale modeling approach was then extended to extensively model CRC metabolism, as well as CRC–E. durans interactions in the context of CRC treatment, using tissue-specific metabolic models of CRC and healthy colon. These findings on further validation can facilitate the development of robust and effective cancer therapy.

Whole-genome-scale identification of novel non-protein-coding RNAs controlling cell proliferation and survival through a functional forward genetics strategy

Identification of cell fate-controlling lncRNAs is essential to our understanding of molecular cell biology. Here we present a human genome-scale forward-genetics approach for the identification of lncRNAs based on gene function. This approach can identify genes that play a causal role, and immediately distinguish them from those that are differentially expressed but do not affect cell function. Our genome-scale library plus next-generation-sequencing and bioinformatic approach, radically upscales the breadth and rate of functional ncRNA discovery. Human gDNA was digested to produce a lentiviral expression library containing inserts in both sense and anti-sense orientation. The library was used to transduce human Jurkat T-leukaemic cells. Cell populations were selected using continuous culture ± anti-FAS IgM, and sequencing used to identify sequences controlling cell proliferation. This strategy resulted in the identification of thousands of new sequences based solely on their function including many ncRNAs previously identified as being able to modulate cell survival or to act as key cancer regulators such as AC084816.1*, AC097103.2, AC087473.1, CASC15*, DLEU1*, ENTPD1-AS1*, HULC*, MIRLET7BHG*, PCAT-1, SChLAP1, and TP53TG1. Independent validation confirmed 4 out of 5 sequences that were identified by this strategy, conferred a striking resistance to anti-FAS IgM-induced apoptosis.

Metabolic flux sampling predicts strain-dependent differences related to aroma production among commercial wine yeasts

BackgroundMetabolomics coupled with genome-scale metabolic modeling approaches have been employed recently to quantitatively analyze the physiological states of various organisms, including Saccharomyces cerevisiae. Although yeast physiology in laboratory strains is well-studied, the metabolic states under industrially relevant scenarios such as winemaking are still not sufficiently understood, especially as there is considerable variation in metabolism between commercial strains. To study the potential causes of strain-dependent variation in the production of volatile compounds during enological conditions, random flux sampling and statistical methods were used, along with experimental extracellular metabolite flux data to characterize the differences in predicted intracellular metabolic states between strains.ResultsIt was observed that four selected commercial wine yeast strains (Elixir, Opale, R2, and Uvaferm) produced variable amounts of key volatile organic compounds (VOCs). Principal component analysis was performed on extracellular metabolite data from the strains at three time points of cell cultivation (24, 58, and 144 h). Separation of the strains was observed at all three time points. Furthermore, Uvaferm at 24 h, for instance, was most associated with propanol and ethyl hexanoate. R2 was found to be associated with ethyl acetate and Opale could be associated with isobutanol while Elixir was most associated with phenylethanol and phenylethyl acetate. Constraint-based modeling (CBM) was employed using the latest genome-scale metabolic model of yeast (Yeast8) and random flux sampling was performed with experimentally derived fluxes at various stages of growth as constraints for the model. The flux sampling simulations allowed us to characterize intracellular metabolic flux states and illustrate the key parts of metabolism that likely determine the observed strain differences. Flux sampling determined that Uvaferm and Elixir are similar while R2 and Opale exhibited the highest degree of differences in the Ehrlich pathway and carbon metabolism, thereby causing strain-specific variation in VOC production. The model predictions also established the top 20 fluxes that relate to phenotypic strain variation (e.g. at 24 h). These fluxes indicated that Opale had a higher median flux for pyruvate decarboxylase reactions compared with the other strains. Conversely, R2 which was lower in all VOCs, had higher median fluxes going toward central metabolism. For Elixir and Uvaferm, the differences in metabolism were most evident in fluxes pertaining to transaminase and hexokinase associated reactions. The applied analysis of metabolic divergence unveiled strain-specific differences in yeast metabolism linked to fusel alcohol and ester production.ConclusionsOverall, this approach proved useful in elucidating key reactions in amino acid, carbon, and glycerophospholipid metabolism which suggest genetic divergence in activity in metabolic subsystems among these wine strains related to the observed differences in VOC formation. The findings in this study could steer more focused research endeavors in developing or selecting optimal aroma-producing yeast stains for winemaking and other types of alcoholic fermentations.

The Candida glabrata Upc2A transcription factor is a global regulator of antifungal drug resistance pathways.

The most commonly used antifungal drugs are the azole compounds, which interfere with biosynthesis of the fungal-specific sterol: ergosterol. The pathogenic yeast Candida glabrata commonly acquires resistance to azole drugs like fluconazole via mutations in a gene encoding a transcription factor called PDR1. These PDR1 mutations lead to overproduction of drug transporter proteins like the ATP-binding cassette transporter Cdr1. In other Candida species, mutant forms of a transcription factor called Upc2 are associated with azole resistance, owing to the important role of this protein in control of expression of genes encoding enzymes involved in the ergosterol biosynthetic pathway. Recently, the C. glabrata Upc2A factor was demonstrated to be required for normal azole resistance, even in the presence of a hyperactive mutant form of PDR1. Using genome-scale approaches, we define the network of genes bound and regulated by Upc2A. By analogy to a previously described hyperactive UPC2 mutation found in Saccharomyces cerevisiae, we generated a similar form of Upc2A in C. glabrata called G898D Upc2A. Analysis of Upc2A genomic binding sites demonstrated that wild-type Upc2A binding to target genes was strongly induced by fluconazole while G898D Upc2A bound similarly, irrespective of drug treatment. Transcriptomic analyses revealed that, in addition to the well-described ERG genes, a large group of genes encoding components of the translational apparatus along with membrane proteins were responsive to Upc2A. These Upc2A-regulated membrane protein-encoding genes are often targets of the Pdr1 transcription factor, demonstrating the high degree of overlap between these two regulatory networks. Finally, we provide evidence that Upc2A impacts the Pdr1-Cdr1 system and also modulates resistance to caspofungin. These studies provide a new perspective of Upc2A as a master regulator of lipid and membrane protein biosynthesis.

In Silico Prediction of Novel Probiotic Species Limiting Pathogenic Vibrio Growth Using Constraint-Based Genome Scale Metabolic Modeling.

The prevalence of bacterial diseases and the application of probiotics to prevent them is a common practice in shrimp aquaculture. A wide range of bacterial species/strains is utilized in probiotic formulations, with proven beneficial effects. However, knowledge of their role in inhibiting the growth of a specific pathogen is restricted. In this study, we employed constraint-based genome-scale metabolic modeling approach to screen and identify the beneficial bacteria capable of limiting the growth of V. harveyi, a common pathogen in shrimp culture. Genome-scale models were built for 194 species (including strains from the genera Bacillus, Lactobacillus, and Lactococcus and the pathogenic strain V. harveyi) to explore the metabolic potential of these strains under different nutrient conditions in a consortium. In silico-based phenotypic analysis on 193 paired models predicted six candidate strains with growth enhancement and pathogen suppression. Growth simulations reveal that mannitol and glucoronate environments mediate parasitic interactions in a pairwise community. Furthermore, in a mannitol environment, the shortlisted six strains were purely metabolite consumers without donating metabolites to V. harveyi. The production of acetate by the screened species in a paired community suggests the natural metabolic end product’s role in limiting pathogen survival. Our study employing in silico approach successfully predicted three novel candidate strains for probiotic applications, namely, Bacillus sp 1 (identified as B. licheniformis in this study), Bacillus weihaiensis Alg07, and Lactobacillus lindneri TMW 1.1993. The study is the first to apply genomic-scale metabolic models for aquaculture applications to detect bacterial species limiting Vibrio harveyi growth.

Genome-scale metabolic modelling when changes in environmental conditions affect biomass composition.

Genome-scale metabolic modeling is an important tool in the study of metabolism by enhancing the collation of knowledge, interpretation of data, and prediction of metabolic capabilities. A frequent assumption in the use of genome-scale models is that the in vivo organism is evolved for optimal growth, where growth is represented by flux through a biomass objective function (BOF). While the specific composition of the BOF is crucial, its formulation is often inherited from similar organisms due to the experimental challenges associated with its proper determination.A cell’s macro-molecular composition is not fixed and it responds to changes in environmental conditions. As a consequence, initiatives for the high-fidelity determination of cellular biomass composition have been launched. Thus, there is a need for a mathematical and computational framework capable of using multiple measurements of cellular biomass composition in different environments. Here, we propose two different computational approaches for directly addressing this challenge: Biomass Trade-off Weighting (BTW) and Higher-dimensional-plane InterPolation (HIP).In lieu of experimental data on biomass composition-variation in response to changing nutrient environment, we assess the properties of BTW and HIP using three hypothetical, yet biologically plausible, BOFs for the Escherichia coli genome-scale metabolic model iML1515. We find that the BTW and HIP formulations have a significant impact on model performance and phenotypes. Furthermore, the BTW method generates larger growth rates in all environments when compared to HIP. Using acetate secretion and the respiratory quotient as proxies for phenotypic changes, we find marked differences between the methods as HIP generates BOFs more similar to a reference BOF than BTW. We conclude that the presented methods constitute a conceptual step in developing genome-scale metabolic modelling approaches capable of addressing the inherent dependence of cellular biomass composition on nutrient environments.

Widespread divergent transcription from bacterial and archaeal promoters is a consequence of DNA-sequence symmetry.

Transcription initiates at promoters, DNA regions recognised by a DNA-dependent RNA polymerase. We previously identified horizontally acquired Escherichia coli promoters where the direction of transcription was unclear. Here, we show that more than half of these promoters are bidirectional. Using genome-scale approaches, we demonstrate that 19% of all transcription start sites detected in E. coli are associated with a bidirectional promoter. Bidirectional promoters are similarly common in diverse bacteria and archaea and have inherent symmetry: specific bases required for transcription initiation are reciprocally co-located on opposite DNA strands. Bidirectional promoters enable co-regulation of divergent genes and are enriched in both intergenic and horizontally acquired regions. Divergent transcription is conserved among bacteria, archaea and eukaryotes, but the underlying mechanisms for bidirectionality are different.

Systems-Level Chemical Biology to Accelerate Antibiotic Drug Discovery.

Drug-resistant bacterial infections pose an imminent and growing threat to public health. The discovery and development of new antibiotics of novel chemical class and mode of action that are unsusceptible to existing resistance mechanisms is imperative for tackling this threat. Modern industrial drug discovery, however, has failed to provide new drugs of this description, as it is dependent largely on a reductionist genes-to-drugs research paradigm. We posit that the lack of success in new antibiotic drug discovery is due in part to a lack of understanding of the bacterial cell system as whole. A fundamental understanding of the architecture and function of bacterial systems has been elusive but is of critical importance to design strategies to tackle drug-resistant bacterial pathogens.Increasingly, systems-level approaches are rewriting our understanding of the cell, defining a dense network of redundant and interacting components that resist perturbations of all kinds, including by antibiotics. Understanding the network properties of bacterial cells requires integrative, systematic, and genome-scale approaches. These methods strive to understand how the phenotypic behavior of bacteria emerges from the many interactions of individual molecular components that constitute the system. With the ability to examine genomic, transcriptomic, proteomic, and metabolomic consequences of, for example, genetic or chemical perturbations, researchers are increasingly moving away from one-gene-at-a-time studies to consider the system-wide response of the cell. Such measurements are demonstrating promise as quantitative tools, powerful discovery engines, and robust hypothesis generators with great value to antibiotic drug discovery.In this Account, we describe our thinking and findings using systems-level studies aimed at understanding bacterial physiology broadly and in uncovering new antibacterial chemical matter of novel mechanism. We share our systems-level toolkit and detail recent technological developments that have enabled unprecedented acquisition of genome-wide interaction data. We focus on three types of interactions: gene-gene, chemical-gene, and chemical-chemical. We provide examples of their use in understanding cell networks and how these insights might be harnessed for new antibiotic discovery. By example, we show the application of these principles in mapping genetic networks that underpin phenotypes of interest, characterizing genes of unknown function, validating small-molecule screening platforms, uncovering novel chemical probes and antibacterial leads, and delineating the mode of action of antibacterial chemicals. We also discuss the importance of computation to these approaches and its probable dominance as a tool for systems approaches in the future. In all, we advocate for the use of systems-based approaches as discovery engines in antibacterial research, both as powerful tools and to stimulate innovation.

Altered Metabolic Strategies: Elaborate Mechanisms Adopted by Oenococcus oeni in Response to Acid Stress.

Oenococcus oeni plays a key role in inducing malolactic fermentation in wine. Acid stress is often encountered under wine conditions. However, the lack of systematic studies of acid resistance mechanisms limits the downstream fermentation applications. In this study, the acid responses of O. oeni were investigated by combining transcriptome, metabolome, and genome-scale metabolic modeling approaches. Metabolite profiling highlighted the decreased abundance of nucleotides under acid stress. The gene-metabolite bipartite network showed negative correlations between nucleotides and genes involved in ribosome assembly, translation, and post-translational processes, suggesting that stringent response could be activated under acid stress. Genome-scale metabolic modeling revealed marked flux rerouting, including reallocation of pyruvate, attenuation of glycolysis, utilization of carbon sources other than glucose, and enhancement of nucleotide salvage and the arginine deiminase pathway. This study provided novel insights into the acid responses of O. oeni, which will be useful for designing strategies to address acid stress in wine malolactic fermentation.

Insights from the Applications of Single-Cell Transcriptomic Analysis in Germ Cell Development and Reproductive Medicine.

Mechanistic understanding of germ cell formation at a genome-scale level can aid in developing novel therapeutic strategies for infertility. Germ cell formation is a complex process that is regulated by various mechanisms, including epigenetic regulation, germ cell-specific gene transcription, and meiosis. Gonads contain a limited number of germ cells at various stages of differentiation. Hence, genome-scale analysis of germ cells at the single-cell level is challenging. Conventional genome-scale approaches cannot delineate the landscape of genomic, transcriptomic, and epigenomic diversity or heterogeneity in the differentiating germ cells of gonads. Recent advances in single-cell genomic techniques along with single-cell isolation methods, such as microfluidics and fluorescence-activated cell sorting, have helped elucidate the mechanisms underlying germ cell development and reproductive disorders in humans. In this review, the history of single-cell transcriptomic analysis and their technical advantages over the conventional methods have been discussed. Additionally, recent applications of single-cell transcriptomic analysis for analyzing germ cells have been summarized.

Blood-Based Detection of Colorectal Cancer Using Cancer-Specific DNA Methylation Markers.

Cancer tissues have characteristic DNA methylation profiles compared with their corresponding normal tissues that can be utilized for cancer diagnosis with liquid biopsy. Using a genome-scale DNA methylation approach, we sought to identify a panel of DNA methylation markers specific for cell-free DNA (cfDNA) from patients with colorectal cancer (CRC). By comparing DNA methylomes between CRC and normal mucosal tissues or blood leukocytes, we identified eight cancer-specific methylated loci (ADGRB1, ANKRD13, FAM123A, GLI3, PCDHG, PPP1R16B, SLIT3, and TMEM90B) and developed a five-marker panel (FAM123A, GLI3, PPP1R16B, SLIT3, and TMEM90B) that detected CRC in liquid biopsies with a high sensitivity and specificity with a droplet digital MethyLight assay. In a set of cfDNA samples from CRC patients (n = 117) and healthy volunteers (n = 60), a panel of five markers on the platform of the droplet digital MethyLight assay detected stages I–III and stage IV CRCs with sensitivities of 45.9% and 95.7%, respectively, and a specificity of 95.0%. The number of detected markers was correlated with the cancer stage, perineural invasion, lymphatic emboli, and venous invasion. Our five-marker panel with the droplet digital MethyLight assay showed a high sensitivity and specificity for the detection of CRC with cfDNA samples from patients with metastatic CRC.