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The mortality caused by tuberculosis (TB) infections is a global concern, and there is a need to improve understanding of the disease. Current in vitro infection models to study the disease have limitations such as short investigation durations and divergent transcriptional signatures. This study aims to overcome these limitations by developing a 3D collagen culture system that mimics the biomechanical and extracellular matrix (ECM) of lung microenvironment (collagen fibers, stiffness comparable to in vivo conditions) as the infection primarily manifests in the lungs. The system incorporates Mycobacterium tuberculosis (Mtb) infected human THP-1 or primary monocytes/macrophages. Dual RNA sequencing reveals higher mammalian gene expression similarity with patient samples than 2D macrophage infections. Similarly, bacterial gene expression more accurately recapitulates in vivo gene expression patterns compared to bacteria in 2D infection models. Key phenotypes observed in humans, such as foamy macrophages and mycobacterial cords, are reproduced in the model. This biomaterial system overcomes challenges associated with traditional platforms by modulating immune cells and closely mimicking in vivo infection conditions, including showing efficacy with clinically relevant concentrations of anti-TB drug pyrazinamide, not seen in any other in vitro infection model, making it reliable and readily adoptable for tuberculosis studies and drug screening., (© 2024 Wiley‐VCH GmbH.)

Resource partitioning is central to the incredible productivity of microbial communities, including gigatons in annual methane emissions through syntrophic interactions. Previous work revealed how a sulfate reducer (Desulfovibrio vulgaris, Dv) and a methanogen (Methanococcus maripaludis, Mm) underwent evolutionary diversification in a planktonic context, improving stability, cooperativity, and productivity within 300-1000 generations. Here, we show that mutations in just 15 Dv and 7 Mm genes within a minimal assemblage of this evolved community gave rise to co-existing ecotypes that were spatially enriched within a few days of culturing in a fluidized bed reactor. The spatially segregated communities partitioned resources in the simulated subsurface environment, with greater lactate utilization by attached Dv but partial utilization of resulting H 2 by low affinity hydrogenases of Mm in the same phase. The unutilized H 2 was scavenged by high affinity hydrogenases of planktonic Mm, producing copious amounts of methane. Our findings show how a few mutations can drive resource partitioning amongst niche-differentiated ecotypes, whose interplay synergistically improves productivity of the entire mutualistic community., (© 2024. The Author(s).)

Poor prognosis and drug resistance in glioblastoma (GBM) can result from cellular heterogeneity and treatment-induced shifts in phenotypic states of tumor cells, including dedifferentiation into glioma stem-like cells (GSCs). This rare tumorigenic cell subpopulation resists temozolomide, undergoes proneural-to-mesenchymal transition (PMT) to evade therapy, and drives recurrence. Through inference of transcriptional regulatory networks (TRNs) of patient-derived GSCs (PD-GSCs) at single-cell resolution, we demonstrate how the topology of transcription factor interaction networks drives distinct trajectories of cell-state transitions in PD-GSCs resistant or susceptible to cytotoxic drug treatment. By experimentally testing predictions based on TRN simulations, we show that drug treatment drives surviving PD-GSCs along a trajectory of intermediate states, exposing vulnerability to potentiated killing by siRNA or a second drug targeting treatment-induced transcriptional programs governing nongenetic cell plasticity. Our findings demonstrate an approach to uncover TRN topology and use it to rationally predict combinatorial treatments that disrupt acquired resistance in GBM.

We have previously reported the transcriptomic and lipidomic profile of the first-generation, hygromycin-resistant (Hyg R ) version of the BCGΔBCG1419c vaccine candidate, under biofilm conditions. We recently constructed and characterized the efficacy, safety, whole genome sequence, and proteomic profile of a second-generation version of BCGΔBCG1419c, a strain lacking the BCG1419c gene and devoid of antibiotic markers. Here, we compared the antibiotic-less BCGΔBCG1419c with BCG. We assessed their colonial and ultrastructural morphology, biofilm, c-di-GMP production in vitro, as well as their transcriptomic and lipidomic profiles, including their capacity to activate macrophages via Mincle and Myd88. Our results show that BCGΔBCG1419c colonial and ultrastructural morphology, c-di-GMP, and biofilm production differed from parental BCG, whereas we found no significant changes in its lipidomic profile either in biofilm or planktonic growth conditions. Transcriptomic profiling suggests changes in BCGΔBCG1419c cell wall and showed reduced transcription of some members of the DosR, MtrA, and ArgR regulons. Finally, induction of TNF-α, IL-6 or G-CSF by bone-marrow derived macrophages infected with either BCGΔBCG1419c or BCG required Mincle and Myd88. Our results confirm that some differences already found to occur in Hyg R BCGΔBCG1419c compared with BCG are maintained in the antibiotic-less version of this vaccine candidate except changes in production of PDIM. Comparison with previous characterizations conducted by OMICs show that some differences observed in BCGΔBCG1419c compared with BCG are maintained whereas others are dependent on the growth condition employed to culture them., (© 2024. The Author(s).)

The sources and sinks of nitrous oxide, as control emissions to the atmosphere, are generally poorly constrained for most environmental systems. Initial depth-resolved analysis of nitrous oxide flux from observation wells and the proximal surface within a nitrate contaminated aquifer system revealed high subsurface production but little escape from the surface. To better understand the environmental controls of production and emission at this site, we used a combination of isotopic, geochemical, and molecular analyses to show that chemodenitrification and bacterial denitrification are major sources of nitrous oxide in this subsurface, where low DO, low pH, and high nitrate are correlated with significant nitrous oxide production. Depth-resolved metagenomes showed that consumption of nitrous oxide near the surface was correlated with an enrichment of Clade II nitrous oxide reducers, consistent with a growing appreciation of their importance in controlling release of nitrous oxide to the atmosphere. Our work also provides evidence for the reduction of nitrous oxide at a pH of 4, well below the generally accepted limit of pH 5.

Grade IV glioma, formerly known as glioblastoma multiforme (GBM) is the most aggressive and lethal type of brain tumor, and its treatment remains challenging in part due to extensive interpatient heterogeneity in disease driving mechanisms and lack of prognostic and predictive biomarkers. Using mechanistic inference of node-edge relationship (MINER), we have analyzed multiomics profiles from 516 patients and constructed an atlas of causal and mechanistic drivers of interpatient heterogeneity in GBM (gbmMINER). The atlas has delineated how 30 driver mutations act in a combinatorial scheme to causally influence a network of regulators (306 transcription factors and 73 miRNAs) of 179 transcriptional "programs", influencing disease progression in patients across 23 disease states. Through extensive testing on independent patient cohorts, we share evidence that a machine learning model trained on activity profiles of programs within gbmMINER significantly augments risk stratification, identifying patients who are super-responders to standard of care and those that would benefit from 2 nd line treatments. In addition to providing mechanistic hypotheses regarding disease prognosis, the activity of programs containing targets of 2 nd line treatments accurately predicted efficacy of 28 drugs in killing glioma stem-like cells from 43 patients. Our findings demonstrate that interpatient heterogeneity manifests from differential activities of transcriptional programs, providing actionable strategies for mechanistically characterizing GBM from a systems perspective and developing better prognostic and predictive biomarkers for personalized medicine.

Background: Individualized treatment decisions for patients with multiple myeloma (MM) requires accurate risk stratification that takes into account patient-specific consequences of genetic abnormalities and tumor microenvironment on disease outcome and therapy responsiveness., Methods: Previously, SYstems Genetic Network AnaLysis (SYGNAL) of multi-omics tumor profiles from 881 MM patients generated the mmSYGNAL network, which uncovered different causal and mechanistic drivers of genetic programs associated with disease progression across MM subtypes. Here, we have trained a machine learning (ML) algorithm on activities of mmSYGNAL programs within individual patient tumor samples to develop a risk classification scheme for MM that significantly outperformed cytogenetics, International Staging System, and multi-gene biomarker panels in predicting risk of PFS across four independent patient cohorts., Results: We demonstrate that, unlike other tests, mmSYGNAL can accurately predict disease progression risk at primary diagnosis, pre- and post-transplant and even after multiple relapses, making it useful for individualized dynamic risk assessment throughout the disease trajectory., Conclusion: mmSYGNAL provides improved individualized risk stratification that accounts for a patient's distinct set of genetic abnormalities and can monitor risk longitudinally as each patient's disease characteristics change.

Clostridioides difficile colonizes up to 30-40% of community-dwelling adults without causing disease. C. difficile infections (CDIs) are the leading cause of antibiotic-associated diarrhea in the U.S. and typically develop in individuals following disruption of the gut microbiota due to antibiotic or chemotherapy treatments. Further treatment of CDI with antibiotics is not always effective and can lead to antibiotic resistance and recurrent infections (rCDI). The most effective treatment for rCDI is the reestablishment of an intact microbiota via fecal microbiota transplants (FMTs). However, the success of FMTs has been difficult to generalize because the microbial interactions that prevent engraftment and facilitate the successful clearance of C. difficile are still only partially understood. Here we show how microbial community-scale metabolic models (MCMMs) accurately predicted known instances of C. difficile colonization susceptibility or resistance in vitro and in vivo . MCMMs provide detailed mechanistic insights into the ecological interactions that govern C. difficile engraftment, like cross-feeding or competition involving metabolites like succinate, trehalose, and ornithine, which differ from person to person. Indeed, three distinct C. difficile metabolic niches emerge from our MCMMs, two associated with positive growth rates and one that represents non-growth, which are consistently observed across 15,204 individuals from five independent cohorts. Finally, we show how MCMMs can predict personalized engraftment and C. difficile growth suppression for a probiotic cocktail (VE303) designed to replace FMTs for the treatment rCDI. Overall, this powerful modeling approach predicts personalized C. difficile engraftment risk and can be leveraged to assess probiotic treatment efficacy. MCMMs could be extended to understand the mechanistic underpinnings of personalized engraftment of other opportunistic bacterial pathogens, beneficial probiotic organisms, or more complex microbial consortia.

Consequential STEM experiences in informal settings can address issues of equity by fully engaging historically marginalized high school students in complex socio-scientific issues. However, inclusive and effective programs are in high demand, and there is little research on what specific aspects, context, and timeframes are most important when scaling these experiences. Using a mixed method approach, this study demonstrates that students make significant gains, in the short and long term, through in-person and remote informal programs ranging between 22-h and 320-h. Progress across STEM learning constructs is attributed to authentic research experiences, students' connections to STEM professionals, direct hands-on participation in projects, and group work. Relative to formal education settings, research-based informal STEM programs can be implemented with minimal resources, can maintain effectiveness while scaling, and work towards addressing the societal challenge of improving STEM learning and outcomes for high school students from historically marginalized communities., Competing Interests: Competing interests The authors declare no competing interests.

Patescibacteria, also known as the candidate phyla radiation (CPR), are a diverse group of bacteria that constitute a disproportionately large fraction of microbial dark matter. Its few cultivated members, belonging mostly to Saccharibacteria, grow as epibionts on host Actinobacteria. Due to a lack of suitable tools, the genetic basis of this lifestyle and other unique features of Patescibacteira remain unexplored. Here, we show that Saccharibacteria exhibit natural competence, and we exploit this property for their genetic manipulation. Imaging of fluorescent protein-labeled Saccharibacteria provides high spatiotemporal resolution of phenomena accompanying epibiotic growth, and a transposon-insertion sequencing (Tn-seq) genome-wide screen reveals the contribution of enigmatic Saccharibacterial genes to growth on their hosts. Finally, we leverage metagenomic data to provide cutting-edge protein structure-based bioinformatic resources that support the strain Southlakia epibionticum and its corresponding host, Actinomyces israelii, as a model system for unlocking the molecular underpinnings of the epibiotic lifestyle., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Data-Independent Acquisition (DIA) is a mass spectrometry-based method to reliably identify and reproducibly quantify large fractions of a target proteome. The peptide-centric data analysis strategy employed in DIA requires a priori generated spectral assay libraries. Such assay libraries allow to extract quantitative data in a targeted approach and have been generated for human, mouse, zebrafish, E. coli and few other organisms. However, a spectral assay library for the extreme halophilic archaeon Halobacterium salinarum NRC-1, a model organism that contributed to several notable discoveries, is not publicly available yet. Here, we report a comprehensive spectral assay library to measure 2,563 of 2,646 annotated H. salinarum NRC-1 proteins. We demonstrate the utility of this library by measuring global protein abundances over time under standard growth conditions. The H. salinarum NRC-1 library includes 21,074 distinct peptides representing 97% of the predicted proteome and provides a new, valuable resource to confidently measure and quantify any protein of this archaeon. Data and spectral assay libraries are available via ProteomeXchange (PXD042770, PXD042774) and SWATHAtlas (SAL00312-SAL00319)., (© 2023. Springer Nature Limited.)

Longitudinal sampling of the stool has yielded important insights into the ecological dynamics of the human gut microbiome. However, human stool samples are available approximately once per day, while commensal population doubling times are likely on the order of minutes-to-hours. Despite this mismatch in timescales, much of the prior work on human gut microbiome time series modeling has assumed that day-to-day fluctuations in taxon abundances are related to population growth or death rates, which is likely not the case. Here, we propose an alternative model of the human gut as a stationary system, where population dynamics occur internally and the bacterial population sizes measured in a bolus of stool represent a steady-state endpoint of these dynamics. We formalize this idea as stochastic logistic growth. We show how this model provides a path toward estimating the growth phases of gut bacterial populations in situ. We validate our model predictions using an in vitro Escherichia coli growth experiment. Finally, we show how this method can be applied to densely-sampled human stool metagenomic time series data. We discuss how these growth phase estimates may be used to better inform metabolic modeling in flow-through ecosystems, like animal guts or industrial bioreactors., (© 2023. Springer Nature Limited.)

The success of Mycobacterium tuberculosis (Mtb) is largely attributed to its ability to physiologically adapt and withstand diverse localized stresses within host microenvironments. Here, we present a data-driven model (EGRIN 2.0) that captures the dynamic interplay of environmental cues and genome-encoded regulatory programs in Mtb. Analysis of EGRIN 2.0 shows how modulation of the MtrAB two-component signaling system tunes Mtb growth in response to related host microenvironmental cues. Disruption of MtrAB by tunable CRISPR interference confirms that the signaling system regulates multiple peptidoglycan hydrolases, among other targets, that are important for cell division. Further, MtrA decreases the effectiveness of antibiotics by mechanisms of both intrinsic resistance and drug tolerance. Together, the model-enabled dissection of complex MtrA regulation highlights its importance as a drug target and illustrates how EGRIN 2.0 facilitates discovery and mechanistic characterization of Mtb adaptation to specific host microenvironments within the host., Competing Interests: Declaration of interests The authors declare no competing interest., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Glutathione (GSH) is an abundant metabolite within eukaryotic cells that can act as a signal, a nutrient source, or serve in a redox capacity for intracellular bacterial pathogens. For Francisella, GSH is thought to be a critical in vivo source of cysteine; however, the cellular pathways permitting GSH utilization by Francisella differ between strains and have remained poorly understood. Using genetic screening, we discovered a unique pathway for GSH utilization in Francisella. Whereas prior work suggested GSH catabolism initiates in the periplasm, the pathway we define consists of a major facilitator superfamily (MFS) member that transports intact GSH and a previously unrecognized bacterial cytoplasmic enzyme that catalyzes the first step of GSH degradation. Interestingly, we find that the transporter gene for this pathway is pseudogenized in pathogenic Francisella, explaining phenotypic discrepancies in GSH utilization among Francisella spp. and revealing a critical role for GSH in the environmental niche of these bacteria., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

The study of bacteria has yielded fundamental insights into cellular biology and physiology, biotechnological advances and many therapeutics. Yet due to a lack of suitable tools, the significant portion of bacterial diversity held within the candidate phyla radiation (CPR) remains inaccessible to such pursuits. Here we show that CPR bacteria belonging to the phylum Saccharibacteria exhibit natural competence. We exploit this property to develop methods for their genetic manipulation, including the insertion of heterologous sequences and the construction of targeted gene deletions. Imaging of fluorescent protein-labeled Saccharibacteria provides high spatiotemporal resolution of phenomena accompanying epibiotic growth and a transposon insertion sequencing genome-wide screen reveals the contribution of enigmatic Saccharibacterial genes to growth on their Actinobacteria hosts. Finally, we leverage metagenomic data to provide cutting-edge protein structure-based bioinformatic resources that support the strain Southlakia epibionticum and its corresponding host, Actinomyces israelii , as a model system for unlocking the molecular underpinnings of the epibiotic lifestyle.

The scale of post-transcriptional regulation and the implications of its interplay with other forms of regulation in environmental acclimation are underexplored for organisms of the domain Archaea . Here, we have investigated the scale of post-transcriptional regulation in the extremely halophilic archaeon Halobacterium salinarum NRC-1 by integrating the transcriptome-wide locations of transcript processing sites (TPSs) and SmAP1 binding, the genome-wide locations of antisense RNAs (asRNAs), and the consequences of RNase_2099C knockout on the differential expression of all genes. This integrated analysis has discovered that 54% of all protein-coding genes in the genome of this haloarchaeon are likely targeted by multiple mechanisms for putative post-transcriptional processing and regulation, with about 20% of genes likely being regulated by combinatorial schemes involving SmAP1, asRNAs, and RNase_2099C. Comparative analysis of mRNA levels (transcriptome sequencing [RNA-Seq]) and protein levels (sequential window acquisition of all theoretical fragment ion spectra mass spectrometry [SWATH-MS]) for 2,579 genes over four phases of batch culture growth in complex medium generated additional evidence for the conditional post-transcriptional regulation of 7% of all protein-coding genes. We demonstrate that post-transcriptional regulation may act to fine-tune specialized and rapid acclimation to stressful environments, e.g., as a switch to turn on gas vesicle biogenesis to promote vertical relocation under anoxic conditions and modulate the frequency of transposition by insertion sequence (IS) elements of the IS 200 /IS 605 , IS 4 , and IS H3 families. Findings from this study are provided as an atlas in a public Web resource (https://halodata.systemsbiology.net). IMPORTANCE While the transcriptional regulation landscape of archaea has been extensively investigated, we currently have limited knowledge about post-transcriptional regulation and its driving mechanisms in this domain of life. In this study, we collected and integrated omics data from multiple sources and technologies to infer post-transcriptionally regulated genes and the putative mechanisms modulating their expression at the protein level in Halobacterium salinarum NRC-1. The results suggest that post-transcriptional regulation may drive environmental acclimation by regulating hallmark biological processes. To foster discoveries by other research groups interested in the topic, we extended our integrated data to the public in the form of an interactive atlas (https://halodata.systemsbiology.net).

There is an urgent need for strategies to discover secondary drugs to prevent or disrupt antimicrobial resistance (AMR), which is causing >700,000 deaths annually. Here, we demonstrate that tetracycline-resistant (Tet R ) Escherichia coli undergoes global transcriptional and metabolic remodeling, including downregulation of tricarboxylic acid cycle and disruption of redox homeostasis, to support consumption of the proton motive force for tetracycline efflux. Using a pooled genome-wide library of single-gene deletion strains, at least 308 genes, including four transcriptional regulators identified by our network analysis, were confirmed as essential for restoring the fitness of Tet R E. coli during treatment with tetracycline. Targeted knockout of ArcA, identified by network analysis as a master regulator of this new compensatory physiological state, significantly compromised fitness of Tet R E. coli during tetracycline treatment. A drug, sertraline, which generated a similar metabolome profile as the arcA knockout strain, also resensitized Tet R E. coli to tetracycline. We discovered that the potentiating effect of sertraline was eliminated upon knocking out arcA , demonstrating that the mechanism of potential synergy was through action of sertraline on the tetracycline-induced ArcA network in the Tet R strain. Our findings demonstrate that therapies that target mechanistic drivers of compensatory physiological states could resensitize AMR pathogens to lost antibiotics. IMPORTANCE Antimicrobial resistance (AMR) is projected to be the cause of >10 million deaths annually by 2050. While efforts to find new potent antibiotics are effective, they are expensive and outpaced by the rate at which new resistant strains emerge. There is desperate need for a rational approach to accelerate the discovery of drugs and drug combinations that effectively clear AMR pathogens and even prevent the emergence of new resistant strains. Using tetracycline-resistant (Tet R ) Escherichia coli, we demonstrate that gaining resistance is accompanied by loss of fitness, which is restored by compensatory physiological changes. We demonstrate that transcriptional regulators of the compensatory physiologic state are promising drug targets because their disruption increases the susceptibility of Tet R E. coli to tetracycline. Thus, we describe a generalizable systems biology approach to identify new vulnerabilities within AMR strains to rationally accelerate the discovery of therapeutics that extend the life span of existing antibiotics.

Glioblastoma (GBM) is a heterogeneous tumor made up of cell states that evolve over time. Here, we modeled tumor evolutionary trajectories during standard-of-care treatment using multi-omic single-cell analysis of a primary tumor sample, corresponding mouse xenografts subjected to standard of care therapy, and recurrent tumor at autopsy. We mined the multi-omic data with single-cell SYstems Genetics Network AnaLysis (scSYGNAL) to identify a network of 52 regulators that mediate treatment-induced shifts in xenograft tumor-cell states that were also reflected in recurrence. By integrating scSYGNAL-derived regulatory network information with transcription factor accessibility deviations derived from single-cell ATAC-seq data, we developed consensus networks that modulate cell state transitions across subpopulations of primary and recurrent tumor cells. Finally, by matching targeted therapies to active regulatory networks underlying tumor evolutionary trajectories, we provide a framework for applying single-cell-based precision medicine approaches to an individual patient in a concurrent, adjuvant, or recurrent setting., (© 2022. The Author(s).)

Stickland amino acid fermentations occur primarily among species of Clostridia. An ancient form of metabolism, Stickland fermentations use amino acids as electron acceptors in the absence of stronger oxidizing agents and provide metabolic capabilities to support growth when other fermentable substrates, such as carbohydrates, are lacking. The reactions were originally described as paired fermentations of amino acid electron donors, such as the branched-chain amino acids, with recipients that include proline and glycine. We present a redox-focused view of Stickland metabolism following electron flow through metabolically diverse oxidative reactions and the defined-substrate reductase systems, including for proline and glycine, and the role of dual redox pathways for substrates such as leucine and ornithine. Genetic studies and Environment and Gene Regulatory Interaction Network (EGRIN) models for the pathogen Clostridioides difficile have improved our understanding of the regulation and metabolic recruitment of these systems, and their functions in modulating inter-species interactions within host-pathogen-commensal systems and uses in industrial and environmental applications., Competing Interests: Declaration of competing interest LB is an inventor on patents for C. difficile microbiota therapeutics, and is the founder, SAB chair and a stock holder in ParetoBio Inc. Remaining authors declare no competing interests., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Mathematical models have many applications in infectious diseases: epidemiologists use them to forecast outbreaks and design containment strategies; systems biologists use them to study complex processes sustaining pathogens, from the metabolic networks empowering microbial cells to ecological networks in the microbiome that protects its host. Here, we (1) review important models relevant to infectious diseases, (2) draw parallels among models ranging widely in scale. We end by discussing a minimal set of information for a model to promote its use by others and to enable predictions that help us better fight pathogens and the diseases they cause., (© 2022 The Author(s).)

There is an urgent need for new drug regimens to rapidly cure tuberculosis. Here, we report the development of drug response assayer (DRonA) and "MLSynergy," algorithms to perform rapid drug response assays and predict response of Mycobacterium tuberculosis (Mtb) to drug combinations. Using a transcriptome signature for cell viability, DRonA detects Mtb killing by diverse mechanisms in broth culture, macrophage infection, and patient sputum, providing an efficient and more sensitive alternative to time- and resource-intensive bacteriologic assays. Further, MLSynergy builds on DRonA to predict synergistic and antagonistic multidrug combinations using transcriptomes of Mtb treated with single drugs. Together, DRonA and MLSynergy represent a generalizable framework for rapid monitoring of drug effects in host-relevant contexts and accelerate the discovery of efficacious high-order drug combinations., Competing Interests: The authors declare no competing interests., (© 2021 The Authors.)

The ability of Mycobacterium tuberculosis (Mtb) to adopt heterogeneous physiological states underlies its success in evading the immune system and tolerating antibiotic killing. Drug tolerant phenotypes are a major reason why the tuberculosis (TB) mortality rate is so high, with over 1.8 million deaths annually. To develop new TB therapeutics that better treat the infection (faster and more completely), a systems-level approach is needed to reveal the complexity of network-based adaptations of Mtb. Here, we report a new predictive model called PRIME (Phenotype of Regulatory influences Integrated with Metabolism and Environment) to uncover environment-specific vulnerabilities within the regulatory and metabolic networks of Mtb. Through extensive performance evaluations using genome-wide fitness screens, we demonstrate that PRIME makes mechanistically accurate predictions of context-specific vulnerabilities within the integrated regulatory and metabolic networks of Mtb, accurately rank-ordering targets for potentiating treatment with frontline drugs., (© 2021. The Author(s).)

We present predictive models for comprehensive systems analysis of Clostridioides difficile, the etiology of pseudomembranous colitis. By leveraging 151 published transcriptomes, we generated an EGRIN model that organizes 90% of C. difficile genes into a transcriptional regulatory network of 297 co-regulated modules, implicating genes in sporulation, carbohydrate transport, and metabolism. By advancing a metabolic model through addition and curation of metabolic reactions including nutrient uptake, we discovered 14 amino acids, diverse carbohydrates, and 10 metabolic genes as essential for C. difficile growth in the intestinal environment. Finally, we developed a PRIME model to uncover how EGRIN-inferred combinatorial gene regulation by transcription factors, such as CcpA and CodY, modulates essential metabolic processes to enable C. difficile growth relative to commensal colonization. The C. difficile interactive web portal provides access to these model resources to support collaborative systems-level studies of context-specific virulence mechanisms in C. difficile., Competing Interests: Declaration of interests L.B. is an inventor on patents for C. difficile microbiota therapeutics. L.B. is an SAB member and holds stock in ParetoBio. Remaining authors declare no competing interests., (Published by Elsevier Inc.)

Leveraging systems biology approaches, we illustrate how metabolically distinct species of Clostridia protect against or worsen Clostridioides difficile infection in mice by modulating the pathogen's colonization, growth, and virulence to impact host survival. Gnotobiotic mice colonized with the amino acid fermenter Paraclostridium bifermentans survive infection with reduced disease severity, while mice colonized with the butyrate-producer, Clostridium sardiniense, succumb more rapidly. Systematic in vivo analyses revealed how each commensal alters the gut-nutrient environment to modulate the pathogen's metabolism, gene regulatory networks, and toxin production. Oral administration of P. bifermentans rescues conventional, clindamycin-treated mice from lethal C. difficile infection in a manner similar to that of monocolonized animals, thereby supporting the therapeutic potential of this commensal species. Our findings lay the foundation for mechanistically informed therapies to counter C. difficile disease using systems biology approaches to define host-commensal-pathogen interactions in vivo., Competing Interests: Declaration of interests L.B. and G.K.G. are co-inventors on patents for C. difficile microbiota therapeutics. L.B., G.K.G., and A.L.S. are SAB members and hold stock in ParetoBio. G.K.G.is an SAB member and holds stock in Kaleido, Inc. A.L.S. is a co-owner of ExArca Pharmaceuticals. The remaining authors declare no competing interests., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Drug resistance is a major problem in treatment of microbial infections and cancers. There is growing evidence that a transient drug tolerant state may precede and potentiate the emergence of drug resistance. Therefore, understanding the mechanisms leading to tolerance is critical for combating drug resistance and for the development of effective therapeutic strategy. Through laboratory evolution of yeast, we recently demonstrated that adaptive prediction (AP), a strategy employed by organisms to anticipate and prepare for a future stressful environment, can emerge within 100 generations by linking the response triggered by a neutral cue (caffeine) to a mechanism of protection against a lethal agent (5-fluoroorotic acid, 5-FOA). Here, we demonstrate that mutations selected across multiple laboratory-evolved lines had linked the neutral cue response to core genes of autophagy. Across these evolved lines, conditional activation of autophagy through AP conferred tolerance, and potentiated subsequent selection of mutations in genes specific to overcoming the toxicity of 5-FOA. These results offer a new perspective on how extensive genome-wide genetic interactions of autophagy could have facilitated the emergence of AP over short evolutionary timescales to potentiate selection of 5-FOA resistance-conferring mutations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Nivedita, Aitchison and Baliga.)

Corals from the northern Red Sea, in particular the Gulf of Aqaba (GoA), have exceptionally high bleaching thresholds approaching >5℃ above their maximum monthly mean (MMM) temperatures. These elevated thresholds are thought to be due to historical selection, as corals passed through the warmer Southern Red Sea during recolonization from the Arabian Sea. To test this hypothesis, we determined thermal tolerance thresholds of GoA versus central Red Sea (CRS) Stylophora pistillata corals using multi-temperature acute thermal stress assays to determine thermal thresholds. Relative thermal thresholds of GoA and CRS corals were indeed similar and exceptionally high (~7℃ above MMM). However, absolute thermal thresholds of CRS corals were on average 3℃ above those of GoA corals. To explore the molecular underpinnings, we determined gene expression and microbiome response of the coral holobiont. Transcriptomic responses differed markedly, with a strong response to the thermal stress in GoA corals and their symbiotic algae versus a remarkably muted response in CRS colonies. Concomitant to this, coral and algal genes showed temperature-induced expression in GoA corals, while exhibiting fixed high expression (front-loading) in CRS corals. Bacterial community composition of GoA corals changed dramatically under heat stress, whereas CRS corals displayed stable assemblages. We interpret the response of GoA corals as that of a resilient population approaching a tipping point in contrast to a pattern of consistently elevated thermal resistance in CRS corals that cannot further attune. Such response differences suggest distinct thermal tolerance mechanisms that may affect the response of coral populations to ocean warming., (© 2021 The Authors. Molecular Ecology published by John Wiley & Sons Ltd.)

Early evolution of mutualism is characterized by big and predictable adaptive changes, including the specialization of interacting partners, such as through deleterious mutations in genes not required for metabolic cross-feeding. We sought to investigate whether these early mutations improve cooperativity by manifesting in synergistic epistasis between genomes of the mutually interacting species. Specifically, we have characterized evolutionary trajectories of syntrophic interactions of Desulfovibrio vulgaris (Dv) with Methanococcus maripaludis (Mm) by longitudinally monitoring mutations accumulated over 1000 generations of nine independently evolved communities with analysis of the genotypic structure of one community down to the single-cell level. We discovered extensive parallelism across communities despite considerable variance in their evolutionary trajectories and the perseverance within many evolution lines of a rare lineage of Dv that retained sulfate-respiration (SR+) capability, which is not required for metabolic cross-feeding. An in-depth investigation revealed that synergistic epistasis across pairings of Dv and Mm genotypes had enhanced cooperativity within SR- and SR+ assemblages, enabling their coexistence within the same community. Thus, our findings demonstrate that cooperativity of a mutualism can improve through synergistic epistasis between genomes of the interacting species, enabling the coexistence of mutualistic assemblages of generalists and their specialized variants., (© 2021. The Author(s).)

Despite recent advancements in the treatment of multiple myeloma (MM), nearly all patients ultimately relapse and many become refractory to multiple lines of therapies. Therefore, we not only need the ability to predict which patients are at high risk for disease progression but also a means to understand the mechanisms underlying their risk. Here, we report a transcriptional regulatory network (TRN) for MM inferred from cross-sectional multi-omics data from 881 patients that predicts how 124 chromosomal abnormalities and somatic mutations causally perturb 392 transcription regulators of 8549 genes to manifest in distinct clinical phenotypes and outcomes. We identified 141 genetic programs whose activity profiles stratify patients into 25 distinct transcriptional states and proved to be more predictive of outcomes than did mutations. The coherence of these programs and accuracy of our network-based risk prediction was validated in two independent datasets. We observed subtype-specific vulnerabilities to interventions with existing drugs and revealed plausible mechanisms for relapse, including the establishment of an immunosuppressive microenvironment. Investigation of the t(4;14) clinical subtype using the TRN revealed that 16% of these patients exhibit an extreme-risk combination of genetic programs (median progression-free survival of 5 months) that create a distinct phenotype with targetable genes and pathways.

Brain tumors are among the most lethal tumors. Glioblastoma, the most frequent primary brain tumor in adults, has a median survival time of approximately 15 months after diagnosis or a five-year survival rate of 10%; the recurrence rate is nearly 90%. Unfortunately, this prognosis has not improved for several decades. The lack of progress in the treatment of brain tumors has been attributed to their high rate of primary therapy resistance. Challenges such as pronounced inter-patient variability, intratumoral heterogeneity, and drug delivery across the blood-brain barrier hinder progress. A comprehensive, multiscale understanding of the disease, from the molecular to the whole tumor level, is needed to address the intratumor heterogeneity resulting from the coexistence of a diversity of neoplastic and non-neoplastic cell types in the tumor tissue. By contrast, inter-patient variability must be addressed by subtyping brain tumors to stratify patients and identify the best-matched drug(s) and therapies for a particular patient or cohort of patients. Accomplishing these diverse tasks will require a new framework, one involving a systems perspective in assessing the immense complexity of brain tumors. This would in turn entail a shift in how clinical medicine interfaces with the rapidly advancing high-throughput (HTP) technologies that have enabled the omics-scale profiling of molecular features of brain tumors from the single-cell to the tissue level. However, several gaps must be closed before such a framework can fulfill the promise of precision and personalized medicine for brain tumors. Ultimately, the goal is to integrate seamlessly multiscale systems analyses of patient tumors and clinical medicine. Accomplishing this goal would facilitate the rational design of therapeutic strategies matched to the characteristics of patients and their tumors. Here, we discuss some of the technologies, methodologies, and computational tools that will facilitate the realization of this vision to practice.

Many of the gene regulatory processes of Plasmodium falciparum, the deadliest malaria parasite, remain poorly understood. To develop a comprehensive guide for exploring this organism's gene regulatory network, we generated a systems-level model of P. falciparum gene regulation using a well-validated, machine-learning approach for predicting interactions between transcription regulators and their targets. The resulting network accurately predicts expression levels of transcriptionally coherent gene regulatory programs in independent transcriptomic data sets from parasites collected by different research groups in diverse laboratory and field settings. Thus, our results indicate that our gene regulatory model has predictive power and utility as a hypothesis-generating tool for illuminating clinically relevant gene regulatory mechanisms within P. falciparum. Using the set of regulatory programs we identified, we also investigated correlates of artemisinin resistance based on gene expression coherence. We report that resistance is associated with incoherent expression across many regulatory programs, including those controlling genes associated with erythrocyte-host engagement. These results suggest that parasite populations with reduced artemisinin sensitivity are more transcriptionally heterogenous. This pattern is consistent with a model where the parasite utilizes bet-hedging strategies to diversify the population, rendering a subpopulation more able to navigate drug treatment., (© The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Mathematical biology and pharmacology models have a long and rich history in the fields of medicine and physiology, impacting our understanding of disease mechanisms and the development of novel therapeutics. With an increased focus on the pharmacology application of system models and the advances in data science spanning mechanistic and empirical approaches, there is a significant opportunity and promise to leverage these advancements to enhance the development and application of the systems pharmacology field. In this paper, we will review milestones in the evolution of mathematical biology and pharmacology models, highlight some of the gaps and challenges in developing and applying systems pharmacology models, and provide a vision for an integrated strategy that leverages advances in adjacent fields to overcome these challenges., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Azer, Kaddi, Barrett, Bai, McQuade, Merrill, Piccoli, Neves-Zaph, Marchetti, Lombardo, Parolo, Immanuel and Baliga.)

Over the last century, leaps in technology for imaging, sampling, detection, high-throughput sequencing, and -omics analyses have revolutionized microbial ecology to enable rapid acquisition of extensive datasets for microbial communities across the ever-increasing temporal and spatial scales. The present challenge is capitalizing on our enhanced abilities of observation and integrating diverse data types from different scales, resolutions, and disciplines to reach a causal and mechanistic understanding of how microbial communities transform and respond to perturbations in the environment. This type of causal and mechanistic understanding will make predictions of microbial community behavior more robust and actionable in addressing microbially mediated global problems. To discern drivers of microbial community assembly and function, we recognize the need for a conceptual, quantitative framework that connects measurements of genomic potential, the environment, and ecological and physical forces to rates of microbial growth at specific locations. We describe the Framework for Integrated, Conceptual, and Systematic Microbial Ecology (FICSME), an experimental design framework for conducting process-focused microbial ecology studies that incorporates biological, chemical, and physical drivers of a microbial system into a conceptual model. Through iterative cycles that advance our understanding of the coupling across scales and processes, we can reliably predict how perturbations to microbial systems impact ecosystem-scale processes or vice versa. We describe an approach and potential applications for using the FICSME to elucidate the mechanisms of globally important ecological and physical processes, toward attaining the goal of predicting the structure and function of microbial communities in chemically complex natural environments., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Lui, Majumder, Smith, Carlson, von Netzer, Fields, Stahl, Zhou, Hazen, Baliga, Adams and Arkin.)

The demand for food will outpace productivity of conventional agriculture due to projected growth of the human population, concomitant with shrinkage of arable land, increasing scarcity of freshwater, and a rapidly changing climate. While aquaponics has potential to sustainably supplement food production with minimal environmental impact, there is a need to better characterize the complex interplay between the various components (fish, plant, microbiome) of these systems to optimize scale up and productivity. Here, we investigated how the commonly-implemented practice of continued microbial community transfer from pre-existing systems might promote or impede productivity of aquaponics. Specifically, we monitored plant growth phenotypes, water chemistry, and microbiome composition of rhizospheres, biofilters, and fish feces over 61-days of lettuce (Lactuca sativa var. crispa) growth in nitrogen-limited aquaponic systems inoculated with bacteria that were either commercially sourced or originating from a pre-existing aquaponic system. Lettuce above- and below-ground growth were significantly reduced across replicates treated with a pre-existing aquaponic system inoculum when compared to replicates treated with a commercial inoculum. Reduced productivity was associated with enrichment in specific bacterial genera in plant roots, including Pseudomonas, following inoculum transfer from pre-existing systems. Increased productivity was associated with enrichment of nitrogen-fixing Rahnella in roots of plants treated with the commercial inoculum. Thus, we show that inoculation from a pre-existing system, rather than from a commercial inoculum, is associated with lower yields. Further work will be necessary to test the putative mechanisms involved., Competing Interests: The authors have declared that no competing interests exist.

Competition between nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) for resources in anoxic environments is generally thought to be governed largely by thermodynamics. It is now recognized that intermediates of nitrogen and sulfur cycling (e.g., hydrogen sulfide, nitrite, etc.) can also directly impact NRB and SRB activities in freshwater, wastewater, and sediment and therefore may play important roles in competitive interactions. Here, through comparative transcriptomic and metabolomic analyses, we have uncovered mechanisms of hydrogen sulfide- and cysteine-mediated inhibition of nitrate respiratory growth for the NRB Intrasporangium calvum C5. Specifically, the systems analysis predicted that cysteine and hydrogen sulfide inhibit growth of I. calvum C5 by disrupting distinct steps across multiple pathways, including branched-chain amino acid (BCAA) biosynthesis, utilization of specific carbon sources, and cofactor metabolism. We have validated these predictions by demonstrating that complementation with BCAAs and specific carbon sources relieves the growth inhibitory effects of cysteine and hydrogen sulfide. We discuss how these mechanistic insights give new context to the interplay and stratification of NRB and SRB in diverse environments. IMPORTANCE Nitrate-reducing bacteria (NRB) and sulfate-reducing bacteria (SRB) colonize diverse anoxic environments, including soil subsurface, groundwater, and wastewater. NRB and SRB compete for resources, and their interplay has major implications on the global cycling of nitrogen and sulfur species, with undesirable outcomes in some contexts. For instance, the removal of reactive nitrogen species by NRB is desirable for wastewater treatment, but in agricultural soils, NRB can drive the conversion of nitrates from fertilizers into nitrous oxide, a potent greenhouse gas. Similarly, the hydrogen sulfide produced by SRB can help sequester and immobilize toxic heavy metals but is undesirable in oil wells where competition between SRB and NRB has been exploited to suppress hydrogen sulfide production. By characterizing how reduced sulfur compounds inhibit growth and activity of NRB, we have gained systems-level and mechanistic insight into the interplay of these two important groups of organisms and drivers of their stratification in diverse environments.

Mycobacterium tuberculosis (MTB) generates phenotypic diversity to persist and survive the harsh conditions encountered during infection. MTB avoids immune effectors and antibacterial killing by entering into distinct physiological states. The surviving cells, persisters, are a major barrier to the timely and relapse-free treatment of tuberculosis (TB). We present for the first time, PerSort, a method to isolate and characterize persisters in the absence of antibiotic or other pressure. We demonstrate the value of PerSort to isolate translationally dormant cells that preexisted in small numbers within Mycobacterium species cultures growing under optimal conditions but that dramatically increased in proportion under stress conditions. The translationally dormant subpopulation exhibited multidrug tolerance and regrowth properties consistent with those of persister cells. Furthermore, PerSort enabled single-cell transcriptional profiling that provided evidence that the translationally dormant persisters were generated through a variety of mechanisms, including vapC30 , mazF , and relA / spoT overexpression. Finally, we demonstrate that notwithstanding the varied mechanisms by which the persister cells were generated, they converge on a similar low-oxygen metabolic state that was reversed through activation of respiration to rapidly eliminate persisters fostered under host-relevant stress conditions. We conclude that PerSort provides a new tool to study MTB persisters, enabling targeted strategies to improve and shorten the treatment of TB. IMPORTANCE Mycobacterium tuberculosis (MTB) persists and survives antibiotic treatments by generating phenotypically heterogeneous drug-tolerant subpopulations. The surviving cells, persisters, are a major barrier to the relapse-free treatment of tuberculosis (TB), which is already killing >1.8 million people every year and becoming deadlier with the emergence of multidrug-resistant strains. This study describes PerSort, a cell sorting method to isolate and characterize, without antibiotic treatment, translationally dormant persisters that preexist in small numbers within Mycobacterium cultures. Characterization of this subpopulation has discovered multiple mechanisms by which mycobacterial persisters emerge and unveiled the physiological basis for their dormant and multidrug-tolerant physiological state. This analysis has discovered that activating oxygen respiratory physiology using l-cysteine eliminates preexisting persister subpopulations, potentiating rapid antibiotic killing of mycobacteria under host-relevant stress. PerSort serves as a new tool to study MTB persisters for enabling targeted strategies to improve and shorten the treatment of TB., (Copyright © 2020 Srinivas et al.)

Data-Independent Acquisition (DIA) is a method to improve consistent identification and precise quantitation of peptides and proteins by mass spectrometry (MS). The targeted data analysis strategy in DIA relies on spectral assay libraries that are generally derived from a priori measurements of peptides for each species. Although Escherichia coli (E. coli) is among the best studied model organisms, so far there is no spectral assay library for the bacterium publicly available. Here, we generated a spectral assay library for 4,014 of the 4,389 annotated E. coli proteins using one- and two-dimensional fractionated samples, and ion mobility separation enabling deep proteome coverage. We demonstrate the utility of this high-quality library with robustness in quantitation of the E. coli proteome and with rapid-chromatography to enhance throughput by targeted DIA-MS. The spectral assay library supports the detection and quantification of 91.5% of all E. coli proteins at high-confidence with 56,182 proteotypic peptides, making it a valuable resource for the scientific community. Data and spectral libraries are available via ProteomeXchange (PXD020761, PXD020785) and SWATHAtlas (SAL00222-28).

With the rapid global spread of SARS-CoV-2, we have become acutely aware of the inadequacies of our ability to respond to viral epidemics. Although disrupting the viral life cycle is critical for limiting viral spread and disease, it has proven challenging to develop targeted and selective therapeutics. Synthetic lethality offers a promising but largely unexploited strategy against infectious viral disease; as viruses infect cells, they abnormally alter the cell state, unwittingly exposing new vulnerabilities in the infected cell. Therefore, we propose that effective therapies can be developed to selectively target the virally reconfigured host cell networks that accompany altered cellular states to cripple the host cell that has been converted into a virus factory, thus disrupting the viral life cycle., (© 2020 Mast et al.)

When organisms encounter an unfavorable environment, they transition to a physiologically distinct, quiescent state wherein abundant transcripts from the previous active growth state continue to persist, albeit their active transcription is downregulated. In order to generate proteins for the new quiescent physiological state, we hypothesized that the translation machinery must selectively translate upregulated transcripts in an intracellular milieu crowded with considerably higher abundance transcripts from the previous active growth state. Here, we have analyzed genome-wide changes in the transcriptome (RNA sequencing [RNA-seq]), changes in translational regulation and efficiency by ribosome profiling across all transcripts (ribosome profiling [Ribo-seq]), and protein level changes in assembled ribosomal proteins (sequential window acquisition of all theoretical mass spectra [SWATH-MS]) to investigate the interplay of transcriptional and translational regulation in Halobacterium salinarum as it transitions from active growth to quiescence. We have discovered that interplay of regulatory processes at different levels of information processing generates condition-specific ribosomal complexes to translate preferentially pools of low abundance and upregulated transcripts. Through analysis of the gene regulatory network architecture of H. salinarum , Escherichia coli , and Saccharomyces cerevisiae , we demonstrate that this conditional, modular organization of regulatory programs governing translational systems is a generalized feature across all domains of life. IMPORTANCE Our findings demonstrate conclusively that low abundance and upregulated transcripts are preferentially translated, potentially by environment-specific translation systems with distinct ribosomal protein composition. We show that a complex interplay of transcriptional and posttranscriptional regulation underlies the conditional and modular regulatory programs that generate ribosomes of distinct protein composition. The modular regulation of ribosomal proteins with other transcription, translation, and metabolic genes is generalizable to bacterial and eukaryotic microbes. These findings are relevant to how microorganisms adapt to unfavorable environments when they transition from active growth to quiescence by generating proteins from upregulated transcripts that are in considerably lower abundance relative to transcripts associated with the previous physiological state. Selective translation of transcripts by distinct ribosomes could form the basis for adaptive evolution to new environments through a modular regulation of the translational systems., (Copyright © 2020 López García de Lomana et al.)