Prevalence and patterns of higher-order drug interactions in Escherichia coli

Interactions within higher-order antibiotic combinations do not influence the rate of adaptation in bacteria

The prevalence and strength of antibiotic resistance has led to an ongoing battle between the development of new treatments and the evolution of resistance. Combining multiple drugs simultaneously is a potential solution for combating antibiotic resistance. However, this approach introduces new factors that must be considered, including the influence of drug interactions on the rate of resistance evolution. When antibiotics are used in combination, their effects can be additive, synergistic, or antagonistic. In this study, we investigated the effect of higher-order interactions involving 3 drugs on resistance evolution in Staphylococcus epidermidis. Previous studies have shown that synergistic interactions can increase the adaptation rate. However, the effects of higher-order interactions on rates of adaptation are unclear. We investigated the adaptation of Staphylococcus epidermidis to single-, 2-, and 3-drug environments to assess how interactions within drug combinations influence the rate of adaptation. We analyzed both the overall interaction and emergent interaction, the latter being a unique interaction that occurs in 3-drug combinations due to the presence of all three drugs, rather than simply strong pairwise interactions. Our results show that neither the overall interactions nor the emergent interactions affect adaptation rates.

Flash optimization of drug combinations for Acinetobacter baumannii with IDentif.AI-AMR

Antimicrobial resistance (AMR) is an emerging threat to global public health. Specifically, Acinetobacter baumannii (A. baumannii), one of the main pathogens driving the rise of nosocomial infections, is a Gram-negative bacillus that displays intrinsic resistance mechanisms and can also develop resistance by acquiring AMR genes from other bacteria. More importantly, it is resistant to nearly 90% of standard of care (SOC) antimicrobial treatments, resulting in unsatisfactory clinical outcomes and a high infection-associated mortality rate of over 30%. Currently, there is a growing challenge to sustainably develop novel antimicrobials in this ever-expanding arms race against AMR. Therefore, a sustainable workflow that properly manages healthcare resources to ultra-rapidly design optimal drug combinations for effective treatment is needed. In this study, the IDentif.AI-AMR platform was harnessed to pinpoint effective regimens against four A. baumannii clinical isolates from a pool of nine US FDA-approved drugs. Notably, IDentif.AI-pinpointed ampicillin-sulbactam/cefiderocol and cefiderocol/polymyxin B/rifampicin combinations were able to achieve 93.89 ± 5.95% and 92.23 ± 11.89% inhibition against the bacteria, respectively, and they may diversify the reservoir of treatment options for the indication. In addition, polymyxin B in combination with rifampicin exhibited broadly applicable efficacy and strong synergy across all tested clinical isolates, representing a potential treatment strategy for A. baumannii. IDentif.AI-pinpointed combinations may potentially serve as alternative treatment strategies for A. baumannii.

Artificial Intelligence Models and Tools for the Assessment of Drug-Herb Interactions

Artificial intelligence (AI) has emerged as a powerful tool in medical sciences that is revolutionizing various fields of drug research. AI algorithms can analyze large-scale biological data and identify molecular targets and pathways advancing pharmacological knowledge. An especially promising area is the assessment of drug interactions. The AI analysis of large datasets, such as drugs' chemical structure, pharmacological properties, molecular pathways, and known interaction patterns, can provide mechanistic insights and identify potential associations by integrating all this complex information and returning potential risks associated with these interactions. In this context, an area where AI may prove valuable is in the assessment of the underlying mechanisms of drug interactions with natural products (i.e., herbs) that are used as dietary supplements. These products pose a challenging problem since they are complex mixtures of constituents with diverse and limited information regarding their pharmacological properties, especially their pharmacokinetic data. As the use of herbal products and supplements continues to grow, it becomes increasingly important to understand the potential interactions between them and conventional drugs and the associated adverse drug reactions. This review will discuss AI approaches and how they can be exploited in providing valuable mechanistic insights regarding the prediction of interactions between drugs and herbs, and their potential exploitation in experimental validation or clinical utilization.

Resolving discrepancies between chimeric and multiplicative measures of higher-order epistasis

Epistasis - the interaction between alleles at different genetic loci - plays a fundamental role in biology. However, several recent approaches quantify epistasis using a chimeric formula that measures deviations from a multiplicative fitness model on an additive scale, thus mixing two scales. Here, we show that for pairwise interactions, the chimeric formula yields a different magnitude but the same sign of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula. We resolve these inconsistencies by deriving mathematical relationships between the different epistasis formulae and different parametrizations of the multivariate Bernoulli distribution. We argue that the chimeric formula does not appropriately model interactions between the Bernoulli random variables. In simulations, we show that the chimeric formula is less accurate than the classical multiplicative/additive epistasis formulae and may falsely detect higher-order epistasis. Analyzing multi-gene knockouts in yeast, multi-way drug interactions in E. coli, and deep mutational scanning of several proteins, we find that approximately 10% to 60% of inferred higher-order interactions change sign using the multiplicative/additive formula compared to the chimeric formula.

Characterizing a stable five-species microbial community for use in experimental evolution and ecology

Model microbial communities are regularly used to test ecological and evolutionary theory as they are easy to manipulate and have fast generation times, allowing for large-scale, high-throughput experiments. A key assumption for most model microbial communities is that they stably coexist, but this is rarely tested experimentally. Here we report the (dis)assembly of a five-species microbial community from a metacommunity of soil microbes that can be used for future experiments. Using reciprocal invasion-from-rare experiments we show that all species can coexist and we demonstrate that the community is stable for a long time (~600 generations). Crucially for future work, we show that each species can be identified by their plate morphologies, even after >1 year in co-culture. We characterise pairwise species interactions and produce high-quality reference genomes for each species. This stable five-species community can be used to test key questions in microbial ecology and evolution.

The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution

In combination therapy, bacteria are challenged with two or more antibiotics simultaneously. Ideally, separate mutations are required to adapt to each of them, which is a priori expected to hinder the evolution of full resistance. Yet, the success of this strategy ultimately depends on how well the combination controls the growth of bacteria with and without resistance mutations. To design a combination treatment, we need to choose drugs and their doses and decide how many drugs get mixed. Which combinations are good? To answer this question, we set up a stochastic pharmacodynamic model and determine the probability to successfully eradicate a bacterial population. We consider bacteriostatic and two types of bactericidal drugs-those that kill independent of replication and those that kill during replication. To establish results for a null model, we consider non-interacting drugs and implement the two most common models for drug independence-Loewe additivity and Bliss independence. Our results show that combination therapy is almost always better in limiting the evolution of resistance than administering just one drug, even though we keep the total drug dose constant for a 'fair' comparison. Yet, exceptions exist for drugs with steep dose-response curves. Combining a bacteriostatic and a bactericidal drug which can kill non-replicating cells is particularly beneficial. Our results suggest that a 50:50 drug ratio-even if not always optimal-is usually a good and safe choice. Applying three or four drugs is beneficial for treatment of strains with large mutation rates but adding more drugs otherwise only provides a marginal benefit or even a disadvantage. By systematically addressing key elements of treatment design, our study provides a basis for future models which take further factors into account. It also highlights conceptual challenges with translating the traditional concepts of drug independence to the single-cell level.

Quantifying higher-order epistasis: beware the chimera

Epistasis, or interactions in which alleles at one locus modify the fitness effects of alleles at other loci, plays a fundamental role in genetics, protein evolution, and many other areas of biology. Epistasis is typically quantified by computing the deviation from the expected fitness under an additive or multiplicative model using one of several formulae. However, these formulae are not all equivalent. Importantly, one widely used formula - which we call the chimeric formula - measures deviations from a multiplicative fitness model on an additive scale, thus mixing two measurement scales. We show that for pairwise interactions, the chimeric formula yields a different magnitude, but the same sign (synergistic vs. antagonistic) of epistasis compared to the multiplicative formula that measures both fitness and deviations on a multiplicative scale. However, for higher-order interactions, we show that the chimeric formula can have both different magnitude and sign compared to the multiplicative formula - thus confusing negative epistatic interactions with positive interactions, and vice versa. We resolve these inconsistencies by deriving fundamental connections between the different epistasis formulae and the parameters of the multivariate Bernoulli distribution . Our results demonstrate that the additive and multiplicative epistasis formulae are more mathematically sound than the chimeric formula. Moreover, we demonstrate that the mathematical issues with the chimeric epistasis formula lead to markedly different biological interpretations of real data. Analyzing multi-gene knockout data in yeast, multi-way drug interactions in E. coli , and deep mutational scanning (DMS) of several proteins, we find that 10 - 60% of higher-order interactions have a change in sign with the multiplicative or additive epistasis formula. These sign changes result in qualitatively different findings on functional divergence in the yeast genome, synergistic vs. antagonistic drug interactions, and and epistasis between protein mutations. In particular, in the yeast data, the more appropriate multiplicative formula identifies nearly 500 additional negative three-way interactions, thus extending the trigenic interaction network by 25%.

Use of multiple pharmacodynamic measures to deconstruct the Nix-TB regimen in a short-course murine model of tuberculosis

A major challenge for tuberculosis (TB) drug development is to prioritize promising combination regimens from a large and growing number of possibilities. This includes demonstrating individual drug contributions to the activity of higher-order combinations. A BALB/c mouse TB infection model was used to evaluate the contributions of each drug and pairwise combination in the clinically relevant Nix-TB regimen [bedaquiline-pretomanid-linezolid (BPaL)] during the first 3 weeks of treatment at human equivalent doses. The rRNA synthesis (RS) ratio, an exploratory pharmacodynamic (PD) marker of ongoing Mycobacterium tuberculosis rRNA synthesis, together with solid culture CFU counts and liquid culture time to positivity (TTP) were used as PD markers of treatment response in lung tissue; and their time-course profiles were mathematically modeled using rate equations with pharmacologically interpretable parameters. Antimicrobial interactions were quantified using Bliss independence and Isserlis formulas. Subadditive (or antagonistic) and additive effects on bacillary load, assessed by CFU and TTP, were found for bedaquiline-pretomanid and linezolid-containing pairs, respectively. In contrast, subadditive and additive effects on rRNA synthesis were found for pretomanid-linezolid and bedaquiline-containing pairs, respectively. Additionally, accurate predictions of the response to BPaL for all three PD markers were made using only the single-drug and pairwise effects together with an assumption of negligible three-way drug interactions. The results represent an experimental and PD modeling approach aimed at reducing combinatorial complexity and improving the cost-effectiveness of in vivo systems for preclinical TB regimen development.

Emerging challenges in antimicrobial resistance: implications for pathogenic microorganisms, novel antibiotics, and their impact on sustainability

Overuse of antibiotics is accelerating the antimicrobial resistance among pathogenic microbes which is a growing public health challenge at the global level. Higher resistance causes severe infections, high complications, longer stays at hospitals and even increased mortality rates. Antimicrobial resistance (AMR) has a significant impact on national economies and their health systems, as it affects the productivity of patients or caregivers due to prolonged hospital stays with high economic costs. The main factor of AMR includes improper and excessive use of antimicrobials; lack of access to clean water, sanitation, and hygiene for humans and animals; poor infection prevention and control measures in hospitals; poor access to medicines and vaccines; lack of awareness and knowledge; and irregularities with legislation. AMR represents a global public health problem, for which epidemiological surveillance systems have been established, aiming to promote collaborations directed at the well-being of human and animal health and the balance of the ecosystem. MDR bacteria such as E. coli, Staphylococcus aureus, Pseudomonas aeruginosa, Enterococcus spp., Acinetobacter spp., and Klebsiella pneumonia can even cause death. These microorganisms use a variety of antibiotic resistance mechanisms, such as the development of drug-deactivating targets, alterations in antibiotic targets, or a decrease in intracellular antibiotic concentration, to render themselves resistant to numerous antibiotics. In context, the United Nations issued the Sustainable Development Goals (SDGs) in 2015 to serve as a worldwide blueprint for a better, more equal, and more sustainable existence on our planet. The SDGs place antimicrobial resistance (AMR) in the context of global public health and socioeconomic issues; also, the continued growth of AMR may hinder the achievement of numerous SDGs. In this review, we discuss the role of environmental pollution in the rise of AMR, different mechanisms underlying the antibiotic resistance, the threats posed by pathogenic microbes, novel antibiotics, strategies such as One Health to combat AMR, and the impact of resistance on sustainability and sustainable development goals.

High-throughput characterization of bacterial responses to complex mixtures of chemical pollutants

Our understanding of how microbes respond to micropollutants, such as pesticides, is almost wholly based on single-species responses to individual chemicals. However, in natural environments, microbes experience multiple pollutants simultaneously. Here we perform a matrix of multi-stressor experiments by assaying the growth of model and non-model strains of bacteria in all 255 combinations of 8 chemical stressors (antibiotics, herbicides, fungicides and pesticides). We found that bacterial strains responded in different ways to stressor mixtures, which could not be predicted simply from their phylogenetic relatedness. Increasingly complex chemical mixtures were both more likely to negatively impact bacterial growth in monoculture and more likely to reveal net interactive effects. A mixed co-culture of strains proved more resilient to increasingly complex mixtures and revealed fewer interactions in the growth response. These results show predictability in microbial population responses to chemical stressors and could increase the utility of next-generation eco-toxicological assays.

Automated and miniaturized screening of antibiotic combinations via robotic-printed combinatorial droplet platform

Antimicrobial resistance (AMR) has become a global health crisis in need of novel solutions. To this end, antibiotic combination therapies, which combine multiple antibiotics for treatment, have attracted significant attention as a potential approach for combating AMR. To facilitate advances in antibiotic combination therapies, most notably in investigating antibiotic interactions and identifying synergistic antibiotic combinations however, there remains a need for automated high-throughput platforms that can create and examine antibiotic combinations on-demand, at scale, and with minimal reagent consumption. To address these challenges, we have developed a Robotic-Printed Combinatorial Droplet (RoboDrop) platform by integrating a programmable droplet microfluidic device that generates antibiotic combinations in nanoliter droplets in automation, a robotic arm that arranges the droplets in an array, and a camera that images the array of thousands of droplets in parallel. We further implement a resazurin-based bacterial viability assay to accelerate our antibiotic combination testing. As a demonstration, we use RoboDrop to corroborate two pairs of antibiotics with known interactions and subsequently identify a new synergistic combination of cefsulodin, penicillin, and oxacillin against a model E. coli strain. We therefore envision RoboDrop becoming a useful tool to efficiently identify new synergistic antibiotic combinations toward combating AMR.

Coassembled Multicomponent Protein Nanoparticles Elicit Enhanced Antibacterial Activity

The waning pipeline of the useful antibacterial arsenal has necessitated the urgent development of more effective antibacterial strategies with distinct mechanisms to rival the continuing emergence of resistant pathogens, particularly Gram-negative bacteria, due to their explicit drug-impermeable, two-membrane-sandwiched cell wall envelope. Herein, we have developed multicomponent coassembled nanoparticles with strong bactericidal activity and simultaneous bacterial cell envelope targeting using a peptide coassembly strategy. Compared to the single-component self-assembled nanoparticle counterparts or cocktail mixtures of these at a similar concentration, coassembled multicomponent nanoparticles showed higher bacterial killing efficiency against Acinetobacter baumannii, Pseudomonas aeruginosa, and Escherichia coli by several orders of magnitude (about 100-1,000,000-fold increase). Comprehensive confocal and electron microscopy suggest that the superior antibacterial activity of the coassembled nanoparticles proceeds via multiple complementary mechanisms of action, including membrane destabilization, disruption, and cell wall hydrolysis, actions that were not observed with the single nanoparticle counterparts. To understand the fundamental working mechanisms behind the improved performance of coassembled nanoparticles, we utilized a "dilution effect" system where the antibacterial components are intermolecularly mixed and coassembled with a non-antibacterial protein in the nanoparticles. We suggest that coassembled nanoparticles mediate enhanced bacterial killing activity by attributes such as optimized local concentration, high avidity, cooperativity, and synergy. The nanoparticles showed no cytotoxic or hemolytic activity against tested eukaryotic cells and erythrocytes. Collectively, these findings reveal potential strategies for disrupting the impermeable barrier that Gram-negative pathogens leverage to restrict antibacterial access and may serve as a platform technology for potential nano-antibacterial design to strengthen the declining antibiotic arsenal.

The evolution of resistance to synergistic multi-drug combinations is more complex than evolving resistance to each individual drug component

Multidrug antibiotic resistance is an urgent public health concern. Multiple strategies have been suggested to alleviate this problem, including the use of antibiotic combinations and cyclic therapies. We examine how adaptation to (1) combinations of drugs affects resistance to individual drugs, and to (2) individual drugs alters responses to drug combinations. To evaluate this, we evolved multiple strains of drug resistant Staphylococcus epidermidis in the lab. We show that evolving resistance to four highly synergistic combinations does not result in cross-resistance to all of their components. Likewise, prior resistance to one antibiotic in a combination does not guarantee survival when exposed to the combination. We also identify four 3-step and four 2-step treatments that inhibit bacterial growth and confer collateral sensitivity with each step, impeding the development of multidrug resistance. This study highlights the importance of considering higher-order drug combinations in sequential therapies and how antibiotic interactions can influence the evolutionary trajectory of bacterial populations.

Synergy between Winter Flounder antimicrobial peptides

Some antimicrobial peptides (AMPs) have potent bactericidal activity and are being considered as potential alternatives to classical antibiotics. In response to an infection, such AMPs are often produced in animals alongside other peptides with low or no perceivable antimicrobial activity, whose role is unclear. Here we show that six AMPs from the Winter Flounder (WF) act in synergy against a range of bacterial pathogens and provide mechanistic insights into how this increases the cooperativity of the dose-dependent bactericidal activity and potency that enable therapy. Only two WF AMPs have potent antimicrobial activity when used alone but we find a series of two-way combinations, involving peptides which otherwise have low or no activity, yield potent antimicrobial activity. Weakly active WF AMPs modulate the membrane interactions of the more potent WF AMPs and enable therapy in a model of Acinetobacter baumannii burn wound infection. The observed synergy and emergent behaviour may explain the evolutionary benefits of producing a family of related peptides and are attractive properties to consider when developing AMPs towards clinical applications.

Novel Synergies and Isolate Specificities in the Drug Interaction Landscape of Mycobacterium abscessus

Mycobacterium abscessus infections are difficult to treat and are often considered untreatable without tissue resection. Due to the intrinsic drug-resistant nature of the bacteria, combination therapy of three or more antibiotics is recommended. A major challenge in treating M. abscessus infections is the absence of a universal combination therapy with satisfying clinical success rates, leaving clinicians to treat infections using antibiotics lacking efficacy data. We systematically measured drug combinations in M. abscessus to establish a resource of drug interaction data and identify patterns of synergy to help design optimized combination therapies. We measured 191 pairwise drug combination effects among 22 antibacterials and identified 71 synergistic pairs, 54 antagonistic pairs, and 66 potentiator-antibiotic pairs. We found that commonly used drug combinations in the clinic, such as azithromycin and amikacin, are antagonistic in the lab reference strain ATCC 19977, whereas novel combinations, such as azithromycin and rifampicin, are synergistic. Another challenge in developing universally effective multidrug therapies for M. abscessus is the significant variation in drug response between isolates. We measured drug interactions in a focused set of 36 drug pairs across a small panel of clinical isolates with rough and smooth morphotypes. We observed strain-dependent drug interactions that cannot be predicted from single-drug susceptibility profiles or known drug mechanisms of action. Our study demonstrates the immense potential to identify synergistic drug combinations in the vast drug combination space and emphasizes the importance of strain-specific combination measurements for designing improved therapeutic interventions.

Higher-Order Interactions and Their Duals Reveal Synergy and Logical Dependence beyond Shannon-Information

Information-theoretic quantities reveal dependencies among variables in the structure of joint, marginal, and conditional entropies while leaving certain fundamentally different systems indistinguishable. Furthermore, there is no consensus on the correct higher-order generalisation of mutual information (MI). In this manuscript, we show that a recently proposed model-free definition of higher-order interactions among binary variables (MFIs), such as mutual information, is a Möbius inversion on a Boolean algebra, except of surprisal instead of entropy. This provides an information-theoretic interpretation to the MFIs, and by extension to Ising interactions. We study the objects dual to mutual information and the MFIs on the order-reversed lattices. We find that dual MI is related to the previously studied differential mutual information, while dual interactions are interactions with respect to a different background state. Unlike (dual) mutual information, interactions and their duals uniquely identify all six 2-input logic gates, the dy- and triadic distributions, and different causal dynamics that are identical in terms of their Shannon information content.

Meta-analysis of three-stressor combinations on population-level fitness reveal substantial higher-order interactions

Although natural populations are typically subjected to multiple stressors, most past research has focused on single-stressor and two-stressor interactions, with little attention paid to higher-order interactions among three or more stressors. However, higher-order interactions increasingly appear to be widespread. Consequently, we used a recently introduced and improved framework to re-analyze higher-order ecological interactions. We conducted a literature review of the last 100 years (1920-2020) and reanalyzed 142 ecological three-stressor interactions on species' populations from 38 published papers; the vast majority of these studies were from the past 10 years. We found that 95.8 % (n = 136) of the three-stressor combinations had either not been categorized before or resulted in different interactions than previously reported. We also found substantial levels of emergent properties-interactions that are not due to strong pairwise interactions within the combination but rather uniquely due to all three stressors being combined. Calculating net interactions-the overall accounting for all possible interactions within a combination including the emergent and all pairwise interactions-we found that the most prevalent interaction type is antagonism, corresponding to a smaller than expected effect based on single stressor effects. In contrast, for emergent interactions, the most prevalent interaction type is synergistic, resulting in a larger than expected effect based on single stressor effects. Additionally, we found that hidden suppressive interactions-where a pairwise interaction is suppressed by a third stressor-are found in the majority of combinations (74 %). Collectively, understanding multiple stressor interactions through applying an appropriate framework is crucial for answering fundamental questions in ecology and has implications for conservation biology and population management. Crucially, identifying emergent properties can reveal hidden suppressive interactions that could be particularly important for the ecological management of at-risk populations.

A CURE on the Evolution of Antibiotic Resistance in Escherichia coli Improves Student Conceptual Understanding

We developed labs on the evolution of antibiotic resistance to assess the costs and benefits of replacing traditional laboratory exercises in an introductory biology course for majors with a course-based undergraduate research experience (CURE). To assess whether participating in the CURE imposed a cost in terms of exam performance, we implemented a quasi-experiment in which four lab sections in the same term of the same course did the CURE labs, while all other students did traditional labs. To assess whether participating in the CURE impacted other aspects of student learning, we implemented a second quasi-experiment in which all students either did traditional labs over a two-quarter sequence or did CURE labs over a two-quarter sequence. Data from the first experiment showed minimal impact on CURE students' exam scores, while data from the second experiment showed that CURE students demonstrated a better understanding of the culture of scientific research and a more expert-like understanding of evolution by natural selection. We did not find disproportionate costs or benefits for CURE students from groups that are minoritized in science, technology, engineering, and mathematics.

Coping with the ESKAPE pathogens: Evolving strategies, challenges and future prospects

Globally, the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are the major cause of nosocomial infections. These pathogens are multidrug resistant, and their negative impacts have brought serious health challenges and economic burden on many countries worldwide. Thus, this narrative review exploits different emerging alternative therapeutic strategies including combination antibiotics, antimicrobial peptides ((AMPs), bacteriophage and photodynamic therapies used in the treatment of the ESKAPE pathogens, their merits, limitations, and future prospects. Our findings indicate that ESKAPE pathogens exhibit resistance to drug using different mechanisms including drug inactivation by irreversible enzyme cleavage, drug-binding site alteration, diminution in permeability of drug or drug efflux increment to reduce accumulation of drug as well as biofilms production. However, the scientific community has shown significant interest in using these novel strategies with numerous benefits although they have some limitations including but not limited to instability and toxicity of the therapeutic agents, or the host developing immune response against the therapeutic agents. Thus, comprehension of resistance mechanisms of these pathogens is necessary to further develop or modify these approaches in order to overcome these health challenges including the barriers of bacterial resistance.

Insights into antibiotic stewardship of lake-rivers-basin complex systems for resistance risk control

Antibiotic stewardship is hindered by a lack of consideration for complicated environmental fate of antibiotics and their role in resistance development, while the current methodology of eco-toxicological risk assessment has not been fully protective against their potential to select for antibiotic resistance. To address this problem, we established a novel methodologic framework to perform comprehensive environmental risk assessment of antibiotics in terms of resistance development, which was based on selection effect, phenotype resistance level, heteroresistance frequency, as well as prevalence and stability of antibiotic resistance genes. We tracked the contribution of antibiotic load reduction to the mitigation of environmental risk of resistance development by fate and transport modeling. The method was instantiated in a lake-river network-basin complex system, taking the Taihu Basin as a case study. Overall, antibiotic load posed no eco-toxicological risk but an average medium-level environmental risk for resistance development in Taihu Lake. The effect of antibiotic load on resistance risk was both seasonal-dependent and category-dependent, while quinolones posed the greatest environmental risk for resistance development. Mass-flow analysis indicated that temporal-spatial variation in hydrological regime and antibiotic fate together exerted a significant effect on antibiotic load in the system. By apportioning antibiotic load to riverine influx, we identified the hotspots for load reduction and predicted the beneficial response of resistance risk under load-reduction scenarios. Our study proposed a risk-oriented strategy of basin-scaled antibiotic load reduction for environmental risk control of resistance development.

Man vs Microbes - The Race of the Century

The complexity of the antimicrobial resistance (AMR) crisis and its global impact on healthcare invokes an urgent need to understand the underlying forces and to conceive and implement innovative solutions. Beyond focusing on a traditional pathogen-centric approach to antibiotic discovery yielding diminishing returns, future therapeutic interventions can expand to focus more comprehensively on host-pathogen interactions. In this manner, increasing the resiliency of our innate immune system or attenuating the virulence mechanisms of the pathogens can be explored to improve therapeutic outcomes. Key pathogen survival strategies such as tolerance, persistence, aggregation, and biofilm formation can be considered and interrupted to sensitize pathogens for more efficient immune clearance. Understanding the evolution and emergence of so-called 'super clones' that drive AMR spread with rapid clonotyping assays may guide more precise antibiotic regimens. Innovative alternatives to classical antibiotics such as bacteriophage therapy, novel engineered peptide antibiotics, ionophores, nanomedicines, and repurposing drugs from other domains of medicine to boost innate immunity are beginning to be successfully implemented to combat AMR. Policy changes supporting shorter durations of antibiotic treatment, greater antibiotic stewardship, and increased surveillance measures can enhance patient safety and enable implementation of the next generation of targeted prevention and control programmes at a global level.

Incorporating Microbial Species Interaction in Management of Freshwater Toxic Cyanobacteria: A Systems Science Challenge

Water resources are critically important, but also pose risks of exposure to toxic and pathogenic microbes. Increasingly, a concern is toxic cyanobacteria, which have been linked to the death and disease of humans, domesticated animals, and wildlife in freshwater systems worldwide. Management approaches successful at reducing cyanobacterial abundance and toxin production have tended to be short-term solutions applied on small scales (e.g., algaecide application) or solutions that entail difficult multifaceted investments (e.g., modification of landscape and land use to reduce nutrient inputs). However, implementation of these approaches can be undermined by microbial species interactions that (a) provide toxic cyanobacteria with protection against the method of control or (b) permit toxic cyanobacteria to be replaced by other significant microbial threats. Understanding these interactions is necessary to avoid such scenarios and can provide a framework for novel strategies to enhance freshwater resource management via systems science (e.g., pairing existing physical and chemical approaches against cyanobacteria with ecological strategies such as manipulation of natural enemies, targeting of facilitators, and reduction of benthic occupancy and recruitment). Here, we review pertinent examples of the interactions and highlight potential applications of what is known.

Antibiotic combinations reduce Staphylococcus aureus clearance

The spread of antibiotic resistance is attracting increased attention to combination-based treatments. Although drug combinations have been studied extensively for their effects on bacterial growth^1–11, much less is known about their effects on bacterial long-term clearance, especially at cidal, clinically relevant concentrations^12–14. Here, using en masse microplating and automated image analysis, we systematically quantify Staphylococcus aureus survival during prolonged exposure to pairwise and higher-order cidal drug combinations. By quantifying growth inhibition, early killing and longer-term population clearance by all pairs of 14 antibiotics, we find that clearance interactions are qualitatively different, often showing reciprocal suppression whereby the efficacy of the drug mixture is weaker than any of the individual drugs alone. Furthermore, in contrast to growth inhibition^6–10 and early killing, clearance efficacy decreases rather than increases as more drugs are added. However, specific drugs targeting non-growing persisters^15–17 circumvent these suppressive effects. Competition experiments show that reciprocal suppressive drug combinations select against resistance to any of the individual drugs, even counteracting methicillin-resistant Staphylococcus aureus both in vitro and in a Galleria mellonella larva model. As a consequence, adding a β-lactamase inhibitor that is commonly used to potentiate treatment against β-lactam-resistant strains can reduce rather than increase treatment efficacy. Together, these results underscore the importance of systematic mapping the long-term clearance efficacy of drug combinations for designing more-effective, resistance-proof multidrug regimes.

Different pairs of antibiotics show qualitatively different bacterial clearance interactions—some pairs show reciprocal suppression whereby the drug mixture efficacy is weaker than the individual drugs alone, and the clearance efficacy decreases as more drugs are added.

Computational models, databases and tools for antibiotic combinations

Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.

Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design

Antibiotic resistance is an important public health problem. One potential solution is the development of synergistic antibiotic combinations, in which the combination is more effective than the component drugs. However, experimental progress in this direction is severely limited by the number of samples required to exhaustively test for synergy, which grows exponentially with the number of drugs combined. We introduce a new metric for antibiotic synergy, motivated by the popular Fractional Inhibitory Concentration Index and the Highest Single Agent model. We also propose a new experimental design that samples along all appropriately normalized diagonals in concentration space, and prove that this design identifies all synergies among a set of drugs while only sampling a small fraction of the possible combinations. We applied our method to screen two- through eight-way combinations of eight antibiotics at 10 concentrations each, which requires sampling only 2,560 unique combinations of antibiotic concentrations.

Antibiotic resistance is a growing public health concern, and there is an increasing need for methods to combat it. One potential approach is the development of synergistic antibiotic combinations, in which a mixture of drugs is more effective than any individual component. Unfortunately, the search for clinically beneficial drug combinations is severely restricted by the pace at which drugs can be screened. To date, most studies of combination therapies have been limited to testing only pairs or triples of drugs. These studies have identified primarily antagonistic drug interactions, in which the combination is less effective than the individual components. There is an acute need for methodologies that enable screening of higher-order drug combinations, both to identify synergies among many drugs and to understand the behavior of higher-order combinations. In this work we introduce a new paradigm for combination testing, the normalized diagonal sampling design, that makes identifying interactions among eight or more drugs feasible for the first time. Screening d drugs at m different combinations requires m ⋅ 2^d samples under our design as opposed to m^d under exhaustive screening, while provably identifying all synergies under mild assumptions about antibiotic behavior. Scientists can use our design to quickly screen for antibiotic interactions, accelerating the pace of combination therapy development.

The IDentif.AI-x pandemic readiness platform: Rapid prioritization of optimized COVID-19 combination therapy regimens

IDentif.AI-x, a clinically actionable artificial intelligence platform, was used to rapidly pinpoint and prioritize optimal combination therapies against COVID-19 by pairing a prospective, experimental validation of multi-drug efficacy on a SARS-CoV-2 live virus and Vero E6 assay with a quadratic optimization workflow. A starting pool of 12 candidate drugs developed in collaboration with a community of infectious disease clinicians was first narrowed down to a six-drug pool and then interrogated in 50 combination regimens at three dosing levels per drug, representing 729 possible combinations. IDentif.AI-x revealed EIDD-1931 to be a strong candidate upon which multiple drug combinations can be derived, and pinpointed a number of clinically actionable drug interactions, which were further reconfirmed in SARS-CoV-2 variants B.1.351 (Beta) and B.1.617.2 (Delta). IDentif.AI-x prioritized promising drug combinations for clinical translation and can be immediately adjusted and re-executed with a new pool of promising therapies in an actionable path towards rapidly optimizing combination therapy following pandemic emergence.

Cell density-dependent antibiotic tolerance to inhibition of the elongation machinery requires fully functional PBP1B

The peptidoglycan (PG) cell wall provides shape and structure to most bacteria. There are two systems to build PG in rod shaped organisms: the elongasome and divisome, which are made up of many proteins including the essential MreB and PBP2, or FtsZ and PBP3, respectively. The elongasome is responsible for PG insertion during cell elongation, while the divisome is responsible for septal PG insertion during division. We found that the main elongasome proteins, MreB and PBP2, can be inhibited without affecting growth rate in a quorum sensing-independent density-dependent manner. Before cells reach a particular cell density, inhibition of the elongasome results in different physiological responses, including intracellular vesicle formation and an increase in cell size. This inhibition of MreB or PBP2 can be compensated for by the presence of the class A penicillin binding protein, PBP1B. Furthermore, we found this density-dependent growth resistance to be specific for elongasome inhibition and was consistent across multiple Gram-negative rods, providing new areas of research into antibiotic treatment.

Epigenome-wide three-way interaction study identifies a complex pattern between TRIM27, KIAA0226, and smoking associated with overall survival of early-stage NSCLC

The interaction between DNA methylation of tripartite motif containing 27 (cg05293407TRIM27 ) and smoking has previously been identified to reveal histologically heterogeneous effects of TRIM27 DNA methylation on early-stage non-small-cell lung cancer (NSCLC) survival. However, to understand the complex mechanisms underlying NSCLC progression, we searched three-way interactions. A two-phase study was adopted to identify three-way interactions in the form of pack-year of smoking (number of cigarettes smoked per day × number of years smoked) × cg05293407TRIM27  × epigenome-wide DNA methylation CpG probe. Two CpG probes were identified with FDR-q ≤ 0.05 in the discovery phase and P ≤ 0.05 in the validation phase: cg00060500KIAA0226 and cg17479956EXT2 . Compared to a prediction model with only clinical information, the model added 42 significant three-way interactions using a looser criterion (discovery: FDR-q ≤ 0.10, validation: P ≤ 0.05) had substantially improved the area under the receiver operating characteristic curve (AUC) of the prognostic prediction model for both 3-year and 5-year survival. Our research identified the complex interaction effects among multiple environment and epigenetic factors, and provided therapeutic target for NSCLC patients.

It More than Adds Up: Interaction of Antibiotic Mixing and Temperature

Use of antibiotics for the treatment and prevention of bacterial infections in humans, agri- and aquaculture as well as livestock rearing leads to antibiotic pollution of fresh water and these antibiotics have an impact on free-living bacteria. While we know which antibiotics are most common in natural environments such as rivers and streams, there is considerable uncertainty regarding antibiotics’ interactions with one another and the effect of abiotic factors such as temperature. Here, we used an experimental approach to explore the effects of antibiotic identity, concentration, mixing and water temperature on the growth of Pseudomonas fluorescens, a common, ubiquitous bacterium. We exposed P. fluorescens to the four antibiotics most commonly found in surface waters (ciprofloxacin, ofloxacin, sulfamethoxazole and sulfapyridine) and investigated antibiotic interactions for single and mixed treatments at different, field-realistic temperatures. We observed an overall dependence of antibiotic potency on temperature, as temperature increased efficacy of ciprofloxacin and ofloxacin with their EC₅₀ lowered by >75% with a 10 °C temperature increase. Further, we show that mixtures of ciprofloxacin and ofloxacin, despite both belonging to the fluoroquinolone class, exhibit low-temperature-dependent synergistic effects in inhibiting bacterial growth. These findings highlight the context dependency of antibiotic efficacy. They further suggest antibiotic-specific off-target effects that only affect the bacteria once they enter a certain temperature range. This has important implications as freshwater systems already contain multi-drug antibiotic cocktails and are changing temperature due to environmental warming. These factors will interact and affect aquatic food webs, and hence this creates an urgent need to adapt and improve laboratory testing conditions to closer reflect natural environments.

Leveraging laboratory and clinical studies to design effective antibiotic combination therapy

Interest in antibiotic combination therapy is increasing due to antimicrobial resistance and a slowing antibiotic pipeline. However, aside from specific indications, combination therapy in the clinic is often not administered systematically; instead, it is used at the physician's discretion as a bet-hedging mechanism to increase the chances of appropriately targeting a pathogen(s) with an unknown antibiotic resistance profile. Some recent clinical trials have been unable to demonstrate superior efficacy of combination therapy over monotherapy. Other trials have shown a benefit of combination therapy in defined circumstances consistent with recent studies indicating that factors including species, strain, resistance profile, and microenvironment affect drug combination efficacy and drug interactions. In this review, we discuss how a careful study design that takes these factors into account, along with the different drug interaction and potency metrics for assessing combination performance, may provide the necessary insight to understand the best clinical use-cases for combination therapy.

Antibiotic resistance in agriculture: Perspectives on upcoming strategies to overcome upsurge in resistance

Antibiotic resistance is a massive problem rising constantly and spreading rapidly since the past decade. The major underlying mechanism responsible for this problem is an overuse or severe misuse of antibiotics. Regardless of this emerging global threat, antibiotics are still being widely used, not only for treatment of human infections, but also to a great extent in agriculture, livestock and animal husbandry. If the current scenario persists, we might enter into a post-antibiotic era where drugs might not be able to treat even the simplest of infections. This review discusses the current status of antibiotic utilization and molecular basis of antibiotic resistance mechanisms acquired by bacteria, along with the modes of transmittance of the resultant resistant genes into human pathogens through their cycling among different ecosystems. The main focus of the article is to provide an insight into the different molecular and other strategies currently being studied worldwide for their use as an alternate to antibiotics with an overall aim to overcome or minimize the global problem of antibiotic resistance.

MuSyC is a consensus framework that unifies multi-drug synergy metrics for combinatorial drug discovery

Drug combination discovery depends on reliable synergy metrics but no consensus exists on the correct synergy criterion to characterize combined interactions. The fragmented state of the field confounds analysis, impedes reproducibility, and delays clinical translation of potential combination treatments. Here we present a mass-action based formalism to quantify synergy. With this formalism, we clarify the relationship between the dominant drug synergy principles, and present a mapping of commonly used frameworks onto a unified synergy landscape. From this, we show how biases emerge due to intrinsic assumptions which hinder their broad applicability and impact the interpretation of synergy in discovery efforts. Specifically, we describe how traditional metrics mask consequential synergistic interactions, and contain biases dependent on the Hill-slope and maximal effect of single-drugs. We show how these biases systematically impact synergy classification in large combination screens, potentially misleading discovery efforts. Thus the proposed formalism can provide a consistent, unbiased interpretation of drug synergy, and accelerate the translatability of synergy studies.

The lack of a unifying metric characterizing combinatorial drug interactions has impeded the development of combinatorial therapies. Here, the authors present MuSyC, a consensus synergy metric that overcomes several caveats associated with other, popular metrics.

The future of antibiotics begins with discovering new combinations

Antibiotic resistance is a worldwide and growing clinical problem. With limited drug development in the antibacterial space, combination therapy has emerged as a promising strategy to combat multidrug-resistant bacteria. Antibacterial combinations can improve antibiotic efficacy and suppress antibacterial resistance through independent, synergistic, or even antagonistic activities. Combination therapies are famously used to treat viral and mycobacterial infections and cancer. However, antibacterial combinations are only now emerging as a common treatment strategy for other bacterial infections owing to challenges in their discovery, development, regulatory approval, and commercial/clinical deployment. Here, we focus on discovery-where the sheer scale of combinatorial chemical spaces represents a significant challenge-and discuss how combination therapy can impact the treatment of bacterial infections. Despite these challenges, recent advancements, including new in silico methods, theoretical frameworks, and microfluidic platforms, are poised to identify the new and efficacious antibacterial combinations needed to revitalize the antibacterial drug pipeline.

Hidden suppressive interactions are common in higher-order drug combinations

The rapid increase of multi-drug resistant bacteria has led to a greater emphasis on multi-drug combination treatments. However, some combinations can be suppressive—that is, bacteria grow faster in some drug combinations than when treated with a single drug. Typically, when studying interactions, the overall effect of the combination is only compared with the single-drug effects. However, doing so could miss “hidden” cases of suppression, which occur when the highest order is suppressive compared with a lower-order combination but not to a single drug. We examined an extensive dataset of 5-drug combinations and all lower-order—single, 2-, 3-, and 4-drug—combinations. We found that a majority of all combinations—54%—contain hidden suppression. Examining hidden interactions is critical to understanding the architecture of higher-order interactions and can substantially affect our understanding and predictions of the evolution of antibiotic resistance under multi-drug treatments.

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Most instances of suppressive interactions are missed by standard methods

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A majority (54%) of all antibiotic combinations tested contain hidden suppression

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Identifying hidden suppression can affect what combinations should be used

Insights on recent approaches in drug discovery strategies and untapped drug targets against drug resistance

BACKGROUND

Despite the various strategies undertaken in the clinical practice, the mortality rate due to antibiotic-resistant microbes has been markedly increasing worldwide. In addition to multidrug-resistant (MDR) microbes, the "ESKAPE" bacteria are also emerging. Of course, the infection caused by ESKAPE cannot be treated even with lethal doses of antibiotics. Now, the drug resistance is also more prevalent in antiviral, anticancer, antimalarial and antifungal chemotherapies.

MAIN BODY

To date, in the literature, the quantum of research reported on the discovery strategies for new antibiotics is remarkable but the milestone is still far away. Considering the need of the updated strategies and drug discovery approaches in the area of drug resistance among researchers, in this communication, we consolidated the insights pertaining to new drug development against drug-resistant microbes. It includes drug discovery void, gene paradox, transposon mutagenesis, vitamin biosynthesis inhibition, use of non-conventional media, host model, target through quorum sensing, genomic-chemical network, synthetic viability to targets, chemical versus biological space, combinational approach, photosensitization, antimicrobial peptides and transcriptome profiling. Furthermore, we optimally briefed about antievolution drugs, nanotheranostics and antimicrobial adjuvants and then followed by twelve selected new feasible drug targets for new drug design against drug resistance. Finally, we have also tabulated the chemical structures of potent molecules against antimicrobial resistance.

CONCLUSION

It is highly recommended to execute the anti-drug resistance research as integrated approach where both molecular and genetic research needs to be as integrative objective of drug discovery. This is time to accelerate new drug discovery research with advanced genetic approaches instead of conventional blind screening.

Habitat loss alters effects of intransitive higher-order competition on biodiversity: a new metapopulation framework

Recent studies have suggested that intransitive competition, as opposed to hierarchical competition, allows more species to coexist. Furthermore, it is recognized that the prevalent paradigm, which assumes that species interactions are exclusively pairwise, may be insufficient. More importantly, whether and how habitat loss, a key driver of biodiversity loss, can alter these complex competition structures (and therefore species coexistence) remain unclear. We thus present a new, simple yet comprehensive metapopulation framework that can account for any competition pattern and more complex higher-order interactions (HOIs) among species. We find that competitive intransitivity increases community diversity and that HOIs generally enhance this effect. Essentially, intransitivity promotes species richness by preventing the dominance of a few species, unlike the hierarchical competition, while HOIs facilitate species coexistence through stabilizing community fluctuations. However, variation in species' vital rates and habitat loss can weaken or even reverse such higher-order effects, as their interaction can lead to a more rapid decline in competitive intransitivity under HOIs. Thus, it is essential to correctly identify the most appropriate interaction model for a given system before models are used to inform conservation efforts. Overall, our simple model framework provides a more parsimonious explanation for biodiversity maintenance than the existing theory.

Antimicrobial resistance screening and profiles: a glimpse from the South African perspective

According to the Centre for Disease Dynamics Economics and Policy, South Africa represents a paradox of antibiotic management similar to other developing countries, with both overuse and underuse (resulting from lack of access) of antibiotics. In addition, wastewater reuse may contribute towards antibiotic resistance through selective pressure that increases resistance in native bacteria and on clinically relevant bacteria, increasing resistance profiles of the common pathogens. Sediments of surface water bodies and wastewater sludge provide a place where antibiotic resistance genes are transferred to other bacteria. Crop irrigation is thought to be a potential source of exposure to antibiotic-resistant bacteria through the transfer from the water or sludge into crops. The objectives of this study were to examine the antibiotic-resistance profiles of Escherishia coli from three agricultural locations in the Western Cape, South Africa. Using a classical microbiology culture approach, the resistance profiles of E. coli species isolated from river water and sediments, farm dams and their sediments and a passive algal wastewater treatment ponds and sediment used for crop irrigation were assessed for resistance to 13 commonly used antibiotics. Randomly selected E. coli isolates from the sediment and water were tested for resistance. 100% of E. coli isolates were resistant to sulphamethoxazole, highlighting its relevance in the South African context. In river water and farm dam samples, only the E. coli isolated from sediment were found to be resistant to fluoroquinolone or fluorifenicol. In the wastewater treatment ponds, the resistance profiles of E. coli isolated from sediments differed from those isolated from effluent, with 90% of the effluent isolates being resistant to ampicillin. Isolates from the sediment were less resistant (40%) to ampicillin, whereas all the isolates from the pond water and sediment samples were resistant to sulphamethoxazole. These results illustrate the importance of developing a better understanding of antibiotic resistance in agriculture and wastewater scenarios to ensure remedial measures take place where the greatest benefit can be realised especially in countries with limited financial and infrastructural resources. Moreover, the potential for passive algal treatment as an effective, feasible alternative for wastewater treatment is highlighted, with comparable resistance profiles and a reducing overall resistance in the sediment samples.

Pristine and Antibiotic-Loaded Nanosheets/Nanoneedles-Based Boron Nitride Films as a Promising Platform to Suppress Bacterial and Fungal Infections

In recent years, bacteria inactivation during their direct physical contact with surface nanotopography has become one of the promising strategies for fighting infection. Contact-killing ability has been reported for several nanostructured surfaces, e.g., black silicon, carbon nanotubes, zinc oxide nanorods, and copper oxide nanosheets. Herein, we demonstrate that Gram-negative antibiotic-resistant Escherichia coli (E. coli) bacteria are killed as a result of their physical destruction while contacting nanostructured h-BN surfaces. BN films, made of spherical nanoparticles formed by numerous nanosheets and nanoneedles with a thickness <15 nm, have been obtained through a reaction of ammonia with amorphous boron. The contact-killing bactericidal effect of BN nanostructures has been compared with a toxic effect of gentamicin released from them. For a wider protection against bacterial and fungal infection, the films have been saturated with a mixture of gentamicin and amphotericin B. Such BN films demonstrate a high antibiotic/antimycotic agent loading capacity and a fast initial and sustained release of therapeutic agents for 170-260 h depending on the loaded dose. The pristine BN films possess high antibacterial activity against E. coli K-261 strain at their initial concentration of 10⁴ cells/mL, attaining >99% inactivation of colony forming units after 24 h, same as gentamicin-loaded (150 μg/cm²) BN sample. The BN films loaded with a mixture of gentamicin (150 and 300 μg/cm²) and amphotericin B (100 μg/cm²) effectively inhibit the growth of E. coli K-261 and Neurospora crassa strains. During immersion in the normal saline solution, the BN film generates reactive oxygen species (ROS), which can lead to accelerated oxidative stress at the site of physical cell damage. The obtained results are valuable for further development of nanostructured surfaces having contact killing, ROS, and biocide release abilities.

Guided screen for synergistic three-drug combinations

Combinations of three or more drugs are routinely used in various medical fields such as clinical oncology and infectious diseases to prevent resistance or to achieve synergistic therapeutic benefits. The very large number of possible high-order drug combinations presents a formidable challenge for discovering synergistic drug combinations. Here, we establish a guided screen to discover synergistic three-drug combinations. Using traditional checkerboard and recently developed diagonal methods, we experimentally measured all pairwise interactions among eight compounds in Erwinia amylovora, the causative agent of fire blight. Showing that synergy measurements of these two methods agree, we predicted synergy/antagonism scores for all possible three-drug combinations by averaging the synergy scores of pairwise interactions. We validated these predictions by experimentally measuring 35 three-drug interactions. Therefore, our guided screen for discovering three-drug synergies is (i) experimental screen of all pairwise interactions using diagonal method, (ii) averaging pairwise scores among components to predict three-drug interaction scores, (iii) experimental testing of top predictions. In our study, this strategy resulted in a five-fold reduction in screen size to find the most synergistic three-drug combinations.

SynergyFinder 2.0: visual analytics of multi-drug combination synergies

SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine.

COMBSecretomics: A pragmatic methodological framework for higher-order drug combination analysis using secretomics

Multi drug treatments are increasingly used in the clinic to combat complex and co-occurring diseases. However, most drug combination discovery efforts today are mainly focused on anticancer therapy and rarely examine the potential of using more than two drugs simultaneously. Moreover, there is currently no reported methodology for performing second- and higher-order drug combination analysis of secretomic patterns, meaning protein concentration profiles released by the cells. Here, we introduce COMBSecretomics (https://github.com/EffieChantzi/COMBSecretomics.git), the first pragmatic methodological framework designed to search exhaustively for second- and higher-order mixtures of candidate treatments that can modify, or even reverse malfunctioning secretomic patterns of human cells. This framework comes with two novel model-free combination analysis methods; a tailor-made generalization of the highest single agent principle and a data mining approach based on top-down hierarchical clustering. Quality control procedures to eliminate outliers and non-parametric statistics to quantify uncertainty in the results obtained are also included. COMBSecretomics is based on a standardized reproducible format and could be employed with any experimental platform that provides the required protein release data. Its practical use and functionality are demonstrated by means of a proof-of-principle pharmacological study related to cartilage degradation. COMBSecretomics is the first methodological framework reported to enable secretome-related second- and higher-order drug combination analysis. It could be used in drug discovery and development projects, clinical practice, as well as basic biological understanding of the largely unexplored changes in cell-cell communication that occurs due to disease and/or associated pharmacological treatment conditions.

The role of multiple global change factors in driving soil functions and microbial biodiversity

Soils underpin terrestrial ecosystem functions, but they face numerous anthropogenic pressures. Despite their crucial ecological role, we know little about how soils react to more than two environmental factors at a time. Here, we show experimentally that increasing the number of simultaneous global change factors (up to 10) caused increasing directional changes in soil properties, soil processes, and microbial communities, though there was greater uncertainty in predicting the magnitude of change. Our study provides a blueprint for addressing multifactor change with an efficient, broadly applicable experimental design for studying the impacts of global environmental change.

Charting the Fragmented Landscape of Drug Synergy

Even as the clinical impact of drug combinations continues to accelerate, no consensus on how to quantify drug synergy has emerged. Rather, surveying the landscape of drug synergy reveals the persistence of historical fissures regarding the appropriate domains of conflicting synergy models - fissures impacting all aspects of combination therapy discovery and deployment. Herein we chronicle the impact of these divisions on: (i) the design, interpretation, and reproducibility of high-throughput combination screens; (ii) the performance of algorithms to predict synergistic mixtures; and (iii) the search for higher-order synergistic interactions. Further progress in each of these subfields hinges on reaching a consensus regarding the long-standing rifts in the field.

Nano-antimicrobials: A New Paradigm for Combating Mycobacterial Resistance

BACKGROUND

Mycobacterium group contains several pathogenic bacteria including M. tuberculosis where the emergence of multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB) is alarming for human and animal health around the world. The condition has further aggravated due to the speed of discovery of the newer drugs has been outpaced by the rate of resistance developed in microorganisms, thus requiring alternative combat strategies. For this purpose, nano-antimicrobials have emerged as a potential option.

OBJECTIVE

The current review is focused on providing a detailed account of nanocarriers like liposome, micelles, dendrimers, solid lipid NPs, niosomes, polymeric nanoparticles, nano-suspensions, nano-emulsion, mesoporous silica and alginate-based drug delivery systems along with the recent updates on developments regarding nanoparticle-based therapeutics, vaccines and diagnostic methods developed or under pipeline with their potential benefits and limitations to combat mycobacterial diseases for their successful eradication from the world in future.

RESULTS

Distinct morphology and the underlying mechanism of pathogenesis and resistance development in this group of organisms urge improved and novel methods for the early and efficient diagnosis, treatment and vaccination to eradicate the disease. Recent developments in nanotechnology have the potential to meet both the aspects: nano-materials are proven components of several efficient targeted drug delivery systems and the typical physicochemical properties of several nano-formulations have shown to possess distinct bacteriocidal properties. Along with the therapeutic aspects, nano-vaccines and theranostic applications of nano-formulations have grown in popularity in recent times as an effective alternative means to combat different microbial superbugs.

CONCLUSION

Nanomedicine holds a bright prospect to perform a key role in global tuberculosis elimination program.

Emergent Gene Expression Responses to Drug Combinations Predict Higher-Order Drug Interactions

Effective design of combination therapies requires understanding the changes in cell physiology that result from drug interactions. Here, we show that the genome-wide transcriptional response to combinations of two drugs, measured at a rigorously controlled growth rate, can predict higher-order antagonism with a third drug in Saccharomyces cerevisiae. Using isogrowth profiling, over 90% of the variation in cellular response can be decomposed into three principal components (PCs) that have clear biological interpretations. We demonstrate that the third PC captures emergent transcriptional programs that are dependent on both drugs and can predict antagonism with a third drug targeting the emergent pathway. We further show that emergent gene expression patterns are most pronounced at a drug ratio where the drug interaction is strongest, providing a guideline for future measurements. Our results provide a readily applicable recipe for uncovering emergent responses in other systems and for higher-order drug combinations. A record of this paper's transparent peer review process is included in the Supplemental Information.

Microbial Interaction Network Inference in Microfluidic Droplets

Microbial interactions are major drivers of microbial community dynamics and functions but remain challenging to identify because of limitations in parallel culturing and absolute abundance quantification of community members across environments and replicates. To this end, we developed Microbial Interaction Network Inference in microdroplets (MINI-Drop). Fluorescence microscopy coupled to computer vision techniques were used to rapidly determine the absolute abundance of each strain in hundreds to thousands of droplets per condition. We showed that MINI-Drop could accurately infer pairwise and higher-order interactions in synthetic consortia. We developed a stochastic model of community assembly to provide insight into the heterogeneity in community states across droplets. Finally, we elucidated the complex web of interactions linking antibiotics and different species in a synthetic consortium. In sum, we demonstrated a robust and generalizable method to infer microbial interaction networks by random encapsulation of sub-communities into microfluidic droplets.

Increasing Temperature and Microplastic Fibers Jointly Influence Soil Aggregation by Saprobic Fungi

Microplastic pollution and increasing temperature have potential to influence soil quality; yet little is known about their effects on soil aggregation, a key determinant of soil quality. Given the importance of fungi for soil aggregation, we investigated the impacts of increasing temperature and microplastic fibers on aggregation by carrying out a soil incubation experiment in which we inoculated soil individually with 5 specific strains of soil saprobic fungi. Our treatments were temperature (ambient temperature of 25°C or temperature increased by 3°C, abruptly versus gradually) and microplastic fibers (control and 0.4% w/w). We evaluated the percentage of water stable aggregates (WSA) and hydrolysis of fluorescein diacetate (FDA) as an indicator of fungal biomass. Microplastic fiber addition was the main factor influencing the WSA, decreasing the percentage of WSA except in soil incubated with strain RLCS 01, and mitigated the effects of temperature or even caused more pronounced decrease in WSA under increasing temperature. We also observed clear differences between temperature change patterns. Our study shows that the interactive effects of warming and microplastic fibers are important to consider when evaluating effects of global change on soil aggregation and potentially other soil processes.