Forecasting antimicrobial resistance evolution.

Antimicrobial resistance (AMR) is a major global health issue. Current measures for tackling it comprise mainly the prudent use of drugs, the development of new drugs, and rapid diagnostics. Relatively little attention has been given to forecasting the evolution of resistance. Here, we argue that forecasting has the potential to be a great asset in our arsenal of measures to tackle AMR. We argue that, if successfully implemented, forecasting resistance will help to resolve the antibiotic crisis in three ways: it will (i) guide a more sustainable use (and therefore lifespan) of antibiotics and incentivize investment in drug development, (ii) reduce the spread of AMR genes and pathogenic microbes in the environment and between patients, and (iii) allow more efficient treatment of persistent infections, reducing the continued evolution of resistance. We identify two important challenges that need to be addressed for the successful establishment of forecasting: (i) the development of bespoke technology that allows stakeholders to empirically assess the risks of resistance evolving during the process of drug development and therapeutic/preventive use, and (ii) the transformative shift in mindset from the current praxis of mostly addressing the problem of antibiotic resistance a posteriori to a concept of a priori estimating, and acting on, the risks of resistance.

Roadmapping the next generation of silicon photonics.

Silicon photonics has developed into a mainstream technology driven by advances in optical communications. The current generation has led to a proliferation of integrated photonic devices from thousands to millions-mainly in the form of communication transceivers for data centers. Products in many exciting applications, such as sensing and computing, are around the corner. What will it take to increase the proliferation of silicon photonics from millions to billions of units shipped? What will the next generation of silicon photonics look like? What are the common threads in the integration and fabrication bottlenecks that silicon photonic applications face, and which emerging technologies can solve them? This perspective article is an attempt to answer such questions. We chart the generational trends in silicon photonics technology, drawing parallels from the generational definitions of CMOS technology. We identify the crucial challenges that must be solved to make giant strides in CMOS-foundry-compatible devices, circuits, integration, and packaging. We identify challenges critical to the next generation of systems and applications-in communication, signal processing, and sensing. By identifying and summarizing such challenges and opportunities, we aim to stimulate further research on devices, circuits, and systems for the silicon photonics ecosystem.

Metastatic cells exploit their stoichiometric niche in the network of cancer ecosystems.

Metastasis is a nonrandom process with varying degrees of organotropism-specific source-acceptor seeding. Understanding how patterns between source and acceptor tumors emerge remains a challenge in oncology. We hypothesize that organotropism results from the macronutrient niche of cells in source and acceptor organs. To test this, we constructed and analyzed a metastatic network based on 9303 records across 28 tissue types. We found that the topology of the network is nested and modular with scale-free degree distributions, reflecting organotropism along a specificity/generality continuum. The variation in topology is significantly explained by the matching of metastatic cells to their stoichiometric niche. Specifically, successful metastases are associated with higher phosphorus content in the acceptor compared to the source organ, due to metabolic constraints in proliferation crucial to the invasion of new tissues. We conclude that metastases are codetermined by processes at source and acceptor organs, where phosphorus content is a limiting factor orchestrating tumor ecology.

Quorum sensing as a mechanism to harness the wisdom of the crowds.

Bacteria release and sense small molecules called autoinducers in a process known as quorum sensing. The prevailing interpretation of quorum sensing is that by sensing autoinducer concentrations, bacteria estimate population density to regulate the expression of functions that are only beneficial when carried out by a sufficiently large number of cells. However, a major challenge to this interpretation is that the concentration of autoinducers strongly depends on the environment, often rendering autoinducer-based estimates of cell density unreliable. Here we propose an alternative interpretation of quorum sensing, where bacteria, by releasing and sensing autoinducers, harness social interactions to sense the environment as a collective. Using a computational model we show that this functionality can explain the evolution of quorum sensing and arises from individuals improving their estimation accuracy by pooling many imperfect estimates - analogous to the 'wisdom of the crowds' in decision theory. Importantly, our model reconciles the observed dependence of quorum sensing on both population density and the environment and explains why several quorum sensing systems regulate the production of private goods.

Delocalized photonic deep learning on the internet's edge.

Advanced machine learning models are currently impossible to run on edge devices such as smart sensors and unmanned aerial vehicles owing to constraints on power, processing, and memory. We introduce an approach to machine learning inference based on delocalized analog processing across networks. In this approach, named Netcast, cloud-based "smart transceivers" stream weight data to edge devices, enabling ultraefficient photonic inference. We demonstrate image recognition at ultralow optical energy of 40 attojoules per multiply (<1 photon per multiply) at 98.8% (93%) classification accuracy. We reproduce this performance in a Boston-area field trial over 86 kilometers of deployed optical fiber, wavelength multiplexed over 3 terahertz of optical bandwidth. Netcast allows milliwatt-class edge devices with minimal memory and processing to compute at teraFLOPS rates reserved for high-power (>100 watts) cloud computers.

Resilience integrates concepts in aging research.

Aging research is unparalleled in the breadth of disciplines it encompasses, from evolutionary studies examining the forces that shape aging to molecular studies uncovering the underlying mechanisms of age-related functional decline. Despite a common focus to advance our understanding of aging, these disciplines have proceeded along distinct paths with little cross-talk. We propose that the concept of resilience can bridge this gap. Resilience describes the ability of a system to respond to perturbations by returning to its original state. Although resilience has been applied in a few individual disciplines in aging research such as frailty and cognitive decline, it has not been explored as a unifying conceptual framework that is able to connect distinct research fields. We argue that because a resilience-based framework can cross broad physiological levels and time scales it can provide the missing links that connect these diverse disciplines. The resulting framework will facilitate predictive modeling and validation and influence targets and directions in research on the biology of aging.

Inferring the dynamics of mutated hematopoietic stem and progenitor cells induced by IFNα in myeloproliferative neoplasms.

Classical BCR-ABL-negative myeloproliferative neoplasms (MPNs) are clonal disorders of hematopoietic stem cells (HSCs) caused mainly by recurrent mutations in genes encoding JAK2 (JAK2), calreticulin (CALR), or the thrombopoietin receptor (MPL). Interferon α (IFNα) has demonstrated some efficacy in inducing molecular remission in MPNs. To determine factors that influence molecular response rate, we evaluated the long-term molecular efficacy of IFNα in patients with MPN by monitoring the fate of cells carrying driver mutations in a prospective observational and longitudinal study of 48 patients over more than 5 years. We measured the clonal architecture of early and late hematopoietic progenitors (84 845 measurements) and the global variant allele frequency in mature cells (409 measurements) several times per year. Using mathematical modeling and hierarchical Bayesian inference, we further inferred the dynamics of IFNα-targeted mutated HSCs. Our data support the hypothesis that IFNα targets JAK2V617F HSCs by inducing their exit from quiescence and differentiation into progenitors. Our observations indicate that treatment efficacy is higher in homozygous than heterozygous JAK2V617F HSCs and increases with high IFNα dose in heterozygous JAK2V617F HSCs. We also found that the molecular responses of CALRm HSCs to IFNα were heterogeneous, varying between type 1 and type 2 CALRm, and a high dose of IFNα correlates with worse outcomes. Our work indicates that the long-term molecular efficacy of IFNα implies an HSC exhaustion mechanism and depends on both the driver mutation type and IFNα dose.

Disease-dependent interaction policies to support health and economic outcomes during the COVID-19 epidemic.

Lockdowns and stay-at-home orders have partially mitigated the spread of Covid-19. However, en masse mitigation has come with substantial socioeconomic costs. In this paper, we demonstrate how individualized policies based on disease status can reduce transmission risk while minimizing impacts on economic outcomes. We design feedback control policies informed by optimal control solutions to modulate interaction rates of individuals based on the epidemic state. We identify personalized interaction rates such that recovered/immune individuals elevate their interactions and susceptible individuals remain at home before returning to pre-lockdown levels. As we show, feedback control policies can yield similar population-wide infection rates to total shutdown but with significantly lower economic costs and with greater robustness to uncertainty compared to optimal control policies. Our analysis shows that test-driven improvements in isolation efficiency of infectious individuals can inform disease-dependent interaction policies that mitigate transmission while enhancing the return of individuals to pre-pandemic economic activity.

Fighting microbial pathogens by integrating host ecosystem interactions and evolution.

Successful therapies to combat microbial diseases and cancers require incorporating ecological and evolutionary principles. Drawing upon the fields of ecology and evolutionary biology, we present a systems-based approach in which host and disease-causing factors are considered as part of a complex network of interactions, analogous to studies of "classical" ecosystems. Centering this approach around empirical examples of disease treatment, we present evidence that successful therapies invariably engage multiple interactions with other components of the host ecosystem. Many of these factors interact nonlinearly to yield synergistic benefits and curative outcomes. We argue that these synergies and nonlinear feedbacks must be leveraged to improve the study of pathogenesis in situ and to develop more effective therapies. An eco-evolutionary systems perspective has surprising and important consequences, and we use it to articulate areas of high research priority for improving treatment strategies.

Paracrine Behaviors Arbitrate Parasite-Like Interactions Between Tumor Subclones.

Explaining the emergence and maintenance of intratumor heterogeneity is an important question in cancer biology. Tumor cells can generate considerable subclonal diversity, which influences tumor growth rate, treatment resistance, and metastasis, yet we know remarkably little about how cells from different subclones interact. Here, we confronted two murine mammary cancer cell lines to determine both the nature and mechanisms of subclonal cellular interactions in vitro. Surprisingly, we found that, compared to monoculture, growth of the "winner" was enhanced by the presence of the "loser" cell line, whereas growth of the latter was reduced. Mathematical modeling and laboratory assays indicated that these interactions are mediated by the production of paracrine metabolites resulting in the winner subclone effectively "farming" the loser. Our findings add a new level of complexity to the mechanisms underlying subclonal growth dynamics.

Disease-dependent interaction policies to support health and economic outcomes during the COVID-19 epidemic.

Lockdowns and stay-at-home orders have partially mitigated the spread of Covid-19. However, the indiscriminate nature of mitigation - applying to all individuals irrespective of disease status - has come with substantial socioeconomic costs. Here, we explore how to leverage the increasing reliability and scale of both molecular and serological tests to balance transmission risks with economic costs involved in responding to Covid-19 epidemics. First, we introduce an optimal control approach that identifies personalized interaction rates according to an individual's test status; such that infected individuals isolate, recovered individuals can elevate their interactions, and activity of susceptible individuals varies over time. Critically, the extent to which susceptible individuals can return to work depends strongly on isolation efficiency. As we show, optimal control policies can yield mitigation policies with similar infection rates to total shutdown but lower socioeconomic costs. However, optimal control policies can be fragile given mis-specification of parameters or mis-estimation of the current disease state. Hence, we leverage insights from the optimal control solutions and propose a feedback control approach based on monitoring of the epidemic state. We utilize genetic algorithms to identify a 'switching' policy such that susceptible individuals (both PCR and serological test negative) return to work after lockdowns insofar as recovered fraction is much higher than the circulating infected prevalence. This feedback control policy exhibits similar performance results to optimal control, but with greater robustness to uncertainty. Overall, our analysis shows that test-driven improvements in isolation efficiency of infectious individuals can inform disease-dependent interaction policies that mitigate transmission while enhancing the return of individuals to pre-pandemic economic activity.

When, why and how tumour clonal diversity predicts survival.

The utility of intratumour heterogeneity as a prognostic biomarker is the subject of ongoing clinical investigation. However, the relationship between this marker and its clinical impact is mediated by an evolutionary process that is not well understood. Here, we employ a spatial computational model of tumour evolution to assess when, why and how intratumour heterogeneity can be used to forecast tumour growth rate and progression-free survival. We identify three conditions that can lead to a positive correlation between clonal diversity and subsequent growth rate: diversity is measured early in tumour development; selective sweeps are rare; and/or tumours vary in the rate at which they acquire driver mutations. Opposite conditions typically lead to negative correlation. In cohorts of tumours with diverse evolutionary parameters, we find that clonal diversity is a reliable predictor of both growth rate and progression-free survival. We thus offer explanations-grounded in evolutionary theory-for empirical findings in various cancers, including survival analyses reported in the recent TRACERx Renal study of clear-cell renal cell carcinoma. Our work informs the search for new prognostic biomarkers and contributes to the development of predictive oncology.

Phage steering of antibiotic-resistance evolution in the bacterial pathogen, Pseudomonas aeruginosa.

BACKGROUND AND OBJECTIVES: Antimicrobial resistance is a growing global concern and has spurred increasing efforts to find alternative therapeutics. Bacteriophage therapy has seen near constant use in Eastern Europe since its discovery over a century ago. One promising approach is to use phages that not only reduce bacterial pathogen loads but also select for phage resistance mechanisms that trade-off with antibiotic resistance-so called 'phage steering'. METHODOLOGY: Recent work has shown that the phage OMKO1 can interact with efflux pumps and in so doing select for both phage resistance and antibiotic sensitivity of the pathogenic bacterium Pseudomonas aeruginosa. We tested the robustness of this approach to three different antibiotics in vitro (tetracycline, erythromycin and ciprofloxacin) and one in vivo (erythromycin). RESULTS: We show that in vitro OMKO1 can reduce antibiotic resistance of P. aeruginosa (Washington PAO1) even in the presence of antibiotics, an effect still detectable after ca.70 bacterial generations in continuous culture with phage. Our in vivo experiment showed that phage both increased the survival times of wax moth larvae (Galleria mellonella) and increased bacterial sensitivity to erythromycin. This increased antibiotic sensitivity occurred both in lines with and without the antibiotic. CONCLUSIONS AND IMPLICATIONS: Our study supports a trade-off between antibiotic resistance and phage sensitivity. This trade-off was maintained over co-evolutionary time scales even under combined phage and antibiotic pressure. Similarly, OMKO1 maintained this trade-off in vivo, again under dual phage/antibiotic pressure. Our findings have implications for the future clinical use of steering in phage therapies. Lay Summary: Given the rise of antibiotic-resistant bacterial infection, new approaches to treatment are urgently needed. Bacteriophages (phages) are bacterial viruses. The use of such viruses to treat infections has been in near-continuous use in several countries since the early 1900s. Recent developments have shown that these viruses are not only effective against routine infections but can also target antibiotic resistant bacteria in a novel, unexpected way. Similar to other lytic phages, these so-called 'steering phages' kill the majority of bacteria directly. However, steering phages also leave behind bacterial variants that resist the phages, but are now sensitive to antibiotics. Treatment combinations of these phages and antibiotics can now be used to greater effect than either one independently. We evaluated the impact of steering using phage OMKO1 and a panel of three antibiotics on Pseudomonas aeruginosa, an important pathogen in hospital settings and in people with cystic fibrosis. Our findings indicate that OMKO1, either alone or in combination with antibiotics, maintains antibiotic sensitivity both in vitro and in vivo, giving hope that phage steering will be an effective treatment option against antibiotic-resistant bacteria.

Patterns of host use by brood parasitic Maculinea butterflies across Europe.

The range of hosts exploited by a parasite is determined by several factors, including host availability, infectivity and exploitability. Each of these can be the target of natural selection on both host and parasite, which will determine the local outcome of interactions, and potentially lead to coevolution. However, geographical variation in host use and specificity has rarely been investigated. Maculinea (= Phengaris) butterflies are brood parasites of Myrmica ants that are patchily distributed across the Palæarctic and have been studied extensively in Europe. Here, we review the published records of ant host use by the European Maculinea species, as well as providing new host ant records for more than 100 sites across Europe. This comprehensive survey demonstrates that while all but one of the Myrmica species found on Maculinea sites have been recorded as hosts, the most common is often disproportionately highly exploited. Host sharing and host switching are both relatively common, but there is evidence of specialization at many sites, which varies among Maculinea species. We show that most Maculinea display the features expected for coevolution to occur in a geographic mosaic, which has probably allowed these rare butterflies to persist in Europe. This article is part of the theme issue 'The coevolutionary biology of brood parasitism: from mechanism to pattern'.

Environmentally Mediated Social Dilemmas.

By consuming and producing environmental resources, organisms inevitably change their habitats. The consequences of such environmental modifications can be detrimental or beneficial not only to the focal organism but also to other organisms sharing the same environment. Social evolution theory has been very influential in studying how social interactions mediated by public 'goods' or 'bads' evolve by emphasizing the role of spatial structure. The environmental dimensions driving these interactions, however, are typically abstracted away. We propose here a new, environment-mediated taxonomy of social behaviors where organisms are categorized by their production or consumption of environmental factors that can help or harm others in the environment. We discuss microbial examples of our classification and highlight the importance of environmental intermediates more generally.

An ecosystem framework for understanding and treating disease.

Pathogens and cancers are pervasive health risks in the human population. I argue that if we are to better understand disease and its treatment, then we need to take an ecological perspective of disease itself. I generalize and extend an emerging framework that views disease as an ecosystem and many of its components as interacting in a community. I develop the framework for biological etiological agents (BEAs) that multiply within humans-focusing on bacterial pathogens and cancers-but the framework could be extended to include other host and parasite species. I begin by describing why we need an ecosystem framework to understand disease, and the main components and interactions in bacterial and cancer disease ecosystems. Focus is then given to the BEA and how it may proceed through characteristic states, including emergence, growth, spread and regression. The framework is then applied to therapeutic interventions. Central to success is preventing BEA evasion, the best known being antibiotic resistance and chemotherapeutic resistance in cancers. With risks of evasion in mind, I propose six measures that either introduce new components into the disease ecosystem or manipulate existing ones. An ecosystem framework promises to enhance our understanding of disease, BEA and host (co)evolution, and how we can improve therapeutic outcomes.

Species dispersal and biodiversity in human-dominated metacommunities.

The concept of the Anthropocene is based on the idea that human impacts are now the primary drivers of changes in the earth's systems, including ecological systems. In many cases, the behavior that causes ecosystem change is itself triggered by ecological factors. Yet most ecological models still treat human impacts as given, and frequently as constant. This undermines our ability to understand the feedbacks between human behavior and ecosystem change. Focusing on the problem of species dispersal, we evaluate the effect of dispersal on biodiversity in a system subject to predation by humans. People are assumed to obtain benefits from (a) the direct consumption of species (provisioning services), (b) the non-consumptive use of species (cultural services), and (c) the buffering effects of the mix of species (regulating services). We find that the effects of dispersal on biodiversity depend jointly on the competitive interactions among species, and on human preferences over species and the services they provide. We find that while biodiversity may be greatest at intermediate levels of dispersal, this depends on structure of preferences across the metacommunity.

Transient negative effects of antibiotics on phages do not jeopardise the advantages of combination therapies.

Phages, the viruses of bacteria, have been proposed as antibacterial agents to complement or replace antibiotics due to the growing problem of resistance. In nature and in the clinic, antibiotics are ubiquitous and may affect phages indirectly via impacts on bacterial hosts. Even if the synergistic association of phages and antibiotics has been shown in several studies, the focus is often on bacteria with little known about the impact on phages. Evolutionary studies have demonstrated that time scale is an important factor in understanding the consequences of antimicrobial strategies, but this perspective is generally overlooked in phage-antibiotic combination studies. Here, we explore the effects of antibiotics on phages targeting the opportunistic pathogen Pseudomonas aeruginosa. We go beyond previous studies by testing the interaction between several types of antibiotics and phages, and evaluate the effects on several important phage parameters during 8 days of experimental co-evolution with bacteria. Our study reveals that antibiotics had a negative effect on phage density and efficacy early on, but not in the later stages of the experiment. The results indicate that antibiotics can affect phage adaptation, but that phages can nevertheless contribute to managing antibiotic resistance levels.

Widely-tunable, narrow-linewidth III-V/silicon hybrid external-cavity laser for coherent communication.

We demonstrate a III-V/silicon hybrid external cavity laser with a tuning range larger than 60 nm at the C-band on a silicon-on-insulator platform. A III-V semiconductor gain chip is hybridized into the silicon chip by edge-coupling the silicon chip through a Si3N4 spot size converter. The demonstrated packaging method requires only passive alignment and is thus suitable for high-volume production. The laser has a largest output power of 11 mW with a maximum wall-plug efficiency of 4.2%, tunability of 60 nm (more than covering the C-band), and a side-mode suppression ratio of 55 dB (>46 dB across the C-band). The lowest measured linewidth is 37 kHz (<80 kHz across the C-band), which is the narrowest linewidth using a silicon-based external cavity. In addition, we successfully demonstrate all silicon-photonics-based transmission of 34 Gbaud (272 Gb/s) dual-polarization 16-QAM using our integrated laser and silicon photonic coherent transceiver. The results show no additional penalty compared to commercially available narrow linewidth tunable lasers. To the best of our knowledge, this is the first experimental demonstration of a complete silicon photonic based coherent link. This is also the first experimental demonstration of >250 Gb/s coherent optical transmission using a silicon micro-ring-based tunable laser.