Monitoring resilience in bursts.

The possibility to anticipate critical transitions through detecting loss of resilience has attracted attention in many fields. Resilience indicators rely on the mathematical concept of critical slowing down, which means that a system recovers more slowly from external perturbations when it gets closer to tipping point. This decrease in recovery rate can be reflected in rising autocorrelation and variance in data. To test whether resilience is changing, resilience indicators are often calculated using a moving window in long, continuous time series of the system. However, for some systems, it may be more feasible to collect several high-resolution time series in short periods of time, i.e., in bursts. Resilience indicators can then be calculated to detect a change of resilience between such bursts. Here, we compare the performance of both methods using simulated data and showcase the possible use of bursts in a case study using mood data to anticipate depression in a patient. With the same number of data points, the burst approach outperformed the moving window method, suggesting that it is possible to downsample the continuous time series and still signal an upcoming transition. We suggest guidelines to design an optimal sampling strategy. Our results imply that using bursts of data instead of continuous time series may improve the capacity to detect changes in resilience. This method is promising for a variety of fields, such as human health, epidemiology, or ecology, where continuous monitoring can be costly or unfeasible.

Nutritional control of developmental processes.

Nutritional and metabolic cues are integral to animal development. Organisms use them both as sustenance and environmental indicators, fueling, informing and influencing developmental decisions. Classical examples, such as the Warburg effect, clearly illustrate how genetic programs control metabolic changes. However, the way that nutrition and metabolism can also modulate or drive genetic programs to instruct developmental trajectories is much more elusive, owing to several difficulties including uncoupling permissive and instructive functions. Here, we discuss recent advancements in the field that highlight the developmental role of nutritional and metabolic cues across multiple levels of organismal complexity.

Trade-offs between cost and information in cellular prediction.

Living cells can leverage correlations in environmental fluctuations to predict the future environment and mount a response ahead of time. To this end, cells need to encode the past signal into the output of the intracellular network from which the future input is predicted. Yet, storing information is costly while not all features of the past signal are equally informative on the future input signal. Here, we show for two classes of input signals that cellular networks can reach the fundamental bound on the predictive information as set by the information extracted from the past signal: Push-pull networks can reach this information bound for Markovian signals, while networks that take a temporal derivative can reach the bound for predicting the future derivative of non-Markovian signals. However, the bits of past information that are most informative about the future signal are also prohibitively costly. As a result, the optimal system that maximizes the predictive information for a given resource cost is, in general, not at the information bound. Applying our theory to the chemotaxis network of Escherichia coli reveals that its adaptive kernel is optimal for predicting future concentration changes over a broad range of background concentrations, and that the system has been tailored to predicting these changes in shallow gradients.

Tradeoffs in bacterial physiology determine the efficiency of antibiotic killing.

Antibiotic effectiveness depends on a variety of factors. While many mechanistic details of antibiotic action are known, the connection between death rate and bacterial physiology is poorly understood. A common observation is that death rate in antibiotics rises linearly with growth rate; however, it remains unclear how other factors, such as environmental conditions and whole-cell physiological properties, affect bactericidal activity. To address this, we developed a high-throughput assay to precisely measure antibiotic-mediated death. We found that death rate is linear in growth rate, but the slope depends on environmental conditions. Growth under stress lowers death rate compared to nonstressed environments with similar growth rate. To understand stress's role, we developed a mathematical model of bacterial death based on resource allocation that includes a stress-response sector; we identify this sector using RNA-seq. Our model accurately predicts the minimal inhibitory concentration (MIC) with zero free parameters across a wide range of growth conditions. The model also quantitatively predicts death and MIC when sectors are experimentally modulated using cyclic adenosine monophosphate (cAMP), including protection from death at very low cAMP levels. The present study shows that different conditions with equal growth rate can have different death rates and establishes a quantitative relation between growth, death, and MIC that suggests approaches to improve antibiotic efficacy.

Trade-offs between the instantaneous growth rate and long-term fitness: Consequences for microbial physiology and predictive computational models.

Microbial systems biology has made enormous advances in relating microbial physiology to the underlying biochemistry and molecular biology. By meticulously studying model microorganisms, in particular Escherichia coli and Saccharomyces cerevisiae, increasingly comprehensive computational models predict metabolic fluxes, protein expression, and growth. The modeling rationale is that cells are constrained by a limited pool of resources that they allocate optimally to maximize fitness. As a consequence, the expression of particular proteins is at the expense of others, causing trade-offs between cellular objectives such as instantaneous growth, stress tolerance, and capacity to adapt to new environments. While current computational models are remarkably predictive for E. coli and S. cerevisiae when grown in laboratory environments, this may not hold for other growth conditions and other microorganisms. In this contribution, we therefore discuss the relationship between the instantaneous growth rate, limited resources, and long-term fitness. We discuss uses and limitations of current computational models, in particular for rapidly changing and adverse environments, and propose to classify microbial growth strategies based on Grimes's CSR framework.

Proteome Dynamics During Transition From Exponential to Stationary Phase Under Aerobic and Anaerobic Conditions in Yeast.

The yeast Saccharomyces cerevisiae is a widely-used eukaryotic model organism and a promising cell factory for industry. However, despite decades of research, the regulation of its metabolism is not yet fully understood, and its complexity represents a major challenge for engineering and optimizing biosynthetic routes. Recent studies have demonstrated the potential of resource and proteomic allocation data in enhancing models for metabolic processes. However, comprehensive and accurate proteome dynamics data that can be used for such approaches are still very limited. Therefore, we performed a quantitative proteome dynamics study to comprehensively cover the transition from exponential to stationary phase for both aerobically and anaerobically grown yeast cells. The combination of highly controlled reactor experiments, biological replicates, and standardized sample preparation procedures ensured reproducibility and accuracy. In addition, we selected the CEN.PK lineage for our experiments because of its relevance for both fundamental and applied research. Together with the prototrophic standard haploid strain CEN.PK113-7D, we also investigated an engineered strain with genetic minimization of the glycolytic pathway, resulting in the quantitative assessment of 54 proteomes. The anaerobic cultures showed remarkably less proteome-level changes compared with the aerobic cultures, during transition from the exponential to the stationary phase as a consequence of the lack of the diauxic shift in the absence of oxygen. These results support the notion that anaerobically growing cells lack resources to adequately adapt to starvation. This proteome dynamics study constitutes an important step toward better understanding of the impact of glucose exhaustion and oxygen on the complex proteome allocation process in yeast. Finally, the established proteome dynamics data provide a valuable resource for the development of resource allocation models as well as for metabolic engineering efforts.

Projecting the Potential Clinical and Economic Impact of Human Immunodeficiency Virus Prevention Resource Reallocation in Tennessee.

BACKGROUND: In 2023, Tennessee replaced $6.2 M in US Centers for Disease Control and Prevention (CDC) human immunodeficiency virus (HIV) prevention funding with state funds to redirect support away from men who have sex with men (MSM), transgender women (TGW), and heterosexual Black women (HSBW) and to prioritize instead first responders (FR), pregnant people (PP), and survivors of sex trafficking (SST). METHODS: We used a simulation model of HIV disease to compare the clinical impact of Current, the present allocation of condoms, preexposure prophylaxis (PrEP), and HIV testing to CDC priority risk groups (MSM/TGW/HSBW); with Reallocation, funding instead increased HIV testing and linkage of Tennessee-determined priority populations (FR/PP/SST). Key model inputs included baseline condom use (45%-49%), PrEP provision (0.1%-8%), HIV testing frequency (every 2.5-4.8 years), and 30-day HIV care linkage (57%-65%). We assumed Reallocation would reduce condom use (-4%), PrEP provision (-26%), and HIV testing (-47%) in MSM/TGW/HSBW, whereas it would increase HIV testing among FR (+47%) and HIV care linkage (to 100%/90%) among PP/SST. RESULTS: Reallocation would lead to 166 additional HIV transmissions, 190 additional deaths, and 843 life-years lost over 10 years. HIV testing reductions were most influential in sensitivity analysis; even a 24% reduction would result in 287 more deaths compared to Current. With pessimistic assumptions, we projected 1359 additional HIV transmissions, 712 additional deaths, and 2778 life-years lost over 10 years. CONCLUSIONS: Redirecting HIV prevention funding in Tennessee would greatly harm CDC priority populations while conferring minimal benefits to new priority populations.

Eye development influences horn size but not patterning in horned beetles.

Understanding the origin of novel morphological traits is a long-standing objective in evolutionary developmental biology. We explored the developmental genetic mechanisms that underpin the formation of a textbook example of evolutionary novelties, the cephalic horns of beetles. Previous work has implicated the gene regulatory networks associated with compound eye and ocellar development in horn formation and suggested that horns and compound eyes may influence each other's sizes. Therefore, we investigated the functional significance of genes central to visual system formation in the initiation, patterning, and size determination of head horns across three horned beetle species. We find that while the downregulation of canonical eye patterning genes reliably reduces or eliminates compound eye formation, it does not alter the position or shape of head horns yet does result in an increase in relative horn length. We discuss the implications of our results for our understanding of the genesis of cephalic horns in particular and evolutionary novelties in general.

Rethinking dopamine-guided action sequence learning.

As opposed to those requiring a single action for reward acquisition, tasks necessitating action sequences demand that animals learn action elements and their sequential order and sustain the behaviour until the sequence is completed. With repeated learning, animals not only exhibit precise execution of these sequences but also demonstrate enhanced smoothness and efficiency. Previous research has demonstrated that midbrain dopamine and its major projection target, the striatum, play crucial roles in these processes. Recent studies have shown that dopamine from the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA) serve distinct functions in action sequence learning. The distinct contributions of dopamine also depend on the striatal subregions, namely the ventral, dorsomedial and dorsolateral striatum. Here, we have reviewed recent findings on the role of striatal dopamine in action sequence learning, with a focus on recent rodent studies.

Variation in bacterial pathotype is consistent with the sit-and-wait hypothesis.

The sit-and-wait hypothesis predicts that bacteria can become more virulent when they survive and transmit outside of their hosts due to circumventing the costs of host mortality. While this hypothesis is largely supported theoretically and through comparative analysis, experimental validation is limited. Here we test this hypothesis in Streptococcus suis, an opportunistic zoonotic pig pathogen, where a pathogenic ecotype proliferated during the change to intensive pig farming that amplifies opportunities for fomite transmission. We show in an in vitro environmental survival experiment that pathogenic ecotypes survive for longer than commensal ecotypes, despite similar rates of decline. The presence of a polysaccharide capsule has no consistent effect on survival. Our findings suggest that extended survival in the food chain may augment the zoonotic capability of S. suis. Moreover, eliminating the long-term environmental survival of bacteria could be a strategy that will both enhance infection control and curtail the evolution of virulence.

Investigating trade-offs between ovary activation and immune protein expression in bumble bee (Bombus impatiens) workers and queens.

Evidence for a trade-off between reproduction and immunity has manifested in many animal species, including social insects. However, investigations in social insect queens present a conundrum: new gynes of many social hymenopterans, such as bumble bees and ants, must first mate, then transition from being solitary to social as they establish their nests, thus experiencing confounding shifts in environmental conditions. Worker bumble bees offer an opportunity to investigate patterns of immune protein expression associated with ovary activation while minimizing extraneous environmental factors and genetic differences. Here, we use proteomics to interrogate the patterns of immune protein expression of female bumble bees (Bombus impatiens) by (i) sampling queens at different stages of their life cycle, then (ii) by sampling workers with different degrees of ovary activation. Patterns of immune protein expression in the haemolymph of queens are consistent with a reproduction-immunity trade-off, but equivalent samples from workers are not. This brings into question whether queen bumble bees really experience a reproduction-immunity trade-off, or if patterns of immune protein expression may actually be due to the selective pressure of the different environmental conditions they are exposed to during their life cycle.

Metabolic impact of heterologous protein production in Pseudomonas putida: Insights into carbon and energy flux control.

For engineered microorganisms, the production of heterologous proteins that are often useless to host cells represents a burden on resources, which have to be shared with normal cellular processes. Within a certain metabolic leeway, this competitive process has no impact on growth. However, once this leeway, or free capacity, is fully utilized, the extra load becomes a metabolic burden that inhibits cellular processes and triggers a broad cellular response, reducing cell growth and often hindering the production of heterologous proteins. In this study, we sought to characterize the metabolic rearrangements occurring in the central metabolism of Pseudomonas putida at different levels of metabolic load. To this end, we constructed a P. putida KT2440 strain that expressed two genes encoding fluorescent proteins, one in the genome under constitutive expression to monitor the free capacity, and the other on an inducible plasmid to probe heterologous protein production. We found that metabolic fluxes are considerably reshuffled, especially at the level of periplasmic pathways, as soon as the metabolic load exceeds the free capacity. Heterologous protein production leads to the decoupling of anabolism and catabolism, resulting in large excess energy production relative to the requirements of protein biosynthesis. Finally, heterologous protein production was found to exert a stronger control on carbon fluxes than on energy fluxes, indicating that the flexible nature of P. putida's central metabolic network is solicited to sustain energy production.

Vibrio natriegens genome-scale modeling reveals insights into halophilic adaptations and resource allocation.

Vibrio natriegens is a Gram-negative bacterium with an exceptional growth rate that has the potential to become a standard biotechnological host for laboratory and industrial bioproduction. Despite this burgeoning interest, the current lack of organism-specific qualitative and quantitative computational tools has hampered the community's ability to rationally engineer this bacterium. In this study, we present the first genome-scale metabolic model (GSMM) of V. natriegens. The GSMM (iLC858) was developed using an automated draft assembly and extensive manual curation and was validated by comparing predicted yields, central metabolic fluxes, viable carbon substrates, and essential genes with empirical data. Mass spectrometry-based proteomics data confirmed the translation of at least 76% of the enzyme-encoding genes predicted to be expressed by the model during aerobic growth in a minimal medium. iLC858 was subsequently used to carry out a metabolic comparison between the model organism Escherichia coli and V. natriegens, leading to an analysis of the model architecture of V. natriegens' respiratory and ATP-generating system and the discovery of a role for a sodium-dependent oxaloacetate decarboxylase pump. The proteomics data were further used to investigate additional halophilic adaptations of V. natriegens. Finally, iLC858 was utilized to create a Resource Balance Analysis model to study the allocation of carbon resources. Taken together, the models presented provide useful computational tools to guide metabolic engineering efforts in V. natriegens.

Carbon and phosphorus exchange rates in arbuscular mycorrhizas depend on environmental context and differ among co-occurring plants.

Phosphorus (P) for carbon (C) exchange is the pivotal function of arbuscular mycorrhiza (AM), but how this exchange varies with soil P availability and among co-occurring plants in complex communities is still largely unknown. We collected intact plant communities in two regions differing c. 10-fold in labile inorganic P. After a 2-month glasshouse incubation, we measured 32P transfer from AM fungi (AMF) to shoots and 13C transfer from shoots to AMF using an AMF-specific fatty acid. AMF communities were assessed using molecular methods. AMF delivered a larger proportion of total shoot P in communities from high-P soils despite similar 13C allocation to AMF in roots and soil. Within communities, 13C concentration in AMF was consistently higher in grass than in blanketflower (Gaillardia aristata Pursh) roots, that is P appeared more costly for grasses. This coincided with differences in AMF taxa composition and a trend of more vesicles (storage structures) but fewer arbuscules (exchange structures) in grass roots. Additionally, 32P-for-13C exchange ratios increased with soil P for blanketflower but not grasses. Contrary to predictions, AMF transferred proportionally more P to plants in communities from high-P soils. However, the 32P-for-13C exchange differed among co-occurring plants, suggesting differential regulation of the AM symbiosis.

Interactions between root hairs and the soil microbial community affect the growth of maize seedlings.

Root hairs are considered important for rhizosphere formation, which affects root system functioning. Through interactions with soil microorganisms mediated by root exudation, root hairs may affect the phenotypes and growth of young plants. We tested this hypothesis by integrating results from two experiments: (1) a factorial greenhouse seedling experiment with Zea mays B73-wt and its root-hairless mutant, B73-rth3, grown in live and autoclaved soil, quantifying 15 phenotypic traits, seven growth rates, and soil microbiomes and (2) a semi-hydroponic system quantifying root exudation of maize genotypes. Possibly as compensation for lacking root hairs, B73-rth3 seedlings allocated more biomass to roots and grew slower than B73-wt seedlings in live soil, whereas B73-wt seedlings grew slowest in autoclaved soil, suggesting root hairs can be costly and their benefits were realized with more complete soil microbial assemblages. There were substantial differences in root exudation between genotypes and in rhizosphere versus non-rhizosphere microbiomes. The microbial taxa enriched in the presence of root hairs generally enhanced growth compared to taxa enriched in their absence. Our findings suggest the root hairs' adaptive value extends to plant-microbe interactions mediated by root exudates, affecting plant phenotypes, and ultimately, growth.

Higher Flower Hydraulic Safety, Drought Tolerance and Structural Resource Allocation Provide Drought Adaptation to Low Mean Annual Precipitation in Caragana Species.

Determining the differences in flower hydraulic traits and structural resource allocation among closely related species adapted to low mean annual precipitation (MAP) can provide insight into plant adaptation to arid environments. Here, we measured the maximum flower hydraulic conductance (Kmax-flower), water potential at induction 50% loss of Kmax-flower (P50-flower), flower pressure-volume parameters, dry mass of individual flowers and structural components (vexillum, wings, keels, stamens and sepals) of six Caragana species growing in regions ranging from 110 to 1400 mm MAP. Compared with species from high-MAP environments, those from low-MAP environments presented lower Kmax-flower, more negative P50-flower, osmotic potential at full turgor (πo) and turgor loss points (πtlp), and a greater bulk modulus of elasticity (ε). Consequently, a negative correlation between Kmax-flower (hydraulic efficiency) and P50-flower (hydraulic safety) was observed across Caragana species. Furthermore, the dry masses of individual flowers and structural components (vexillum, wings, keels, stamens and sepals) were greater in the species from the low-MAP environment than in those from the high-MAP environment. These findings suggest that greater flower hydraulic safety and drought tolerance combined with greater structural resource allocation promote drought adaptation in Caragana species to low-MAP environments.

Proteome reorganization and amino acid metabolism during germination and seedling establishment in Lupinus albus.

During germination plants rely entirely on their seed storage compounds to provide energy and precursors for the synthesis of macromolecular structures until the seedling has emerged from the soil and photosynthesis can be established. Lupin seeds use proteins as their major storage compounds, accounting for up to 40% of the seed dry weight. Lupins are therefore a valuable complement to soy as a source of plant protein for human and animal nutrition. The aim of this study was to elucidate how storage protein metabolism is coordinated with other metabolic processes to meet the requirements of the growing seedling. In a quantitative approach, we analysed seedling growth, as well as alterations in biomass composition, the proteome, and metabolite profiles during germination and seedling establishment in Lupinus albus. The reallocation of nitrogen resources from seed storage proteins to functional seed proteins was mapped based on a manually curated functional protein annotation database. Although classified as a protein crop, Lupinus albus does not use amino acids as a primary substrate for energy metabolism during germination. However, fatty acid and amino acid metabolism may be integrated at the level of malate synthase to combine stored carbon from lipids and proteins into gluconeogenesis.

Dietary restriction and life-history trade-offs: insights into mTOR pathway regulation and reproductive investment in Japanese quail.

Resources are needed for growth, reproduction and survival, and organisms must trade off limited resources among competing processes. Nutritional availability in organisms is sensed and monitored by nutrient-sensing pathways that can trigger physiological changes or alter gene expression. Previous studies have proposed that one such signalling pathway, the mechanistic target of rapamycin (mTOR), underpins a form of adaptive plasticity when individuals encounter constraints in their energy budget. Despite the fundamental importance of this process in evolutionary biology, how nutritional limitation is regulated through the expression of genes governing this pathway and its consequential effects on fitness remain understudied, particularly in birds. We used dietary restriction to simulate resource depletion and examined its effects on body mass, reproduction and gene expression in Japanese quails (Coturnix japonica). Quails were subjected to feeding at 20%, 30% and 40% restriction levels or ad libitum for 2 weeks. All restricted groups exhibited reduced body mass, whereas reductions in the number and mass of eggs were observed only under more severe restrictions. Additionally, dietary restriction led to decreased expression of mTOR and insulin-like growth factor 1 (IGF1), whereas the ribosomal protein S6 kinase 1 (RPS6K1) and autophagy-related genes (ATG9A and ATG5) were upregulated. The pattern in which mTOR responded to restriction was similar to that for body mass. Regardless of the treatment, proportionally higher reproductive investment was associated with individual variation in mTOR expression. These findings reveal the connection between dietary intake and the expression of mTOR and related genes in this pathway.

How important is hidden phenotypic plasticity arising from alternative but converging developmental trajectories, and what limits it?

Developmental plasticity -- the capacity for a genotype to develop into different phenotypes, depending on the environment - is typically viewed from the perspective of the resulting phenotype. Thus, if development is viewed as a trajectory towards a target, then developmental plasticity allows environmentally induced alterations to the target. However, there can also be variations in the trajectory. This is seen with compensatory responses, for instance where growth accelerates after an earlier period of food shortage, or where investment in sexual ornaments is maintained even when resources are limiting. If the compensation is complete, the adult phenotype can appear 'normal' (i.e. the different developmental trajectories converge on the same target). However, alternative trajectories to a common target can have multiple long-term consequences, including altered physiological programming and rates of senescence, possibly owing to trade-offs between allocating resources to the prioritized trait versus to body maintenance. This suggests that plasticity in developmental trajectories towards a common target leads to variation in the resilience and robustness of the adult body. This form of developmental plasticity is far more hidden than plasticity in final adult target, but it may be more common. Here, I discuss the causes, consequences and limitations of these different kinds of plasticity, with a special focus on whether they are likely to be adaptive. I emphasize the need to study plasticity in developmental trajectories, and conclude with suggestions for future research to tease apart the different forms of developmental plasticity and the factors that influence their evolution and expression.

Models for malaria control optimization-a systematic review.

BACKGROUND: Despite advances made in curbing the global malaria burden since the 2000s, progress has stalled, in part due to a plateauing of the financing available to implement needed interventions. In 2020, approximately 3.3 billion USD was invested globally for malaria interventions, falling short of the targeted 6.8 billion USD set by the GTS, increasing the financial gap between desirable and actual investment. Models for malaria control optimization are used to disentangle the most efficient interventions or packages of interventions for inherently constrained budgets. This systematic review aimed to identify and characterise models for malaria control optimization for resource allocation in limited resource settings and assess their strengths and limitations. METHODS: Following the Prospective Register of Systematic Reviews and Preferred reporting Items for Systematic Reviews and Meta-Analysis guidelines, a comprehensive search across PubMed and Embase databases was performed of peer-reviewed literature published from inception until June 2024. The following keywords were used: optimization model; malaria; control interventions; elimination interventions. Editorials, commentaries, opinion papers, conference abstracts, media reports, letters, bulletins, pre-prints, grey literature, non-English language studies, systematic reviews and meta-analyses were excluded from the search. RESULTS: The search yielded 2950 records, of which 15 met the inclusion criteria. The studies were carried out mainly in countries in Africa (53.3%), such as Ghana, Nigeria, Tanzania, Uganda, and countries in Asia (26.7%), such as Thailand and Myanmar. The most used interventions for analyses were insecticide-treated bed nets (93.3%), IRS (80.0%), Seasonal Malaria Chemoprevention (33.3%) and Case management (33.3%). The methods used for estimating health benefits were compartmental models (40.0%), individual-based models (40.0%), static models (13.0%) and linear regression model (7%). Data used in the analysis were validated country-specific data (60.0%) or non-country-specific data (40.0%) and were analysed at national only (40.0%), national and subnational levels (46.7%), or subnational only levels (13.3%). CONCLUSION: This review identified available optimization models for malaria resource allocation. The findings highlighted the need for country-specific analysis for malaria control optimization, the use of country-specific epidemiological and cost data in performing modelling analyses, performing cost sensitivity analyses and defining the perspective for the analysis, with an emphasis on subnational tailoring for data collection and analysis for more accurate and good quality results. It is critical that the future modelling efforts account for fairness and target at risk malaria populations that are hard-to-reach to maximize impact. TRIAL REGISTRATION: PROSPERO Registration number: CRD42023436966.