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Biology subjects

Marino, J.

Publications and source records attributed to Marino, J..

6 recordsLinked to original sources
bioRxiv2024

Dynamic energy budget model for a bumble bee colony: Predicting the spatial distribution and dynamics of colonies across multiple seasons

Bumble bees are important pollinators of many crops around the world. In recent decades, agricultural intensification has resulted in significant declines in bumble bee populations and the pollination services they provide. Empirical studies have shown that this trend can be reversed, however, by enhancing the agricultural landscape with natural habitat, such as adding wildflower patches adjacent to crops. Despite the empirical evidence, the mechanisms behind these positive effects are not fully understood, and the specific characteristics of the enhanced natural habitat that would maximize benefits are unclear at this time. Theoretical studies, in the form of mathematical models, have proven useful in elucidating the underlying mechanisms and determining the optimal natural habitat configurations. Existing models, however, generally focus only on particular aspects of bumble bee behaviour; some models are accurate at describing population dynamics, while others are accurate at describing their spatial distribution. In this work, we build a unique model coupling population dynamics, using a whole-colony Dynamic Energy Budget (DEB) approach, to a spatial distribution model based on the maximum energy principle. This coupling gives valuable new insights into the effects of spatial arrangements on population dynamics, and vice-versa. With our model, we answer questions such as when, how much, or what type of wildflower patches should be planted to maximize crop pollination services and minimize bee decline. We find that planting wildflowers that bloom before and after crop bloom is crucial to achieve high pollination services and preserving wild pollinator populations. We also find that small quantities of natural habitat are needed when the crop is nutritionally rich, but higher quantities are most beneficial when the crop is nutritionally deficient.

ecology↗
bioRxiv2021

Resource and seasonality drive interspecific variability in a Dynamic Energy Budget model

Animals show a vast array of phenotypic traits in time and space. These variation patterns have traditionally been described as ecogeographical rules; for example, the tendency of size and clutch size to increase with latitude (Bergmans and Lacks rules, respectively). Despite considerable research into these patterns, the processes behind trait variation remain controversial. Here, we show how food variability, which determines individual energy input and allocation trade-offs, can drive interspecific trait variation. Using a dynamic energy budget (DEB) model, we simulated different food environments as well as interspecific variability in the parameters for energy assimilation, mobilization, and allocation to soma. We found that interspecific variability is greater when the resource is non-limiting in both constant and seasonal environments. Our findings further show that individuals can reach larger biomass and greater reproductive output in a seasonal environment than in a constant environment of equal average resource due to the peaks of food surplus. Our results agree with the classical patterns of interspecific trait variation and provide a mechanistic understanding that supports recent hypotheses which explain them: the resource and the eNPP (net primary production during the growing season) rules. Due to the current alterations to ecosystems and communities, disentangling trait variation is increasingly important to understand and predict biodiversity dynamics under environmental change.

ecology↗
bioRxiv2021

Symbiosis increases population size and buffers environmental fluctuations in a physiologically-structured model parameterized for thyasirid bivalves

Symbioses whereby one partner provisions a nutritional resource to the other may alter energy allocation towards reproduction and survival in the recipient partner, potentially impacting population dynamics. Asymbiotic thyasirid bivalves feed predominantly on free-living bacteria, which fluctuate in abundance due to seasonality-driven temperature variations. Symbiotic thyasirids are mixotrophs, gaining nutrients from free-living bacteria and symbiotic bacteria that they host on their enlarged gills. Symbiotic bacteria may function as an additional energy reserve for thyasirids, allowing the hosts to allocate more energy to reproduction. We hypothesize that, for symbiotic thyasirids, the symbionts are a nutritional source that mitigates resource limitation. Using Dynamic Energy Budget theory, we built a physiologically-structured population model assuming equal mortality rates in both species. We find that without seasonal fluctuations, symbiotic thyasirids have higher abundances than asymbiotic thyasirids since the symbionts increase reproduction. Both species have similar population sizes in fluctuating environments, suggesting different adaptations to seasonality: asymbiotic thyasirids have adapted their physiology, while symbiotic thyasirids have adapted through mixotrophy. Our results highlight the significance of linking individual energetics and life-history traits to population dynamics and are the first step to-wards understanding the role of symbioses in population and community dynamics.

ecology↗
bioRxiv2020

A set of common movements within GPCR-G-protein complexes from variability analysis of cryo-EM datasets

G-protein coupled receptors (GPCRs) are among the most versatile signal transducers in the cell. Once activated, GPCRs sample a large conformational space and couple to G-proteins to initiate distinct signaling pathways. The dynamical behavior of GPCR-G-protein complexes is difficult characterize structurally. Here, we report on the use of variability analysis to characterize the inherent flexibility within the cryo-EM dataset of the rhodopsin-Gi-protein complex (Tsai et al., 2019), on which this article builds on. We compare the outcome of this analysis with recently published results obtained on the cannabinoid-Gi- and secretin-Gs-receptor complexes. Despite differences related to the biochemical compositions of the three samples, a set of consensus movements emerges. We anticipate that systematic variability analysis on GPCR-G-protein complexes may provide useful information not only at the biological level, but also for improving the preparation of more stable samples for cryo-EM single-particle analysis.

bioinformatics↗
bioRxiv2019

Cryo-EM structure of the rhodopsin-Gαi-βγ complex reveals binding of the rhodopsin C-terminal tail to the Gβ subunit

G protein-coupled receptors (GPCRs) are the largest class of integral membrane proteins and represent key targets for pharmacological research. GPCRs modulate cell physiology by engaging and activating a diversity of intracellular transducers, prominently heterotrimeric G proteins, but also G protein-receptor kinases (GRKs) and arrestins. The recent surge in the number of structures of GPCR-G protein complexes has expanded our understanding of G protein recognition and GPCR-mediated signal transduction. However, many aspects of these mechanisms, including the existence of transient interactions with transducers, have remained elusive. Here, we present the cryo-EM structure of the light-sensitive GPCR rhodopsin in complex with heterotrimeric Gi. In contrast to all reported structures, our density map reveals the receptor C-terminal tail bound to the G{beta} subunit of the G protein heterotrimer. This observation provides a structural foundation for the role of the C-terminal tail in GPCR signaling, and of G{beta} as scaffold for recruiting G subunits and GRKs. By comparing all available complex structures, we found a small set of common anchoring points that are G protein-subtype specific. Taken together, our structure and analysis provide new structural basis for the molecular events of the GPCR signaling pathway.

biochemistry↗