Collective behavior in the spatial spreading of obesity

Obesity prevalence is increasing in many countries at alarming levels. A difficulty in the conception of policies to reverse these trends is the identification of the drivers behind the obesity epidemics. Here, we implement a spatial spreading analysis to investigate whether obesity shows spatial correlations, revealing the effect of collective and global factors acting above individual choices. We find a regularity in the spatial fluctuations of their prevalence revealed by a pattern of scale-free long-range correlations. The fluctuations are anomalous, deviating in a fundamental way from the weaker correlations found in the underlying population distribution indicating the presence of collective behavior, i.e., individual habits may have negligible influence in shaping the patterns of spreading. Interestingly, we find the same scale-free correlations in economic activities associated with food production. These results motivate future interventions to investigate the causality of this relation providing guidance for the implementation of preventive health policies

Network resilience

Many systems on our planet are known to shift abruptly and irreversibly from one state to another when they are forced across a "tipping point," such as mass extinctions in ecological networks, cascading failures in infrastructure systems, and social convention changes in human and animal networks. Such a regime shift demonstrates a system's resilience that characterizes the ability of a system to adjust its activity to retain its basic functionality in the face of internal disturbances or external environmental changes. In the past 50 years, attention was almost exclusively given to low dimensional systems and calibration of their resilience functions and indicators of early warning signals without considerations for the interactions between the components. Only in recent years, taking advantages of the network theory and lavish real data sets, network scientists have directed their interest to the real-world complex networked multidimensional systems and their resilience function and early warning indicators. This report is devoted to a comprehensive review of resilience function and regime shift of complex systems in different domains, such as ecology, biology, social systems and infrastructure. We cover the related research about empirical observations, experimental studies, mathematical modeling, and theoretical analysis. We also discuss some ambiguous definitions, such as robustness, resilience, and stability.Comment: Review chapter

Network topology and community function in spatial microbial communities

Complex communities of microbes act collectively to regulate human health, provide sources of clean energy, and ripen aromatic cheese. The efficient functioning of these communities can be directly related to competitive and cooperative interactions between species. Physical constraints and local environment affect the stability of these interactions. Here we explore the role of spatial habitat and interaction networks in microbial ecology and human disease. In the first part of the dissertation, we model mutualism to understand how spatial microbial communities survive number fluctuations in physical habitats. We explicitly account for the production, consumption, and diffusion of public goods in a two-species microbial community. We show that increased sharing of nutrients breaks down coexistence, and that species may benefit from making slower-diffusing nutrients. In multi-species communities, indirect and higher order interactions may affect community function. We find that the requirement for spatial proximity severely restricts the network of possible microbial interactions. While cooperation between two species is stable, higher-order mutualism requiring three or more species succumbs easily to number fluctuations. Additional cyclic or reciprocal interactions between pairs can stabilize multi-species communities. Inter-species interactions also affect human health via the human microbiome: microbial communities in the gut, lungs and skin. In the second part of the dissertation, we use machine learning and statistics to establish links between microbiota abundance and composition, and the incidence of chronic diseases. We study the gut fungal profile to probe the effects of diet and fungal dysbiosis in a cohort of Saudi children with Crohn's disease. While statistical microbiome studies established that each disease phenotype is associated with a distinct state of intestinal dysbiosis, they often produced conflicting results and identified a very large number of microbes associated with disease. We show that a handful of taxa could drive the dynamics of ecosystem-level abundance changes due to strong inter-species interactions. Using maximum entropy methods, we propose a simple statistical approach (Direct Association Analysis or DAA) to account for interspecific interactions. When applied to the largest dataset on IBD, DAA detects a small subset of associations directly linked to the disease, avoids p-value inflation and identifies most predictive features of the microbiome

Psr1p interacts with SUN/sad1p and EB1/mal3p to establish the bipolar spindle

Regular Abstracts - Sunday Poster Presentations: no. 382During mitosis, interpolar microtubules from two spindle pole bodies (SPBs) interdigitate to create an antiparallel microtubule array for accommodating numerous regulatory proteins. Among these proteins, the kinesin-5 cut7p/Eg5 is the key player responsible for sliding apart antiparallel microtubules and thus helps in establishing the bipolar spindle. At the onset of mitosis, two SPBs are adjacent to one another with most microtubules running nearly parallel toward the nuclear envelope, creating an unfavorable microtubule configuration for the kinesin-5 kinesins. Therefore, how the cell organizes the antiparallel microtubule array in the first place at mitotic onset remains enigmatic. Here, we show that a novel protein psrp1p localizes to the SPB and plays a key role in organizing the antiparallel microtubule array. The absence of psr1+ leads to a transient monopolar spindle and massive chromosome loss. Further functional characterization demonstrates that psr1p is recruited to the SPB through interaction with the conserved SUN protein sad1p and that psr1p physically interacts with the conserved microtubule plus tip protein mal3p/EB1. These results suggest a model that psr1p serves as a linking protein between sad1p/SUN and mal3p/EB1 to allow microtubule plus ends to be coupled to the SPBs for organization of an antiparallel microtubule array. Thus, we conclude that psr1p is involved in organizing the antiparallel microtubule array in the first place at mitosis onset by interaction with SUN/sad1p and EB1/mal3p, thereby establishing the bipolar spindle.postprin

Removal of antagonistic spindle forces can rescue metaphase spindle length and reduce chromosome segregation defects

Regular Abstracts - Tuesday Poster Presentations: no. 1925Metaphase describes a phase of mitosis where chromosomes are attached and oriented on the bipolar spindle for subsequent segregation at anaphase. In diverse cell types, the metaphase spindle is maintained at a relatively constant length. Metaphase spindle length is proposed to be regulated by a balance of pushing and pulling forces generated by distinct sets of spindle microtubules and their interactions with motors and microtubule-associated proteins (MAPs). Spindle length appears important for chromosome segregation fidelity, as cells with shorter or longer than normal metaphase spindles, generated through deletion or inhibition of individual mitotic motors or MAPs, showed chromosome segregation defects. To test the force balance model of spindle length control and its effect on chromosome segregation, we applied fast microfluidic temperature-control with live-cell imaging to monitor the effect of switching off different combinations of antagonistic forces in the fission yeast metaphase spindle. We show that spindle midzone proteins kinesin-5 cut7p and microtubule bundler ase1p contribute to outward pushing forces, and spindle kinetochore proteins kinesin-8 klp5/6p and dam1p contribute to inward pulling forces. Removing these proteins individually led to aberrant metaphase spindle length and chromosome segregation defects. Removing these proteins in antagonistic combination rescued the defective spindle length and, in some combinations, also partially rescued chromosome segregation defects. Our results stress the importance of proper chromosome-to-microtubule attachment over spindle length regulation for proper chromosome segregation.postprin

Biophysical Tools to Study Cellular Mechanotransduction

The cell membrane is the interface that volumetrically isolates cellular components from the cell’s environment. Proteins embedded within and on the membrane have varied biological functions: reception of external biochemical signals, as membrane channels, amplification and regulation of chemical signals through secondary messenger molecules, controlled exocytosis, endocytosis, phagocytosis, organized recruitment and sequestration of cytosolic complex proteins, cell division processes, organization of the cytoskeleton and more. The membrane’s bioelectrical role is enabled by the physiologically controlled release and accumulation of electrochemical potential modulating molecules across the membrane through specialized ion channels (e.g., Na+, Ca2+, K+ channels). The membrane’s biomechanical functions include sensing external forces and/or the rigidity of the external environment through force transmission, specific conformational changes and/or signaling through mechanoreceptors (e.g., platelet endothelial cell adhesion molecule (PECAM), vascular endothelial (VE)-cadherin, epithelial (E)-cadherin, integrin) embedded in the membrane. Certain mechanical stimulations through specific receptor complexes induce electrical and/or chemical impulses in cells and propagate across cells and tissues. These biomechanical sensory and biochemical responses have profound implications in normal physiology and disease. Here, we discuss the tools that facilitate the understanding of mechanosensitive adhesion receptors. This article is structured to provide a broad biochemical and mechanobiology background to introduce a freshman mechano-biologist to the field of mechanotransduction, with deeper study enabled by many of the references cited herein

Allometric and fluctuation scaling in health, well-being and mortality

In the urban scaling hypothesis, it has been noted that cities exhibit self-similar behaviour. Much of the scaling literature focuses on cities, whilst not including rural regions. Initially, rural-urban population density scaling was investigated using a diverse set of indicators (crime, property, mortality and age) in England and Wales. These were fitted using either a single or segmented powerlaw (PL) model where preference was chosen using a Davies test and confirmed using both Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). In this study, it was found that most indicators exhibited a change point between rural-urban regions typically around 27 people per hectare. Mortality, for example, declined above the change point showing that urban regions have a ‘protective’ effect influenced by age demographics. Residuals obtained from the preferred PL model were analysed using methods such as hierarchical clustering and self-organising maps (SOM) displaying regional clusters, strong extensive correlations and disparities. The most interesting finding was that age demographics break the self-similarity behaviour that is a fundamental and underlying part of the urban scaling hypothesis. Scaling is usually cumulatively data over a large timeframe. Finer granularity of data is not easily accessible, although the COVID-19 pandemic was a unique opportunity to obtain and explore the scaling of daily data. It is thought that scaling exponent is slow changing and exhibited little fluctuation. However, it was found that COVID-19 cases revealed that the scaling exponents along with residual variance and skew varied with considerable complexity. Scaling exponents continually evolved and reversed where preference of propagation between ruralurban regions switched 6 times. Regional homogeneity occurred in periods with low variance where regions are located close to the PL. Contrary, regional heterogeneity occurred in periods with high variance where regions are located further away from the PL. Skew also exhibited both positive and 4 negative skew; both important features of propagation where the latter is not appreciated in the modelling of community propagation. Positive skew indicates a long tail of ‘hotspots’ and ‘superspreading’ events whilst negative skew indicates a long tail of ‘cold spots’ and ‘super-isolators’. In contrast, COVID-19 deaths exhibit near constant scale, variance and skew despite the extended studied timeframe, government intervention, different testing regimes and the national vaccination programme. This was also evident in the regions position relative to the PL where it remained either below or above the expectation throughout the pandemic. In the initial study of COVID-19, residual variance did not meet the conditions of standard linear regression. The variance expanded and contracted over time and residual distributions included both positive and negative skew, thus, normality and homoscedasticity assumptions of standard linear regression were not always met. This investigation stimulated the development of the generalised logistic distribution (GLD) within a Bayesian framework to model expectation and dispersion using Markov chain Monte Carlo (MCMC) methods. The advantage of the GLD is its flexibility when looking at skewed or otherwise nonnormally distributed data. The GLD regression model and its key features are demonstrated using COVID-19 data. However, the proposed framework will benefit a range of systems with linear structure. The additional dispersion regression coefficients account for heteroscedasticity together with the parameters of the GLD to provide more realistic shapes (e.g., skew). The normal regression model assumes a normally distributed homoscedastic system, producing relatively large model bias, relative to the improved GLD regression model. Gelman-Rubin diagnostics and deviance information criterion (DIC) was included in the proposed framework showing good convergence and data were explained well by the fitted GLD regression model