Interindividual variation among Culex pipiens larvae in terms of thermal response.

This study aims to determine the phenological characteristics of thermal responses in the larvae of a Culex pipiens complex field population at the individual level under the influence of thermal regime of its habitat. The analysis is based on a structured population model quantifying the thermal responses of development time and survival under variable conditions and characterising the variety between the larvae (interindividual variety). During the study performed in Turkish Thrace on a monthly basis between May 2021 and June 2022, a total of 3744 larvae were reared as peer larval cohorts and 2330 larvae as siblings in artificial containers to be fully exposed to the natural thermal condition that was recorded hourly. The development process of larvae was monitored daily from egg to adult. As a result, a total of 4788 adult mosquitoes emerged, with a development period ranging from 8 to 52 days in the females and 7 to 50 days in the males, and the survival rate was found to range from 0% to 100%. Both parameters varied by month and individuals, and the variations manifested itself, particularly in the colder periods. The results indicate that the variation between the individuals in terms of thermal response in the larvae of C. pipiens, along with the thermal acclimation ability, appears to be fate determinant in resisting fluctuating thermal regimes, surviving in concurrent climate change and adapting to new conditions with modifications in the seasonal phenology, such as maintaining reproductive dynamics throughout the winter thanks to global warming.

Climate change and human health in the Eastern Mediterranean and Middle East: Literature review, research priorities and policy suggestions.

Human health is linked to climatic factors in complex ways, and climate change can have profound direct and indirect impacts on the health status of any given region. Susceptibility to climate change is modulated by biological, ecological and socio-political factors such as age, gender, geographic location, socio-economic status, occupation, health status and housing conditions, among other. In the Eastern Mediterranean and Middle East (EMME), climatic factors known to affect human health include extreme heat, water shortages and air pollution. Furthermore, the epidemiology of vector-borne diseases (VBDs) and the health consequences of population displacement are also influenced by climate change in this region. To inform future policies for adaptation and mitigation measures, and based on an extensive review of the available knowledge, we recommend several research priorities for the region. These include the generation of more empirical evidence on exposure-response functions involving climate change and specific health outcomes, the development of appropriate methodologies to evaluate the physical and psychological effects of climate change on vulnerable populations, determining how climate change alters the ecological determinants of human health, improving our understanding of the effects of long-term exposure to heat stress and air pollution, and evaluating the interactions between adaptation and mitigation strategies. Because national boundaries do not limit most climate-related factors expected to impact human health, we propose that adaptation/mitigation policies must have a regional scope, and therefore require collaborative efforts among EMME nations. Policy suggestions include a decisive region-wide decarbonisation, the integration of environmentally driven morbidity and mortality data throughout the region, advancing the development and widespread use of affordable technologies for the production and management of drinking water by non-traditional means, the development of comprehensive strategies to improve the health status of displaced populations, and fostering regional networks for monitoring and controlling the spread of infectious diseases and disease vectors.

Evidence for activation of the unfolded protein response in collagen IV nephropathies.

Thin-basement-membrane nephropathy (TBMN) and Alport syndrome (AS) are progressive collagen IV nephropathies caused by mutations in COL4A3/A4/A5 genes. These nephropathies invariably present with microscopic hematuria and frequently progress to proteinuria and CKD or ESRD during long-term follow-up. Nonetheless, the exact molecular mechanisms by which these mutations exert their deleterious effects on the glomerulus remain elusive. We hypothesized that defective trafficking of the COL4A3 chain causes a strong intracellular effect on the cell responsible for COL4A3 expression, the podocyte. To this end, we overexpressed normal and mutant COL4A3 chains (G1334E mutation) in human undifferentiated podocytes and tested their effects in various intracellular pathways using a microarray approach. COL4A3 overexpression in the podocyte caused chain retention in the endoplasmic reticulum (ER) that was associated with activation of unfolded protein response (UPR)-related markers of ER stress. Notably, the overexpression of normal or mutant COL4A3 chains differentially activated the UPR pathway. Similar results were observed in a novel knockin mouse carrying the Col4a3-G1332E mutation, which produced a phenotype consistent with AS, and in biopsy specimens from patients with TBMN carrying a heterozygous COL4A3-G1334E mutation. These results suggest that ER stress arising from defective localization of collagen IV chains in human podocytes contributes to the pathogenesis of TBMN and AS through activation of the UPR, a finding that may pave the way for novel therapeutic interventions for a variety of collagenopathies.

Machine Learning Modeling of Aedes albopictus Habitat Suitability in the 21st Century.

The Asian tiger mosquito, Aedes albopictus, is an important vector of arboviruses that cause diseases such as dengue, chikungunya, and zika. The vector is highly invasive and adapted to survive in temperate northern territories outside its native tropical and sub-tropical range. Climate and socio-economic change are expected to facilitate its range expansion and exacerbate the global vector-borne disease burden. To project shifts in the global habitat suitability of the vector, we developed an ensemble machine learning model, incorporating a combination of a Random Forest and XGBoost binary classifiers, trained with a global collection of vector surveillance data and an extensive set of climate and environmental constraints. We demonstrate the reliable performance and wide applicability of the ensemble model in comparison to the known global presence of the vector, and project that suitable habitats will expand globally, most significantly in the northern hemisphere, putting at least an additional billion people at risk of vector-borne diseases by the middle of the 21st century. We project several highly populated areas of the world will be suitable for Ae. albopictus populations, such as the northern parts of the USA, Europe, and India by the end of the century, which highlights the need for coordinated preventive surveillance efforts of potential entry points by local authorities and stakeholders.

Entomological surveillance and spatiotemporal risk assessment of sand fly-borne diseases in Cyprus.

Visceral and cutaneous leishmaniases are important public health concerns in Cyprus. Although the diseases, historically prevalent on the island, were nearly eradicated by 1996, an increase in frequency and geographical spread has recently been recorded. Upward trends in leishmaniasis prevalence have largely been attributed to environmental changes that amplify the abundance and activity of its vector, the phlebotomine sand flies. Here, we performed an extensive field study across the island to map the sand fly fauna and compared the presence and distribution of the species found with historical records. We mapped the habitat preferences of Phlebotomus papatasi and P. tobbi, two medically important species, and predicted the seasonal abundance of P. papatasi at unprecedented spatiotemporal resolution using a climate-sensitive population dynamics model driven by high-resolution meteorological forecasting. Our compendium holds a record of 18 species and the locations of a subset, including those of potential public and veterinary health concern. We confirmed that P. papatasi is widespread, especially in densely urbanized areas, and predicted that its abundance uniformly peaks across the island at the end of summer. We identified potential hotspots of P. papatasi activity even after this peak. Our results form a foundation to inform public health planning and contribute to the development of effective, efficient, and environmentally sensitive strategies to control sand fly populations and prevent sand fly-borne diseases.

A dynamically structured matrix population model for insect life histories observed under variable environmental conditions.

Various environmental drivers influence life processes of insect vectors that transmit human disease. Life histories observed under experimental conditions can reveal such complex links; however, designing informative experiments for insects is challenging. Furthermore, inferences obtained under controlled conditions often extrapolate poorly to field conditions. Here, we introduce a pseudo-stage-structured population dynamics model to describe insect development as a renewal process with variable rates. The model permits representing realistic life stage durations under constant and variable environmental conditions. Using the model, we demonstrate how random environmental variations result in fluctuating development rates and affect stage duration. We apply the model to infer environmental dependencies from the life history observations of two common disease vectors, the southern (Culex quinquefasciatus) and northern (Culex pipiens) house mosquito. We identify photoperiod, in addition to temperature, as pivotal in regulating larva stage duration, and find that carefully timed life history observations under semi-field conditions accurately predict insect development throughout the year. The approach we describe augments existing methods of life table design and analysis, and contributes to the development of large-scale climate- and environment-driven population dynamics models for important disease vectors.

The impact of climatic factors on tick-related hospital visits and borreliosis incidence rates in European Russia.

Tick-borne diseases are among the challenges associated with warming climate. Many studies predict, and already note, expansion of ticks' habitats to the north, bringing previously non-endemic diseases, such as borreliosis and encephalitis, to the new areas. In addition, higher temperatures accelerate phases of ticks' development in areas where ticks have established populations. Earlier works have shown that meteorological parameters, such as temperature and humidity influence ticks' survival and define their areas of habitat. Here, we study the link between climatic parameters and tick-related hospital visits as well as borreliosis incidence rates focusing on European Russia. We have used yearly incidence rates of borreliosis spanning a period of 20 years (1997-2016) and weekly tick-related hospital visits spanning two years (2018-2019). We identify regions in Russia characterized by similar dynamics of incidence rates and dominating tick species. For each cluster, we find a set of climatic parameters that are significantly correlated with the incidence rates, though a linear regression approach using exclusively climatic parameters to incidence prediction was less than 50% effective. On a weekly timescale, we find correlations of different climatic parameters with hospital visits. Finally, we trained two long short-term memory neural network models to project the tick-related hospital visits until the end of the century, under the RCP8.5 climate scenario, and present our findings in the evolution of the tick season length for different regions in Russia. Our results show that the regions with an expected increase in both tick season length and borreliosis incidence rates are located in the southern forested areas of European Russia. Oppositely, our projections suggest no prolongation of the tick season length in the northern areas with already established tick population.

ABC-SysBio--approximate Bayesian computation in Python with GPU support.

MOTIVATION: The growing field of systems biology has driven demand for flexible tools to model and simulate biological systems. Two established problems in the modeling of biological processes are model selection and the estimation of associated parameters. A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions. RESULTS: Here we present a Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework. ABC-SysBio combines three algorithms: ABC rejection sampler, ABC SMC for parameter inference and ABC SMC for model selection. It is designed to work with models written in Systems Biology Markup Language (SBML). Deterministic and stochastic models can be analyzed in ABC-SysBio. AVAILABILITY: http://abc-sysbio.sourceforge.net

A climate-driven and field data-assimilated population dynamics model of sand flies.

Sand flies are responsible for the transmission of leishmaniasis, a neglected tropical disease claiming more than 50,000 lives annually. Leishmaniasis is an emerging health risk in tropical and Mediterranean countries as well as temperate regions in North America and Europe. There is an increasing demand for predicting population dynamics and spreading of sand flies to support management and control, yet phenotypic diversity and complex environmental dependence hamper model development. Here, we present the principles for developing predictive species-specific population dynamics models for important disease vectors. Based on these principles, we developed a sand fly population dynamics model with a generic structure where model parameters are inferred using a surveillance dataset collected from Greece and Cyprus. The model incorporates distinct life stages and explicit dependence on a carefully selected set of environmental variables. The model successfully replicates the observations and demonstrates high predictive capacity on the validation dataset from Turkey. The surveillance datasets inform about biological processes, even in the absence of laboratory experiments. Our findings suggest that the methodology can be applied to other vector species to predict abundance, control dispersion, and help to manage the global burden of vector-borne diseases.

Statistical interpretation of the interplay between noise and chaos in the stochastic logistic map.

A recurring problem in population biology--as well as other stochastic dynamical systems in biology, the physical and social sciences--is the distinction between the 'true' dynamics of a system and observational noise: i.e. can we from present data reliably infer e.g. biological mechanisms, or are signals swamped by noise. Here, we approach this problem using the canonical model for simple systems that exhibit complex behaviour, the logistic map. At each time-point noise is added, which allows us to study the long-term behaviour of a system which exhibits both non-linear dynamics and intrinsic noise. We show that the interplay between deterministic non-linear dynamics and simple Gaussian noise results in a perplexingly simple system when viewed statistically. In particular we show that for the case of Gaussian noise it is possible to derive at very reliable approximations for the time until the system has reached an absorbing state. This generic model allows us, for example, to study the life-time of molecular species involved in noisy feedback loops.

sPop: Age-structured discrete-time population dynamics model in C, Python, and R.

This article describes the sPop packages implementing the deterministic and stochastic versions of an age-structured discrete-time population dynamics model. The packages enable mechanistic modelling of a population by monitoring the age and development stage of each individual. Survival and development are included as the main effectors and they progress at a user-defined pace: follow a fixed rate, delay for a given time, or progress at an age-dependent manner. The model is implemented in C, Python, and R with a uniform design to ease usage and facilitate adoption. Early versions of the model were previously employed for investigating climate-driven population dynamics of the tiger mosquito and the chikungunya disease spread by this vector. The sPop packages presented in this article enable the use of the model in a range of applications extending from vector-borne diseases towards any age-structured population including plant and animal populations, microbial dynamics, host-pathogen interactions, infectious diseases, and other time-dependent epidemiological processes.

A large-scale stochastic spatiotemporal model for Aedes albopictus-borne chikungunya epidemiology.

Chikungunya is a viral disease transmitted to humans primarily via the bites of infected Aedes mosquitoes. The virus caused a major epidemic in the Indian Ocean in 2004, affecting millions of inhabitants, while cases have also been observed in Europe since 2007. We developed a stochastic spatiotemporal model of Aedes albopictus-borne chikungunya transmission based on our recently developed environmentally-driven vector population dynamics model. We designed an integrated modelling framework incorporating large-scale gridded climate datasets to investigate disease outbreaks on Reunion Island and in Italy. We performed Bayesian parameter inference on the surveillance data, and investigated the validity and applicability of the underlying biological assumptions. The model successfully represents the outbreak and measures of containment in Italy, suggesting wider applicability in Europe. In its current configuration, the model implies two different viral strains, thus two different outbreaks, for the two-stage Reunion Island epidemic. Characterisation of the posterior distributions indicates a possible relationship between the second larger outbreak on Reunion Island and the Italian outbreak. The model suggests that vector control measures, with different modes of operation, are most effective when applied in combination: adult vector intervention has a high impact but is short-lived, larval intervention has a low impact but is long-lasting, and quarantining infected territories, if applied strictly, is effective in preventing large epidemics. We present a novel approach in analysing chikungunya outbreaks globally using a single environmentally-driven mathematical model. Our study represents a significant step towards developing a globally applicable Ae. albopictus-borne chikungunya transmission model, and introduces a guideline for extending such models to other vector-borne diseases.

Neonatal Nav1.5 protein expression in normal adult human tissues and breast cancer.

Expression of the neonatal splice variant of the voltage-gated sodium channel α-subunit (VGSC) subtype Nav1.5 (nNav1.5), encoded by the gene SCN5A, was shown earlier to be upregulated in human breast cancer (BCa), both in vitro and in vivo. Channel activity promoted BCa invasion of Matrigel®in vitro and metastasis in vivo. Consequently, expression of nNav1.5 has been proposed as a functional biomarker of BCa cells with metastatic potential. Here, we have determined immunohistochemically both nNav1.5 and total VGSC (tVGSC) protein expression in a range of adult human tissues. Some VGSC protein was expressed in normal colon, small intestine, stomach, prostate, bladder and breast. As expected, high levels of VGSC protein were expressed in brain, skeletal muscle and cardiac muscle. On the other hand, nNav1.5 protein was not expressed in any of the normal tissues tested except breast where a low-level of protein was present. In comparison to normal breast, nNav1.5 protein expression in BCa was consistently widespread and occurred at a significantly higher level. We also questioned whether there was any relationship between the nNav1.5 protein expression and the estrogen receptor (ERα) status of BCa and obtained the following results. First, all cases lacking nNav1.5 were positive for ERα. Second, in all ERα-negative tissues, nNav1.5 protein was expressed in plasma membrane. Third, however, in ERα-positive cases, nNav1.5 protein expression was observed in both plasma membrane and cytoplasm. In conclusion, nNav1.5 protein has a restricted expression pattern among human tissues. High level expression occurs in BCa and associates with ERα status. These results further support the proposition that nNav1.5 is a novel biomarker of metastatic BCa.

Citrate enhances in vitro metastatic behaviours of PC-3M human prostate cancer cells: status of endogenous citrate and dependence on aconitase and fatty acid synthase.

Prostate is a unique organ that produces and releases large amounts of citrate. This is reduced significantly in cancer and it is possible that citrate is (re)taken up and used as a metabolite to enhance cellular activity. The main purpose of this study was to determine how cytosolic citrate might affect in vitro metastatic cell behaviours (lateral motility, endocytosis and adhesion). Normal (PNT2-C2) and metastatic (PC-3M) human prostate cancer cells were used in a comparative approach. As regards intermediary metabolic enzymes, aconitase and fatty acid synthase, already implicated in prostate cancer, were evaluated. The level of intracellular citrate was significantly higher in PNT2-C2 cells under both control conditions and following preincubation in extracellular citrate. Supply of exogenous citrate enhanced endocytosis, lateral motility, decreased cell adhesion of PC-3M cells but failed to produce any effect on normal cells. Real-time PCR measurements showed that the mRNA levels of mitochondrial and cytosolic aconitases and fatty acid synthase were significantly higher in PC-3M cells. Correspondingly, aconitase activity was also higher in PC-3M cells. Using cerulenin (an inhibitor of fatty acid synthase), oxalomalate and fluorocitrate (inhibiting aconitases), we investigated the dependence of citrate-induced down-regulation of cellular adhesion on aconitase and fatty acid synthase activities. It was concluded: (1) that strongly metastatic PC-3M cells stored less/utilised more cytosolic citrate than the normal PNT2-C2 cells and (2) that cancer cells could metabolise cytoplasmic citrate via aconitase and fatty acid synthase to enhance their metastatic behaviour.

Large-Scale Modelling of the Environmentally-Driven Population Dynamics of Temperate Aedes albopictus (Skuse).

The Asian tiger mosquito, Aedes albopictus, is a highly invasive vector species. It is a proven vector of dengue and chikungunya viruses, with the potential to host a further 24 arboviruses. It has recently expanded its geographical range, threatening many countries in the Middle East, Mediterranean, Europe and North America. Here, we investigate the theoretical limitations of its range expansion by developing an environmentally-driven mathematical model of its population dynamics. We focus on the temperate strain of Ae. albopictus and compile a comprehensive literature-based database of physiological parameters. As a novel approach, we link its population dynamics to globally-available environmental datasets by performing inference on all parameters. We adopt a Bayesian approach using experimental data as prior knowledge and the surveillance dataset of Emilia-Romagna, Italy, as evidence. The model accounts for temperature, precipitation, human population density and photoperiod as the main environmental drivers, and, in addition, incorporates the mechanism of diapause and a simple breeding site model. The model demonstrates high predictive skill over the reference region and beyond, confirming most of the current reports of vector presence in Europe. One of the main hypotheses derived from the model is the survival of Ae. albopictus populations through harsh winter conditions. The model, constrained by the environmental datasets, requires that either diapausing eggs or adult vectors have increased cold resistance. The model also suggests that temperature and photoperiod control diapause initiation and termination differentially. We demonstrate that it is possible to account for unobserved properties and constraints, such as differences between laboratory and field conditions, to derive reliable inferences on the environmental dependence of Ae. albopictus populations.

Modelling Anopheles gambiae s.s. Population Dynamics with Temperature- and Age-Dependent Survival.

Climate change and global warming are emerging as important threats to human health, particularly through the potential increase in vector- and water-borne diseases. Environmental variables are known to affect substantially the population dynamics and abundance of the poikilothermic vectors of disease, but the exact extent of this sensitivity is not well established. Focusing on malaria and its main vector in Africa, Anopheles gambiae sensu stricto, we present a set of novel mathematical models of climate-driven mosquito population dynamics motivated by experimental data suggesting that in An. gambiae, mortality is temperature and age dependent. We compared the performance of these models to that of a "standard" model ignoring age dependence. We used a longitudinal dataset of vector abundance over 36 months in sub-Saharan Africa for comparison between models that incorporate age dependence and one that does not, and observe that age-dependent models consistently fitted the data better than the reference model. This highlights that including age dependence in the vector component of mosquito-borne disease models may be important to predict more reliably disease transmission dynamics. Further data and studies are needed to enable improved fitting, leading to more accurate and informative model predictions for the An. gambiae malaria vector as well as for other disease vectors.

A mathematical model of the unfolded protein stress response reveals the decision mechanism for recovery, adaptation and apoptosis.

BACKGROUND: The unfolded protein response (UPR) is a major signalling cascade acting in the quality control of protein folding in the endoplasmic reticulum (ER). The cascade is known to play an accessory role in a range of genetic and environmental disorders including neurodegenerative and cardiovascular diseases, diabetes and kidney diseases. The three major receptors of the ER stress involved with the UPR, i.e. IRE1 α, PERK and ATF6, signal through a complex web of pathways to convey an appropriate response. The emerging behaviour ranges from adaptive to maladaptive depending on the severity of unfolded protein accumulation in the ER; however, the decision mechanism for the switch and its timing have so far been poorly understood. RESULTS: Here, we propose a mechanism by which the UPR outcome switches between survival and death. We compose a mathematical model integrating the three signalling branches, and perform a comprehensive bifurcation analysis to investigate possible responses to stimuli. The analysis reveals three distinct states of behaviour, low, high and intermediate activity, associated with stress adaptation, tolerance, and the initiation of apoptosis. The decision to adapt or destruct can, therefore, be understood as a dynamic process where the balance between the stress and the folding capacity of the ER plays a pivotal role in managing the delivery of the most appropriate response. The model demonstrates for the first time that the UPR is capable of generating oscillations in translation attenuation and the apoptotic signals, and this is supplemented with a Bayesian sensitivity analysis identifying a set of parameters controlling this behaviour. CONCLUSIONS: This work contributes largely to the understanding of one of the most ubiquitous signalling pathways involved in protein folding quality control in the metazoan ER. The insights gained have direct consequences on the management of many UPR-related diseases, revealing, in addition, an extended list of candidate disease modifiers. Demonstration of stress adaptation sheds light to how preconditioning might be beneficial in manifesting the UPR outcome to prevent untimely apoptosis, and paves the way to novel approaches for the treatment of many UPR-related conditions.

A miR-1207-5p binding site polymorphism abolishes regulation of HBEGF and is associated with disease severity in CFHR5 nephropathy.

Heparin binding epidermal growth factor (HBEGF) is expressed in podocytes and was shown to play a role in glomerular physiology. MicroRNA binding sites on the 3'UTR of HBEGF were predicted using miRWalk algorithm and followed by DNA sequencing in 103 patients diagnosed with mild or severe glomerulopathy. A single nucleotide polymorphism, miRSNP C1936T (rs13385), was identified at the 3'UTR of HBEGF that corresponds to the second base of the hsa-miR-1207-5p seed region. When AB8/13 undifferentiated podocytes were transfected with miRNA mimics of hsa-miR-1207-5p, the HBEGF protein levels were reduced by about 50%. A DNA fragment containing the miRSNP allele-1936C was cloned into the pMIR-Report Luciferase vector and co-transfected with miRNA mimics of hsa-miR-1207-5p into AB8/13 podocytes. In agreement with western blot data, this resulted in reduced luciferase expression demonstrating the ability of hsa-miR-1207-5p to directly regulate HBEGF expression. On the contrary, in the presence of the miRSNP 1936T allele, this regulation was abolished. Collectively, these results demonstrate that variant 1936T of this miRSNP prevents hsa-miR-1207-5p from down-regulating HBEGF in podocytes. We hypothesized that this variant has a functional role as a genetic modifier. To this end, we showed that in a cohort of 78 patients diagnosed with CFHR5 nephropathy (also known as C3-glomerulopathy), inheritance of miRSNP 1936T allele was significantly increased in the group demonstrating progression to chronic renal failure on long follow-up. No similar association was detected in a cohort of patients with thin basement membrane nephropathy. This is the first report associating a miRSNP as genetic modifier to a monogenic renal disorder.

Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models.

The size and complexity of cellular systems make building predictive models an extremely difficult task. In principle dynamical time-course data can be used to elucidate the structure of the underlying molecular mechanisms, but a central and recurring problem is that many and very different models can be fitted to experimental data, especially when the latter are limited and subject to noise. Even given a model, estimating its parameters remains challenging in real-world systems. Here we present a comprehensive analysis of 180 systems biology models, which allows us to classify the parameters with respect to their contribution to the overall dynamical behaviour of the different systems. Our results reveal candidate elements of control in biochemical pathways that differentially contribute to dynamics. We introduce sensitivity profiles that concisely characterize parameter sensitivity and demonstrate how this can be connected to variability in data. Systematically linking data and model sloppiness allows us to extract features of dynamical systems that determine how well parameters can be estimated from time-course measurements, and associates the extent of data required for parameter inference with the model structure, and also with the global dynamical state of the system. The comprehensive analysis of so many systems biology models reaffirms the inability to estimate precisely most model or kinetic parameters as a generic feature of dynamical systems, and provides safe guidelines for performing better inferences and model predictions in the context of reverse engineering of mathematical models for biological systems.