The demographic consequences of mutualism: ants increase host-plant fruit production but not population growth

The impact of mutualists on a partner’s demography depends on how they affect the partner’s multiple vital rates and how those vital rates, in turn, affect population growth. However, mutualism studies rarely measure effects on multiple vital rates or integrate them to assess the ultimate impact on population growth. We used vital rate data, population models and simulations of long-term population dynamics to quantify the demographic impact of a guild of ant species on the plant Ferocactus wislizeni. The ants feed at the plant’s extrafloral nectaries and attack herbivores attempting to consume reproductive organs. Ant-guarded plants produced significantly more fruit, but ants had no significant effect on individual growth or survival. After integrating ant effects across these vital rates, we found that projected population growth was not significantly different between unguarded and ant-guarded plants because population growth was only weakly influenced by differences in fruit production (though strongly influenced by differences in individual growth and survival). However, simulations showed that ants could positively affect long-term plant population dynamics through services provided during rare but important events (herbivore outbreaks that reduce survival or years of high seedling recruitment associated with abundant precipitation). Thus, in this seemingly clear example of mutualism, the interaction may actually yield no clear benefit to plant population growth, or if it does, may only do so through the actions of the ants during rare events. These insights demonstrate the value of taking a demographic approach to studying the consequences of mutualism.     

Brain signal predictions from multi-scale networks using a linearized framework

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 10⁶ neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials (‘spikes’) or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin.Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for.The work also constitutes a major advance in computational efficiency of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.     

Increased sensitivity to dietary cholesterol in diabetic and hypothyroid rats associated with low levels of hepatic HMG-CoA reductase expression

We recently postulated that hepatic 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase functions as a cholesterol buffer to protect against the serum and tissue cholesterol raising action of dietary cholesterol. This postulate predicts that diminished basal expression of hepatic HMG-CoA reductase results in increased sensitivity to dietary cholesterol. Because diabetic and hypothyroid animals are known to have markedly reduced hepatic HMG-CoA reductase, these animals were selected as models to test our postulate. When rats were rendered diabetic with streptozotocin, their hepatic HMG-CoA reductase activity decreased from 314 to 22 pmol. min(-1). mg(-1), and their serum cholesterol levels increased slightly. When the diabetic animals were challenged with a diet containing 1% cholesterol, their serum cholesterol levels doubled, and their hepatic reductase activity decreased further to 0.9 pmol. min(-1). mg(-1). Hepatic low-density lipoprotein (LDL) receptor immunoreactive protein levels were unaffected in the diabetic rats whether fed cholesterol-supplemented diets or not. In rats rendered hypothyroid by thyroparathyroidectomy, serum cholesterol levels rose from 100 to 386 mg/dl in response to the 1% cholesterol challenge, whereas HMG-CoA reductase activity dropped from 33.8 to 3.4 pmol. min(-1). mg(-1). Hepatic LDL receptor immunoreactive protein levels decreased only slightly in the hypothyroid rats fed cholesterol-supplemented diets. Taken together, these results show that rats deficient in either insulin or thyroid hormone are extremely sensitive to dietary cholesterol largely due to low basal expression of hepatic HMG-CoA reductase.     

Contrasting exotic Solenopsis invicta and native Forelius pruinosus ants as mutualists with Catalpa bignonioides, a native plant

Abstract. 1. The suitability of the red imported fire ant Solenopsis invicta Buren and a native ant Forelius pruinosus (Roger) as participants in a food-for-protection mutualism with a native nectaried tree Catalpa bignonioides Walter was compared.
2. The mean mortality of folivore larvae of Ceratomia catalpae Boisduval was similar for S. invicta and F. pruinosus although S. invicta attacked fewer caterpillar aggregations and was a devastating pupal predator. Solenopsis invicta also differed from the native ant in that it attacked the parasitoid Cotesia congregata Say, another plant mutualist, and visited extrafloral nectaries less frequently.
3. Habitats invaded by S. invicta are characterised by a scarcity of both herbivores and of beneficial insects that visit extrafloral nectaries. The plants do not require protection, and extrafloral nectaries are visited rarely. Although plants are defended incidentally by S. invicta , the insect-plant mutualism therein is greatly simplified or defunct.     

High-order SNP combinations associated with complex diseases: efficient discovery, statistical power and functional interactions

There has been increased interest in discovering combinations of single-nucleotide polymorphisms (SNPs) that are strongly associated with a phenotype even if each SNP has little individual effect. Efficient approaches have been proposed for searching two-locus combinations from genome-wide datasets. However, for high-order combinations, existing methods either adopt a brute-force search which only handles a small number of SNPs (up to few hundreds), or use heuristic search that may miss informative combinations. In addition, existing approaches lack statistical power because of the use of statistics with high degrees-of-freedom and the huge number of hypotheses tested during combinatorial search. Due to these challenges, functional interactions in high-order combinations have not been systematically explored. We leverage discriminative-pattern-mining algorithms from the data-mining community to search for high-order combinations in case-control datasets. The substantially improved efficiency and scalability demonstrated on synthetic and real datasets with several thousands of SNPs allows the study of several important mathematical and statistical properties of SNP combinations with order as high as eleven. We further explore functional interactions in high-order combinations and reveal a general connection between the increase in discriminative power of a combination over its subsets and the functional coherence among the genes comprising the combination, supported by multiple datasets. Finally, we study several significant high-order combinations discovered from a lung-cancer dataset and a kidney-transplant-rejection dataset in detail to provide novel insights on the complex diseases. Interestingly, many of these associations involve combinations of common variations that occur in small fractions of population. Thus, our approach is an alternative methodology for exploring the genetics of rare diseases for which the current focus is on individually rare variations.     

Radiation inactivation analysis of enzymes. Effect of free radical scavengers on apparent target sizes

In most cases the apparent target size obtained by radiation inactivation analysis corresponds to the subunit size or to the size of a multimeric complex. In this report, we examined whether the larger than expected target sizes of some enzymes could be due to secondary effects of free radicals. To test this proposal we carried out radiation inactivation analysis on Escherichia coli DNA polymerase I, Torula yeast glucose-6-phosphate dehydrogenase, Chlorella vulgaris nitrate reductase, and chicken liver sulfite oxidase in the presence and absence of free radical scavengers (benzoic acid and mannitol). In the presence of free radical scavengers, inactivation curves are shifted toward higher radiation doses. Plots of scavenger concentration versus enzyme activity showed that the protective effect of benzoic acid reached a maximum at 25 mM then declined. Mannitol alone had little effect, but appeared to broaden the maximum protective range of benzoic acid relative to concentration. The apparent target size of the polymerase activity of DNA polymerase I in the presence of free radical scavengers was about 40% of that observed in the absence of these agents. This is considerably less than the minimum polypeptide size and may reflect the actual size of the polymerase functional domain. Similar effects, but of lesser magnitude, were observed for glucose-6-phosphate dehydrogenase, nitrate reductase, and sulfite oxidase. These results suggest that secondary damage due to free radicals generated in the local environment as a result of ionizing radiation can influence the apparent target size obtained by this method.     

Survival of Escherichia coli cells exposed to iodoacetate and chlorodinitrobenzene is independent of the glutathione-gated K+ efflux systems KefB and KefC.

The KefB and KefC systems of Escherichia coli cells are activated by iodoacetate (IOA) and chlorodinitrobenzene (CDNB), leading to a rapid drop in the intracellular pH. However, survival of exposure to IOA or CDNB was found to be essentially independent of KefB and KefC activation. No correlation was found between the toxicity of the compound and its ability to elicit protective acidification via activation of KefB and KefC.