Investigating the predictability of essential genes across distantly related organisms using an integrative approach

Rapid and accurate identification of new essential genes in under-studied microorganisms will significantly improve our understanding of how a cell works and the ability to re-engineer microorganisms. However, predicting essential genes across distantly related organisms remains a challenge. Here, we present a machine learning-based integrative approach that reliably transfers essential gene annotations between distantly related bacteria. We focused on four bacterial species that have well-characterized essential genes, and tested the transferability between three pairs among them. For each pair, we trained our classifier to learn traits associated with essential genes in one organism, and applied it to make predictions in the other. The predictions were then evaluated by examining the agreements with the known essential genes in the target organism. Ten-fold cross-validation in the same organism yielded AUC scores between 0.86 and 0.93. Cross-organism predictions yielded AUC scores between 0.69 and 0.89. The transferability is likely affected by growth conditions, quality of the training data set and the evolutionary distance. We are thus the first to report that gene essentiality can be reliably predicted using features trained and tested in a distantly related organism. Our approach proves more robust and portable than existing approaches, significantly extending our ability to predict essential genes beyond orthologs.     

Density-dependent population dynamics in clonal organisms: a modelling approach

1. Density dependence may act at several stages in an organisms life-cycle (e.g. on mortality, fecundity, etc.), but not all density-dependent processes necessarily regulate population size. In this paper I use a density manipulation experiment to determine the effects of density on the transition rates between different size classes of the clonal zoanthid Palythoa caesia Dana 1846. I then formulate a density-dependent matrix model of population dynamics of Palythoa , and perform a series of sensitivity analyses on the model to determine at what stage in the life-cycle regulation acts.
2. Seven of the 16 transition probabilities decreased with density, most of them being shrinkage (due to loss of tissue or fission) and stasis (the self–self transition) of medium and large colonies. The only probability to increase was for the stasis of large colonies. Recruitment was quadratically dependent on density, peaking at intermediate densities.
3. Equilibrium cover in the model was 84% and was reached in ≈40 years. To determine which density-dependent transitions were involved in population regulation, the strength of density dependence was varied in each independently. This sensitivity analysis showed that only changes in the probabilities of large colonies remaining large and producing medium colonies, were regulating.
4. These results suggest that regulation is primarily acting on fission of large colonies to produce intermediate-sized colonies, in combination with size specific growth rates. Fission rates decrease greatly with density, resulting in a greater proportion of large colonies at high densities and large colonies grow more slowly than small. Overall, this behaviour is very similar to that of clonal plants which have a phalanx type life history.     

Advances in Ecological Speciation: an integrative approach

The role of natural selection in promoting reproductive isolation has received substantial renewed interest within the last two decades. As a consequence, the study of ecological speciation has become an extremely productive research area in modern evolutionary biology. Recent innovations in sequencing technologies offer an unprecedented opportunity to study the mechanisms involved in ecological speciation. Genome scans provide significant insights but have some important limitations; efforts are needed to integrate them with other approaches to make full use of the sequencing data deluge. An international conference ‘Advances in Ecological Speciation’ organized by the University of Porto (Portugal) aimed to review current progress in ecological speciation. Using some of the examples presented at the conference, we highlight the benefits of integrating ecological and genomic data and discuss different mechanisms of parallel evolution. Finally, future avenues of research are suggested to advance our knowledge concerning the role of natural selection in the establishment of reproductive isolation during ecological speciation.     

Natural product sciences: an integrative approach to the innovations of plant natural products

Science China Life Sciences - The study on plant natural products not only helps us understand that their structural diversity is the inevitable result of plant species diversity, but also helps us...     

Comparative phylogeography as an integrative approach to historical biogeography

Phylogeography has become a powerful approach for elucidating contemporary geographical patterns of evolutionary subdivision within species and species complexes. A recent extension of this approach is the comparison of phylogeographic patterns of multiple co-distributed taxonomic groups, or 'comparative phylogeography.' Recent comparative phylogeographic studies have revealed pervasive and previously unrecognized biogeographic patterns which suggest that vicariance has played a more important role in the historical development of modern biotic assemblages than current taxonomy would indicate. Despite the utility of comparative phylogeography for uncovering such 'cryptic vicariance', this approach has yet to be embraced by some researchers as a valuable complement to other approaches to historical biogeography. We address here some of the common misconceptions surrounding comparative phylogeography, provide an example of this approach based on the boreal mammal fauna of North America, and argue that together with other approaches, comparative phylogeography can contribute importantly to our understanding of the relationship between earth history and biotic diversification.     

Economic context and pigeons' risk-taking: an integrative approach

The present work tested pigeons' (Columba livia) risk sensitivity to changes in resource availability in the concurrent-chains choice procedure. Subjects were provided choices, generally between variable-ratio (VR) and fixed-ratio (FR) outcome schedules with equal or different mean values. Predictions from ecology's risk sensitivity, behavioral economic's rate maximization, and psychology's delay-reduction theory were contrasted under settings where budget was adversely affected by one of three manipulations in seven experiments: (1) session length was dramatically reduced; (2) choice-phase duration was substantially increased by increasing the response requirements in the choice phase, or (3) outcome duration was significantly increased by increasing the response requirements in the outcome phase while holding session length constant. Although preference measures were sensitive to every budget manipulation, the nature of the changes observed were dependent upon the manner in which resource availability was manipulated and the choice alternatives offered. For example, choice was affected in an opposite manner when budget was adversely altered by lengthening choice duration ("search time") as opposed to lengthening outcome duration ("handling time"). Findings revealed partial support for pigeons' sensitivity both to changes in budget and to changes in conditioned reinforcer value. The overall pattern of results thus favors an integrative approach to describing the effect of economic context on general risk sensitivity.     

Neuromechanics: an integrative approach for understanding motor control

Neuromechanics seeks to understand how muscles, sense organs,motor pattern generators, and brain interact to produce coordinatedmovement, not only in complex terrain but also when confrontedwith unexpected perturbations. Applications of neuromechanicsinclude ameliorating human health problems (including prosthesisdesign and restoration of movement following brain or spinalcord injury), as well as the design, actuation and control ofmobile robots. In animals, coordinated movement emerges fromthe interplay among descending output from the central nervoussystem, sensory input from body and environment, muscle dynamics,and the emergent dynamics of the whole animal. The inevitablecoupling between neural information processing and the emergentmechanical behavior of animals is a central theme of neuromechanics.Fundamentally, motor control involves a series of transformationsof information, from brain and spinal cord to muscles to body,and back to brain. The control problem revolves around the specifictransfer functions that describe each transformation. The transferfunctions depend on the rules of organization and operationthat determine the dynamic behavior of each subsystem (i.e.,central processing, force generation, emergent dynamics, andsensory processing). In this review, we (1) consider the contributionsof muscles, (2) sensory processing, and (3) central networksto motor control, (4) provide examples to illustrate the interplayamong brain, muscles, sense organs and the environment in thecontrol of movement, and (5) describe advances in both roboticsand neuromechanics that have emerged from application of biologicalprinciples in robotic design. Taken together, these studiesdemonstrate that (1) intrinsic properties of muscle contributeto dynamic stability and control of movement, particularly immediatelyafter perturbations; (2) proprioceptive feedback reinforcesthese intrinsic self-stabilizing properties of muscle; (3) controlsystems must contend with inevitable time delays that can simplifyor complicate control; and (4) like most animals under a varietyof circumstances, some robots use a trial and error processto tune central feedforward control to emergent body dynamics.     

Functional demands of dynamic biological adhesion: an integrative approach

Climbing organisms are constantly challenged to make their way rapidly and reliably across varied and often novel terrain. A diversity of morphologically and mechanically disparate attachment strategies have evolved across widely distributed phylogenetic groups to aid legged animals in scaling these surfaces, notable among them some very impressive adhesive pads. Despite the differences between, for example, the dry fibrillar pads of geckos and the smooth, secretion-aided pads of stick insects, I hypothesize that they face similar functional demands in their environment. I outline three broad criteria defining dynamic biological adhesion: reusability, reversibility, and substrate tolerance. Organismal adhesive pads must be able to attach repeatedly without significant decline in performance, detach easily at will, and adhere strongly to the broadest possible range of surfaces in their habitat. A survey of the literature suggests that evidence for these general principles can be found in existing research, but that many gaps remain to be filled. By taking a comparative, integrative approach to biological dynamic adhesion, rather than focusing on a few model organisms, investigators will continue to discover new and interesting attachment strategies in natural systems.     

Genomics and genome-wide association studies: an integrative approach to expression QTL mapping

Expression QTL mapping by integrating genome-wide gene expression and genotype data is a promising approach to identifying functional genetic variation, but is hampered by the large number of multiple comparisons inherent in such studies. A novel approach to addressing multiple testing problems in genome-wide family-based association studies is screening candidate markers using heritability or conditional power. We apply these methods in settings in which microarray gene expression data are used as phenotypes, screening for SNPs near the expressed genes. We perform association analyses for phenotypes using a univariate approach. We also perform simulations on trios with large numbers of causal SNPs to determine the optimal number of markers to use in a screen. We demonstrate that our family-based screening approach performs well in the analysis of integrative genomic datasets and that screening using either heritability or conditional power produces similar, though not identical, results.     

Assessing the quality of products from cloned cattle: an integrative approach

Scientific expertise was developed during a 3-year study to evaluate a large number of bovine female clones (n=37; from 4 to 36 months of age) and their products through a multidisciplinary approach and compare them to non-cloned breed, age and sex-matched contemporary control animals (n=38) maintained under the same conditions at the same experimental farm of INRA. In clone and control groups, most parameters measured for health and development of the animals as well as evaluation of milk and meat products were within the normal range for the breed. The strict comparison between cloned animals and controls allowed us to detect slight significant differences between the two groups. Cloned heifers reached puberty significantly later (+62 days) and at higher body weight (+56kg) than controls. There were slight differences in antigen-specific induced proliferation of lymphocytes after vaccination with ovalbumin before 10 months of age, but responses were normal responses in older animals. There were differences in the fatty acid (FA) composition of milk and muscle arising from two families of clones, suggesting a possible deviation in lipid metabolism as assessed by higher Delta-9 desaturase activity indices in both milk and muscle from clones compared to controls. Nutritional evaluation of milk and meat using the rat model did not reveal any difference between products derived from clones versus controls.     

An approach to the biometeorology of decomposer organisms

A search for surrogate variables of weather's control over rate of decay by decomposer organisms has revealed that Actual Evapotranspiration (AE), a water budget term, correlates well (r = 0.976) with measured values of litter decomposition rate. Using data from many biomes of the earth, a curve-fit of AE with measured decomposition rate has been formulated. This curve-fit has been used to prepare a map which displays the geography of predicted decay rate for North America. The physical properties of the litter also controls decomposition rates. Work is in progress to refine the AE to decomposer relationship by considering the lignin content of decomposing litter. Preliminary results suggest that control of decomposition rates by lignin increases with AE so that in high AE environments small changes in lignin concentration result in large changes in litter decay rates. This relationship perhaps explains the great variability in decay rates reported in tropical ecosystems.     

Generalized estimating equations: A pragmatic and flexible approach to the marginal GLM modelling of correlated data in the behavioural sciences

Within behavioural research, non‐normally distributed data with a complicated structure are common. For instance, data can represent repeated observations of quantities on the same individual. The regression analysis of such data is complicated both by the interdependency of the observations (response variables) and by their non‐normal distribution. Over the last decade, such data have been more and more frequently analysed using generalized mixed‐effect models. Some researchers invoke the heavy machinery of mixed‐effect modelling to obtain the desired population‐level (marginal) inference, which can be achieved by using simpler tools—namely by marginal models. This paper highlights marginal modelling (using generalized estimating equations [GEE]) as an alternative method. In various situations, GEE can be based on fewer assumptions and directly generate estimates (population‐level parameters) which are of immediate interest to the behavioural researcher (such as population means). Using four examples from behavioural research, we demonstrate the use, advantages, and limits of the GEE approach as implemented within the functions of the ‘geepack’ package in R.     

An integrative approach to understanding mechanosensation

The ability for a living organism to sense and respond to its external environment is crucial to its survival. Understanding mechanosensation, the mechanism by which organisms react in response to mechanical stimuli, presents many interesting and challenging problems for both experimental and computational biologists. A major difficulty in studying mechanosensors is their inherent multiscale nature. The systems involved in mechanosesnsing can span eight orders of magnitude in length scale and up to 10 orders of magnitude in time scale. Trying to ascertain information across these length and time scales simultaneously is challenging. This problem has led to the need to approach these types of problems using an integrative approach, combining both computational and experimental biology. This review classifies the major types of mechanosensors and explains methods that have been employed in understanding their behavior, both using modeling and experimental techniques. Multiscale modeling methods combined with experimental techniques in an integrative approach are suggested as ways of undertaking the study of such systems.     

Protein complex prediction using an integrative bioinformatics approach

Since protein complexes play a crucial role in biological cells, one of the major goals in bioinformatics is the elucidation of protein complexes. A general approach is to build a prediction rule based on multiple data sources, e.g. gene expression data and protein interaction data, to assess the likelihood of two proteins having complex association. We critically revisit the step of predictor construction, i.e. the determination of a proper training set, an optimal classifier, and, most importantly, an optimal feature set. We use an exhaustive set of features, which includes the 2hop-feature as introduced by Wong et al. for predicting synthetic sick or lethal interactions. Post-processing of the likelihoods of protein interaction is then required to extract protein complexes. We propose a new protocol for combining these likelihood estimates. The protocol interprets the probabilities of complex association as output by the prediction rule as distances and employs hierarchical clustering to find groups of interacting proteins. In contrast to the computationally expensive search-and-score approach of Sharan et al., this protocol is very fast and can be applied to fully connected graphs. The protocol identifies trusted protein complexes with high confidence. We show that the 2hop-feature is relevant for predicting protein complexes. Furthermore, several interesting hypotheses about new protein complexes have been generated. For example, our approach linked the protein FYV4 to the mitochondrial ribosomal subunit. Interestingly, it is known that this protein is located in the mitochondrion, but its biological role is unknown. Vid22 and YGR071C were also linked, which corresponds to the new TAP data of Krogan et al.