Mixed anhydrides at the intersection between peptide and RNA autocatalytic sets: evolution of biological coding.

We present a scenario for the origin of biological coding, a semiotic relationship between chemical information stored in one location that links to chemical information stored in a separate location. Coding originated from cooperation between two, originally separate, collectively autocatalytic sets (CASs), one for nucleic acids and one for peptides. Upon interaction, a series of RNA folding-directed processes led to their joint cooperativity. The aminoacyl adenylate was the first covalent association made by these two CASs and solidified their interdependence, and is a palimpsest of this era, a relic of the original semiotic relationship between RNA and proteins. Coding was driven by selection pressure to eliminate waste in CASs. Eventually a 1 : 1 relationship between single amino acids and short RNA pieces was established, i.e. the 'genetic code'. The two classes of aaRS enzymes are remnants of the complementary information in two RNA strands, as postulated by Rodin and Ohno. Every stage in the evolution of coding was driven by the downward selection on the components of a system to satisfy the Kantian whole. Coding was engendered because there were two chemically distinct classes of polymers needed for open-ended evolution; systems with only one polymer cannot exhibit this characteristic. Coding is thus synonymous with life as we know it.

Human cervicovaginal mucus contains an activity that hinders HIV-1 movement.

Cervical and vaginal epithelia are primary barriers against HIV type I (HIV-1) entry during male-to-female transmission. Cervical mucus (CM) is produced by the endocervix and forms a layer locally as well as in the vaginal compartment in the form of cervicovaginal mucus (CVM). To study the potential barrier function of each mucus type during HIV-1 transmission, we quantified HIV-1 mobility in CM and CVM ex vivo using fluorescent microscopy. Virions and 200-nm PEGylated beads were digitally tracked and mean-squared displacement was calculated. The mobility of beads increased significantly in CVM compared with CM, consistent with the known decreased mucin concentration of CVM. Unexpectedly, HIV-1 diffusion was significantly hindered in the same CVM samples in which bead diffusion was unhindered. Inhibition of virus transport was envelope-independent. Our results reveal a previously unknown activity in CVM that is capable of impeding HIV-1 mobility to enhance mucosal barrier function.

Quantitative simulation tools to analyze up- and downstream interactions of soil and water conservation measures: supporting policy making in the Green Water Credits program of Kenya.

Upstream soil and water conservation measures in catchments can have positive impact both upstream in terms of less erosion and higher crop yields, but also downstream by less sediment flow into reservoirs and increased groundwater recharge. Green Water Credits (GWC) schemes are being developed to encourage upstream farmers to invest in soil and water conservation practices which will positively effect upstream and downstream water availability. Quantitative information on water and sediment fluxes is crucial as a basis for such financial schemes. A pilot design project in the large and strategically important Upper-Tana Basin in Kenya has the objective to develop a methodological framework for this purpose. The essence of the methodology is the integration and use of a collection of public domain tools and datasets: the so-called Green water and Blue water Assessment Toolkit (GBAT). This toolkit was applied in order to study different options to implement GWC in agricultural rainfed land for the pilot study. Impact of vegetative contour strips, mulching, and tied ridges were determined for: (i) three upstream key indicators: soil loss, crop transpiration and soil evaporation, and (ii) two downstream indicators: sediment inflow in reservoirs and groundwater recharge. All effects were compared with a baseline scenario of average conditions. Thus, not only actual land management was considered but also potential benefits of changed land use practices. Results of the simulations indicate that especially applying contour strips or tied ridges significantly reduces soil losses and increases groundwater recharge in the catchment. The model was used to build spatial expressions of the proposed management practices in order to assess their effectiveness. The developed procedure allows exploring the effects of soil conservation measures in a catchment to support the implementation of GWC.

Cell-cell interaction and diversity of emergent behaviours.

Despite myriads of possible gene expression profiles, cells tend to be found in a confined number of expression patterns. The dynamics of Boolean models of gene regulatory networks has proven to be a likely candidate for the description of such self-organisation phenomena. Because cells do not live in isolation, but they constantly shape their functions to adapt to signals from other cells, this raises the question of whether the cooperation among cells entails an expansion or a reduction of their possible steady states. Multi random Boolean networks are introduced here as a model for interaction among cells that might be suitable for the investigation of some generic properties regarding the influence of communication on the diversity of cell behaviours. In spite of its simplicity, the model exhibits a non-obvious phenomenon according to which a moderate exchange of products among adjacent cells fosters the variety of their possible behaviours, which on the other hand are more similar to one another. On the contrary, a more invasive coupling would lead cells towards homogeneity.

Robustness analysis of a Boolean model of gene regulatory network with memory.

The response to different kinds of perturbations of a discrete model of gene regulatory network, which is a generalization of the random Boolean network (RBN) model, is discussed. The model includes memory effects, and the analysis pays particular attention to the influence on the system stability of a parameter (i.e., the decay time of the gene products) that determines the duration of the memory effects. It is shown that this parameter deeply affects the overall behavior of the system, with special regard to the dynamical regimes and the sensitivity. Furthermore, a noteworthy divergence in the response of systems characterized by different memory lengths in the presence of either temporary or permanent damages is highlighted, as is the substantial difference, with respect to classical RBNs, between the specific dynamical regime and the landscape of the attractors.

Phase transition in a class of nonlinear random networks.

We discuss the complex dynamics of a nonlinear random networks model as a function of the connectivity k between the elements of the network. We show that this class of networks exhibits an order-chaos phase transition for a critical connectivity k{c}=2 . Also, we show that both pairwise correlation and complexity measures are maximized in dynamically critical networks. These results are in good agreement with the previously reported studies on random Boolean networks and random threshold networks, and show once again that critical networks provide an optimal coordination of diverse behavior.

On the dynamics of random Boolean networks subject to noise: attractors, ergodic sets and cell types.

The asymptotic dynamics of random Boolean networks subject to random fluctuations is investigated. Under the influence of noise, the system can escape from the attractors of the deterministic model, and a thorough study of these transitions is presented. We show that the dynamics is more properly described by sets of attractors rather than single ones. We generalize here a previous notion of ergodic sets, and we show that the Threshold Ergodic Sets so defined are robust with respect to noise and, at the same time, that they do not suffer from a major drawback of ergodic sets. The system jumps from one attractor to another of the same Threshold Ergodic Set under the influence of noise, never leaving it. By interpreting random Boolean networks as models of genetic regulatory networks, we also propose to associate cell types to Threshold Ergodic Sets rather than to deterministic attractors or to ergodic sets, as it had been previously suggested. We also propose to associate cell differentiation to the process whereby a Threshold Ergodic Set composed by several attractors gives rise to another one composed by a smaller number of attractors. We show that this approach accounts for several interesting experimental facts about cell differentiation, including the possibility to obtain an induced pluripotent stem cell from a fully differentiated one by overexpressing some of its genes.

Maximal information transfer and behavior diversity in Random Threshold Networks.

Random Threshold Networks (RTNs) are an idealized model of diluted, non-symmetric spin glasses, neural networks or gene regulatory networks. RTNs also serve as an interesting general example of any coordinated causal system. Here we study the conditions for maximal information transfer and behavior diversity in RTNs. These conditions are likely to play a major role in physical and biological systems, perhaps serving as important selective traits in biological systems. We show that the pairwise mutual information is maximized in dynamically critical networks. Also, we show that the correlated behavior diversity is maximized for slightly chaotic networks, close to the critical region. Importantly, critical networks maximize coordinated, diverse dynamical behavior across the network and across time: the information transmission between source and receiver nodes and the diversity of dynamical behaviors, when measured with a time delay between the source and receiver, are maximized for critical networks.

Enrichment and immunoprecipitation of 22 kDa human growth hormone spiked into human urine.

Approaches to detect whether an athlete has used growth hormone have been intensely investigated by sport organizations for 20 years. This effort has led to a human growth hormone (hGH) isoform ratio test in serum that has been approved by WADA and deployed at three Olympic Games, although a positive case has yet to be reported. We set out to determine whether the ratio test could be applied to urine. First we investigated various ways to extract hGH from spiked urine. We were able to recover 95% using selective centrifugal concentration. This fraction was then subjected to four different commercially available immunoprecipitation kits. The highest yield was obtained with the Invitrogen Dynabeads Protein G kit. Nevertheless it is apparent that these methods do not recover enough hGH for subsequent analysis by mass spectrometry. With further effort greater recovery of the 22 kDa isoform might be achieved, however it is very unlikely that the 20 kDa isoform could be detected. This method may be significantly improved by the application of both nanoparticle and aptamer technology.

Heuristic approach to sparse approximation of gene regulatory networks.

Determining the structure of the gene regulatory network using the information in genomewide profiles of mRNA abundance, such as microarray data, poses several challenges. Typically, "static" rather than dynamical profile measurements, such as those taken from steady state tissues in various conditions, are the starting point. This makes the inference of causal relationships between genes difficult. Moreover, the paucity of samples relative to the gene number leads to problems such as overfitting and underconstrained regression analysis. Here we present a novel method for the sparse approximation of gene regulatory networks that addresses these issues. It is formulated as a sparse combinatorial optimization problem which has a globally optimal solution in terms of l(0) norm error. In order to seek an approximate solution of the l(0) optimization problem, we consider a heuristic approach based on iterative greedy algorithms. We apply our method to a set of gene expression profiles comprising of 24,102 genes measured over 79 human tissues. The inferred network is a signed directed graph, hence predicts causal relationships. It exhibits typical characteristics of regulatory networks organism with partially known network topology, such as the average number of inputs per gene as well as the in-degree and out-degree distribution.

On the sparse reconstruction of gene networks.

We discuss a heuristic method for the sparse reconstruction of gene networks. The method is based on iterative greedy algorithms, and uses gene expression data from microarray experiments. Also, we show numerically that the greedy algorithms are able to give good approximative solutions to the sparse reconstruction problem even in the presence of significant levels of noise.

Monte Carlo simulation of a simple gene network yields new evolutionary insights.

Monte Carlo simulations of a genetic toggle switch show that its behavior can be more complex than analytic models would suggest. We show here that as a result of the interplay between frequent and infrequent reaction events, such a switch can have more stable states than an analytic model would predict, and that the number and character of these states depend to a large extent on the propensity of transcription factors to bind to and dissociate from promoters. The effects of gene duplications differ even more; in analytic models, these seem to result in the disappearance of bi-stability and thus a loss of the switching function, but a Monte Carlo simulation shows that they can result in the appearance of new stable states without the loss of old ones, and thus in an increase of the complexity of the switch's behavior which may facilitate the evolution of new cellular functions. These differences are of interest with respect to the evolution of gene networks, particularly in clonal lines of cancer cells, where the duplication of active genes is an extremely common event, and often seems to result in the appearance of viable new cellular phenotypes.

A physical analogy of the genetic toggle switch.

We show that there is a physical analogy between a stochastic model of a genetic toggle switch system and a thermostated particle moving in a potential field, derived from the probability distribution of the toggle switch. This result suggests that one can actually simulate the dynamics of a more complex gene network by considering an ensemble of thermostated particles moving in a potential field, derived from the stationary distribution of the chemical stochastic model describing the gene network.

Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data.

In a previous study it was shown that a simple random Boolean network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1.

Network growth models and genetic regulatory networks.

We study a class of growth algorithms for directed graphs that are candidate models for the evolution of genetic regulatory networks. The algorithms involve partial duplication of nodes and their links, together with the innovation of new links, allowing for the possibility that input and output links from a newly created node may have different probabilities of survival. We find some counterintuitive trends as the parameters are varied, including the broadening of the in-degree distribution when the probability for retaining input links is decreased. We also find that both the scaling of transcription factors with genome size and the measured degree distributions for genes in yeast can be reproduced by the growth algorithm if and only if a special seed is used to initiate the process.

A system for studying genetic changes in Candida albicans during infection.

Candida albicans is a diploid yeast with a dimorphic life history. It exists commensally in many healthy humans but becomes a potent pathogen in immunocompromised hosts. The underlying genetic mechanisms by which C. albicans switches from a commensal to a pathogenic form in the host are not well understood. To study the evolution of virulence in mammalian hosts, we used GAL1 as selectable marker system that allows for both positive and negative selection in selective media. We show that the deletion of one or both copies of GAL1 in the C. albicans genome does not change virulence in a systemic mouse model. We obtained estimates for the frequency of mitotic recombination at the GAL1 locus during systemic infection. Our observations suggest that genetic changes such as mitotic recombination and gene conversion occur at a high enough frequency to be important in the transition of C. albicans from a commensal to a pathogenic organism.

Scaling in ordered and critical random boolean networks.

Random Boolean networks, originally invented as models of genetic regulatory networks, are simple models for a broad class of complex systems that show rich dynamical structures. From a biological perspective, the most interesting networks lie at or near a critical point in parameter space that divides "ordered" from "chaotic" attractor dynamics. We study the scaling of the average number of dynamically relevant nodes and the median number of distinct attractors in such networks. Our calculations indicate that the correct asymptotic scalings emerge only for very large systems.

WdChs2p, a class I chitin synthase, together with WdChs3p (class III) contributes to virulence in Wangiella (Exophiala) dermatitidis.

The chitin synthase structural gene WdCHS2 was isolated by screening a subgenomic DNA library of Wangiella dermatitidis by using a 0.6-kb PCR product of the gene as a probe. The nucleotide sequence revealed a 2,784-bp open reading frame, which encoded 928 amino acids, with a 59-bp intron near its 5' end. Derived protein sequences showed highest amino acid identities with those derived from the CiCHS1 gene of Coccidioides immitis and the AnCHSC gene of Aspergillus nidulans. The derived sequence also indicated that WdChs2p is an orthologous enzyme of Chs1p of Saccharomyces cerevisiae, which defines the class I chitin synthases. Disruptions of WdCHS2 produced strains that showed no obvious morphological defects in yeast vegetative growth or in ability to carry out polymorphic transitions from yeast cells to hyphae or to isotropic forms. However, assays showed that membranes of wdchs2Delta mutants were drastically reduced in chitin synthase activity. Other assays of membranes from a wdchs1Deltawdchs3Deltawdchs4Delta triple mutant showed that their residual chitin synthase activity was extremely sensitive to trypsin activation and was responsible for the majority of zymogenic activity. Although no loss of virulence was detected when wdchs2Delta strains were tested in a mouse model of acute infection, wdchs2Deltawdchs3Delta disruptants were considerably less virulent in the same model, even though wdchs3Delta strains also had previously shown no loss of virulence. This virulence attenuation in the wdchs2Deltawdchs3Delta mutants was similarly documented in a limited fashion in more-sensitive cyclophosphamide-induced immunocompromised mice. The importance of WdChs2p and WdChs3p to the virulence of W. dermatitidis was then confirmed by reconstituting virulence in the double mutant by the reintroduction of either WdCHS2 or WdCHS3 into the wdchs2Deltawdchs3Delta mutant background.

Prolegomenon to a general biology.

This chapter is a slightly modified version of Chapter 1 of my book, Investigations. I discuss "autonomous agents," the origin of life, autocatalytic sets of polymers, work cycles, puzzles-about evolutionary theory, and the birth of a general biology dealing with biospheres anywhere in the universe.

Prostasin is a glycosylphosphatidylinositol-anchored active serine protease.

A recombinant human prostasin serine protease was expressed in several human cell lines. Subcellular fractionation showed that this serine protease is synthesized as a membrane-bound protein while a free-form prostasin is secreted into the culture medium. Prostasin was identified in nuclear and membrane fractions. Membrane-bound prostasin can be released by phosphatidylinositol-specific phospholipase C treatment, or labeled by [(3)H]ethanolamine, indicating a glycosylphosphatidylinositol anchorage. A prostasin-binding protein was identified in mouse and human seminal vesicle fluid. Both the secreted and the membrane-bound prostasin were able to form a covalently linked 82-kDa complex when incubated with seminal vesicle fluid. The complex formation between prostasin and the prostasin-binding protein was inhibited by a prostasin antibody, heparin, and serine protease inhibitors. Prostasin's serine protease activity was inhibited when bound to the prostasin-binding protein in mouse seminal vesicle fluid. This study indicates that prostasin is an active serine protease in its membrane-bound form.