Transcriptomic landscape of the interaction between the entomopathogenic fungus Beauveria bassiana and its tolerant host Tribolium castaneum revealed by dual RNA-seq

Entomopathogenic fungi such as Beauveria bassiana are the only insect pathogens able to start the infection process by penetrating through the host cuticle. However, some insects try to avoid fungal infection by embedding their cuticle with antifungal compounds. This is the case of the red flour beetle Tribolium castaneum, which generates economical loss of great significance in stored product environments worldwide. In this study, T. castaneum adults were fed during different time periods (from 3 to 72 h) on B. bassiana conidia-covered corn kernels. The progression of fungal infection was monitored using the dual RNA-seq technique to reconstruct the temporal transcriptomic profile and to perform gene enrichment analyses in both interacting organisms. After mapping the total reads with the B. bassiana genome, 904 genes were identified during this process. The more expressed fungal genes were related to carbon catabolite repression, cation binding, peptidase inhibition, redox processes, and stress response. Several immune-related genes from Toll, IMD, and JNK pathways, as well as genes related to chitin modification, were found to be differentially expressed in fungus-exposed T. castaneum. This study represents the first dual transcriptomic approach to help understand the interaction between the entomopathogenic fungus B. bassiana and its tolerant host T. castaneum.

Dicodon-based measures for modeling gene expression

MOTIVATION

Codon usage preference patterns have been associated with modulation of translation efficiency, protein folding, and mRNA decay. However, new studies support that codon pair usage has also a remarkable effect at the gene expression level. Here, we expand the concept of CAI to answer if codon pair usage patterns can be understood in terms of codon usage bias, or if they offer new information regarding coding translation efficiency.

RESULTS

Through the implementation of a weighting strategy to consider the dicodon contributions, we observe that the dicodon-based measure has greater correlations with gene expression level than CAI. Interestingly, we have noted that dicodons associated with a low value of adaptiveness are related to dicodons which mediate strong translational inhibition in yeast. We have also noticed that some codon-pairs have a smaller dicodon contribution than estimated by the product of the respective codon contributions.

AVAILABILITY AND IMPLEMENTATION

Scripts, implemented in Python, are freely available for download at https://zenodo.org/record/7738276#.ZBIDBtLMIdU.

Analysis of the Interactome of the Toxoplasma gondii Tgj1 HSP40 Chaperone

Toxoplasma gondii is an obligate intracellular apicomplexan that causes toxoplasmosis in humans and animals. Central to its dissemination and pathogenicity is the ability to rapidly divide in the tachyzoite stage and infect any type of nucleated cell. Adaptation to different cell contexts requires high plasticity in which heat shock proteins (Hsps) could play a fundamental role. Tgj1 is a type I Hsp40 of T. gondii, an ortholog of the DNAJA1 group, which is essential during the tachyzoite lytic cycle. Tgj1 consists of a J-domain, ZFD, and DNAJ_C domains with a CRQQ C-terminal motif, which is usually prone to lipidation. Tgj1 presented a mostly cytosolic subcellular localization overlapping partially with endoplasmic reticulum. Protein-protein Interaction (PPI) analysis showed that Tgj1 could be implicated in various biological pathways, mainly translation, protein folding, energy metabolism, membrane transport and protein translocation, invasion/pathogenesis, cell signaling, chromatin and transcription regulation, and cell redox homeostasis among others. The combination of Tgj1 and Hsp90 PPIs retrieved only 70 interactors linked to the Tgj1-Hsp90 axis, suggesting that Tgj1 would present specific functions in addition to those of the Hsp70/Hsp90 cycle, standing out invasion/pathogenesis, cell shape motility, and energy pathway. Within the Hsp70/Hsp90 cycle, translation-associated pathways, cell redox homeostasis, and protein folding were highly enriched in the Tgj1-Hsp90 axis. In conclusion, Tgj1 would interact with a wide range of proteins from different biological pathways, which could suggest a relevant role in them.

Cell annotation using scRNA-seq data: A protein-protein interaction network approach

Pathway analysis is an important step in the interpretation of single cell transcriptomic data, as it provides powerful information to detect which cellular processes are active in each individual cell. We have recently developed a protein-protein interaction network-based framework to quantify pluripotency associated pathways from scRNA-seq data. On this occasion, we extend this approach to quantify the activity of a pathway associated with any biological process, or even any list of genes. A systems-level characterization of pathway activities across multiple cell types provides a broadly applicable tool for the analysis of pathways in both healthy and disease conditions. Dysregulated cellular functions are a hallmark of a wide spectrum of human disorders, including cancer and autoimmune diseases. Here, we illustrate our method by analyzing various biological processes in healthy and cancer breast samples. Using this approach we found that tumor breast cells, even when they form a single group in the UMAP space, keep diverse biological programs active in a differentiated manner within the cluster.•We implement a protein-protein interaction network-based approach to quantify the activity of different biological processes.•The methodology can be used for cell annotation in scRNA-seq studies and is freely available as R package.

ORIGINS: a protein network-based approach to quantify cell pluripotency from scRNA-seq data

Trajectory inference is a common application of scRNA-seq data. However, it is often necessary to previously determine the origin of the trajectories, the stem or progenitor cells. In this work, we propose a computational tool to quantify pluripotency from single cell transcriptomics data. This approach uses the protein-protein interaction (PPI) network associated with the differentiation process as a scaffold and the gene expression matrix to calculate a score that we call differentiation activity. This score reflects how active the differentiation network is in each cell. We benchmark the performance of our algorithm with two previously published tools, LandSCENT (Chen et al., 2019) and CytoTRACE (Gulati et al., 2020), for four healthy human data sets: breast, colon, hematopoietic and lung. We show that our algorithm is more efficient than LandSCENT and requires less RAM memory than the other programs. We also illustrate a complete workflow from the count matrix to trajectory inference using the breast data set.•

ORIGINS is a methodology to quantify pluripotency from scRNA-seq data implemented as a freely available R package.

•

ORIGINS uses the protein-protein interaction network associated with differentiation and the data set expression matrix to calculate a score (differentiation activity) that quantifies pluripotency for each cell.

Single cell gene expression profiling of nasal ciliated cells reveals distinctive biological processes related to epigenetic mechanisms in patients with severe COVID-19

Objective

To explore the molecular processes associated with cellular regulatory programs in patients with COVID-19, including gene activation or repression mediated by epigenetic mechanisms. We hypothesized that a comprehensive gene expression profiling of nasopharyngeal epithelial cells might expand our understanding of the pathogenic mechanisms of severe COVID-19.

Methods

We used single-cell RNA sequencing (scRNAseq) profiling of ciliated cells (n = 12,725) from healthy controls (SARS-CoV-2 negative n = 13) and patients with mild/moderate (n = 13) and severe (n = 14) COVID-19. ScRNAseq data at the patient level were used to perform gene set and pathway enrichment analyses. We prioritized candidate miRNA-target interactions and epigenetic mechanisms.

Results

We found that mild/moderate COVID-19 compared to healthy controls had upregulation of gene expression signatures associated with mitochondrial function, misfolded proteins, and membrane permeability.

In addition, we found that compared to mild/moderate disease, severe COVID-19 had downregulation of epigenetic mechanisms, including DNA and histone H3K4 methylation and chromatin remodelling regulation. Furthermore, we found 11-ranked miRNAs that may explain miRNA-dependent regulation of histone methylation, some of which share seed sequences with SARS-CoV-2 miRNAs.

Conclusion

Our results may provide novel insights into the epigenetic mechanisms mediating the clinical course of SARS-CoV-2 infection.

Synonymous variants in holoprosencephaly alter codon usage and impact the Sonic Hedgehog protein

Synonymous single nucleotide variants (sSNVs) have been implicated in various genetic disorders through alterations of pre-mRNA splicing, mRNA structure and miRNA regulation. However, their impact on synonymous codon usage and protein translation remains to be elucidated in clinical context. Here, we explore the functional impact of sSNVs in the Sonic Hedgehog (SHH) gene, identified in patients affected by holoprosencephaly, a congenital brain defect resulting from incomplete forebrain cleavage. We identified eight sSNVs in SHH, selectively enriched in holoprosencephaly patients as compared to healthy individuals, and systematically assessed their effect at both transcriptional and translational levels using a series of in silico and in vitro approaches. Although no evidence of impact of these sSNVs on splicing, mRNA structure or miRNA regulation was found, five sSNVs introduced significant changes in codon usage and were predicted to impact protein translation. Cell assays demonstrated that these five sSNVs are associated with a significantly reduced amount of the resulting protein, ranging from 5% to 23%. Inhibition of the proteasome rescued the protein levels for four out of five sSNVs, confirming their impact on protein stability and folding. Remarkably, we found a significant correlation between experimental values of protein reduction and computational measures of codon usage, indicating the relevance of in silico models in predicting the impact of sSNVs on translation. Considering the critical role of SHH in brain development, our findings highlight the clinical relevance of sSNVs in holoprosencephaly and underline the importance of investigating their impact on translation in human pathologies.

SARS-CoV-2 Codon Usage Bias Downregulates Host Expressed Genes With Similar Codon Usage

Severe acute respiratory syndrome has spread quickly throughout the world and was declared a pandemic by the World Health Organization (WHO). The pathogenic agent is a new coronavirus (SARS-CoV-2) that infects pulmonary cells with great effectiveness. In this study we focus on the codon composition for the viral protein synthesis and its relationship with the protein synthesis of the host. Our analysis reveals that SARS-CoV-2 preferred codons have poor representation of G or C nucleotides in the third position, a characteristic which could result in an unbalance in the tRNAs pools of the infected cells with serious implications in host protein synthesis. By integrating this observation with proteomic data from infected cells, we observe a reduced translation rate of host proteins associated with highly expressed genes and that they share the codon usage bias of the virus. The functional analysis of these genes suggests that this mechanism of epistasis can contribute to understanding how this virus evades the immune response and the etiology of some deleterious collateral effect as a result of the viral replication. In this manner, our finding contributes to the understanding of the SARS-CoV-2 pathogeny and could be useful for the design of a vaccine based on the live attenuated strategy.

Alternating regimes of motion in a model with cell-cell interactions

Cellular movement is a complex dynamic process, resulting from the interaction of multiple elements at the intra- and extracellular levels. This epiphenomenon presents a variety of behaviors, which can include normal and anomalous diffusion or collective migration. In some cases, cells can get neighborhood information through chemical or mechanical cues. A unified understanding about how such information can influence the dynamics of cell movement is still lacking. In order to improve our comprehension of cell migration we have considered a cellular Potts model where cells move actively in the direction of a driving field. The intensity of this driving field is constant, while its orientation can evolve according to two alternative dynamics based on the Ornstein-Uhlenbeck process. In one case, the next orientation of the driving field depends on the previous direction of the field. In the other case, the direction update considers the mean orientation performed by the cell in previous steps. Thus, the latter update rule mimics the ability of cells to perceive the environment, avoiding obstacles and thus increasing the cellular displacement. Different cell densities are considered to reveal the effect of cell-cell interactions. Our results indicate that both dynamics introduce temporal and spatial correlations in cell velocity in a friction-coefficient and cell-density-dependent manner. Furthermore, we observe alternating regimes in the mean-square displacement, with normal and anomalous diffusion. The crossovers between diffusive and directed motion regimes are strongly affected by both the driving field dynamics and cell-cell interactions. In this sense, when cell polarization update grants information about the previous cellular displacement, the duration of the diffusive regime decreases, particularly in high-density cultures.

Prediction of cell position using single-cell transcriptomic data: an iterative procedure

Single-cell sequencing reveals cellular heterogeneity but not cell localization. However, by combining single-cell transcriptomic data with a reference atlas of a small set of genes, it would be possible to predict the position of individual cells and reconstruct the spatial expression profile of thousands of genes reported in the single-cell study. With the purpose of developing new algorithms, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) consortium organized a crowd-sourced competition known as DREAM Single Cell Transcriptomics Challenge (SCTC). Within this context, we describe here our proposed procedures for adequate reference genes selection, and an iterative procedure to predict spatial expression profile of other genes.

A new analogue of islet neogenesis associated protein with higher structural and plasma stability

Islet Neogenesis Associated Protein pentadecapeptide (INGAP-PP) increases β-cell mass and function in experimental animals. A short clinical trial also yielded promising results. However, HTD4010, a new peptide derived from INGAP-PP, was developed in order to optimize its specific effects by minimizing its side effects. To study and compare the tertiary structure, stability dynamics, and plasma stability of HTD4010, an INGAP-PP analogue. Both peptides were pre-incubated in human, rat and mouse plasma at 37 °C, and their presence was identified and quantified by high performance liquid chromatography at different time-points. GROMACS 2019 package and the Gromos 54A7 force field were used to evaluate overall correlated motion of the peptide molecule during molecular dynamics simulation by essential dynamics. HTD4010 exhibited significantly larger plasma stability than INGAP-PP, and its structural stability was almost 3.36-fold higher than INGAP-PP. These results suggest that HTD4010 may facilitate longer tissue interaction, thereby developing higher potential biological effects. If so, HTD4010 may become a promising therapeutic agent to treat people with diabetes. Communicated by Ramaswamy H. Sarma.

Gene target discovery with network analysis in Toxoplasma gondii

Infectious diseases are of great relevance for global health, but needed drugs and vaccines have not been developed yet or are not effective in many cases. In fact, traditional scientific approaches with intense focus on individual genes or proteins have not been successful in providing new treatments. Hence, innovations in technology and computational methods provide new tools to further understand complex biological systems such as pathogen biology. In this paper, we apply a gene regulatory network approach to analyze transcriptomic data of the parasite Toxoplasma gondii. By means of an optimization procedure, the phenotypic transitions between the stages associated with the life cycle of T. gondii were embedded into the dynamics of a gene regulatory network. Thus, through this methodology we were able to reconstruct a gene regulatory network able to emulate the life cycle of the pathogen. The community network analysis has revealed that nodes of the network can be organized in seven communities which allow us to assign putative functions to 338 previously uncharacterized genes, 25 of which are predicted as new pathogenic factors. Furthermore, we identified a small gene circuit that drives a series of phenotypic transitions that characterize the life cycle of this pathogen. These new findings can contribute to the understanding of parasite pathogenesis.

Information propagation in a noisy gene cascade

We use information theory to study the information transmission through a simple gene cascade where the product of an unregulated gene regulates the expression activity of a cooperative genetic switch. While the input signal is provided by the upstream gene with two states, we consider that the expression of downstream gene is controlled by a cis-regulatory system with three binding sites for the regulator product, which can bind cooperatively. By computing exactly the associated probability distributions, we estimate information transmission thought the mutual information measure. We found that the mutual information associated with unimodal input signal is lower than the associated with bimodal inputs. We also observe that mutual information presents a maximum in the cooperativity intensity, and the position of this maximum depends on the kinetic rates of the promoter. Furthermore, we found that the bursting dynamics of the input signal can enhance the information transmission capacity.

Phylogenomics of tomato chloroplasts using assembly and alignment-free method

Phylogenetics and population genetics are central disciplines in evolutionary biology. Both are based on the comparison of single DNA sequences, or a concatenation of a number of these. However, with the advent of next-generation DNA sequencing technologies, the approaches that consider large genomic data sets are of growing importance for the elucidation of evolutionary relationships among species. Among these approaches, the assembly and alignment-free methods which allow an efficient distance computation and phylogeny reconstruction are of great importance. However, it is not yet clear under what quality conditions and abundance of genomic data such methods are able to infer phylogenies accurately. In the present study we assess the method originally proposed by Fan et al. for whole genome data, in the elucidation of Tomatoes' chloroplast phylogenetics using short read sequences. We find that this assembly and alignment-free method is capable of reproducing previous results under conditions of high coverage, given that low frequency k-mers (i.e. error prone data) are effectively filtered out. Finally, we present a complete chloroplast phylogeny for the best data quality candidates of the recently published 360 tomato genomes.

Bicodon bias can determine the role of synonymous SNPs in human diseases

Background

For a long time synonymous single nucleotide polymorphisms were considered as silent mutations. However, nowadays it is well known that they can affect protein conformation and function, leading to altered disease susceptibilities, differential prognosis and/or drug responses, among other clinically relevant genetic traits. This occurs through different mechanisms: by disrupting the splicing signals of precursor mRNAs, affecting regulatory binding-sites of transcription factors and miRNAs, or by modifying the secondary structure of mRNAs.

Results

In this paper we considered 22 human genetic diseases or traits, linked to 35 synonymous single nucleotide polymorphisms in 27 different genes. We performed a local sequence context analysis in terms of the ribosomal pause propensity affected by synonymous single nucleotide polymorphisms. We found that synonymous mutations related to the above mentioned mechanisms presented small pause propensity changes, whereas synonymous mutations that were not related to those mechanisms presented large pause propensity changes. On the other hand, we did not observe large variations in the codon usage of codons associated with these mutations. Furthermore, we showed that the changes in the pause propensity associated with benign sSNPs are significantly lower than the pause propensity changes related to sSNPs associated to diseases.

Conclusions

These results suggest that the genetic diseases or traits related to synonymous mutations with large pause propensity changes, could be the consequence of another mechanism underlying non-silent synonymous mutations. Namely, alternative protein configuration related, in turn, to alterations in the ribosome-mediated translational attenuation program encoded by pairs of consecutive codons, not codons. These findings shed light on the latter mechanism based on the perturbation of the co-translational folding process.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3609-6) contains supplementary material, which is available to authorized users.

Modeling the flux of metabolites in the juvenile hormone biosynthesis pathway using generalized additive models and ordinary differential equations

Juvenile hormone (JH) regulates development and reproductive maturation in insects. The corpora allata (CA) from female adult mosquitoes synthesize fluctuating levels of JH, which have been linked to the ovarian development and are influenced by nutritional signals. The rate of JH biosynthesis is controlled by the rate of flux of isoprenoids in the pathway, which is the outcome of a complex interplay of changes in precursor pools and enzyme levels. A comprehensive study of the changes in enzymatic activities and precursor pool sizes have been previously reported for the mosquito Aedes aegypti JH biosynthesis pathway. In the present studies, we used two different quantitative approaches to describe and predict how changes in the individual metabolic reactions in the pathway affect JH synthesis. First, we constructed generalized additive models (GAMs) that described the association between changes in specific metabolite concentrations with changes in enzymatic activities and substrate concentrations. Changes in substrate concentrations explained 50% or more of the model deviances in 7 of the 13 metabolic steps analyzed. Addition of information on enzymatic activities almost always improved the fitness of GAMs built solely based on substrate concentrations. GAMs were validated using experimental data that were not included when the model was built. In addition, a system of ordinary differential equations (ODE) was developed to describe the instantaneous changes in metabolites as a function of the levels of enzymatic catalytic activities. The results demonstrated the ability of the models to predict changes in the flux of metabolites in the JH pathway, and can be used in the future to design and validate experimental manipulations of JH synthesis.

Promoters Architecture-Based Mechanism for Noise-Induced Oscillations in a Single-Gene Circuit

It is well known that single-gene circuits with negative feedback loop can lead to oscillatory gene expression when they operate with time delay. In order to generate these oscillations many processes can contribute to properly timing such delay. Here we show that the time delay coming from the transitions between internal states of the cis-regulatory system (CRS) can drive sustained oscillations in an auto-repressive single-gene circuit operating in a small volume like a cell. We found that the cooperative binding of repressor molecules is not mandatory for a oscillatory behavior if there are enough binding sites in the CRS. These oscillations depend on an adequate balance between the CRS kinetic, and the synthesis/degradation rates of repressor molecules. This finding suggest that the multi-site CRS architecture can play a key role for oscillatory behavior of gene expression. Finally, our results can also help to synthetic biologists on the design of the promoters architecture for new genetic oscillatory circuits.

Systems Biology Approach to Model the Life Cycle of Trypanosoma cruzi

Due to recent advances in reprogramming cell phenotypes, many efforts have been dedicated to developing reverse engineering procedures for the identification of gene regulatory networks that emulate dynamical properties associated with the cell fates of a given biological system. In this work, we propose a systems biology approach for the reconstruction of the gene regulatory network underlying the dynamics of the Trypanosoma cruzi’s life cycle. By means of an optimisation procedure, we embedded the steady state maintenance, and the known phenotypic transitions between these steady states in response to environmental cues, into the dynamics of a gene network model. In the resulting network architecture we identified a small subnetwork, formed by seven interconnected nodes, that controls the parasite’s life cycle. The present approach could be useful for better understanding other single cell organisms with multiple developmental stages.

Cooperative binding: a multiple personality

Cooperative binding has been described in many publications and has been related to or defined by several different properties of the binding behavior of the ligand to the target molecule. In addition to the commonly used Hill coefficient, other characteristics such as a sigmoidal shape of the overall titration curve in a linear plot, a change of ligand affinity of the other binding sites when a site of the target molecule becomes occupied, or complex roots of the binding polynomial have been used to define or to quantify cooperative binding. In this work, we analyze how the different properties are related in the most general model for binding curves based on the grand canonical partition function and present several examples which highlight differences between the cooperativity characterizing properties which are discussed. Our results mainly show that among the presented definitions there are not two which fully coincide. Moreover, this work poses the question whether it can make sense to distinguish between positive and negative cooperativity based on the macroscopic binding isotherm only. This article shall emphasize that scientists who investigate cooperative effects in biological systems could help avoiding misunderstandings by stating clearly which kind of cooperativity they discuss.

Cooperativity to increase Turing pattern space for synthetic biology

It is hard to bridge the gap between mathematical formulations and biological implementations of Turing patterns, yet this is necessary for both understanding and engineering these networks with synthetic biology approaches. Here, we model a reaction-diffusion system with two morphogens in a monostable regime, inspired by components that we recently described in a synthetic biology study in mammalian cells.1 The model employs a single promoter to express both the activator and inhibitor genes and produces Turing patterns over large regions of parameter space, using biologically interpretable Hill function reactions. We applied a stability analysis and identified rules for choosing biologically tunable parameter relationships to increase the likelihood of successful patterning. We show how to control Turing pattern sizes and time evolution by manipulating the values for production and degradation relationships. More importantly, our analysis predicts that steep dose-response functions arising from cooperativity are mandatory for Turing patterns. Greater steepness increases parameter space and even reduces the requirement for differential diffusion between activator and inhibitor. These results demonstrate some of the limitations of linear scenarios for reaction-diffusion systems and will help to guide projects to engineer synthetic Turing patterns.

Abortive and propagating intracellular calcium waves: analysis from a hybrid model

The functional properties of inositol(1,4,5)-triphosphate (IP3) receptors allow a variety of intracellular Ca(2+) phenomena. In this way, global phenomena, such as propagating and abortive Ca(2+) waves, as well as local events such as puffs, have been observed. Several experimental studies suggest that many features of global phenomena (e.g., frequency, amplitude, speed wave) depend on the interplay of biophysical processes such as diffusion, buffering, efflux and influx rates, which in turn depend on parameters such as buffer concentration, Ca(2+) pump density, cytosolic IP3 level, and intercluster distance. Besides, it is known that cells are able to modify some of these parameters in order to regulate the Ca(2+) signaling. By using a hybrid model, we analyzed different features of the hierarchy of calcium events as a function of two relevant parameters for the calcium signaling, the intercluster distance and the pump strength or intensity. In the space spanned by these two parameters, we found two modes of calcium dynamics, one dominated by abortive calcium waves and the other by propagating waves. Smaller distances between the release sites promote propagating calcium waves, while the increase of the efflux rate makes the transition from propagating to abortive waves occur at lower values of intercluster distance. We determined the frontier between these two modes, in the parameter space defined by the intercluster distance and the pump strength. Furthermore, we found that the velocity of simulated calcium waves accomplishes Luther's law, and that an effective rate constant for autocatalytic calcium production decays linearly with both the intercluster distance and the pump strength.

Genetically encoded sender-receiver system in 3D mammalian cell culture

Engineering spatial patterning in mammalian cells, employing entirely genetically encoded components, requires solving several problems. These include how to code secreted activator or inhibitor molecules and how to send concentration-dependent signals to neighboring cells, to control gene expression. The Madin-Darby Canine Kidney (MDCK) cell line is a potential engineering scaffold as it forms hollow spheres (cysts) in 3D culture and tubulates in response to extracellular hepatocyte growth factor (HGF). We first aimed to graft a synthetic patterning system onto single developing MDCK cysts. We therefore developed a new localized transfection method to engineer distinct sender and receiver regions. A stable reporter line enabled reversible EGFP activation by HGF and modulation by a secreted repressor (a truncated HGF variant, NK4). By expanding the scale to wide fields of cysts, we generated morphogen diffusion gradients, controlling reporter gene expression. Together, these components provide a toolkit for engineering cell-cell communication networks in 3D cell culture.

An insight into the transcriptome of the digestive tract of the bloodsucking bug, Rhodnius prolixus

The bloodsucking hemipteran Rhodnius prolixus is a vector of Chagas' disease, which affects 7-8 million people today in Latin America. In contrast to other hematophagous insects, the triatomine gut is compartmentalized into three segments that perform different functions during blood digestion. Here we report analysis of transcriptomes for each of the segments using pyrosequencing technology. Comparison of transcript frequency in digestive libraries with a whole-body library was used to evaluate expression levels. All classes of digestive enzymes were highly expressed, with a predominance of cysteine and aspartic proteinases, the latter showing a significant expansion through gene duplication. Although no protein digestion is known to occur in the anterior midgut (AM), protease transcripts were found, suggesting secretion as pro-enzymes, being possibly activated in the posterior midgut (PM). As expected, genes related to cytoskeleton, protein synthesis apparatus, protein traffic, and secretion were abundantly transcribed. Despite the absence of a chitinous peritrophic membrane in hemipterans - which have instead a lipidic perimicrovillar membrane lining over midgut epithelia - several gut-specific peritrophin transcripts were found, suggesting that these proteins perform functions other than being a structural component of the peritrophic membrane. Among immunity-related transcripts, while lysozymes and lectins were the most highly expressed, several genes belonging to the Toll pathway - found at low levels in the gut of most insects - were identified, contrasting with a low abundance of transcripts from IMD and STAT pathways. Analysis of transcripts related to lipid metabolism indicates that lipids play multiple roles, being a major energy source, a substrate for perimicrovillar membrane formation, and a source for hydrocarbons possibly to produce the wax layer of the hindgut. Transcripts related to amino acid metabolism showed an unanticipated priority for degradation of tyrosine, phenylalanine, and tryptophan. Analysis of transcripts related to signaling pathways suggested a role for MAP kinases, GTPases, and LKBP1/AMP kinases related to control of cell shape and polarity, possibly in connection with regulation of cell survival, response of pathogens and nutrients. Together, our findings present a new view of the triatomine digestive apparatus and will help us understand trypanosome interaction and allow insights into hemipteran metabolic adaptations to a blood-based diet.

Noise minimisation in gene expression switches

Gene expression is subject to stochastic variation which leads to fluctuations in the rate of protein production. Recently, a study in yeast at a genomic scale showed that, in some cases, gene expression variability alters phenotypes while, in other cases, these remain unchanged despite fluctuations in the expression of other genes. These studies suggested that noise in gene expression is a physiologically relevant trait and, to prevent harmful stochastic variation in the expression levels of some genes, it can be subject to minimisation. However, the mechanisms for noise minimisation are still unclear. In the present work, we analysed how noise expression depends on the architecture of the cis-regulatory system, in particular on the number of regulatory binding sites. Using analytical calculations and stochastic simulations, we found that the fluctuation level in noise expression decreased with the number of regulatory sites when regulatory transcription factors interacted with only one other bound transcription factor. In contrast, we observed that there was an optimal number of binding sites when transcription factors interacted with many bound transcription factors. This finding suggested a new mechanism for preventing large fluctuations in the expression of genes which are sensitive to the concentration of regulators.

Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes

The microarray technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining these data one can identify the dynamics of the gene expression time series. The detection of genes that are periodically expressed is an important step that allows us to study the regulatory mechanisms associated with the circadian cycle. The problem of finding periodicity in biological time series poses many challenges. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, outliers and unevenly sampled time points. Consequently, the method for finding periodicity should preferably be robust against such anomalies in the data. In this paper, we propose a general and robust procedure for identifying genes with a periodic signature at a given significance level. This identification method is based on autoregressive models and the information theory. By using simulated data we show that the suggested method is capable of identifying rhythmic profiles even in the presence of noise and when the number of data points is small. By recourse of our analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis.

Inositol (1,4,5)-trisphosphate receptor microarchitecture shapes Ca2+ puff kinetics

Inositol (1,4,5)-trisphosphate receptors (IP(3)Rs) release intracellular Ca(2+) as localized Ca(2+) signals (Ca(2+) puffs) that represent the activity of small numbers of clustered IP(3)Rs spaced throughout the endoplasmic reticulum. Although much emphasis has been placed on estimating the number of active Ca(2+) release channels supporting Ca(2+) puffs, less attention has been placed on understanding the role of cluster microarchitecture. This is important as recent data underscores the dynamic nature of IP(3)R transitions between heterogeneous cellular architectures and the differential behavior of IP(3)Rs socialized into clusters. Here, we applied a high-resolution model incorporating stochastically gating IP(3)Rs within a three-dimensional cytoplasmic space to demonstrate: 1), Ca(2+) puffs are supported by a broad range of clustered IP(3)R microarchitectures; 2), cluster ultrastructure shapes Ca(2+) puff characteristics; and 3), loosely corralled IP(3)R clusters (>200 nm interchannel separation) fail to coordinate Ca(2+) puffs, owing to inefficient triggering and impaired coupling due to reduced Ca(2+)-induced Ca(2+) release microwave velocity (<10 nm/s) throughout the channel array. Dynamic microarchitectural considerations may therefore influence Ca(2+) puff occurrence/properties in intact cells, contrasting with a more minimal role for channel number over the same simulated conditions in shaping local Ca(2+) dynamics.

Neuropeptide precursor gene discovery in the Chagas disease vector Rhodnius prolixus

We show a straightforward workflow combining homology search in Rhodnius prolixus genome sequence with cloning by rapid amplification of cDNA ends and mass spectrometry. We have identified 32 genes and their transcripts that encode a number of neuropeptide precursors leading to 194 putative peptides. We validated by mass spectrometry 82 of those predicted neuropeptides in the brain of R. prolixus to achieve the first comprehensive genomic, transcriptomic and neuropeptidomic analysis of an insect disease vector. Comparisons of available insect neuropeptide sequences revealed that the R. prolixus genome contains most of the conserved neuropeptides in insects, many of them displaying specific features at the sequence level. Some gene families reported here are identified for the first time in the order Hemiptera, a highly biodiverse group of insects that includes many human, animal and plant disease agents.

Localization and socialization: experimental insights into the functional architecture of IP3 receptors

Inositol 1,4,5-trisphosphate (IP(3))-evoked Ca(2+) signals display great spatiotemporal malleability. This malleability depends on diversity in both the cellular organization and in situ functionality of IP(3) receptors (IP(3)Rs) that regulate Ca(2+) release from the endoplasmic reticulum (ER). Recent experimental data imply that these considerations are not independent, such that-as with other ion channels-the local organization of IP(3)Rs impacts their functionality, and reciprocally IP(3)R activity impacts their organization within native ER membranes. Here, we (i) review experimental data that lead to our understanding of the "functional architecture" of IP(3)Rs within the ER, (ii) propose an updated terminology to span the organizational hierarchy of IP(3)Rs observed in intact cells, and (iii) speculate on the physiological significance of IP(3)R socialization in Ca(2+) dynamics, and consequently the emerging need for modeling studies to move beyond gridded, planar, and static simulations of IP(3)R clustering even over short experimental timescales.

Infradian Rhythmicity in Sleep/Wake Ratio in Developing Infants

Although there are several reports on ultradian and circadian rhythms in newborns, we found only one report in which infradian periodicities are described for heart-rate measurements in the early stages of human development. Here, we report infradian rhythms in the monthly range in the sleep/wake cycle of four infants studied along 24 consecutive weeks. Our procedure was applied to sleep diary records from four healthy newborns. The data were arranged in binary time series representing sleep (-1) or wake (1) states. These time series were integrated in order to obtain the cumulative sleep/wake time. A measure of the sleep/wake ratio (SWR) was obtained by computing the average slope of the cumulative sleep/wake time. To extract periodicities we applied the Fourier periodogram to the temporal course of the SWR. We found a notorious difference in the SWR pattern among infants. In two infants the SWR showed a marked linear decay, spending more time asleep than awake, while in the two other infants oscillated near zero. We found robust oscillations in all children. In all cases the Fourier periodogram results present significant power in the infradian range. From these results, we suggest that sleep and wake durations are probably modulated by some internal stimuli.

Role of cooperative binding on noise expression

The origin of stochastic fluctuations in gene expression has received considerable attention recently. Fluctuations in gene expression are particularly pronounced in cellular systems because of the small copy number of species undergoing transitions between discrete chemical states and the small size of biological compartments. In this paper, we propose a stochastic model for gene expression regulation including several binding sites, considering elementary reactions only. The model is used to investigate the role of cooperativity on the intrinsic fluctuations of gene expression by means of master-equation formalism. We found that the Hill coefficient and the level of noise increase as the interaction energy between activators increases. Additionally, we show that the model allows one to distinguish between two cooperative binding mechanisms.

Pattern formation in a gene network model with boundary shape dependence

A fundamental task in developmental biology is to identify the mechanisms which drive morphogenesis. Traditionally pattern formation have been modeled mainly using Turing-type mechanisms, where complex patterns arise by symmetry breaking. However, there is a growing experimental evidence that the influence of signals derived from surrounding tissues can contribute to the patterning processes. In this paper, we show that the interplay between the shape of surrounding tissues and a hierarchically organized gene regulatory network can be able to induce stable complex patterns. The rise of these patterns depends strongly on the shape of the surrounding tissues.

Modeling stochastic Ca2+ release from a cluster of IP3-sensitive receptors

We focused our attention on Ca(2+) release from the endoplasmic reticulum through a cluster of inositol(1,4,5)-trisphosphate (IP(3)) receptor channels. The random opening and closing of these receptors introduce stochastic effects that have been observed experimentally. Here, we present a stochastic version of Othmer-Tang model (OTM) for IP(3) receptor clusters. We address the average behavior of the channels in response to IP(3) stimuli. In our stochastic simulation we found that the fraction of open channels versus [IP(3)] follows a Hill curve, whose associate Hill coefficient increases when intracellular Ca(2+) level increase. This finding suggests that feedback from cytosolic Ca(2+) plays a key role in the channel response to IP(3). We also study several aspects of the stochastic properties of Ca(2+) release and we compare with experimental observations.

Complex networks approach to gene expression driven phenotype imaging

MOTIVATION

The need is to visualize and quantify gene expression spatial patterns. Because of their generality for representation of interaction among several elements, complex networks are used to measure the spatial interactions and adjacencies defined by gene expression patterns.

RESULTS

Enhanced visualization of spatial interactions between elements where genes are expressed is possible, allowing the identification of structures which would go unnoticed by using conventional imaging. The quantification of the expression intensity in terms of the node degree and clustering coefficient allows the identification of different types of interactions, yielding insights about cell signaling and differentiation, and providing the basis for comparison and discrimination of the patterns along the developmental stages.

AVAILABILITY

Supplementary Material, including visualizations as well as the basic routines for translating gene expression images into complex networks and obtaining node degree and clustering coefficient measurements, are provided.

CONTACT

luciano@if.sc.usp.br; diambra@univap.br.

Topographical maps as complex networks

The neuronal networks in the mammalian cortex are characterized by the coexistence of hierarchy, modularity, short and long range interactions, spatial correlations, and topographical connections. Particularly interesting, the latter type of organization implies special demands on developing systems in order to achieve precise maps preserving spatial adjacencies, even at the expense of isometry. Although the object of intensive biological research, the elucidation of the main anatomic-functional purposes of the ubiquitous topographical connections in the mammalian brain remains an elusive issue. The present work reports on how recent results from complex network formalism can be used to quantify and model the effect of topographical connections between neuronal cells over the connectivity of the network. While the topographical mapping between two cortical modules is achieved by connecting nearest cells from each module, four kinds of network models are adopted for implementing intramodular connections, including random, preferential-attachment, short-range, and long-range networks. It is shown that, though spatially uniform and simple, topographical connections between modules can lead to major changes in the network properties in some specific cases, depending on intramodular connections schemes, fostering more effective intercommunication between the involved neuronal cells and modules. The possible implications of such effects on cortical operation are discussed.

Detecting epileptic spikes

We present a technique for automatic detection of epileptic spikes in electroencephalogram (EEG) recordings. We used a nonlinear modeling method that enables us to detect rapidly and accurately epileptic behavior in the EEG signal. Our approach is illustrated by an application to interictal activity from a patient with focal epilepsy.

Ciclograma: a tool for detection of rhythmicities in sleep/wake cycles

The Fourier spectral analysis of binary time series (or rectangular signals) causes methodological problems, due to the fact that it is based on sinusoidal functions. We propose a new tool for the detection of periodicities in binary time series, focusing on sleep/wake cycles. This methodology is based on a weighted histogram of cycle durations. In this paper, we compare our methodology with the Fourier spectral analysis on the basis of simulated and real binary data sets of various lengths. We also provide an approach to statistical validation of the periodicities determined with our methodology. Furthermore, we analyze the discriminating power of both methods in terms of standard deviation. Our results indicate that the Ciclograma is much more powerful than Fourier analysis when applied on this type of time series.

Chaos in two-loop negative feedback systems

Multiloop delayed negative feedback systems, with each feedback loop having its own characteristic time lag (delay), are used to describe a great variety of systems: optical systems, neural networks, physiological control systems, etc. Previous investigations have shown that if the number of delayed feedback loops is greater than two, the system can exhibit complex dynamics and chaos, but in the case of two delayed loops only periodic solutions were found. Here we show that a period-doubling cascade and chaotic dynamics are also found in systems with two coupled delayed negative feedback loops.

Information theory approach to learning of the perceptron rule

By recourse to a method based on information theory, we have studied the generalization problem in perceptrons. We considered different a priori distributions about the weights of the teacher perceptron. Our approach allows us to define the information gain from the examples used in the training procedure. The information gain can be used to choose a convenient example set for training the perceptron and to select the transfer function of the student perceptron.

Divergence measure between chaotic attractors

We propose a measure of divergence of probability distributions for quantifying the dissimilarity of two chaotic attractors. This measure is defined in terms of a generalized entropy. We illustrate our procedure by considering the effect of additive noise in the well known Hénon attractor. Finally, we show how our approach allows one to detect nonstationary events in a time series.