Assessing the emergence time of SARS-CoV-2 zoonotic spillover.

Understanding the evolution of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) and its relationship to other coronaviruses in the wild is crucial for preventing future virus outbreaks. While the origin of the SARS-CoV-2 pandemic remains uncertain, mounting evidence suggests the direct involvement of the bat and pangolin coronaviruses in the evolution of the SARS-CoV-2 genome. To unravel the early days of a probable zoonotic spillover event, we analyzed genomic data from various coronavirus strains from both human and wild hosts. Bayesian phylogenetic analysis was performed using multiple datasets, using strict and relaxed clock evolutionary models to estimate the occurrence times of key speciation, gene transfer, and recombination events affecting the evolution of SARS-CoV-2 and its closest relatives. We found strong evidence supporting the presence of temporal structure in datasets containing SARS-CoV-2 variants, enabling us to estimate the time of SARS-CoV-2 zoonotic spillover between August and early October 2019. In contrast, datasets without SARS-CoV-2 variants provided mixed results in terms of temporal structure. However, they allowed us to establish that the presence of a statistically robust clade in the phylogenies of gene S and its receptor-binding (RBD) domain, including two bat (BANAL) and two Guangdong pangolin coronaviruses (CoVs), is due to the horizontal gene transfer of this gene from the bat CoV to the pangolin CoV that occurred in the middle of 2018. Importantly, this clade is closely located to SARS-CoV-2 in both phylogenies. This phylogenetic proximity had been explained by an RBD gene transfer from the Guangdong pangolin CoV to a very recent ancestor of SARS-CoV-2 in some earlier works in the field before the BANAL coronaviruses were discovered. Overall, our study provides valuable insights into the timeline and evolutionary dynamics of the SARS-CoV-2 pandemic.

Intelligent personalized shopping recommendation using clustering and supervised machine learning algorithms.

Next basket recommendation is a critical task in market basket data analysis. It is particularly important in grocery shopping, where grocery lists are an essential part of shopping habits of many customers. In this work, we first present a new grocery Recommender System available on the MyGroceryTour platform. Our online system uses different traditional machine learning (ML) and deep learning (DL) algorithms, and provides recommendations to users in a real-time manner. It aims to help Canadian customers create their personalized intelligent weekly grocery lists based on their individual purchase histories, weekly specials offered in local stores, and product cost and availability information. We perform clustering analysis to partition given customer profiles into four non-overlapping clusters according to their grocery shopping habits. Then, we conduct computational experiments to compare several traditional ML algorithms and our new DL algorithm based on the use of a gated recurrent unit (GRU)-based recurrent neural network (RNN) architecture. Our DL algorithm can be viewed as an extension of DREAM (Dynamic REcurrent bAsket Model) adapted to multi-class (i.e. multi-store) classification, since a given user can purchase recommended products in different grocery stores in which these products are available. Among traditional ML algorithms, the highest average F-score of 0.516 for the considered data set of 831 customers was obtained using Random Forest, whereas our proposed DL algorithm yielded the average F-score of 0.559 for this data set. The main advantage of the presented Recommender System is that our intelligent recommendation is personalized, since a separate traditional ML or DL model is built for each customer considered. Such a personalized approach allows us to outperform the prediction results provided by general state-of-the-art DL models.

Applying novel connectivity networks to wood turtle populations to provide comprehensive conservation management strategies for species at risk.

Genetic diversity within and among populations is frequently used in prioritization processes to rank populations based on their vulnerability or distinctiveness, however, connectivity and gene flow are rarely considered within these frameworks. Using a wood turtle (Glyptemys insculpta) population graph, we introduce BRIDES as a new tool to evaluate populations for conservation purpose without focusing solely on individual nodes. BRIDES characterizes different types of shortest paths among the nodes of a subgraph and compares the shortest paths among the same nodes in a complete network. The main objectives of this study were to (1) introduce a BRIDES selection process to assist conservation biologists in the prioritization of populations, and (2) use different centrality indices and node removal statistics to compare BRIDES results and assess gene flow among wood turtle populations. We constructed six population subgraphs and used a stepwise selection algorithm to choose the optimal number of additional nodes, representing different populations, required to maximize network connectivity under different weighting schemes. Our results demonstrate the robustness of the BRIDES selection process for a given scenario, while inconsistencies were observed among node-based metrics. Results showed repeated selection of certain wood turtle populations, which could have not been predicted following only genetic diversity and distinctiveness estimation, node-based metrics and node removal analysis. Contrary to centrality measures focusing on static networks, BRIDES allowed for the analysis of evolving networks. To our knowledge, this study is the first to apply graph theory for turtle conservation genetics. We show that population graphs can reveal complex gene flow dynamics and population resiliency to local extinction. As such, BRIDES offers an interesting complement to node-based metrics and node removal to better understand the global processes at play when addressing population prioritization frameworks.

SimPlot++: a Python application for representing sequence similarity and detecting recombination.

MOTIVATION: Accurate detection of sequence similarity and homologous recombination are essential parts of many evolutionary analyses. RESULTS: We have developed SimPlot++, an open-source multiplatform application implemented in Python, which can be used to produce publication quality sequence similarity plots using 63 nucleotide and 20 amino acid distance models, to detect intergenic and intragenic recombination events using Φ, Max-χ2, NSS or proportion tests, and to generate and analyze interactive sequence similarity networks. SimPlot++ supports multicore data processing and provides useful distance calculability diagnostics. AVAILABILITY AND IMPLEMENTATION: SimPlot++ is freely available on GitHub at: https://github.com/Stephane-S/Simplot_PlusPlus, as both an executable file (for Windows) and Python scripts (for Windows/Linux/MacOS).

Proteolytic Processing of Plant Proteins by Potyvirus NIa Proteases.

The NIa protease of potyviruses is a chymotrypsin-like cysteine protease related to the picornavirus 3C protease. It is also a multifunctional protein known to play multiple roles during virus infection. Picornavirus 3C proteases cleave hundreds of host proteins to facilitate virus infection. However, whether or not potyvirus NIa proteases cleave plant proteins has so far not been tested. Regular expression search using the cleavage site consensus sequence [EQN]xVxH[QE]/[SGTA] for the plum pox virus (PPV) protease identified 90 to 94 putative cleavage events in the proteomes of Prunus persica (a crop severely affected by PPV), Arabidopsis thaliana, and Nicotiana benthamiana (two experimental hosts). In vitro processing assays confirmed cleavage of six A. thaliana and five P. persica proteins by the PPV protease. These proteins were also cleaved in vitro by the protease of turnip mosaic virus (TuMV), which has a similar specificity. We confirmed in vivo cleavage of a transiently expressed tagged version of AtEML2, an EMSY-like protein belonging to a family of nuclear histone readers known to be involved in pathogen resistance. Cleavage of AtEML2 was efficient and was observed in plants that coexpressed the PPV or TuMV NIa proteases or in plants that were infected with TuMV. We also showed partial in vivo cleavage of AtDUF707, a membrane protein annotated as lysine ketoglutarate reductase trans-splicing protein. Although cleavage of the corresponding endogenous plant proteins remains to be confirmed, the results show that a plant virus protease can cleave host proteins during virus infection and highlight a new layer of plant-virus interactions. IMPORTANCE Viruses are highly adaptive and use multiple molecular mechanisms to highjack or modify the cellular resources to their advantage. They must also counteract or evade host defense responses. One well-characterized mechanism used by vertebrate viruses is the proteolytic cleavage of host proteins to inhibit the activities of these proteins and/or to produce cleaved protein fragments that are beneficial to the virus infection cycle. Even though almost half of the known plant viruses encode at least one protease, it was not known whether plant viruses employ this strategy. Using an in silico prediction approach and the well-characterized specificity of potyvirus NIa proteases, we were able to identify hundreds of putative cleavage sites in plant proteins, several of which were validated by downstream experiments. It can be anticipated that many other plant virus proteases also cleave host proteins and that the identification of these cleavage events will lead to novel antiviral strategies.

Genomic Profiling of Virulence in the Soybean Cyst Nematode Using Single-Nematode Sequencing.

Soybean cyst nematode (SCN) is one of the most important diseases in soybean. Currently, the main management strategy relies on planting resistant cultivars. However, the overuse of a single resistance source has led to the selection of virulent SCN populations, although the mechanisms by which the nematode overcomes the resistance genes remain unknown. In this study, we used a nematode-adapted single-cell RNA-seq approach to identify SCN genes potentially involved in resistance breakdown in Peking and PI 88788 parental soybean lines. We established for the first time the full transcriptome of single SCN individuals allowing us to identify a list of putative virulence genes against both major SCN resistance sources. Our analysis identified 48 differentially expressed putative effectors (secreted proteins required for infection) alongside 40 effectors showing evidence of novel structural variants, and 11 effector genes containing phenotype-specific sequence polymorphisms. Additionally, a differential expression analysis revealed an interesting phenomenon of coexpressed gene regions with some containing putative effectors. The selection of virulent SCN individuals on Peking resulted in a profoundly altered transcriptome, especially for genes known to be involved in parasitism. Several sequence polymorphisms were also specific to these virulent nematodes and could potentially play a role in the acquisition of nematode virulence. On the other hand, the transcriptome of virulent individuals on PI 88788 was very similar to avirulent ones with the exception of a few genes, which suggest a distinct virulence strategy to Peking.

Introducing Trait Networks to Elucidate the Fluidity of Organismal Evolution Using Palaeontological Data.

Explaining the evolution of animals requires ecological, developmental, paleontological, and phylogenetic considerations because organismal traits are affected by complex evolutionary processes. Modeling a plurality of processes, operating at distinct time-scales on potentially interdependent traits, can benefit from approaches that are complementary treatments to phylogenetics. Here, we developed an inclusive network approach, implemented in the command line software ComponentGrapher, and analyzed trait co-occurrence of rhinocerotoid mammals. We identified stable, unstable, and pivotal traits, as well as traits contributing to complexes, that may follow to a common developmental regulation, that point to an early implementation of the postcranial Bauplan among rhinocerotoids. Strikingly, most identified traits are highly dissociable, used repeatedly in distinct combinations and in different taxa, which usually do not form clades. Therefore, the genes encoding these traits are likely recruited into novel gene regulation networks during the course of evolution. Our evo-systemic framework, generalizable to other evolved organizations, supports a pluralistic modeling of organismal evolution, including trees and networks.

What determines host specificity in hyperspecialized plant parasitic nematodes?

BACKGROUND: In hyperspecialized parasites, the ability to grow on a particular host relies on specific virulence factors called effectors. These excreted proteins are involved in the molecular mechanisms of parasitism and distinguish virulent pathogens from non-virulent related species. The potato cyst nematodes (PCN) Globodera rostochiensis and G. pallida are major plant-parasitic nematodes developing on numerous solanaceous species including potato. Their close relatives, G. tabacum and G. mexicana are stimulated by potato root diffusate but unable to establish a feeding site on this plant host. RESULTS: RNA sequencing was used to characterize transcriptomic differences among these four Globodera species and to identify genes associated with host specificity. We identified seven transcripts that were unique to PCN species, including a protein involved in ubiquitination. We also found 545 genes that were differentially expressed between PCN and non-PCN species, including 78 genes coding for effector proteins, which represent more than a 6-fold enrichment compared to the whole transcriptome. Gene polymorphism analysis identified 359 homozygous non-synonymous variants showing a strong evidence for selection in PCN species. CONCLUSIONS: Overall, we demonstrated that the determinant of host specificity resides in the regulation of essential effector gene expression that could be under the control of a single or of very few regulatory genes. Such genes are therefore promising targets for the development of novel and more sustainable resistances against potato cyst nematodes.

The draft genome of Ditylenchus dipsaci.

Ditylenchus dipsaci is a devastating pest to many crops worldwide. We present the first genome sequence for this species, produced with PacBio sequencing and assembled with CANU.Ditylenchus dipsaci is a devastating pest to many crops worldwide. We present the first genome sequence for this species, produced with PacBio sequencing and assembled with CANU.

The genome of the soybean cyst nematode (Heterodera glycines) reveals complex patterns of duplications involved in the evolution of parasitism genes.

BACKGROUND: Heterodera glycines, commonly referred to as the soybean cyst nematode (SCN), is an obligatory and sedentary plant parasite that causes over a billion-dollar yield loss to soybean production annually. Although there are genetic determinants that render soybean plants resistant to certain nematode genotypes, resistant soybean cultivars are increasingly ineffective because their multi-year usage has selected for virulent H. glycines populations. The parasitic success of H. glycines relies on the comprehensive re-engineering of an infection site into a syncytium, as well as the long-term suppression of host defense to ensure syncytial viability. At the forefront of these complex molecular interactions are effectors, the proteins secreted by H. glycines into host root tissues. The mechanisms of effector acquisition, diversification, and selection need to be understood before effective control strategies can be developed, but the lack of an annotated genome has been a major roadblock. RESULTS: Here, we use PacBio long-read technology to assemble a H. glycines genome of 738 contigs into 123 Mb with annotations for 29,769 genes. The genome contains significant numbers of repeats (34%), tandem duplicates (18.7 Mb), and horizontal gene transfer events (151 genes). A large number of putative effectors (431 genes) were identified in the genome, many of which were found in transposons. CONCLUSIONS: This advance provides a glimpse into the host and parasite interplay by revealing a diversity of mechanisms that give rise to virulence genes in the soybean cyst nematode, including: tandem duplications containing over a fifth of the total gene count, virulence genes hitchhiking in transposons, and 107 horizontal gene transfers not reported in other plant parasitic nematodes thus far. Through extensive characterization of the H. glycines genome, we provide new insights into H. glycines biology and shed light onto the mystery underlying complex host-parasite interactions. This genome sequence is an important prerequisite to enable work towards generating new resistance or control measures against H. glycines.

Genome Scans Reveal Homogenization and Local Adaptations in Populations of the Soybean Cyst Nematode.

Determining the adaptive potential of alien invasive species in a new environment is a key concern for risk assessment. As climate change is affecting local climatic conditions, widespread modifications in species distribution are expected. Therefore, the genetic mechanisms underlying local adaptations must be understood in order to predict future species distribution. The soybean cyst nematode (SCN), Heterodera glycines Ichinohe, is a major pathogen of soybean that was accidentally introduced in most soybean-producing countries. In this study, we explored patterns of genetic exchange between North American populations of SCN and the effect of isolation by geographical distance. Genotyping-by-sequencing was used to sequence and compare 64 SCN populations from the United States and Canada. At large scale, only a weak correlation was found between genetic distance (Wright's fixation index, FST) and geographic distance, but local effects were strong in recently infested states. Our results also showed a high level of genetic differentiation within some populations, allowing them to adapt to new environments and become established in new soybean-producing areas. Bayesian genome scan methods identified 15 loci under selection for climatic or geographic co-variables. Among these loci, two non-synonymous mutations were detected in SMAD-4 (mothers against decapentaplegic homolog 4) and DOP-3 (dopamine receptor 3). High-impact variants linked to these loci by genetic hitchhiking were also highlighted as putatively involved in local adaptation of SCN populations to new environments. Overall, it appears that strong selective pressure by resistant cultivars is causing a large scale homogenization with virulent populations.

Subtle differences in selective pressures applied on the envelope gene of HIV-1 in pregnant versus non-pregnant women.

Pregnancy is associated with modulations of maternal immunity that contribute to foeto-maternal tolerance. To understand whether and how these alterations impact antiviral immunity, a detailed cross-sectional analysis of selective pressures exerted on HIV-1 envelope amino-acid sequences was performed in a group of pregnant (n = 32) and non-pregnant (n = 44) HIV-infected women in absence of treatment with antiretroviral therapy (ART). Independent of HIV-1 subtype, p-distance, dN and dS were all strongly correlated with one another but were not significantly different in pregnant as compared to non-pregnant patients. Differential levels of selective pressure applied on different Env subdomains displayed similar yet non-identical patterns between the two groups, with pressure applied on C1 being significantly lower in constant regions C1 and C2 than in V1, V2, V3 and C3. To draw a general picture of the selection applied on the envelope and compensate for inter-individual variations, we performed a binomial test on selection frequency data pooled from pregnant and non-pregnant women. This analysis uncovered 42 positions, present in both groups, exhibiting statistically-significant frequency of selection that invariably mapped to the surface of the Env protein, with the great majority located within epitopes recognized by Env-specific antibodies or sites associated with the development of cross-reactive neutralizing activity. The median frequency of occurrence of positive selection per site was significantly lower in pregnant versus non-pregnant women. Furthermore, examination of the distribution of positively selected sites using a hypergeometric test revealed that only 2 positions (D137 and S142) significantly differed between the 2 groups. Taken together, these result indicate that pregnancy is associated with subtle yet distinctive changes in selective pressures exerted on the HIV-1 Env protein that are compatible with transient modulations of maternal immunity.

Using hybridization networks to retrace the evolution of Indo-European languages.

BACKGROUND: Curious parallels between the processes of species and language evolution have been observed by many researchers. Retracing the evolution of Indo-European (IE) languages remains one of the most intriguing intellectual challenges in historical linguistics. Most of the IE language studies use the traditional phylogenetic tree model to represent the evolution of natural languages, thus not taking into account reticulate evolutionary events, such as language hybridization and word borrowing which can be associated with species hybridization and horizontal gene transfer, respectively. More recently, implicit evolutionary networks, such as split graphs and minimal lateral networks, have been used to account for reticulate evolution in linguistics. RESULTS: Striking parallels existing between the evolution of species and natural languages allowed us to apply three computational biology methods for reconstruction of phylogenetic networks to model the evolution of IE languages. We show how the transfer of methods between the two disciplines can be achieved, making necessary methodological adaptations. Considering basic vocabulary data from the well-known Dyen's lexical database, which contains word forms in 84 IE languages for the meanings of a 200-meaning Swadesh list, we adapt a recently developed computational biology algorithm for building explicit hybridization networks to study the evolution of IE languages and compare our findings to the results provided by the split graph and galled network methods. CONCLUSION: We conclude that explicit phylogenetic networks can be successfully used to identify donors and recipients of lexical material as well as the degree of influence of each donor language on the corresponding recipient languages. We show that our algorithm is well suited to detect reticulate relationships among languages, and present some historical and linguistic justification for the results obtained. Our findings could be further refined if relevant syntactic, phonological and morphological data could be analyzed along with the available lexical data.

BRIDES: A New Fast Algorithm and Software for Characterizing Evolving Similarity Networks Using Breakthroughs, Roadblocks, Impasses, Detours, Equals and Shortcuts.

Various types of genome and gene similarity networks along with their characteristics have been increasingly used for retracing different kinds of evolutionary and ecological relationships. Here, we present a new polynomial time algorithm and the corresponding software (BRIDES) to provide characterization of different types of paths existing in evolving (or augmented) similarity networks under the constraint that such paths contain at least one node that was not present in the original network. These different paths are denoted as Breakthroughs, Roadblocks, Impasses, Detours, Equal paths, and Shortcuts. The analysis of their distribution can allow discriminating among different evolutionary hypotheses concerning genomes or genes at hand. Our approach is based on an original application of the popular shortest path Dijkstra's and Yen's algorithms. The C++ and R versions of the BRIDES program are freely available at: https://github.com/etiennelord/BRIDES.

An integrative approach to identify hexaploid wheat miRNAome associated with development and tolerance to abiotic stress.

BACKGROUND: Wheat is a major staple crop with broad adaptability to a wide range of environmental conditions. This adaptability involves several stress and developmentally responsive genes, in which microRNAs (miRNAs) have emerged as important regulatory factors. However, the currently used approaches to identify miRNAs in this polyploid complex system focus on conserved and highly expressed miRNAs avoiding regularly those that are often lineage-specific, condition-specific, or appeared recently in evolution. In addition, many environmental and biological factors affecting miRNA expression were not yet considered, resulting still in an incomplete repertoire of wheat miRNAs. RESULTS: We developed a conservation-independent technique based on an integrative approach that combines machine learning, bioinformatic tools, biological insights of known miRNA expression profiles and universal criteria of plant miRNAs to identify miRNAs with more confidence. The developed pipeline can potentially identify novel wheat miRNAs that share features common to several species or that are species specific or clade specific. It allowed the discovery of 199 miRNA candidates associated with different abiotic stresses and development stages. We also highlight from the raw data 267 miRNAs conserved with 43 miRBase families. The predicted miRNAs are highly associated with abiotic stress responses, tolerance and development. GO enrichment analysis showed that they may play biological and physiological roles associated with cold, salt and aluminum (Al) through auxin signaling pathways, regulation of gene expression, ubiquitination, transport, carbohydrates, gibberellins, lipid, glutathione and secondary metabolism, photosynthesis, as well as floral transition and flowering. CONCLUSION: This approach provides a broad repertoire of hexaploid wheat miRNAs associated with abiotic stress responses, tolerance and development. These valuable resources of expressed wheat miRNAs will help in elucidating the regulatory mechanisms involved in freezing and Al responses and tolerance mechanisms as well as for development and flowering. In the long term, it may help in breeding stress tolerant plants.

Classification of bioinformatics workflows using weighted versions of partitioning and hierarchical clustering algorithms.

BACKGROUND: Workflows, or computational pipelines, consisting of collections of multiple linked tasks are becoming more and more popular in many scientific fields, including computational biology. For example, simulation studies, which are now a must for statistical validation of new bioinformatics methods and software, are frequently carried out using the available workflow platforms. Workflows are typically organized to minimize the total execution time and to maximize the efficiency of the included operations. Clustering algorithms can be applied either for regrouping similar workflows for their simultaneous execution on a server, or for dispatching some lengthy workflows to different servers, or for classifying the available workflows with a view to performing a specific keyword search. RESULTS: In this study, we consider four different workflow encoding and clustering schemes which are representative for bioinformatics projects. Some of them allow for clustering workflows with similar topological features, while the others regroup workflows according to their specific attributes (e.g. associated keywords) or execution time. The four types of workflow encoding examined in this study were compared using the weighted versions of k-means and k-medoids partitioning algorithms. The Calinski-Harabasz, Silhouette and logSS clustering indices were considered. Hierarchical classification methods, including the UPGMA, Neighbor Joining, Fitch and Kitsch algorithms, were also applied to classify bioinformatics workflows. Moreover, a novel pairwise measure of clustering solution stability, which can be computed in situations when a series of independent program runs is carried out, was introduced. CONCLUSIONS: Our findings based on the analysis of 220 real-life bioinformatics workflows suggest that the weighted clustering models based on keywords information or tasks execution times provide the most appropriate clustering solutions. Using datasets generated by the Armadillo and Taverna scientific workflow management system, we found that the weighted cosine distance in association with the k-medoids partitioning algorithm and the presence-absence workflow encoding provided the highest values of the Rand index among all compared clustering strategies. The introduced clustering stability indices, PS and PSG, can be effectively used to identify elements with a low clustering support.

Armadillo 1.1: an original workflow platform for designing and conducting phylogenetic analysis and simulations.

In this paper we introduce Armadillo v1.1, a novel workflow platform dedicated to designing and conducting phylogenetic studies, including comprehensive simulations. A number of important phylogenetic and general bioinformatics tools have been included in the first software release. As Armadillo is an open-source project, it allows scientists to develop their own modules as well as to integrate existing computer applications. Using our workflow platform, different complex phylogenetic tasks can be modeled and presented in a single workflow without any prior knowledge of programming techniques. The first version of Armadillo was successfully used by professors of bioinformatics at Université du Quebec à Montreal during graduate computational biology courses taught in 2010-11. The program and its source code are freely available at: .

Murine preosteoblast differentiation induced by a peptide derived from bone morphogenetic proteins-9.

Bone morphogenetic proteins (BMPs) increase the differentiation of osteoblasts implicated in bone formation and repair. In a previous study, we demonstrated that a peptide derived from BMP-9 (pBMP-9) at 400 ng/mL inhibited murine preosteoblasts MC3T3-E1 proliferation. Here, we compared the effects of equimolar concentrations of BMP-2 (50 ng/mL), BMP-9 (42.3 ng/mL), and pBMP-9 (4.52 ng/mL) on the differentiation of MC3T3-E1 in a serum-free medium. Like BMP-2, BMP-9 and pBMP-9 activated the Smad pathway. In contrary to BMP-2, the Smad phosphorylation induced by BMP-9 and pBMP-9 is not prevented by noggin, an extracellular antagonist of BMP-2. Further, BMP-9 and pBMP-9 increased, dose dependently, alkaline phosphatase activity, an early marker of osteoblast differentiation, after 1 day. Quantitative real-time polymerase chain reaction analysis demonstrated that BMP-2, BMP-9, and pBMP-9 (4.52 or 400 ng/mL) all activated the transcription of Runx2, Osterix, type I collagen alpha1 chain, and Osteocalcin genes within day 6. Alizarin red S quantification demonstrated that pBMP-9 (400 ng/mL) and pBMP-9 (4.52 ng/mL) allowed a slight deposition of Ca(2+) in the extracellular matrix of cells within 12 and 18 days, respectively. Therefore, pBMP-9 might be a promising replacement for costly BMP in tissue engineering applications that require a well-defined serum-free medium.

Bone cells-biomaterials interactions.

With the aging population, the incidence of bone defects due to fractures, tumors and infection will increase. Therefore, bone replacement will become an ever bigger and more costly problem. The current standard for bone replacement is autograft, because these transplants are osteoconductive and osteoinductive. However, harvesting an autograft requires additional surgery at the donor site that is related to high level of morbidity. In addition, the quantity of bone tissue that can be harvested is limited. These limitations have necessitated the pursuit of alternatives using biomaterials. The control of bone tissue cell adhesion to biomaterials is an important requirement for the successful incorporation of implants or the colonization of scaffolds for tissue repair. Controlling cells-biomaterials interactions appears of prime importance to influence subsequent biological processes such as cell proliferation and differentiation. Therefore, interactions of cells with biomaterials have been widely studied especially on two-dimensional systems. This review focuses on these interactions.

Short-term effects of adhesion peptides on the responses of preosteoblasts to pBMP-9.

Adhesion peptides are currently used to enhance the interactions of osteoblasts with biomaterials. However, little is known about the effects of adhesion peptides on cell responses to growth factors, especially the bone morphogenetic proteins (BMPs). We used adhesion peptides Ac-CGGNGERPRGDTYRAY-NH(2) (pRGD), derived from bone sialoprotein, and Ac-CGGDGEA-NH(2) (pDGEA), derived from collagen, which interact with alpha(v)beta(3) and alpha(2)beta(1) integrins, respectively. We analyzed the effects of pRGD- and pDGEA-coated polystyrene (PS) on the responses of murine MC3T3-E1 preosteoblasts to a peptide derived from human BMP-9 (pBMP-9) in serum-free medium. After 1h, pRGD favoured interactions with alpha(v) while pDGEA bound beta(1) integrin subunits. Adding pBMP-9 (400 ng/mL) increased the amount of alpha(v) integrin subunits in cell membranes on pRGD-coated PS, but had no effect on beta(1) integrin subunits. Only on this substratum, collagen type I mRNA was enhanced and the addition of pBMP-9 promoted the early cell differentiation, increasing their alkaline phosphatase (ALP) activity within 24 h. These cells also organized beta(1) integrin subunits at their focal adhesion points. Inhibiting alpha(2)beta(1) integrins by pDGEA pre-treatment decreased this ALP activity. It is therefore important to understand the impact of adhesion peptides on the early cell responses to growth factors in order to improve biomimetic materials.