Microalgal co-cultivation -recent methods, trends in omic-studies, applications, and future challenges.

The burgeoning human population has resulted in an augmented demand for raw materials and energy sources, which in turn has led to a deleterious environmental impact marked by elevated greenhouse gas (GHG) emissions, acidification of water bodies, and escalating global temperatures. Therefore, it is imperative that modern society develop sustainable technologies to avert future environmental degradation and generate alternative bioproduct-producing technologies. A promising approach to tackling this challenge involves utilizing natural microbial consortia or designing synthetic communities of microorganisms as a foundation to develop diverse and sustainable applications for bioproduct production, wastewater treatment, GHG emission reduction, energy crisis alleviation, and soil fertility enhancement. Microalgae, which are photosynthetic microorganisms that inhabit aquatic environments and exhibit a high capacity for CO2 fixation, are particularly appealing in this context. They can convert light energy and atmospheric CO2 or industrial flue gases into valuable biomass and organic chemicals, thereby contributing to GHG emission reduction. To date, most microalgae cultivation studies have focused on monoculture systems. However, maintaining a microalgae monoculture system can be challenging due to contamination by other microorganisms (e.g., yeasts, fungi, bacteria, and other microalgae species), which can lead to low productivity, culture collapse, and low-quality biomass. Co-culture systems, which produce robust microorganism consortia or communities, present a compelling strategy for addressing contamination problems. In recent years, research and development of innovative co-cultivation techniques have substantially increased. Nevertheless, many microalgae co-culturing technologies remain in the developmental phase and have yet to be scaled and commercialized. Accordingly, this review presents a thorough literature review of research conducted in the last few decades, exploring the advantages and disadvantages of microalgae co-cultivation systems that involve microalgae-bacteria, microalgae-fungi, and microalgae-microalgae/algae systems. The manuscript also addresses diverse uses of co-culture systems, and growing methods, and includes one of the most exciting research areas in co-culturing systems, which are omic studies that elucidate different interaction mechanisms among microbial communities. Finally, the manuscript discusses the economic viability, future challenges, and prospects of microalgal co-cultivation methods.

Reshaping the research landscape in Brazil.

Brazil would benefit from a long-term strategy for science and innovation that improves the standing of both science and scientists in the country.

From Feasting to Fasting: The Arginine Pathway as a Metabolic Switch in Nitrogen-Deprived Chlamydomonas reinhardtii.

The metabolism of the model microalgae Chlamydomonas reinhardtii under nitrogen deprivation is of special interest due to its resulting increment of triacylglycerols (TAGs), that can be applied in biotechnological applications. However, this same condition impairs cell growth, which may limit the microalgae's large applications. Several studies have identified significant physiological and molecular changes that occur during the transition from an abundant to a low or absent nitrogen supply, explaining in detail the differences in the proteome, metabolome and transcriptome of the cells that may be responsible for and responsive to this condition. However, there are still some intriguing questions that reside in the core of the regulation of these cellular responses that make this process even more interesting and complex. In this scenario, we reviewed the main metabolic pathways that are involved in the response, mining and exploring, through a reanalysis of omics data from previously published datasets, the commonalities among the responses and unraveling unexplained or non-explored mechanisms of the possible regulatory aspects of the response. Proteomics, metabolomics and transcriptomics data were reanalysed using a common strategy, and an in silico gene promoter motif analysis was performed. Together, these results identified and suggested a strong association between the metabolism of amino acids, especially arginine, glutamate and ornithine pathways to the production of TAGs, via the de novo synthesis of lipids. Furthermore, our analysis and data mining indicate that signalling cascades orchestrated with the indirect participation of phosphorylation, nitrosylation and peroxidation events may be essential to the process. The amino acid pathways and the amount of arginine and ornithine available in the cells, at least transiently during nitrogen deprivation, may be in the core of the post-transcriptional, metabolic regulation of this complex phenomenon. Their further exploration is important to the discovery of novel advances in the understanding of microalgae lipids' production.

The experience of teaching introductory programming skills to bioscientists in Brazil.

Computational biology has gained traction as an independent scientific discipline over the last years in South America. However, there is still a growing need for bioscientists, from different backgrounds, with different levels, to acquire programming skills, which could reduce the time from data to insights and bridge communication between life scientists and computer scientists. Python is a programming language extensively used in bioinformatics and data science, which is particularly suitable for beginners. Here, we describe the conception, organization, and implementation of the Brazilian Python Workshop for Biological Data. This workshop has been organized by graduate and undergraduate students and supported, mostly in administrative matters, by experienced faculty members since 2017. The workshop was conceived for teaching bioscientists, mainly students in Brazil, on how to program in a biological context. The goal of this article was to share our experience with the 2020 edition of the workshop in its virtual format due to the Coronavirus Disease 2019 (COVID-19) pandemic and to compare and contrast this year's experience with the previous in-person editions. We described a hands-on and live coding workshop model for teaching introductory Python programming. We also highlighted the adaptations made from in-person to online format in 2020, the participants' assessment of learning progression, and general workshop management. Lastly, we provided a summary and reflections from our personal experiences from the workshops of the last 4 years. Our takeaways included the benefits of the learning from learners' feedback (LLF) that allowed us to improve the workshop in real time, in the short, and likely in the long term. We concluded that the Brazilian Python Workshop for Biological Data is a highly effective workshop model for teaching a programming language that allows bioscientists to go beyond an initial exploration of programming skills for data analysis in the medium to long term.

A Reductionist Approach Using Primary and Metastatic Cell-Derived Extracellular Vesicles Reveals Hub Proteins Associated with Oral Cancer Prognosis.

Oral squamous cell carcinoma (OSCC) has high mortality rates that are largely associated with lymph node metastasis. However, the molecular mechanisms that drive OSCC metastasis are unknown. Extracellular vesicles (EVs) are membrane-bound particles that play a role in intercellular communication and impact cancer development and progression. Thus, profiling EVs would be of great significance to decipher their role in OSCC metastasis. For that purpose, we used a reductionist approach to map the proteomic, miRNA, metabolomic, and lipidomic profiles of EVs derived from human primary tumor (SCC-9) cells and matched lymph node metastatic (LN1) cells. Distinct omics profiles were associated with the metastatic phenotype, including 670 proteins, 217 miRNAs, 26 metabolites, and 63 lipids differentially abundant between LN1 cell- and SCC-9 cell-derived EVs. A multi-omics integration identified 11 'hub proteins' significantly decreased at the metastatic site compared with primary tumor-derived EVs. We confirmed the validity of these findings with analysis of data from multiple public databases and found that low abundance of seven 'hub proteins' in EVs from metastatic lymph nodes (ALDH7A1, CAD, CANT1, GOT1, MTHFD1, PYGB, and SARS) is correlated with reduced survival and tumor aggressiveness in patients with cancer. In summary, this multi-omics approach identified proteins transported by EVs that are associated with metastasis and which may potentially serve as prognostic markers in OSCC.

Introduction: Advances in Plant Omics and Systems Biology.

How the complexity of biological systems can be understood is currently limited by the amount of biological information we have available to be incorporate in the vastitude of possibilities that could represent how a biological organism function. This point of view is, of course, alive under the paradigm that describes a living thing as a whole that could never be interpreted as to the sole understanding of its separated parts.If we are going to achieve the knowledge to understand all the complex relations between the molecules, pathways, organelles, cells, organs, phenotypes, and environments is unknown. However, that is exactly what moves us toward digging the most profound nature of relationships present in the living organisms.During the last 20 years, a big workforce was dedicated to the development of techniques, instruments, and scientific approaches that guided a whole new generation of scientists into the universe of omics approaches. The implementation of technological advances in several omics applications, such as transcriptomics, proteomics, and metabolomics, has brought to light the information that nowadays reshape our previous thinking on specific aspects of plant sciences, including growth, development, organ communication, chromatin states, and metabolism, not to mention the underpinning role of regulatory mechanisms that in many cases are essentially the basis for the phenotypical expression of a biological phenomenon and plants adaptation to their environment.In this chapter, some of the original concepts of complex systems theory were briefly discussed, and examples of omics approaches that are contributing to uncovering emergent characteristics of plants are presented and discussed. The combination of several experimental and computational or mathematical approaches indicated that there is room for improvements and novel discoveries. However, the level of complexity of biological systems seems to require and demand us to unify efforts toward the integration of the large omics datasets already available and the ones to come. This unification may represent the necessary breakthrough to the achievement of the understanding of complex phenomena. To do so, the inclusion of systems biology thinking into the training of undergraduate and graduate students of plant sciences and related areas seems to be also a contribution that is necessary to be organized and implemented in a worldwide scale.

Plant Proteomics and Systems Biology.

Proteome analysis of model and non-model plants is a genuine scientific field in expansion. Several technological advances have contributed to the implementation of different proteomics approaches for qualitative and quantitative analysis of the dynamics of cellular responses at the protein level. The design of time-resolved experiments and the emergent use of multiplexed proteome analysis using chemical or isotopic and isobaric labeling strategies as well as label-free approaches are generating a vast amount of proteomics data that is going to be essential for analysis of protein posttranslational modifications and implementation of systems biology approaches. Through the target proteomics analysis, especially the ones that combine the untargeted methods, we should expect an improvement in the completeness of the identification of proteome and reveal nuances of regulatory cellular mechanisms related to plant development and responses to environmental stresses. Both genomic sequencing and proteomic advancements in the last decades coupled to integrative data analysis are enriching biological information that was once confined to model plants. Therewith, predictions of a changing environment places proteomics as an especially useful tool for crops performance.

Gene regulatory networks on transfer entropy (GRNTE): a novel approach to reconstruct gene regulatory interactions applied to a case study for the plant pathogen Phytophthora infestans.

BACKGROUND: The increasing amounts of genomics data have helped in the understanding of the molecular dynamics of complex systems such as plant and animal diseases. However, transcriptional regulation, although playing a central role in the decision-making process of cellular systems, is still poorly understood. In this study, we linked expression data with mathematical models to infer gene regulatory networks (GRN). We present a simple yet effective method to estimate transcription factors' GRNs from transcriptional data. METHOD: We defined interactions between pairs of genes (edges in the GRN) as the partial mutual information between these genes that takes into account time and possible lags in time from one gene in relation to another. We call this method Gene Regulatory Networks on Transfer Entropy (GRNTE) and it corresponds to Granger causality for Gaussian variables in an autoregressive model. To evaluate the reconstruction accuracy of our method, we generated several sub-networks from the GRN of the eukaryotic yeast model, Saccharomyces cerevisae. Then, we applied this method using experimental data of the plant pathogen Phytophthora infestans. We evaluated the transcriptional expression levels of 48 transcription factors of P. infestans during its interaction with one moderately resistant and one susceptible cultivar of yellow potato (Solanum tuberosum group Phureja), using RT-qPCR. With these data, we reconstructed the regulatory network of P. infestans during its interaction with these hosts. RESULTS: We first evaluated the performance of our method, based on the transfer entropy (GRNTE), on eukaryotic datasets from the GRNs of the yeast S. cerevisae. Results suggest that GRNTE is comparable with the state-of-the-art methods when the parameters for edge detection are properly tuned. In the case of P. infestans, most of the genes considered in this study, showed a significant change in expression from the onset of the interaction (0 h post inoculum - hpi) to the later time-points post inoculation. Hierarchical clustering of the expression data discriminated two distinct periods during the infection: from 12 to 36 hpi and from 48 to 72 hpi for both the moderately resistant and susceptible cultivars. These distinct periods could be associated with two phases of the life cycle of the pathogen when infecting the host plant: the biotrophic and necrotrophic phases. CONCLUSIONS: Here we presented an algorithmic solution to the problem of network reconstruction in time series data. This analytical perspective makes use of the dynamic nature of time series data as it relates to intrinsically dynamic processes such as transcription regulation, were multiple elements of the cell (e.g., transcription factors) act simultaneously and change over time. We applied the algorithm to study the regulatory network of P. infestans during its interaction with two hosts which differ in their level of resistance to the pathogen. Although the gene expression analysis did not show differences between the two hosts, the results of the GRN analyses evidenced rewiring of the genes' interactions according to the resistance level of the host. This suggests that different regulatory processes are activated in response to different environmental cues. Applications of our methodology showed that it could reliably predict where to place edges in the transcriptional networks and sub-networks. The experimental approach used here can help provide insights on the biological role of these interactions on complex processes such as pathogenicity. The code used is available at https://github.com/jccastrog/GRNTE under GNU general public license 3.0.

Systems Analysis of the Response of Photosynthesis, Metabolism, and Growth to an Increase in Irradiance in the Photosynthetic Model Organism Chlamydomonas reinhardtii.

We investigated the systems response of metabolism and growth after an increase in irradiance in the nonsaturating range in the algal model Chlamydomonas reinhardtii. In a three-step process, photosynthesis and the levels of metabolites increased immediately, growth increased after 10 to 15 min, and transcript and protein abundance responded by 40 and 120 to 240 min, respectively. In the first phase, starch and metabolites provided a transient buffer for carbon until growth increased. This uncouples photosynthesis from growth in a fluctuating light environment. In the first and second phases, rising metabolite levels and increased polysome loading drove an increase in fluxes. Most Calvin-Benson cycle (CBC) enzymes were substrate-limited in vivo, and strikingly, many were present at higher concentrations than their substrates, explaining how rising metabolite levels stimulate CBC flux. Rubisco, fructose-1,6-biosphosphatase, and seduheptulose-1,7-bisphosphatase were close to substrate saturation in vivo, and flux was increased by posttranslational activation. In the third phase, changes in abundance of particular proteins, including increases in plastidial ATP synthase and some CBC enzymes, relieved potential bottlenecks and readjusted protein allocation between different processes. Despite reasonable overall agreement between changes in transcript and protein abundance (R2 = 0.24), many proteins, including those in photosynthesis, changed independently of transcript abundance.

A step-by-step protocol for formaldehyde-assisted isolation of regulatory elements from Arabidopsis thaliana.

The control of gene expression by transcriptional regulators and other types of functionally relevant DNA transactions such as chromatin remodeling and replication underlie a vast spectrum of biological processes in all organisms. DNA transactions require the controlled interaction of proteins with DNA sequence motifs which are often located in nucleosome-depleted regions (NDRs) of the chromatin. Formaldehyde-assisted isolation of regulatory elements (FAIRE) has been established as an easy-to-implement method for the isolation of NDRs from a number of eukaryotic organisms, and it has been successfully employed for the discovery of new regulatory segments in genomic DNA from, for example, yeast, Drosophila, and humans. Until today, however, FAIRE has only rarely been employed in plant research and currently no detailed FAIRE protocol for plants has been published. Here, we provide a step-by-step FAIRE protocol for NDR discovery in Arabidopsis thaliana. We demonstrate that NDRs isolated from plant chromatin are readily amenable to quantitative polymerase chain reaction and next-generation sequencing. Only minor modification of the FAIRE protocol will be needed to adapt it to other plants, thus facilitating the global inventory of regulatory regions across species.

Genome-wide identification of regulatory elements and reconstruction of gene regulatory networks of the green alga Chlamydomonas reinhardtii under carbon deprivation.

The unicellular green alga Chlamydomonas reinhardtii is a long-established model organism for studies on photosynthesis and carbon metabolism-related physiology. Under conditions of air-level carbon dioxide concentration [CO2], a carbon concentrating mechanism (CCM) is induced to facilitate cellular carbon uptake. CCM increases the availability of carbon dioxide at the site of cellular carbon fixation. To improve our understanding of the transcriptional control of the CCM, we employed FAIRE-seq (formaldehyde-assisted Isolation of Regulatory Elements, followed by deep sequencing) to determine nucleosome-depleted chromatin regions of algal cells subjected to carbon deprivation. Our FAIRE data recapitulated the positions of known regulatory elements in the promoter of the periplasmic carbonic anhydrase (Cah1) gene, which is upregulated during CCM induction, and revealed new candidate regulatory elements at a genome-wide scale. In addition, time series expression patterns of 130 transcription factor (TF) and transcription regulator (TR) genes were obtained for cells cultured under photoautotrophic condition and subjected to a shift from high to low [CO2]. Groups of co-expressed genes were identified and a putative directed gene-regulatory network underlying the CCM was reconstructed from the gene expression data using the recently developed IOTA (inner composition alignment) method. Among the candidate regulatory genes, two members of the MYB-related TF family, Lcr1 (Low-CO 2 response regulator 1) and Lcr2 (Low-CO2 response regulator 2), may play an important role in down-regulating the expression of a particular set of TF and TR genes in response to low [CO2]. The results obtained provide new insights into the transcriptional control of the CCM and revealed more than 60 new candidate regulatory genes. Deep sequencing of nucleosome-depleted genomic regions indicated the presence of new, previously unknown regulatory elements in the C. reinhardtii genome. Our work can serve as a basis for future functional studies of transcriptional regulator genes and genomic regulatory elements in Chlamydomonas.

Carbon acquisition and accumulation in microalgae Chlamydomonas: Insights from "omics" approaches.

Understanding the processes and mechanisms of carbon acquisition and accumulation in microalgae is fundamental to enhance the cellular capabilities aimed to environmental and industrial applications. The "omics" approaches have greatly contributed to expanding the knowledge on these carbon-related cellular responses, reporting large data sets on microalgae transcriptome, proteome and metabolome. This review emphasizes the advances made on Chlamydomonas exploration; however, some knowledge acquired from studying this model organism, may be extrapolated to close algae species. The large data sets available for this organism revealed the identity of a vast range of genes and proteins which are integrating carbon-related mechanisms. Nevertheless, these data sets have also highlighted the need for integrative analysis in order to fully explore the information enclosed. Here, some of the main results from "omics" approaches which may contribute to the understanding of carbon acquisition and accumulation in Chlamydomonas were reviewed and possible applications were discussed. BIOLOGICAL SIGNIFICANCE: A number of important publications in the field of "omics" technologies have been published reporting studies of the model green microalga Chlamydomonas reinhardtii and related to microalgal biomass production. However, there are only few attempts to integrate these data. Publications showing the results from "omics" approaches, such as transcriptome, metabolome and proteome, focused in the study of mechanisms of carbon acquisition and accumulation in microalgae were reviewed. This review contributes to highlight the knowledge recently generated on such "omics" studies and it discusses how these results may be important for the advance of applied sciences, such as microalgae biotechnology.

AN OPTIMIZED METHOD FOR THE ISOLATION OF NUCLEI FROM CHLAMYDOMONAS REINHARDTII (CHLOROPHYCEAE)(1).

The cell nucleus harbors a large number of proteins involved in transcription, RNA processing, chromatin remodeling, nuclear signaling, and ribosome assembly. The nuclear genome of the model alga Chlamydomonas reinhardtii P. A. Dang. was recently sequenced, and many genes encoding nuclear proteins, including transcription factors and transcription regulators, have been identified through computational discovery tools. However, elucidating the specific biological roles of nuclear proteins will require support from biochemical and proteomics data. Cellular preparations with enriched nuclei are important to assist in such analyses. Here, we describe a simple protocol for the isolation of nuclei from Chlamydomonas, based on a commercially available kit. The modifications done in the original protocol mainly include alterations of the differential centrifugation parameters and detergent-based cell lysis. The nuclei-enriched fractions obtained with the optimized protocol show low contamination with mitochondrial and plastid proteins. The protocol can be concluded within only 3 h, and the proteins extracted can be used for gel-based and non-gel-based proteomic approaches.

Isolation and characterization of a new serine protease with thrombin-like activity (TLBm) from the venom of the snake Bothrops marajoensis.

The thrombin-like serine protease TLBm from Bothrops marajoensis was isolated in one chromatographic step in reverse phase HPLC. Its molecular mass was 33239.95 Da, as based on the determined primary structure and confirmed experimentally by MALDI-TOF mass spectrometry (33332.5 Da) and it contains 12 half-cysteine residues. This TLBm exhibited high specificity for BArhoNA, Michaelis-Menten behavior with K(m) 2.3x10(-1)M and the V(max) 0.52x10(-1) nmoles rho-NA/lt/min for this substrate. TLBm also showed ability to coagulate bovine fibrinogen and was inhibited by soybean trypsin inhibitor, EDTA and S(Dm) from the serum of the species Didelphis marsupialis. The primary structure of TLBm showed the presence of His(45), Asp(103) and Ser(228) residues in the corresponding positions of the catalytic triad established in the serine proteases and Ser(228) are inhibited by phenylmethylsulfonyl fluoride (PMSF). Amino acid analysis showed a high content of Asp, Glu, Gly, Ser, Ala and Pro as well as 12 half-cysteine residues and calculated pI of 6.47; TLBm presented 285 amino acid residues. In this work, we investigated the ability of TLBm to degrade fibrinogen and we observed that it is able to cause alpha- and beta-chain cleavage. Enzymatic as well as the platelet aggregation activities were strongly inhibited when incubated with PMSF, a specific inhibitor of serine protease. Also, TLBm induced platelet aggregation in washed and platelet-rich plasma, and in both cases, PMSF inhibited its activity.

Xylella fastidiosa disturbs nitrogen metabolism and causes a stress response in sweet orange Citrus sinensis cv. Pera.

Xylella fastidiosa (Xf) is a fastidious bacterium that grows exclusively in the xylem of several important crop species, including grape and sweet orange (Citrus sinensis L. Osb.) causing Pierce disease and citrus variegated chlorosis (CVC), respectively. The aim of this work was to study the nitrogen metabolism of a highly susceptible variety of sweet orange cv. 'Pêra' (C. sinensis L. Osbeck) infected with Xf. Plants were artificially infected and maintained in the greenhouse until they have developed clear disease symptoms. The content of nitrogen compounds and enzymes of the nitrogen metabolism and proteases in the xylem sap and leaves of diseased (DP) and uninfected healthy (HP) plants was studied. The activity of nitrate reductase in leaves did not change in DP, however, the activity of glutamine synthetase was significantly higher in these leaves. Although amino acid concentration was slightly higher in the xylem sap of DP, the level dropped drastically in the leaves. The protein contents were lower in the sap and in leaves of DP. DP and HP showed the same amino acid profiles, but different proportions were observed among them, mainly for asparagine, glutamine, and arginine. The polyamine putrescine was found in high concentrations only in DP. Protease activity was higher in leaves of DP while, in the xylem sap, activity was detected only in DP. Bidimensional electrophoresis showed a marked change in the protein pattern in DP. Five differentially expressed proteins were identified (2 from HP and 3 from DP), but none showed similarity with the genomic (translated) and proteomic database of Xf, but do show similarity with the proteins thaumatin, mucin, peroxidase, ABC-transporter, and strictosidine synthase. These results showed that significant changes take place in the nitrogen metabolism of DP, probably as a response to the alterations in the absorption, assimilation and distribution of N in the plant.

Absence of classical heat shock response in the citrus pathogen Xylella fastidiosa.

The fastidious bacterium Xylella fastidiosa is associated with important crop diseases worldwide. We have recently shown that X. fastidiosa is a peculiar organism having unusually low values of gene codon bias throughout its genome and, unexpectedly, in the group of the most abundant proteins. Here, we hypothesized that the lack of codon usage optimization in X. fastidiosa would incapacitate this organism to undergo quick and massive changes in protein expression as occurs in a classical stress response. Proteomic analysis of the response to heat stress in X. fastidiosa revealed that no changes in protein expression can be detected. Moreover, stress-inducible proteins identified in the closely related citrus pathogen Xanthomonas axonopodis pv citri were found to be constitutively expressed in X. fastidiosa. These proteins have extremely high codon bias values in the X. citri and other well-studied organisms, but low values in X. fastidiosa. Because biased codon usage is well known to correlate to the rate of protein synthesis, we speculate that the peculiar codon bias distribution in X. fastidiosa is related to the absence of a classical stress response, and, probably, alternative strategies for survival of X. fastidiosa under stressfull conditions.

Proteome analysis of the plant pathogen Xylella fastidiosa reveals major cellular and extracellular proteins and a peculiar codon bias distribution.

The bacteria Xylella fastidiosa is the causative agent of a number of economically important crop diseases, including citrus variegated chlorosis. Although its complete genome is already sequenced, X. fastidiosa is very poorly characterized by biochemical approaches at the protein level. In an initial effort to characterize protein expression in X. fastidiosa we used one- and two-dimensional gel electrophoresis and mass spectrometry to identify the products of 142 genes present in a whole cell extract and in an extracellular fraction of the citrus isolated strain 9a5c. Of particular interest for the study of pathogenesis are adhesion and secreted proteins. Homologs to proteins from three different adhesion systems (type IV fimbriae, mrk pili and hsf surface fibrils) were found to be coexpressed, the last two being detected only as multimeric complexes in the high molecular weight region of one-dimensional electrophoresis gels. Using a procedure to extract secreted proteins as well as proteins weakly attached to the cell surface we identified 30 different proteins including toxins, adhesion related proteins, antioxidant enzymes, different types of proteases and 16 hypothetical proteins. These data suggest that the intercellular space of X. fastidiosa colonies is a multifunctional microenvironment containing proteins related to in vivo bacterial survival and pathogenesis. A codon usage analysis of the most expressed proteins from the whole cell extract revealed a low biased distribution, which we propose is related to the slow growing nature of X. fastidiosa. A database of the X. fastidiosa proteome was developed and can be accessed via the internet (URL: www.proteome.ibi.unicamp.br).