Por meio da compreensão de interações do conjunto de circuitos gênicos que atuam na célula, os cientistas têm como objetivo sintetizar novos circuitos. Esse conhecimento favorece a elucidação da engrenagem a ponto de poder atuar no controle de processos biológicos. Os pesquisadores esperam desenvolver novos circuitos gênicos sintéticos para várias doenças complexas como câncer, doenças metabólicas, neurológicas e psiquiátricas. 5 Doença de Gaucher e Biologia Sintética O gene da Doença de Gaucher foi caracterizado na década de 80 e junto com a engenharia genética após uma década foi possível o primeiro ensaio de terapia gênica por meio de um gene recombinante. Atualmente, grupo de pesquisadores do Departamento de Genética e Cirurgia e Anatomia da Faculdade de Medicina de Ribeirão Preto da USP, juntamente com o MIT e com apoio da FAPESP e Agilent estão desenvolvendo uma nova terapia por meio de um DNA sintético (Figura 4) modulado para uma terapia capaz de reverter os efeitos danosos da doença de Gaucher. A abordagem utilizada envolve vetores lentivirais, muitos dos quais são desenvolvidos pelo Prof. Stanton Gerson, também colaborador desse projeto. Colaboradores:

Figura 5- DNA lentiviral portador do gene sintético que codifica GBA. Autores: Professores da Escola Básica: Claudio Paris e Luciana M. Bimbati Escolas: Liceu Albert Sabin – Ribeirão Preto – SP Centro Ed. Miguel Nayme – São Simão – SP Desenhos: Sandra Navarro Bresciani Revisão: Profª Drª Aparecida Maria Fontes Profa Dra Daniela P. Cunha Tirapelli Prof. Dr. Carlos Gilberto Carlotti Júnior Apoio:

Biologia Sintética e Doenças

Genéticas 1 Introdução A aprovação do Projeto FAPESP-MIT possibilitou o contato com a Profa Natalie Kudell do MIT, que desenvolve vários materiais sobre biologia sintética com professores da escola básica nos Estados Unidos através da BioBuilder Foundation http://biobuilder.org/ Nessa caminhada pouco conhecida, a opção pelo tema em estudo, como é o propósito do formato Folhetim é apresentar a Biologia sintética e Doenças Genéticas. Muitos cientistas afirmam que o estudo da a Biologia Sintética colabora para se obter conhecimentos novos em processos básicos da biologia, como controle da expressão gênica, bem como, oferecer novos tipos de terapia para diversas doenças genéticas, como por exemplo, a doença de Gaucher. 2. Doença de Gaucher É uma doença metabólica hereditária que afeta os lisossomos pela falta de uma enzima. Em pessoas sem a doença, a enzima “quebra” um tipo de gordura dentro da célula. Pessoas com essa doença, pela falta dessa enzima, acumulam essa gordura nos lisossomos do interior dos macrófagos. Tais células do sangue são responsáveis pela fagocitose no combate a microorganismos e células

tumorais. Devido esse acúmulo, os macrófagos são impedidos de realizar suas funções, ficam pesados, “gordos” e se acumulam, aumentando o tamanho de órgãos como fígado, baço, pulmão e medula óssea. Em alguns casos afeta o sistema nervoso central. 3. Doenças Mendelianas Estudos apontam que atualmente há mais de 7 mil tipos de doenças metabólicas mendelianas, que podem apresentar um padrão de herança dominante ou recessivo e também mitocondrial. A Doença de Gaucher é autossômica recessiva. Assim, se o pai e a mãe forem portadores do gene mutado (heterozigoto) há 25% de probabilidade de ter um filho com essa doença metabólica hereditária. 4. Biologia Sintética A Biologia Sintética é uma convergência de saberes, seu conhecimento se fundamenta na interação de biologia, engenharia, computação, bioquímica etc. Cientistas que desenvolvem pesquisa utilizando a biologia sintética percebem o potencial de suas aplicações para compreensão da complexidade das doenças genéticas, como a Doença de Gaucher, por desvendar as redes moleculares regulatórias e suas interações. Na fase inicial, os cientistas sintetizaram genes completos como demonstrado pelo grupo do Prof. Steven Benner na década de 80 com a síntese completa de um gene de 300 bp (Figura 1) (K.P. Nambiar et al Science 223, 1299; 1984).

Figura 1 - Fragmento do gene sintético que codifica a enzima Ribonuclease S (retirado de Nambiar et al 1984).

Após 26 anos, o grupo do Prof. J.C. Venter mostrou que é possível a síntese de um genoma completo de 1.08 milhões de pares de base (Figura 2) (D.G. Gibson et al Science 329, 52; 2010).

Figura 2 – Esquema utilizado para construção do genoma de Mycoplasma (retirado e modificado de Gibson et al 2010).

Dois anos mais tarde, o grupo do Prof. Markus Covert criou o modelo computacional de uma bactéria constituído por 28 modulos que descrevem as diferentes funções celulares (Figura 3) (J.R. Karr et al Cell 150:389-401, 2012 e M.W. Covert Scient. Amer. Brasil 12:40-47, 2012).

Figura 3- Exemplo de um dos módulos (DNA) que se interage com diferentes funções celulares (retirado e modificado de A. Gelfand Biomed. Comput. Rev. 8-13, 2013). No mesmo ano, o grupo do Prof. Ron Weiss desenvolveu 4 módulos sintéticos que se interagiram e permitiram a célula-tronco multiplicar-se e diferenciar–se em uma população β produtora de insulina de maneira controlada (Figura 4) (M. Miller et al PloS Comput Biol 8:e1002579).

Figura 4 – Processo de proliferação e diferenciação da célula-tronco controlado por dois mecanismos de feedback (retirado e modificado de Miller et al 2012).

synthesized oligonucleotides by Blue Heron(Bothell, Washington). Each cassette was individ-ually synthesized and sequence-verified by themanufacturer. To aid in the building process, DNAcassettes and assembly intermediates were de-signed to contain Not I restriction sites at theirtermini and recombined in the presence of vectorelements to allow for growth and selection in yeast(7, 11). A hierarchical strategy was designed toassemble the genome in three stages bytransformation and homologous recombination inyeast from 1078 1-kb cassettes (Fig. 1) (12, 13).

Assembly of 10-kb synthetic intermediates.In the first stage, cassettes and a vector wererecombined in yeast and transferred toEscherichia

coli (11). Plasmid DNA was then isolated fromindividual E. coli clones and digested to screen forcells containing a vector with an assembled 10-kbinsert. One successful 10-kb assembly is repre-sented (Fig. 2A). In general, at least one 10-kbassembled fragment could be obtained byscreening 10 yeast clones. However, the rate ofsuccess varied from 10 to 100%. All of the first-stage intermediates were sequenced. Nineteen outof 111 assemblies contained errors. Alternateclones were selected, sequence-verified, andmoved on to the next assembly stage (11).

Assembly of 100-kb synthetic intermediates.The pooled 10-kb assemblies and their respectivecloning vectors were transformed into yeast as

above to produce 100-kb assembly intermediates(11). Our results indicated that these productscannot be stably maintained in E. coli, sorecombined DNA had to be extracted from yeast.Multiplex polymerase chain reaction (PCR) wasperformed on selected yeast clones (fig. S3 andtable S2). Because every 10-kb assemblyintermediate was represented by a primer pair inthis analysis, the presence of all amplicons wouldsuggest an assembled 100-kb intermediate. Ingeneral, 25% or more of the clones screenedcontained all of the amplicons expected for acomplete assembly. One of these clones wasselected for further screening. Circular plasmidDNA was extracted and sized on an agarose gelalongside a supercoiled marker. Successful second-stage assemblies with the vector sequence are ~105kb in length (Fig. 2B). When all amplicons wereproduced following multiplex PCR, a second-stage assembly intermediate of the correct sizewasusually produced. In some cases, however, smalldeletions occurred. In other instances, multiple 10-kb fragments were assembled, which produced alarger second-stage assembly intermediate. Fortu-nately, these differences could easily be detectedon an agarose gel before complete genomeassembly.

Complete genome assembly. In preparationfor the final stage of assembly, it was necessary toisolate microgram quantities of each of the 11second-stage assemblies (11). As reported (14),circular plasmids the size of our second-stageassemblies could be isolated from yeast sphero-plasts after an alkaline-lysis procedure. To furtherpurify the 11 assembly intermediates, they weretreated with exonuclease and passed through ananion-exchange column. A small fraction of thetotal plasmid DNA (1/100) was digested withNot I and analyzed by field-inversion gel electro-phoresis (FIGE) (Fig. 2C). This method produced~1 mg of each assembly per 400 ml of yeastculture (~1011 cells).

The method above does not completely re-move all of the linear yeast chromosomal DNA,which we found could substantially decrease theyeast transformation and assembly efficiency. Tofurther enrich for the 11 circular assembly inter-mediates, ~200 ng samples of each assemblywere pooled and mixed with molten agarose. Asthe agarose solidifies, the fibers thread throughand topologically “trap” circular DNA (15).Untrapped linear DNA can then be separatedout of the agarose plug by electrophoresis, thusenriching for the trapped circular molecules. The11 circular assembly intermediates were digestedwith Not I so that the inserts could be released.Subsequently, the fragments were extracted fromthe agarose plug, analyzed by FIGE (Fig. 2D),and transformed into yeast spheroplasts (11). Inthis third and final stage of assembly, an addi-tional vector sequence was not required becausethe yeast cloning elements were already presentin assembly 811-900.

To screen for a complete genome, multiplexPCR was carried out with 11 primer pairs,

A CG T

1,080 bp cassettes (1,078)(Assemble109X)

10,080 bp assemblies (109)(Assemble 11X)

100,000 bp assemblies (11)(Assemble 1X)

1,077,947 bp

OligonucleotideSynthesizer

Oligonucleotides

Yeast

BssH II

WM4

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94D

WM3

WM1

WM2

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11,077,947

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Fig. 1. The assembly of a synthetic M. mycoides genome in yeast. A synthetic M. mycoides genomewas assembled from 1078 overlapping DNA cassettes in three steps. In the first step, 1080-bpcassettes (orange arrows), produced from overlapping synthetic oligonucleotides, were recombinedin sets of 10 to produce 109 ~10-kb assemblies (blue arrows). These were then recombined in sets of10 to produce 11 ~100-kb assemblies (green arrows). In the final stage of assembly, these 11fragments were recombined into the complete genome (red circle). With the exception of twoconstructs that were enzymatically pieced together in vitro (27) (white arrows), assemblies werecarried out by in vivo homologous recombination in yeast. Major variations from the natural genomeare shown as yellow circles. These include four watermarked regions (WM1 to WM4), a 4-kb regionthat was intentionally deleted (94D), and elements for growth in yeast and genome transplantation.In addition, there are 20 locations with nucleotide polymorphisms (asterisks). Coordinates of thegenome are relative to the first nucleotide of the natural M. mycoides sequence. The designedsequence is 1,077,947 bp. The locations of the Asc I and BssH II restriction sites are shown. Cassettes1 and 800-810 were unnecessary and removed from the assembly strategy (11). Cassette 2 overlapscassette 1104, and cassette 799 overlaps cassette 811.

www.sciencemag.org SCIENCE VOL 329 2 JULY 2010 53

RESEARCH ARTICLE

10 BIOMEDICAL COMPUTATION REVIEW Winter 2013 www.biomedicalcomputationreview.org

and histone marks were most important toprediction. As reported in a recent paper inNucleic Acids Research, the team found dis-tinct differences in predictive strengthbased on location, with transcription fac-tors achieving their highest predictivepower in a small region of DNA centeredaround the transcription start sites, and hi-stone modifications demonstrating highpredictive power across a wide regionaround the genes. As a final step, Gersteinand his colleagues built a model that in-cluded both histone modifications andtranscription factors, but discovered thatintegrating the two did not improve accu-racy. “They’re actually somewhat redun-dant; you can’t do better by combiningthem,” says Gerstein—a surprising resultthat may help illuminate the basic biologyof transcriptional regulation.

Interestingly, Gerstein doesn’t considerthe integrative aspect of the undertakingto have been especially challenging. “In asense, the integration is carried out in theactual mathematical machinery as it’s puttogether,” he says, referring to the auto-mated manner in which the machine-learning algorithms go about sorting andmultiplying, adding and predicting. Instead,most of the heavy lifting comes earlier: be-fore the data on the various regulators canbe fed into the models, they must first benormalized and placed in the same coordi-nate system, put in the correct format andproperly scaled. “There’s a huge amount ofupstream work [required] to be able to do

this integration,” Gerstein says, adding thatthe project is “a nice case study” of “theoverall process of putting all this informa-tion together and making predictions.”

Integrating a Whole Cell Model

The idea that the “integrative” part of anambitious integrative analysis project shouldturn out to be fairly straightforward mightseem surprising. But it’s hardly uncommon.

For example, yoking together 28 indi-

Whole-Cell Model Integrates 28 Submodels of Diverse Cellular Processes. (A) The 28 sub-models in Covert’s whole-cell model are represented by colored words in the context of the flask-like shape of an M. genitalium cell. Submodels are connected through common metabolites, RNA,protein, and chromosome, which are depicted as orange, green, blue, and red arrows, respectively. (B)The model integrates cellular function submodels (right-hand column) through 16 cell variables (left-hand column). For each one-second timestep (dark black arrows), the submodels retrieve the currentvalues of the cellular variables, perform their computations, and update the values of the cellularvariables accordingly. This process is repeated thousands of times during the course of each simula-tion, ending only when the cell divides. (Colored lines between the variables and submodels indicatethe cell variables predicted by each submodel, while the number of genes associated with each sub-model is indicated in parentheses.) Reprinted with permission from Karr JR, et al., A Whole-Cell Com-putational Model Predicts Phenotype from Genotype, Cell 150:2:389-401 (2012).

“There’s a huge amount of upstream

work [required] to be able to do this integration,” Gerstein says.