Structures of N-Glycans of Bothrops Venoms Revealed as Molecular Signatures that Contribute to Venom Phenotype in Viperid Snakes.

The complexity of snake venoms has long been investigated to explore a myriad of biologically active proteins and peptides that are used for immobilizing or killing prey, and are responsible for the pathological effects observed on envenomation. Glycosylation is the main post-translational modification (PTM) of viperid venoms but currently there is little understanding of how protein glycosylation impacts the variation of venom proteomes. We have previously reported that Bothrops venom glycoproteomes contain a core of components that markedly define their composition and parallel their phylogenetic classification. Here we extend those observations to eight Bothrops species evaluating the N-glycomes by LC-MS as assigned cartoon structures and detailing those structures separately as methylated analogs using ion-trap mass spectrometry (MSn). Following ion disassembly through multiple steps provided sequence and linkage isomeric details that characterized 52 unique compositions in Bothrops venoms. These occurred as 60 structures, of which 26 were identified in the venoms of the Jararaca Complex (B. alcatraz, B. insularis, and B. jararaca), 20 in B. erythromelas, B. jararacussu, B. moojeni and B. neuwiedi venoms, and 22 in B. cotiara venom. Further, quantitative analysis of these N-glycans showed variable relative abundances in the venoms. For the first time a comprehensive set of N-glycan structures present in snake venoms are defined. Despite the fact that glycosylation is not template-defined, the N-glycomes of these venoms mirror the phylogeny cladograms of South American bothropoid snakes reported in studies on morphological, molecular data and feeding habits, exhibiting distinct molecular signatures for each venom. Considering the complexity of N-glycan moieties generally found in glycoproteins, characterized by different degrees of branching, isomer structures, and variable abundances, our findings point to these factors as another level of complexity in Bothrops venoms, features that could dramatically contribute to their distinct biological activities.

Quantitative Proteomic Analysis of Replicative and Nonreplicative Forms Reveals Important Insights into Chromatin Biology of Trypanosoma cruzi.

Chromatin associated proteins are key regulators of many important processes in the cell. Trypanosoma cruzi, a protozoa flagellate that causes Chagas disease, alternates between replicative and nonreplicative forms accompanied by a shift on global transcription levels and by changes in its chromatin architecture. Here, we investigated the T. cruzi chromatin proteome using three different protocols and compared it between replicative (epimastigote) and nonreplicative (trypomastigote) forms by high-resolution mass spectrometry. More than 2000 proteins were identified and quantified both in chromatin and nonchromatin extracts. Besides histones and other known nuclear proteins, trypanosomes chromatin also contains metabolic (mainly from carbohydrate pathway), cytoskeleton and many other proteins with unknown functions. Strikingly, the two parasite forms differ greatly regarding their chromatin-associated factors composition and amount. Although the nucleosome content is the same for both life forms (as seen by MNase digestion), the remaining proteins were much less detected in nonreplicative forms, suggesting that they have a naked chromatin. Proteins associated to DNA proliferation, such as PCNA, RPA, and DNA topoisomerases were exclusively found in the chromatin of replicative stages. On the other hand, the nonreplicative stages have an enrichment of a histone H2B variant. Furthermore, almost 20% of replicative stages chromatin-associated proteins are expressed in nonreplicative forms, but located at nonchromatin space. We identified different classes of proteins including phosphatases and a Ran-binding protein, that may shuttle between chromatin and nonchromatin space during differentiation. Seven proteins, including those with unknown functions, were selected for further validation. We confirmed their location in chromatin and their differential expression, using Western blotting assays and chromatin immunoprecipitation (ChIP). Our results indicate that the replicative state in trypanosomes involves an increase of chromatin associated proteins content. We discuss in details, the qualitative and quantitative implication of this chromatin set in trypanosome chromatin biology. Because trypanosomes are early-branching organisms, this data can boost our understanding of chromatin-associated processes in other cell types.

Identification of Novel Interspersed DNA Repetitive Elements in the Trypanosoma cruzi Genome Associated with the 3'UTRs of Surface Multigenic Families.

Trypanosoma cruzi is the etiological agent of Chagas disease, which affects millions of people in Latin America. No transcriptional control of gene expression has been demonstrated in this organism, and 50% of its genome consists of repetitive elements and members of multigenic families. In this study, we applied a novel bioinformatics approach to predict new repetitive elements in the genome sequence of T. cruzi. A new repetitive sequence measuring 241 nt was identified and found to be interspersed along the genome sequence from strains of different DTUs. This new repeat was mostly on intergenic regions, and upstream and downstream regions of the 241 nt repeat were enriched in surface protein genes. RNAseq analysis revealed that the repeat was part of processed mRNAs and was predominantly found in the 3' untranslated regions (UTRs) of genes of multigenic families encoding surface proteins. Moreover, we detected a correlation between the presence of the repeat in the 3'UTR of multigenic family genes and the level of differential expression of these genes when comparing epimastigote and trypomastigote transcriptomes. These data suggest that this sequence plays a role in the posttranscriptional regulation of the expression of multigenic families.

An efficient, parallelized algorithm for optimal conditional entropy-based feature selection

In Machine Learning, feature selection is an important step in classifier design. It consists of finding a subset of features that is optimum for a given cost function. One possibility to solve feature selection is to organize all possible feature subsets into a Boolean lattice and to exploit the fact that the costs of chains in that lattice describe U-shaped curves. Minimization of such cost function is known as the U-curve problem. Recently, a study proposed U-Curve Search (UCS), an optimal algorithm for that problem, which was successfully used for feature selection. However, despite of the algorithm optimality, the UCS required time in computational assays was exponential on the number of features. Here, we report that such scalability issue arises due to the fact that the U-curve problem is NP-hard. In the sequence, we introduce the Parallel U-Curve Search (PUCS), a new algorithm for the U-curve problem. In PUCS, we present a novel way to partition the search space into smaller Boolean lattices, thus rendering the algorithm highly parallelizable. We also provide computational assays with both synthetic data and Machine Learning datasets, where the PUCS performance was assessed against UCS and other golden standard algorithms in feature selection

Optimal Boolean lattice-based algorithms for the U-curve optimization problem

The U-curve optimization problem is characterized by a decomposable in U-shaped curves cost function over the chains of a Boolean lattice. This problem can be applied to model the classical feature selection problem in Machine Learning. In this paper, we point out that the firstly proposed algorithm to tackle the U-curve problem, the RBM algorithm, is in fact suboptimal. We also present two new algorithms: UCS, which is actually optimal to tackle this problem; and UCSR, a variation of UCS that solves a special case of the U-curve problem and relies on a reduced, ordered binary decision diagram to control the search space. We provide results of two computational assays with these new algorithms: first, W-operator design for filtering of binary images; second, linear SVM design for classification of data sets from the UCI Machine Learning Repository. We show that, in these assays, UCS and UCSR outperformed an exhaustive search and also three widely used heuristics: the SFFS sequential selection, the BFS graph-based search, and the CHCGA genetic algorithm. Finally, we analyze the obtained results and point out improvements that might enhance the performance of these two novel algorithms.

An efficient, parallelized algorithm for optimal conditional entropy-based feature selection

In Machine Learning, feature selection is an important step in classifier design. It consists of finding a subset of features that is optimum for a given cost function. One possibility to solve feature selection is to organize all possible feature subsets into a Boolean lattice and to exploit the fact that the costs of chains in that lattice describe U-shaped curves. Minimization of such cost function is known as the U-curve problem. Recently, a study proposed U-Curve Search (UCS), an optimal algorithm for that problem, which was successfully used for feature selection. However, despite of the algorithm optimality, the UCS required time in computational assays was exponential on the number of features. Here, we report that such scalability issue arises due to the fact that the U-curve problem is NP-hard. In the sequence, we introduce the Parallel U-Curve Search (PUCS), a new algorithm for the U-curve problem. In PUCS, we present a novel way to partition the search space into smaller Boolean lattices, thus rendering the algorithm highly parallelizable. We also provide computational assays with both synthetic data and Machine Learning datasets, where the PUCS performance was assessed against UCS and other golden standard algorithms in feature selection

Comparative analysis of the high molecular mass subproteomes of eight Bothrops snake venoms

Snake venoms are extremely active biological secretions composed primarily of various classes of enzymes. The genusBothropscomprises various pit viper speciesthat represent the most medically significant taxa in Central and South America, accounting for more human envenomations and fatalities than any other snakes inthe region. Venom proteomes of manyBothropsspecies have been well-characterized but investigations have focused almost exclusively on proteins smaller than100 kDa despite expression of larger components being documented in severalBothropsvenoms. This study sought to achieve detailed identification of majorcomponents in the high molecular mass subproteome of venoms from eightBothropsspecies (B.brazili,B.cotiara,B.insularis,B.jararaca,B.jararacussu,B.leucurus,B.moojeniandB. neuwiedi). Enzymes such as metalloproteinases and L-amino acid oxidases were the most prominent components identified in the first size-exclusionchromatography fractions of these venoms. Minor components also identified in the first peaks included 5'-nucleotidase, aminopeptidase, phosphodiesterase, andphospholipases A2and B. Most of these components disappeared in electrophoretic profiles under reducing conditions, suggesting that they may be composed of morethan one polypeptide chain. A significant shift in the molecular masses of these protein bands was observed following enzymatic N-deglycosylation, indicating thatthey may contain N-glycans. Furthermore, none of the identified high molecular mass proteins were shared by all eight species, revealing a high level of interspecificvariability among these venom components.

Identification of novel interspersed DNA repetitive elements in the Trypanosoma cruzi genome associated with the 3′UTRs of surface multigenic families

Trypanosoma cruzi is the etiological agent of Chagas disease, which affects millions of people in Latin America. No transcriptional control of gene expression has been demonstrated in this organism, and 50% of its genome consists of repetitive elements and members of multigenic families. In this study, we applied a novel bioinformatics approach to predict new repetitive elements in the genome sequence of T. cruzi. A new repetitive sequence measuring 241 nt was identified and found to be interspersed along the genome sequence from strains of different DTUs. This new repeat was mostly on intergenic regions, and upstream and downstream regions of the 241 nt repeat were enriched in surface protein genes. RNAseq analysis revealed that the repeat was part of processed mRNAs and was predominantly found in the 3′ untranslated regions (UTRs) of genes of multigenic families encoding surface proteins. Moreover, we detected a correlation between the presence of the repeat in the 3′UTR of multigenic family genes and the level of differential expression of these genes when comparing epimastigote and trypomastigote transcriptomes. These data suggest that this sequence plays a role in the posttranscriptional regulation of the expression of multigenic families.

Transcription activity contributes to the firing of non-constitutive origins in African trypanosomes helping to maintain robustness in S-phase duration

The co-synthesis of DNA and RNA potentially generates conflicts between replication and transcription, which can lead to genomic instability. In trypanosomatids, eukaryotic parasites that perform polycistronic transcription, this phenomenon and its consequences are still little studied. Here, we showed that the number of constitutive origins mapped in the Trypanosoma brucei genome is less than the minimum required to complete replication within S-phase duration. By the development of a mechanistic model of DNA replication considering replication-transcription conflicts and using immunofluorescence assays and DNA combing approaches, we demonstrated that the activation of non-constitutive (backup) origins are indispensable for replication to be completed within S-phase period. Together, our findings suggest that transcription activity during S phase generates R-loops, which contributes to the emergence of DNA lesions, leading to the firing of backup origins that help maintain robustness in S-phase duration. The usage of this increased pool of origins, contributing to the maintenance of DNA replication, seems to be of paramount importance for the survival of this parasite that affects million people around the world.

Fibroblast growth factor 2 lethally sensitizes cancer cells to stress-targeted therapeutic inhibitors

In malignant transformation, cellular stress-response pathways are dynami-cally mobilized to counterbalance oncogenic activity, keeping cancer cellsviable. Therapeutic disruption of this vulnerable homeostasis might changethe outcome of many human cancers, particularly those for which no effec-tive therapy is available. Here, we report the use of fibroblast growth factor2 (FGF2) to demonstrate that further mitogenic activation disrupts cellularhomeostasis and strongly sensitizes cancer cells to stress-targeted therapeu-tic inhibitors. We show that FGF2 enhanced replication and proteotoxicstresses in a K-Ras-driven murine cancer cell model, and combinations ofFGF2 and proteasome or DNA damage response-checkpoint inhibitorstriggered cell death. CRISPR/Cas9-mediated K-Ras depletion suppressedthe malignant phenotype and prevented these synergic toxicities in thesemurine cells. Moreover, in a panel of human Ewing’s sarcoma family tumorcells, sublethal concentrations of bortezomib (proteasome inhibitor) or VE-821 (ATR inhibitor) induced cell death when combined with FGF2. Sus-tained MAPK-ERK1/2 overactivation induced by FGF2 appears to under-lie these synthetic lethalities, as late pharmacological inhibition of thispathway restored cell homeostasis and prevented these described synergies.Our results highlight how mitotic signaling pathways which are frequentlyoverridden in malignant transformation might be exploited to disrupt therobustness of cancer cells, ultimately sensitizing them to stress-targeted ther-apies. This approach provides a new therapeutic rationale for human can-cers, with important implications for tumors still lacking effectivetreatment, and for those that frequently relapse after treatment with avail-able therapies.

Optimal Boolean lattice-based algorithms for the U-curve optimization problem

The U-curve optimization problem is characterized by a decomposable in U-shaped curves cost function over the chains of a Boolean lattice. This problem can be applied to model the classical feature selection problem in Machine Learning. In this paper, we point out that the firstly proposed algorithm to tackle the U-curve problem, the RBM algorithm, is in fact suboptimal. We also present two new algorithms: UCS, which is actually optimal to tackle this problem; and UCSR, a variation of UCS that solves a special case of the U-curve problem and relies on a reduced, ordered binary decision diagram to control the search space. We provide results of two computational assays with these new algorithms: first, W-operator design for filtering of binary images; second, linear SVM design for classification of data sets from the UCI Machine Learning Repository. We show that, in these assays, UCS and UCSR outperformed an exhaustive search and also three widely used heuristics: the SFFS sequential selection, the BFS graph-based search, and the CHCGA genetic algorithm. Finally, we analyze the obtained results and point out improvements that might enhance the performance of these two novel algorithms.

A fast Branch-and-Bound algorithm for U-curve feature selection

We introduce a fast Branch-and-Bound algorithm for optimal feature selection based on a U-curve assumption for the cost function. The U-curve assumption, which is based on the peaking phenomenon of the classification error, postulates that the cost over the chains of the Boolean lattice that represents the search space describes a U-shaped curve. The proposed algorithm is an improvement over the original algorithm for U-curve feature selection introduced recently. Extensive simulation experiments are carried out to assess the performance of the proposed algorithm (IUBB), comparing it to the original algorithm (UBB), as well as exhaustive search and Generalized Sequential Forward Search. The results show that the IUBB algorithm makes fewer evaluations and achieves better solutions under a fixed computational budget. We also show that the IUBB algorithm is robust with respect to violations of the U-curve assumption. We investigate the application of the IUBB algorithm in the design of imaging W-operators and in classification feature selection, using the average mean conditional entropy (MCE) as the cost function for the search.

Comparative analysis of the high molecular mass subproteomes of eight Bothrops snake venoms

Snake venoms are extremely active biological secretions composed primarily of various classes of enzymes. The genusBothropscomprises various pit viper speciesthat represent the most medically significant taxa in Central and South America, accounting for more human envenomations and fatalities than any other snakes inthe region. Venom proteomes of manyBothropsspecies have been well-characterized but investigations have focused almost exclusively on proteins smaller than100 kDa despite expression of larger components being documented in severalBothropsvenoms. This study sought to achieve detailed identification of majorcomponents in the high molecular mass subproteome of venoms from eightBothropsspecies (B.brazili,B.cotiara,B.insularis,B.jararaca,B.jararacussu,B.leucurus,B.moojeniandB. neuwiedi). Enzymes such as metalloproteinases and L-amino acid oxidases were the most prominent components identified in the first size-exclusionchromatography fractions of these venoms. Minor components also identified in the first peaks included 5'-nucleotidase, aminopeptidase, phosphodiesterase, andphospholipases A2and B. Most of these components disappeared in electrophoretic profiles under reducing conditions, suggesting that they may be composed of morethan one polypeptide chain. A significant shift in the molecular masses of these protein bands was observed following enzymatic N-deglycosylation, indicating thatthey may contain N-glycans. Furthermore, none of the identified high molecular mass proteins were shared by all eight species, revealing a high level of interspecificvariability among these venom components.

Structures of N-glycans of Bothrops venoms revealed as molecular signatures that contribute to venom phenotype in viperid snakes

The complexity of snake venoms has long been investigated to explore a myriad of biologically active proteins and peptides that are used for immobilizing or killing prey, and are responsible for the pathological effects observed on envenomation. Glycosylation is the main post-translational modification (PTM) of viperid venoms but currently there is little understanding of how protein glycosylation impacts the variation of venom proteomes. We have previously reported that Bothrops venom glycoproteomes contain a core of components that markedly define their composition and parallel their phylogenetic classification. Here we extend those observations to eight Bothrops species evaluating the N-glycomes by LC-MS as assigned cartoon structures and detailing those structures separately as methylated analogs using ion-trap mass spectrometry (MSn). Following ion disassembly through multiple steps provided sequence and linkage isomeric details that characterized 52 unique compositions in Bothrops venoms. These occurred as 60 structures, of which 26 were identified in the venoms of the Jararaca Complex (B. alcatraz, B. insularis, and B. jararaca), 20 in B. erythromelas, B. jararacussu, B. moojeni and B. neuwiedi venoms, and 22 in B. cotiara venom. Further, quantitative analysis of these N-glycans showed variable relative abundances in the venoms. For the first time a comprehensive set of N-glycan structures present in snake venoms are defined. Despite the fact that glycosylation is not template-defined, the N-glycomes of these venoms mirror the phylogeny cladograms of South American bothropoid snakes reported in studies on morphological, molecular data and feeding habits, exhibiting distinct molecular signatures for each venom. Considering the complexity of N-glycan moieties generally found in glycoproteins, characterized by different degrees of branching, isomer structures, and variable abundances, our findings point to these factors as another level of complexity in Bothrops venoms, features that could dramatically contribute to their distinct biological activities.

Transcription activity contributes to the firing of non-constitutive origins in African trypanosomes helping to maintain robustness in S-phase duration

The co-synthesis of DNA and RNA potentially generates conflicts between replication and transcription, which can lead to genomic instability. In trypanosomatids, eukaryotic parasites that perform polycistronic transcription, this phenomenon and its consequences are still little studied. Here, we showed that the number of constitutive origins mapped in the Trypanosoma brucei genome is less than the minimum required to complete replication within S-phase duration. By the development of a mechanistic model of DNA replication considering replication-transcription conflicts and using immunofluorescence assays and DNA combing approaches, we demonstrated that the activation of non-constitutive (backup) origins are indispensable for replication to be completed within S-phase period. Together, our findings suggest that transcription activity during S phase generates R-loops, which contributes to the emergence of DNA lesions, leading to the firing of backup origins that help maintain robustness in S-phase duration. The usage of this increased pool of origins, contributing to the maintenance of DNA replication, seems to be of paramount importance for the survival of this parasite that affects million people around the world.

Fibroblast growth factor 2 lethally sensitizes cancer cells to stress-targeted therapeutic inhibitors

In malignant transformation, cellular stress-response pathways are dynami-cally mobilized to counterbalance oncogenic activity, keeping cancer cellsviable. Therapeutic disruption of this vulnerable homeostasis might changethe outcome of many human cancers, particularly those for which no effec-tive therapy is available. Here, we report the use of fibroblast growth factor2 (FGF2) to demonstrate that further mitogenic activation disrupts cellularhomeostasis and strongly sensitizes cancer cells to stress-targeted therapeu-tic inhibitors. We show that FGF2 enhanced replication and proteotoxicstresses in a K-Ras-driven murine cancer cell model, and combinations ofFGF2 and proteasome or DNA damage response-checkpoint inhibitorstriggered cell death. CRISPR/Cas9-mediated K-Ras depletion suppressedthe malignant phenotype and prevented these synergic toxicities in thesemurine cells. Moreover, in a panel of human Ewing’s sarcoma family tumorcells, sublethal concentrations of bortezomib (proteasome inhibitor) or VE-821 (ATR inhibitor) induced cell death when combined with FGF2. Sus-tained MAPK-ERK1/2 overactivation induced by FGF2 appears to under-lie these synthetic lethalities, as late pharmacological inhibition of thispathway restored cell homeostasis and prevented these described synergies.Our results highlight how mitotic signaling pathways which are frequentlyoverridden in malignant transformation might be exploited to disrupt therobustness of cancer cells, ultimately sensitizing them to stress-targeted ther-apies. This approach provides a new therapeutic rationale for human can-cers, with important implications for tumors still lacking effectivetreatment, and for those that frequently relapse after treatment with avail-able therapies.

FGF2 antiproliferative stimulation induces proteomic dynamic changes and high expression of FOSB and JUNB in K-ras-driven mouse tumor cells

Fibroblast growth factor 2 (FGF2) is a well-known cell proliferation promoter; however, it can also induce cell cycle arrest. To gain insight into the molecular mechanisms of this antiproliferative effect, for the first time, the early systemic proteomic differences induced by this growth factor in a K-Ras-driven mouse tumor cell line using a quantitative proteomics approach are investigated. More than 2900 proteins are quantified, indicating that terms associated with metabolism, RNA processing, replication, and transcription are enriched among proteins differentially expressed upon FGF2 stimulation. Proteomic trend dynamics indicate that, for proteins mainly associated with DNA replication and carbohydrate metabolism, an FGF2 stimulus delays their abundance changes, whereas FGF2 stimulation accelerates other metabolic programs. Transcription regulatory network analysis indicates master regulators of FGF2 stimulation, including two critical transcription factors, FOSB and JUNB. Their expression dynamics, both in the Y1 cell line (a murine model of adenocarcinoma cells) and in two other human cell lines (SK-N-MC and UM-UC-3) also susceptible to FGF2 antiproliferative effects, are investigated. Both protein expression levels depend on fibroblast growth factor receptor (FGFR) and src signaling. JUNB and FOSB knockdown do not rescue cells from the growth arrest induced by FGF2; however, FOSB knockdown rescue cells from DNA replication delay, indicating that FOSB expression underlies one of the FGF2 antiproliferative effects, namely, S-phase progression delay.

A fast Branch-and-Bound algorithm for U-curve feature selection

We introduce a fast Branch-and-Bound algorithm for optimal feature selection based on a U-curve assumption for the cost function. The U-curve assumption, which is based on the peaking phenomenon of the classification error, postulates that the cost over the chains of the Boolean lattice that represents the search space describes a U-shaped curve. The proposed algorithm is an improvement over the original algorithm for U-curve feature selection introduced recently. Extensive simulation experiments are carried out to assess the performance of the proposed algorithm (IUBB), comparing it to the original algorithm (UBB), as well as exhaustive search and Generalized Sequential Forward Search. The results show that the IUBB algorithm makes fewer evaluations and achieves better solutions under a fixed computational budget. We also show that the IUBB algorithm is robust with respect to violations of the U-curve assumption. We investigate the application of the IUBB algorithm in the design of imaging W-operators and in classification feature selection, using the average mean conditional entropy (MCE) as the cost function for the search.

Structures of N-glycans of Bothrops venoms revealed as molecular signatures that contribute to venom phenotype in viperid snakes

The complexity of snake venoms has long been investigated to explore a myriad of biologically active proteins and peptides that are used for immobilizing or killing prey, and are responsible for the pathological effects observed on envenomation. Glycosylation is the main post-translational modification (PTM) of viperid venoms but currently there is little understanding of how protein glycosylation impacts the variation of venom proteomes. We have previously reported that Bothrops venom glycoproteomes contain a core of components that markedly define their composition and parallel their phylogenetic classification. Here we extend those observations to eight Bothrops species evaluating the N-glycomes by LC-MS as assigned cartoon structures and detailing those structures separately as methylated analogs using ion-trap mass spectrometry (MSn). Following ion disassembly through multiple steps provided sequence and linkage isomeric details that characterized 52 unique compositions in Bothrops venoms. These occurred as 60 structures, of which 26 were identified in the venoms of the Jararaca Complex (B. alcatraz, B. insularis, and B. jararaca), 20 in B. erythromelas, B. jararacussu, B. moojeni and B. neuwiedi venoms, and 22 in B. cotiara venom. Further, quantitative analysis of these N-glycans showed variable relative abundances in the venoms. For the first time a comprehensive set of N-glycan structures present in snake venoms are defined. Despite the fact that glycosylation is not template-defined, the N-glycomes of these venoms mirror the phylogeny cladograms of South American bothropoid snakes reported in studies on morphological, molecular data and feeding habits, exhibiting distinct molecular signatures for each venom. Considering the complexity of N-glycan moieties generally found in glycoproteins, characterized by different degrees of branching, isomer structures, and variable abundances, our findings point to these factors as another level of complexity in Bothrops venoms, features that could dramatically contribute to their distinct biological activities.

Quantitative proteomic analysis of replicative and nonreplicative forms reveals important insights into chromatin biology of trypanosoma cruzi

Chromatin associated proteins are key regulators of many important processes in the cell. Trypanosome cruzi, a protozoa flagellate that causes Chagas disease, alternates between replicative and nonreplicative forms accompanied by a shift on global transcription levels and by changes in its chromatin architecture. Here, we investigated the T. cruzi chromatin proteome using three different protocols and compared it between replicative (epimastigote) and nonreplicative (trypomastigote) forms by high-resolution mass spectrometry. More than 2000 proteins were identified and quantified both in chromatin and nonchromatin extracts. Besides histones and other known nuclear proteins, trypanosomes chromatin also contains metabolic (mainly from carbohydrate pathwa)4, cytoskeleton and many other proteins with unknown functions. Strikingly, the two parasite forms differ greatly regarding their chromatin-associated factors composition and amount. Although the nucleosome content is the same for both life forms (as seen by MNase digestion), the remaining proteins were much less detected in nonreplicative forms, suggesting that they have a naked chromatin. Proteins associated to DNA proliferation, such as PCNA. RPA, and DNA topoisomerases were exclusively found in the chromatin of replicative stages. On the other hand, the nonreplicative stages have an enrichment of a histone H2B variant. Furthermore, almost 20% of replicative stages chromatin-associated proteins are expressed in nonreplicative forms, but located at nonchromatin space. We identified different classes of proteins including phosphatases and a Ran-binding protein, that may shuttle between chromatin and nonchromatin space during differentiation. Seven proteins, including those with unknown functions, were selected for further validation. We confirmed their location in chromatin and their differential expression, using Western blotting assays and chromatin immunoprecipitation (ChIP). Our results indicate that the replicative state in trypanosomes involves an increase of chromatin associated proteins content. We discuss in details, the qualitative and quantitative implication of this chromatin set in trypanosome chromatin biology. Because trypanosomes are early-branching organisms, this data can boost our understanding of chromatin-associated processes in other cell types.