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de Souza, A. P.

Publications and source records attributed to de Souza, A. P..

At least 37 records · Page 2Linked to original sources
bioRxiv2022

A divide-and-conquer approach for genomic prediction in rubber tree using machine learning

Rubber tree (Hevea brasiliensis) is the main feedstock for commercial rubber; however, its long vegetative cycle has hindered the development of more productive varieties via breeding programs. With the availability of H. brasiliensis genomic data, several linkage maps with associated quantitative trait loci (QTLs) have been constructed and suggested as a tool for marker-assisted selection (MAS). Nonetheless, novel genomic strategies are still needed, and genomic selection (GS) may facilitate rubber tree breeding programs aimed at reducing the required cycles for performance assessment. Even though such a methodology has already been shown to be a promising tool for rubber tree breeding, increased model predictive capabilities and practical application are still needed. Here, we developed a novel machine learning-based approach for predicting rubber tree stem circumference based on molecular markers. Through a divide-and-conquer strategy, we propose a neural network prediction system with two stages: (1) subpopulation prediction and (2) phenotype estimation. This approach yielded higher accuracies than traditional statistical models in a single-environment scenario. By delivering large accuracy improvements, our methodology represents a powerful tool for use in Hevea GS strategies. Therefore, the incorporation of machine learning techniques into rubber tree GS represents an opportunity to build more robust models and optimize Hevea breeding programs.

genetics↗
bioRxiv2022

Differential adaptive potential and vulnerability to climate-driven habitat loss in Brazilian mangroves

Geographic and environmental differences have been identified as factors influencing Brazilian mangrove trees genetic diversity. Geographically, distinct species have convergent spatial genetic structures, indicating a limited gene flow between northern and southern populations. Environmentally, genomic studies and common garden experiments have found evidence of local adaptations along the latitudinal gradient of the Brazilian coast. However, little is known about how such adaptive heterogeneity could be affected by a rapidly changing climate in the coming decades, and the combination of deforestation and climate-induced habitat loss may affect these forests and their genetic diversity. Here, we applied two genomic-environmental association methods to model the turnover of potentially adaptive alleles for two dominant mangrove trees: Avicennia germinans and A. schaueriana. We analyzed a total of 134 individuals from six populations of A. germinans and ten populations of A. schaueriana spanning the Brazilian coast from 1 {degrees}S to 28 {degrees}S. Gradient forest models identified temperature-related variables as the most important predictors for A. germinans outlier loci, whereas both temperature and precipitation were important for A. schaueriana. We modeled allele frequencies and projected them for future climatic scenarios to estimate adaptively driven vulnerability. We assessed climate-driven habitat loss through climate-only distribution models and calculated annual deforestation rates for each sampled region. Finally, to assess the vulnerability of individual populations, we combined the environmental suitability, deforestation data, and adaptive vulnerability projections. For both species, subtropical populations presented a higher vulnerability than equatorial populations to climate-driven habitat loss. We also identified deforestation rates at the sampled sites that were alarmingly higher than the global average mangrove deforestation rate. Our results provide improved estimates of the impacts of ongoing climate change and human-caused habitat loss on the distribution of mangroves and highlight the importance of site-based conservation strategies that consider individual subtropical and equatorial mangrove forests.

evolutionary biology↗
bioRxiv2022

Whole-Genome Sequencing and Comparative Genomic Analysis of Potential Biotechnological Strains from Trichoderma harzianum, Trichoderma atroviride, and Trichoderma reesei

Trichoderma atroviride and Trichoderma harzianum are widely used as commercial biocontrol agents against plant diseases. Recently, T. harzianum IOC-3844 (Th3844) and T. harzianum CBMAI-0179 (Th0179) demonstrated great potential in the enzymatic conversion of lignocellulose into fermentable sugars. Herein, we performed whole-genome sequencing and assembly of the Th3844 and Th0179 strains. To assess the genetic diversity within the genus Trichoderma, the results of both strains were compared with strains of T. atroviride CBMAI-00020 (Ta0020) and T. reesei CBMAI-0711 (Tr0711). The sequencing coverage value of all genomes evaluated in this study was higher than that of previously reported genomes for the same species of Trichoderma. The resulting assembly revealed total lengths of 40 Mb (Th3844), 39 Mb (Th0179), 36 Mb (Ta0020), and 32 Mb (Tr0711). A genome-wide phylogenetic analysis provided details on the relationships of the newly sequenced species with other Trichoderma species. Structural variants revealed genomic rearrangements among Th3844, Th0179, Ta0020, and Tr0711 relative to the T. reesei QM6a reference genome and showed the functional effects of such variants. In conclusion, the findings presented herein allow the visualization of genetic diversity in the evaluated strains and offer opportunities to explore such fungal genomes in future biotechnological and industrial applications.

genomics↗
bioRxiv2021

Novel insights into the cold resistance of Hevea brasiliensis through coexpression networks

BackgroundHevea brasiliensis is the main global source of natural rubber. Due to fungal disease pressure in hot, humid regions, rubber plantations have been moved to drier "escape areas" with lower temperatures. In order to analyze gene expression regulation during cold exposure, we studied young GT1 and RRIM 600 rubber tree clones with different cold tolerance strategies. ResultsAlongside traditional differential expression approaches, an RNA-seq gene coexpression network (GCN) was developed with 27,220 genes grouped into 205 gene clusters. The GCN related most rubber tree cold stress molecular responses to 31 clusters across three GCN modules: a downregulated group with 16 clusters and two upregulated groups with twelve and three clusters. The hub genes of the cold-responsive modules were also identified and analyzed. We observed that the general response to short-term cold exposure involves complex regulation of the jasmonic acid (JA) stress response and programmed cell death (PCD), upregulation of ethylene-responsive genes, and relaxation of florigen gene inhibition. As a result, we identified single DEGs and gained insights into the mechanisms involved in the response to cold stress in young rubber trees. ConclusionsOur findings may represent the species genetic stress responses developed during the course of evolution, since the examined varieties were genotypes selected during the early years of rubber tree domestication. Understanding the cold response mechanisms in H. brasiliensis could improve breeding strategies for this crop, which has a narrow genetic base, is being impacted by climate change and is the only source for large-scale rubber production.

genomics↗
bioRxiv2021

Selective signatures and high genome-wide diversity in traditional Brazilian manioc (Manihot esculenta Crantz) varieties

Knowledge about crops genetic diversity is essential to promote effective use and conservation of their genetic resources, because genetic diversity enables farmers to adapt their crops to specific needs and is the raw material for breeding efforts. Currently, manioc (Manihot esculenta ssp. esculenta) is one of the most important food crops in the world and has the potential to help achieve food security in the context of on-going climate changes. In this study we assessed the patterns of genome-wide diversity of traditional Brazilian manioc varieties conserved in the gene bank of the Luiz de Queiroz College of Agriculture, University of Sao Paulo. We used single nucleotide polymorphisms to evaluate the organization of genetic diversity and to identify selective signatures contrasting varieties from different biomes with samples of maniocs wild relative M. esculenta ssp. flabellifolia. We identified signatures of selection putatively associated with resistance genes, plant development and response to abiotic stresses. This presumed adaptive variation might have been important for the initial domestication and for the crops diversification in response to cultivation in different environments. The neutral variation revealed high levels of genetic diversity within groups of varieties from different biomes and low to moderate genetic divergence among biomes. These results reflect the complexity of maniocs biology and its evolutionary dynamics under traditional cultivation. Our results exemplify how the smallholder practices contribute to the conservation of maniocs genetic resources, maintaining variation of potential adaptive significance and high levels of neutral genetic diversity.

genetics↗
bioRxiv2021

Deciphering Rubber Tree Growth Using Network-Based Multi Omics Approaches

Hevea brasiliensis (rubber tree) is a large tree species of the Euphorbiaceae family with inestimable economic importance. Rubber tree breeding programs currently aim to improve growth and production, and the use of early genotype selection technologies can accelerate such processes, mainly with the incorporation of genomic tools, such as marker-assisted selection (MAS). However, few quantitative trait loci (QTLs) have been used successfully in MAS for complex characteristics. Recent research shows the efficiency of genome-wide association studies (GWAS) for locating QTL regions in different populations. In this way, the integration of GWAS, RNA-sequencing (RNA-Seq) methodologies, coexpression networks and enzyme networks can provide a better understanding of the molecular relationships involved in the definition of the phenotypes of interest, supplying research support for the development of appropriate genomic based strategies for breeding. In this context, this work presents the potential of using combined multiomics to decipher the mechanisms of genotype and phenotype associations involved in the growth of rubber trees. Using GWAS from a genotyping-by-sequencing (GBS) Hevea population, we were able to identify molecular markers in QTL regions with a main effect on rubber tree plant growth under constant water stress. The underlying genes were evaluated and incorporated into a gene coexpression network modelled with an assembled RNA-Seq-based transcriptome of the species, where novel gene relationships were estimated and evaluated through in silico methodologies, including an estimated enzymatic network. From all these analyses, we were able to estimate not only the main genes involved in defining the phenotype but also the interactions between a core of genes related to rubber tree growth at the transcriptional and translational levels. This work was the first to integrate multiomics analysis into the in-depth investigation of rubber tree plant growth, producing useful data for future genetic studies in the species and enhancing the efficiency of the species improvement programs.

plant biology↗
bioRxiv2021

Temporal Gene Expression in Apical Culms Shows Early Changes in Cell Wall Biosynthesis Genes in Sugarcane

Multiple genes in sugarcane control sucrose accumulation and the biosynthesis of cell wall components; however, it is unclear how these genes are expressed in its apical culms. To better understand this process, we sequenced mRNA from +1 stem internodes collected from four genotypes with different concentrations of soluble solids. Culms were collected at four different time points, ranging from six to 12-month-old plants. Here we show differentially expressed genes related to sucrose metabolism and cell wall biosynthesis, including genes encoding invertases, sucrose synthase and cellulose synthase. Our results showed increased expression of invertases in IN84-58, the genotype with lower sugar and higher fiber content, as well as delayed expression of secondary cell wall-related cellulose synthase for the other genotypes. Interestingly, genes involved with hormone metabolism were differentially expressed across time points in the three genotypes with higher soluble solids content. A similar result was observed for genes controlling maturation and transition to reproductive stages, possibly a result of selection against flowering in sugarcane breeding programs. These results indicate that carbon partitioning in apical culms of contrasting genotypes is mainly associated with differential cell wall biosynthesis, and may include early modifications for subsequent sucrose accumulation. Co-expression network analysis identified transcription factors related to growth and development, showing a probable time shift for carbon partitioning occurred in 10-month-old plants.

genomics↗
bioRxiv2021

An Automated SNP-Based Approach for Contaminant Identification in Biparental Polyploid Populations of Tropical Forage Grasses

Artificial hybridization plays a fundamental role in plant breeding programs since it generates new genotypic combinations that can result in desirable phenotypes. Depending on the species and mode of reproduction, controlled crosses may be challenging, and contaminating individuals can be introduced accidentally. In this context, the identification of such contaminants is important to avoid compromising further selection cycles, as well as genetic and genomic studies. The main objective of this work was to propose an automated multivariate methodology for the detection and classification of putative contaminants, including apomictic clones, self-fertilized individuals, half-siblings and full contaminants, in biparental polyploid progenies of tropical forage grasses. We established a pipeline to identify contaminants in genotyping-by-sequencing (GBS) data encoded as allele dosages of single nucleotide polymorphism (SNP) markers by integrating principal component analysis (PCA), genotypic analysis (GA) measures based on Mendelian segregation and clustering analysis (CA). The combination of these methods allowed the correct identification of all contaminants in all simulated progenies and the detection of putative contaminants in three real progenies of tropical forage grasses, providing an easy and promising methodology for the identification of contaminants in biparental progenies of tetraploid and hexaploid species. The proposed pipeline was made available through the polyCID Shiny app and can be easily coupled with traditional genetic approaches, such as linkage map construction, thereby increasing the efficiency of breeding programs.

genomics↗
bioRxiv2021

Genomic prediction with allele dosage information in highly polyploid species

Several studies have shown how to leverage allele dosage information to improve the accuracy of genomic selection models in autotetraploids. In this study we expanded the methodology used for genomic selection in autotetraploids to higher (and mixed) ploidy levels. We adapted the models to build covariance matrices of both additive and digenic dominance effects that are subsequently used in genomic selection models. We applied these models using estimates of ploidy and allele dosage to sugarcane and sweet potato datasets and validated our results by also applying the models in simulated data. For the simulated datasets, including allele dosage information led up to 140% higher mean predictive abilities in comparison to using diploidized markers. Including dominance effects was highly advantageous when using diploidized markers, leading to mean predictive abilities which were up to 115% higher in comparison to only including additive effects. When the frequency of heterozygous genotypes in the population was low, such as in the sugarcane and sweet potato datasets, there was little advantage in including allele dosage information in the models. Overall, we show that including allele dosage can improve genomic selection in highly polyploid species under higher frequency of different heterozygous genotypic classes and high dominance degree levels.

genetics↗
bioRxiv2021

Integrative Analysis of Sorghum Bicolor Green Prop Roots Under Elevated CO2 and Water Deficit Conditions

Elevated CO2 (E[CO2]) improves the biomass and yield when combined with water-stress in C4 plants. Although several studies described the molecular response of the C4 plant Sorghum bicolor during drought exposure, none reported its combinatorial effect with E[CO2] in the roots. We decided to perform a molecular analysis using green prop roots, the portion of the radicular system photosynthetically active and more sensible to drought. Whole-transcriptome analysis identified 394 up- and 1,471 down-regulated genes. Among the E[CO2] induced pathways, photosynthesis stood out. Carbon fixation, phenylpropanoid, phenolic compounds, and fatty acid biosynthesis-related pathways were repressed. Protein family analysis showed induction of chlorophyll a-b binding protein family, and repression of glutathione-related enzymes. Protein-protein interaction networks exhibited well-defined clusters, including genes related to cell organization and biogenesis, oxi-reduction process, and photosynthesis being induced. The findings suggest that the E[CO2] mitigates the water deficit by antioxidant and osmoregulation activity, as well as by accumulation of sugar-alcohols in the green prop roots, which may be responsible by the increase in biomass together with the cell proliferation. The higher carbon uptake explains the increase in photosynthetic and primary metabolism activities. Our data revealed that green prop roots present an intriguing metabolism under water deficit and E[CO2], showing its crucial role in the drought tolerance acquisition in a predicted future global atmosphere.

molecular biology↗
bioRxiv2020

Novel fungal metal-dependent GH54 α-L-arabinofuranosidase: expanded substrate specificity and potential use for plant biomass degradation

Trichoderma genus fungi present great potential for the production of carbohydrate-active enzymes (CAZYmes), including glycoside hydrolase (GH) family members. From a renewability perspective, CAZYmes can be biotechnologically exploited to convert plant biomass into free sugars for the production of advanced biofuels and other high-value chemicals. GH54 is an attractive enzyme family for biotechnological applications because many GH54 enzymes are bifunctional. Thus, GH54 enzymes are interesting targets in the search for new enzymes for use in industrial processes such as plant biomass conversion. Herein, a novel metal-dependent GH54 arabinofuranosidase (ThABF) from the cellulolytic fungus Trichoderma harzianum was identified and biochemically characterized. Initial in silico searches were performed to identify the GH54 sequence. Next, the gene was cloned and heterologously overexpressed in Escherichia coli. The recombinant protein was purified, and the enzymes biochemical and biophysical properties were assessed. The GH54 members show wide functional diversity and specifically remove plant cell decorations including arabinose and galactose, in the presence of a metallic cofactor. Plant cell wall decoration have a major impact on lignocellulosic substrate conversion into high-value chemicals. These results expand the known functional diversity within the GH54 family, showing the potential of a novel arabinofuranosidase for plant biomass degradation.

biochemistry↗
bioRxiv2020

The sugarcane and sorghum kinomes: insights into evolutionary expansion and diversification

The protein kinase (PK) superfamily is one of the largest superfamilies in plants and is the core regulator of cellular signaling. Even considering this substantial importance, the kinomes of sugarcane and sorghum have not been profiled. Here we identified and profiled the complete kinomes of the polyploid Saccharum spontaneum (Ssp) and Sorghum bicolor (Sbi), a close diploid relative. The Sbi kinome was composed of 1,210 PKs; for Ssp, we identified 2,919 PKs when disregarding duplications and allelic copies, which were related to 1,345 representative gene models. The Ssp and Sbi PKs were grouped into 20 groups and 120 subfamilies and exhibited high compositional similarities and evolutionary divergences. By utilizing the collinearity between these species, this study offers insights about Sbi and Ssp speciation, PK differentiation and selection. We assessed the PK subfamily expression profiles via RNA-Seq, identifying significant similarities between Sbi and Ssp. Moreover, through coexpression networks, we inferred a core structure of kinase interactions with specific key elements. This study is the first to categorize the allele specificity of a kinome and provides a wide reservoir of molecular and genetic information, enhancing the understanding of the evolutionary history of Sbi and Ssp PKs. HighlightThis study describes the catalog of kinase gene family in Saccharum spontaneum and Sorghum bicolor, providing a reservoir of molecular features and expression patterns based on RNA-Seq and co-expression networks.

genomics↗
bioRxiv2020

Genome-Wide Association Mapping of Sugarcane Yellow Leaf Virus Resistance

A major disease affecting sugarcane, a leading sugar and energy crop, is sugarcane yellow leaf (SCYL), caused by the sugarcane yellow leaf virus (SCYLV). Despite damages caused by SCYLV, the genetic basis of resistance to this virus remains largely unknown. Several methodologies have arisen to identify molecular markers associated with SCYLV resistance, which are crucial for marker-assisted selection and understanding response mechanisms to this virus. We investigated the genetic basis of SCYLV resistance using dominant and codominant markers and genotypes of interest for breeding. A sugarcane panel inoculated with SCYLV was analyzed for SCYL symptoms, and viral titer was estimated by RT-qPCR. This panel was genotyped with 662 dominant markers and 70,888 SNPs and indels with allele proportion information. We used polyploid-adapted genome-wide association analyses and machine-learning algorithms coupled with feature selection methods to establish marker-trait associations. While each approach identified unique marker sets associated with phenotypes, convergences were observed between them, demonstrating their complementarity. Lastly, we annotated these markers, identifying genes encoding emblematic participants in virus resistance mechanisms and previously unreported candidates involved in viral responses. Our approach could accelerate sugarcane breeding targeting SCYLV resistance and facilitate studies on biological processes leading to this trait.

plant biology↗
bioRxiv2020

Genetic structure and molecular diversity of Brazilian grapevine germplasm: management and use in the breeding programs

The management of germplasm banks is complex, especially when many accessions are involved. Microsatellite markers are an efficient tool for assessing the genetic diversity of germplasm collections, optimizing their use in breeding programs. This study genetically characterizes a large collection of 410 grapevine accessions maintained at the Agronomic Institute of Campinas (IAC) (Brazil). The accessions were genotyped with 17 highly polymorphic microsatellite markers. Genetic data were analyzed to determine the genetic structure of the germplasm, quantify its allelic diversity, suggest the composition of a core collection, and discover cases of synonymy, duplication, and misnaming. A total of 304 alleles were obtained, and 334 unique genotypes were identified. The molecular profiles of 145 accessions were confirmed according to the literature and databases, and the molecular profiles of more than 100 genotypes were reported for the first time. The analysis of the genetic structure revealed different levels of stratification. The primary division was between accessions related to Vitis vinifera and V. labrusca, followed by their separation from wild grapevine. A core collection of 120 genotypes captured 100% of all detected alleles. The accessions selected for the core collection may be used in future phenotyping efforts, in genome association studies, and for conservation purposes. Genetic divergence among accessions has practical applications in grape breeding programs, as the choice of relatively divergent parents will maximize the frequency of progeny with superior characteristics. Together, our results can enhance the management of grapevine germplasm and guide the efficient exploitation of genetic diversity to facilitate the development of new grape cultivars for fresh fruits, wine, and rootstock.

molecular biology↗
bioRxiv2020

Comparative gene coexpression networks analysis reveals different strategies of Trichoderma spp. associated with XYR1 and CRE1 during cellulose degradation

Trichoderma harzianum, whose gene expression is tightly controlled by the transcription factors (TFs) XYR1 and CRE1, is a potential candidate for hydrolytic enzyme production. Here, we performed a network analysis of T. harzianum IOC-3844 and T. harzianum CBMAI-0179 to explore how the regulation of these TFs varies between these strains. In addition, we explored the evolutionary relationships of XYR1 and CRE1 protein sequences among Trichoderma spp. The results of the T. harzianum strains were compared with those of Trichoderma atroviride CBMAI-0020, a mycoparasitic species. Although transcripts encoding carbohydrate-active enzymes (CAZymes), TFs, transporters, and proteins with unknown functions were coexpressed with cre1 or xyr1, other proteins indirectly related to cellulose degradation were identified. The enriched GO terms describing the transcripts of these groups differed across all strains, and several metabolic pathways with high similarity between both regulators but strain-specific differences were identified. In addition, the CRE1 and XYR1 subnetworks presented different topology profiles in each strain, likely indicating differences in the influences of these regulators according to the fungi. The hubs of the cre1 and xyr1 groups included transcripts not yet characterized or described as being related to cellulose degradation. The first-neighbor analyses confirmed the results of the profile of the coexpressed transcripts in cre1 and xyr1. The analyses of the shortest paths revealed that CAZymes upregulated under cellulose degradation conditions are most closely related to both regulators, and new targets between such signaling pathways were discovered. Although the evaluated T. harzianum strains are phylogenetically close and their amino acid sequences related to XYR1 and CRE1 are very similar, the set of transcripts related to xyr1 and cre1 differed, suggesting that each T. harzianum strain used a specific regulation strategy for cellulose degradation. More interestingly, our findings may suggest that XYR1 and CRE1 indirectly regulate genes encoding proteins related to cellulose degradation in the evaluated T. harzianum strains. An improved understanding of the basic biology of fungi during the cellulose degradation process can contribute to the use of their enzymes in several biotechnological applications and pave the way for further studies on the differences across strains of the same species.

bioinformatics↗
bioRxiv2020

Machine learning approaches reveal genomic regions associated with sugarcane brown rust resistance

Sugarcane is an economically important crop, but its genomic complexity has hindered advances in molecular approaches for genetic breeding. New cultivars are released based on the identification of interesting traits, and for sugarcane, brown rust resistance is a desirable characteristic due to the large economic impact of the disease. Although marker-assisted selection for rust resistance has been successful, the genes involved are still unknown, and the associated regions vary among cultivars, thus restricting methodological generalization. We used genotyping by sequencing of full-sib progeny to relate genomic regions with brown rust phenotypes. We established a pipeline to identify reliable SNPs in complex polyploid data, which were used for phenotypic prediction via machine learning. We identified 14,540 SNPs, which led to a mean prediction accuracy of 50% by using different models. We also tested feature selection algorithms to increase predictive accuracy, resulting in a reduced dataset with more explanatory power for rust phenotypes. Using different feature selection techniques, we achieved accuracy of up to 95% with a dataset of 131 SNPs related to brown rust QTL regions and auxiliary genes. Therefore, our novel strategy has the potential to assist studies of the genomic organization of brown rust resistance in sugarcane.

bioinformatics↗
bioRxiv2020

The synergistic actions of hydrolytic genes in coexpression networks reveal the potential of Trichoderma harzianum for cellulose degradation

Bioprospecting genes and proteins related to plant biomass degradation is an attractive approach for the identification of target genes for biotechnological purposes, especially genes with potential applications in the biorefinery industry that can enhance second-generation ethanol production technology. Trichoderma harzianum is a potential candidate for cellulolytic enzyme prospection and production. Herein, the enzymatic activities, transcriptome, exoproteome, and coexpression networks of the T. harzianum strain CBMAI-0179 were examined under biomass degradation conditions. We used RNA-Seq to identify differentially expressed genes (DEGs) and carbohydrate-active enzyme (CAZyme) genes related to plant biomass degradation and compared them with the genes of strains from congeneric species (T. harzianum IOC-3844 and T. atroviride CBMAI-0020). T. harzianum CBMAI-0179 harbors strain- and treatment-specific CAZyme genes and transcription factors. We detected important proteins related to biomass degradation, including {beta}-glucosidases, endoglucanases, cellobiohydrolases, lytic polysaccharide monooxygenases, endo-1,4-{beta}-xylanases and {beta}-mannanases, in the exoproteome under cellulose growth conditions. Coexpression networks were constructed to explore the relationships among the genes and corresponding secreted proteins that act synergistically for cellulose degradation. An enriched cluster with degradative enzymes was described, and the subnetwork of CAZymes revealed strong correlations among the secreted proteins (AA9, GH6, GH10, GH11 and CBM1) and differentially expressed CAZyme genes (GH45, GH7, AA7 and GH1). Our results provide valuable information for future studies on the genetic regulation of plant cell wall-degrading enzymes. This knowledge can be exploited for the improvement of enzymatic reactions in biomass degradation for bioethanol production. Key pointsDifferent biotechnological approaches were used to understand the mechanism of cellulose degradation of Trichoderma spp. T. harzianum CBMAI-0179 is a potential candidate for the production of cellulolytic enzymes. Coexpression networks revealed genes and proteins acting synergistically for cellulose hydrolysis.

bioinformatics↗