Defining functional diversity for lignocellulose degradation in a microbial community using multi-omics studies

Abstract\ud \ud Background\ud Lignocellulose is one of the most abundant forms of fixed carbon in the biosphere. Current industrial approaches to the degradation of lignocellulose employ enzyme mixtures, usually from a single fungal species, which are only effective in hydrolyzing polysaccharides following biomass pre-treatments. While the enzymatic mechanisms of lignocellulose degradation have been characterized in detail in individual microbial species, the microbial communities that efficiently breakdown plant materials in nature are species rich and secrete a myriad of enzymes to perform “community-level” metabolism of lignocellulose. Single-species approaches are, therefore, likely to miss important aspects of lignocellulose degradation that will be central to optimizing commercial processes.\ud \ud \ud Results\ud Here, we investigated the microbial degradation of wheat straw in liquid cultures that had been inoculated with wheat straw compost. Samples taken at selected time points were subjected to multi-omics analysis with the aim of identifying new microbial mechanisms for lignocellulose degradation that could be applied in industrial pre-treatment of feedstocks. Phylogenetic composition of the community, based on sequenced bacterial and eukaryotic ribosomal genes, showed a gradual decrease in complexity and diversity over time due to microbial enrichment. Taxonomic affiliation of bacterial species showed dominance of Bacteroidetes and Proteobacteria and high relative abundance of genera Asticcacaulis, Leadbetterella and Truepera. The eukaryotic members of the community were enriched in peritrich ciliates from genus Telotrochidium that thrived in the liquid cultures compared to fungal species that were present in low abundance. A targeted metasecretome approach combined with metatranscriptomics analysis, identified 1127 proteins and showed the presence of numerous carbohydrate-active enzymes extracted from the biomass-bound fractions and from the culture supernatant. This revealed a wide array of hydrolytic cellulases, hemicellulases and carbohydrate-binding modules involved in lignocellulose degradation. The expression of these activities correlated to the changes in the biomass composition observed by FTIR and ssNMR measurements.\ud \ud \ud Conclusions\ud A combination of mass spectrometry-based proteomics coupled with metatranscriptomics has enabled the identification of a large number of lignocellulose degrading enzymes that can now be further explored for the development of improved enzyme cocktails for the treatment of plant-based feedstocks. In addition to the expected carbohydrate-active enzymes, our studies reveal a large number of unknown proteins, some of which may play a crucial role in community-based lignocellulose degradation.This work was funded by Biotechnology and Biological Sciences Research\ud Council (BBSRC) Grants BB/1018492/1, BB/K020358/1 and BB/P027717/1, the\ud BBSRC Network in Biotechnology and Bioenergy BIOCATNET and São Paulo\ud Research Foundation (FAPESP) Grant 10/52362-5. ERdA thanks EMBRAPA\ud Instrumentation São Carlos and Dr. Luiz Alberto Colnago for providing the\ud NMR facility and CNPq Grant 312852/2014-2. The authors would like to thank\ud Deborah Rathbone and Susan Heywood from the Biorenewables Develop‑\ud ment Centre for technical assistance in rRNA amplicon sequencing

A list of land plants of Parque Nacional do Caparaó, Brazil, highlights the presence of sampling gaps within this protected area

Brazilian protected areas are essential for plant conservation in the Atlantic Forest domain, one of the 36 global biodiversity hotspots. A major challenge for improving conservation actions is to know the plant richness, protected by these areas. Online databases offer an accessible way to build plant species lists and to provide relevant information about biodiversity. A list of land plants of “Parque Nacional do Caparaó” (PNC) was previously built using online databases and published on the website "Catálogo de Plantas das Unidades de Conservação do Brasil." Here, we provide and discuss additional information about plant species richness, endemism and conservation in the PNC that could not be included in the List. We documented 1,791 species of land plants as occurring in PNC, of which 63 are cited as threatened (CR, EN or VU) by the Brazilian National Red List, seven as data deficient (DD) and five as priorities for conservation. Fifity-one species were possible new ocurrences for ES and MG states

Guidelines for the management of neuroendocrine tumours by the Brazilian gastrointestinal tumour group

Neuroendocrine tumours are a heterogeneous group of diseases with a significant variety of diagnostic tests and treatment modalities. Guidelines were developed by North American and European groups to recommend their best management. However, local particularities and relativisms found worldwide led us to create Brazilian guidelines. Our consensus considered the best feasible strategies in an environment involving more limited resources. We believe that our recommendations may be extended to other countries with similar economic standards.Univ Sao Paulo, Inst Canc Estado Sao Paulo, BR-01246000 Sao Paulo, BrazilUniv Sao Paulo, Fac Med, Dept Radiol & Oncol, BR-01246903 Sao Paulo, BrazilHosp Sirio Libanes, BR-01308050 Sao Paulo, BrazilHosp Moinhos de Vento Porto Alegre, BR-90035000 Porto Alegre, RS, BrazilOncoctr, BR-30360680 Belo Horizonte, MG, BrazilUniv Fed Rio Grande do Sul, Dept Cirurgia, BR-90040060 Porto Alegre, RS, BrazilHosp Clin Porto Alegre, BR-90035903 Porto Alegre, RS, BrazilUniv Fed Ceara, Fac Med, Dept Fisiol & Farmacol, BR-60020180 Fortaleza, Ceara, BrazilHosp Univ Walter Cantidio, BR-60430370 Fortaleza, Ceara, BrazilInst Nacl Canc, BR-20230240 Rio De Janeiro, BrazilUniv Sao Paulo, Fac Med, Disciplina Endocrinol & Metabol, BR-01246903 Sao Paulo, BrazilAC Camargo Canc Ctr, Dept Surg, BR-01509010 Sao Paulo, BrazilUniv Sao Paulo, Fac Med, Dept Gastroenterol, Sao Paulo, BrazilUniv Fed Ciencias Saude Porto Alegre, BR-90050170 Porto Alegre, RS, BrazilHosp Albert Einstein, BR-05652900 Sao Paulo, BrazilHosp Base, Fac Med Sao Jose do Rio Preto, BR-15090000 Sao Paulo, BrazilSanta Casa Sao Jose do Rio Preto, BR-15025500 Sao Jose Do Rio Preto, BrazilPontificia Univ Catolica Parana, Hosp Erasto Gaertner, BR-81520060 Curitiba, Parana, BrazilUniv Fed Rio Grande do Norte, BR-59300000 Natal, RN, BrazilUniv Sao Paulo, Inst Coracao, BR-05403900 Sao Paulo, BrazilAC Camargo Canc Ctr, Med Oncol, BR-01509010 Sao Paulo, BrazilUniv Fed Sao Paulo, Disciplina Gastroenterol, BR-04021001 Sao Paulo, BrazilHosp Sao Rafael, BR-41253190 Salvador, BA, BrazilHosp Canc Barretos, Dept Cirurgia Aparelho Digest Alto & Hepatobiliop, BR-14784400 Sao Paulo, BrazilUniv Sao Paulo, Fac Med, Dept Patol, BR-01246903 Sao Paulo, BrazilClin AMO, BR-1950640 Salvador, BA, BrazilHosp Sao Jose, BR-01323001 Sao Paulo, BrazilUniv Nove de Julho, BR-02111030 Sao Paulo, BrazilUniv Fed Sao Paulo, Disciplina Gastroenterol, BR-04021001 Sao Paulo, BrazilWeb of Scienc

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio