Characterization of novel antibodies and glycan binding proteins against cancer

Cancer is the largest cause of death worldwide and it is increasingly urgent to develop new approaches to diagnosis and therapy. The glycosylation pattern of the cancer cells differ from that of healthy cells and therefore the aberrant expression of glycans are promising targets for diagnosis and therapy. Sialyl-Tn (STn) is a glycan, whose expression is increased in cancer cells and almost absent in normal cells. It is involved in processes such as cancer growth, progression and metastasis. In addition to STn other glycans present an aberrant pattern in cancer. As is the case of Sialyl Lewis X (sLeX) shown overexpressed in tumors. The objective of this work consisted in the characterization of monoclonal antibodies against STn and sLeX produced through the hybridoma technology. The antibodies’ affinity and specificity were tested by flow cytometry and ELISA. In case of STn, different batches of a L2A5 antibody were tested. To select and isolate a better performant hybridoma the original L2A5 hybridoma population was subcloned. Cancer cell lines expressing STn and bovine submaxillary mucins expressing STn, were used for screening by flow cytometry and ELISA, respectively. Both techniques showed that the antibodies produced by the L2A5 hybridoma had affinity and specificity for STn, although these were slightly different between batches. We then cloned the hybridoma cells by limiting dilution. The results allowed selecting the subclone 4E11 which produce antibody with the optimized characteristics. In case of sLeX, a novel antibody is being developed by immunizing mice with glycoproteins carrying sLeX. Mice serum was screened by ELISA and results showed the presence of sLeX-recognizing antibodies. Then the supernatants of hybridomas were screened by ELISA to select those producing a candidate monoclonal antibody that recognize sLeX . Both studies have contributed to the development of new monoclonal antibodies with potential use in the diagnosis and treatment of cancer

Proximal and mineral composition of native fish species from Amazonas, Brazil

For decades, researches were developed about the diversity of fishes in the Amazon basin and their consumption by the people. In this context, an important gap identified was a lack of information about the nutritional composition of some of the main Amazon fish species consumed and traded. Front this, the objective of this study was to evaluate the chemical composition and mineral content of filets from 10 fish species with the highest landing volume in Amazonas State. The fish species selected were curimatã, jaraqui, mapará, matrinxã, pacu, piramutaba, sardinha, surubim, tambaqui and tucunaré. Were collected 20 samples (fishes) from each species according to the hydrological cycles of the region (20 samples in the flood and 20 samples in drought). Ten fish samples were processed to determine the proximal composition and 10 fish samples were used to determine mineral content (macro and micro minerals). The proximal composition of fish species analysed varied widely between species and seasons, with an emphasis on moisture and lipid content. Fishes in the flood season presented higher content of nutrients than drought season. This result also was observed in the minerals profile, where fishes in the flood season presented the highest (p < 0.05) minerals content

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

ALDH1A2 (RALDH2) genetic variation in human congenital heart disease

Abstract\ud \ud \ud \ud Background\ud \ud Signaling by the vitamin A-derived morphogen retinoic acid (RA) is required at multiple steps of cardiac development. Since conversion of retinaldehyde to RA by retinaldehyde dehydrogenase type II (ALDH1A2, a.k.a RALDH2) is critical for cardiac development, we screened patients with congenital heart disease (CHDs) for genetic variation at the ALDH1A2 locus.\ud \ud \ud \ud Methods\ud \ud One-hundred and thirty-three CHD patients were screened for genetic variation at the ALDH1A2 locus through bi-directional sequencing. In addition, six SNPs (rs2704188, rs1441815, rs3784259, rs1530293, rs1899430) at the same locus were studied using a TDT-based association approach in 101 CHD trios. Observed mutations were modeled through molecular mechanics (MM) simulations using the AMBER 9 package, Sander and Pmemd programs. Sequence conservation of observed mutations was evaluated through phylogenetic tree construction from ungapped alignments containing ALDH8 s, ALDH1Ls, ALDH1 s and ALDH2 s. Trees were generated by the Neighbor Joining method. Variations potentially affecting splicing mechanisms were cloned and functional assays were designed to test splicing alterations using the pSPL3 splicing assay.\ud \ud \ud \ud Results\ud \ud We describe in Tetralogy of Fallot (TOF) the mutations Ala151Ser and Ile157Thr that change non-polar to polar residues at exon 4. Exon 4 encodes part of the highly-conserved tetramerization domain, a structural motif required for ALDH oligomerization. Molecular mechanics simulation studies of the two mutations indicate that they hinder tetramerization. We determined that the SNP rs16939660, previously associated with spina bifida and observed in patients with TOF, does not affect splicing. Moreover, association studies performed with classical models and with the transmission disequilibrium test (TDT) design using single marker genotype, or haplotype information do not show differences between cases and controls.\ud \ud \ud \ud Conclusion\ud \ud In summary, our screen indicates that ALDH1A2 genetic variation is present in TOF patients, suggesting a possible causal role for this gene in rare cases of human CHD, but does not support the hypothesis that variation at the ALDH1A2 locus is a significant modifier of the risk for CHD in humans.Work supported by grants from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 01/000090; 00/030722; 01/142381; 02/113402; 03/099982; 04/116068; 04/157044 and Conselho Nacional de Desenvolvimento Científico e Tecnológico 481872/20078. We would like to thank the careful work and thoughtful suggestions of the two reviewers responsible for the reviewing editorial process.Work supported by grants from Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 01/00009-0; 00/03072-2; 01/14238-1; 02/11340-2; 03/09998-2; 04/11606-8; 04/15704-4 and Conselho Nacional de Desenvolvimento Científico e Tecnológico 481872/2007-8. We would like to thank the careful work and thoughtful suggestions of the two reviewers responsible for the reviewing editorial process

PESQUISAS DESENVOLVIDAS NO PROGRAMA DE PÓS-GRADUAÇÃO EM GERONTOLOGIA DA UNIVERSIDADE FEDERAL DE SÃO CARLOS E SUA IMPORTÂNCIA NO CONTEXTO DO ENVELHECIMENTO

Esta revisão narrativa tem como objetivo apresentar e discutir a relevância dos estudos desenvolvidos no Programa de Pós-Graduação em Gerontologia da Universidade Federal de São Carlos (PPGGero/UFSCar). Os dados foram coletados na disciplina “Seminários Avançados em Gerontologia”, em que os docentes autores apresentaram suas linhas de pesquisa, as quais foram compiladas pelos discentes autores. Os resultados desta revisão apresentam os estudos realizados em um programa interdisciplinar de mestrado acadêmico que possui duas linhas de pesquisa denominadas: 1) Saúde, Biologia e Envelhecimento; e 2) Gestão, Tecnologia e Inovação em Gerontologia. Os estudos se inserem em diversas temáticas da área do envelhecimento, que se articulam entre ambas as linhas de pesquisa do programa. Na linha 1 destacam-se estudos relacionados à cognição, à demência e suas consequências aos pacientes e cuidadores, bem como a pesquisas relacionadas ao seu diagnóstico e cuidado. Destacam-se também pesquisas epidemiológicas e aquelas relacionadas a condições ou doenças comuns no envelhecimento, como dor crônica, osteoartrite e, mais recentemente, a infecções em idosos, incluindo a COVID-19. Na linha 2 destacam-se pesquisas relacionadas a tecnologias assistivas voltadas aos idosos, bem como estratégias de marketing e políticas de atenção para esse público. É notório que a multidimensionalidade de abordagens das questões do envelhecimento exige enfoque multidisciplinar sobre um contexto de excelência acadêmica. Assim, conclui-se que as pesquisas do PPGGero/UFSCar, ao abrangerem várias áreas do conhecimento – as quais permeiam as ciências humanas, biológicas, sociais e exatas no contexto de tecnologia e inovação –, promovem a formação de mestres e pesquisadores que contribuem para a produção de conhecimento em Gerontologia no país

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost

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 understanding 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,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 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 underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities 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 organism 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 neglected 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 lost