A novel panel of short mononucleotide repeats linked to informative polymorphisms enabling effective high volume low cost discrimination between mismatch repair deficient and proficient tumours

<div><p>Somatic mutations in mononucleotide repeats are commonly used to assess the mismatch repair status of tumours. Current tests focus on repeats with a length above 15bp, which tend to be somatically more unstable than shorter ones. These longer repeats also have a substantially higher PCR error rate, and tests that use capillary electrophoresis for fragment size analysis often require expert interpretation. In this communication, we present a panel of 17 short repeats (length 7–12bp) for sequence-based microsatellite instability (MSI) testing. Using a simple scoring procedure that incorporates the allelic distribution of the mutant repeats, and analysis of two cohort of tumours totalling 209 samples, we show that this panel is able to discriminate between MMR proficient and deficient tumours, even when constitutional DNA is not available. In the training cohort, the method achieved 100% concordance with fragment analysis, while in the testing cohort, 4 discordant samples were observed (corresponding to 97% concordance). Of these, 2 showed discrepancies between fragment analysis and immunohistochemistry and one was reclassified after re-testing using fragment analysis. These results indicate that our approach offers the option of a reliable, scalable routine test for MSI.</p></div

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

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

Análise de KIR e HLA em alta resolução em uma população euro-descendente e em pacientes com carcinoma mamário esporádico

Orientador: Prof. Dr. Danillo Gardenal AugustoCoorientadora: Profª Drª. Marcia H. BeltrameTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Biológicas, Programa de Pós-Graduação em Genética. Defesa : Curitiba, 19/09/2022Inclui referênciasResumo: As células assassinas naturais (ou NK, do inglês natural killer) são importantes efetores da imunidade inata e adaptativa, atuando no combate a células infectadas e células tumorais. Os genes KIR (killer-cell immunoglobulinlike receptors) codificam um grupo de receptores que modulam as respostas dessas células através do reconhecimento de antígenos leucocitários humanos (HLA, do inglês, human leukocyte antigens). Combinações de variantes KIR-HLA já foram associadas com diversas doenças, incluindo vários tipos de câncer, doenças autoimunes e infecciosas. No entanto, devido à complexidade estrutural da família de genes KIR, poucos estudos atingiram um nível de profundidade de análise que permitiu explorar a variação alélica e de número de cópias desses genes em larga escala. Com o objetivo de preencher essa lacuna, nosso primeiro objetivo foi descrever a variação de KIR em alta resolução em uma grande amostra populacional, aplicando sequenciamento de nova geração em 2130 Euro-descendentes dos Estados Unidos. A determinação precisa do número de cópias de KIR nos permitiu identificar um conjunto de haplótipos KIR incomuns, responsáveis por 5,2% da variação estrutural total. Observamos que KIR2DL4 foi o gene moldura que mais variou em número de cópias (6,5% de todos os indivíduos). Além disso, identificamos 250 alelos com resolução de 5 dígitos, dos quais 90 têm frequências >ou igual a 1%. Encontramos padrões de sequência que eram consistentes com a presença de novos alelos em 398 (18,7%) indivíduos e contextualizamos vários polimorfismos de nucleotídeo únicos (SNP) órfãos dentro do complexo. Também identificamos uma nova variante de KIR2DL1 (Pro151Arg), a qual demonstramos por dinâmica molecular que esta substituição possivelmente afeta a interação com HLA-C. No contexto de KIR e doenças, nós escolhemos o câncer de mama como foco do nosso estuado devido à falta de estudos que analisam KIR e HLA de classe I e classe II nessa doença. O câncer de mama (BC) é a neoplasia mais comum entre as mulheres, com tumores que variam em agressividade, tipo histológico, subtipo molecular e desenvolvimento de metástases entre outros. Neste trabalho, analisamos o DNA de 550 mulheres com câncer de mama esporádico e 747 controles. Nós identificamos que a presença de três cópias de KIR2DL5 estava associada à proteção (OR = 0,21, pcorr = 0,027), enquanto a presença de três cópias de KIR3DL1S1 foi associada ao risco aumentado para BC (OR = 2,44, pcorr = 0,009). Também identificamos que o par KIR2DL3*001+HLA-C1 estava significativamente reduzido em pacientes com o subtipo HER2+ não luminal (ER e PR negativo) em comparação com todos os outros subtipos (OR = 0,23, pcorr = 0,016), além de uma forte associação de KIR2DS1*002+HLA-C2 em pacientes com tumores triplo negativos em comparação com todos os outros subtipos (OR = 3,34, pcorr or equal to 1%. We also found sequence patterns that were consistent with the presence of new alleles in 398 (18.7%) individuals and contextualized several orphan single nucleotide polymorphisms (SNP) within the complex. We also identified a new variant of KIR2DL1 (Pro151Arg), which we demonstrated by molecular dynamics that this substitution possibly affects the interaction with HLA-C. In the context of KIR and disease, we chose breast cancer as the focus of our study due to the lack of studies that analyze KIR and HLA class I and class II in this disease. Breast cancer (BC) is the most common malignancy among women, with tumors that vary in aggressiveness, histological type, molecular subtype, and development of metastases, among others. In this work, we analyzed the DNA of 550 women with sporadic breast cancer and 747 controls. We found that the presence of three copies of KIR2DL5 was associated with protection (OR = 0.21, pcorr = 0.027), while the presence of three copies of KIR3DL1S1 was associated with increased risk for BC (OR = 2.44, pcorr = 0.009). We also identified that the KIR2DL3*001+HLA-C1 pair was significantly reduced in patients with the non-luminal HER2+ subtype (ER and PR negative) compared to all other subtypes (OR = 0.23, pcorr = 0.016), in addition to a strong association of KIR2DS1*002+HLA-C2 in patients with triple negative tumors compared to all other subtypes (OR = 3.34, pcorr < 1.4 x10-4). Finally, we observed a strong protective effect of the KIR2DL1*001+HLA-C2 pair, significantly increased in patients with grade I BC compared to the more aggressive grades II+III (OR = 0.25, pcorr = 0.038). We also explored the diversity of HLA class I and class II in BC and their subtypes. HLA-C*12:03 was observed significantly more frequent in patients with triple negative breast cancer (OR = 2.7, p = 0.0025, pcorr = 0.033), as well as variations in amino acids at positions 11 and 12 of HLA-B. HLADPB1* 17:01 was associated with protection against the development of lymph node metastases (OR = 0.21, p = 0.0019, pcorr = 0.037). Finally, the presence of the amino acid arginine at position 151 of HLA-A was associated with the development of grade III breast cancer (OR = 3.36, p = 0.0016, pcorr = 0.016). The amino acids lysine and aspartic acid at positions 9 and 11 of HLA-DRB1 were associated with protection against grade III breast cancer (OR = 0.13, p = 0.0033, pcorr = 0.033). No previous study has explored the structural and sequence variation of KIR in any population or disease to the depth that we have presented in this work. We demonstrate that the combination of high-throughput sequencing with state-of-the-art computational tools allows for the exploration of all aspects of KIR variation, including the determination of haplotype diversity at the population level, improving the understanding of this complex gene family, and providing an important reference for future studies

Diagnóstico de câncer colorretal hereditário sem polipose por sequenciamento de nova geração

Orientador: Nina A. B. PagnanMonografia (Bacharelado) - Universidade Federal do Paraná. Setor de Ciências Biológicas. Curso de Graduação em Ciências Biológica