Effect of polysaccharide sources on the physicochemical properties of bromelainchitosan nanoparticles

Bromelain, a set of proteolytic enzymes potential pharmaceutical applications, was encapsulated in chitosan nanoparticles to enhance enzyme stability, and the effect of different chitosan sources was evaluated. Chitosan types (i.e., low molecular weight chitosan, chitosan oligosaccharide lactate, and chitosan from shrimp shells) produced nanoparticles with different physicochemical properties, however in all cases, particle size and zeta potential decreased, and polydispersity index increased after bromelain addition. Bromelain encapsulation was higher than 84% and 79% for protein content and enzymatic activity, respectively, with low molecular weight chitosan presenting the highest encapsulation efficiency. Nanoparticle suspension was also tested for accelerated stability and rheological behavior. For the chitosan–bromelain nanoparticles, an instability index below 0.3 was recorded and, in general, the loading of bromelain in chitosan nanoparticles decreased the cohesiveness of the final suspension.This research was granted by FAPESP (2016/03444-5,2017/05275-9,and2017/05333-9), CNPq and FAEPEX, and by the Portuguese Science and Technology Foundation, Ministry of Science and Education (FCT/MEC) through national funds, and co-financed by FEDER, under the project reference M-ERA-NET/0004/2015 (PAIRED) Partnership Agreement PT2020.info:eu-repo/semantics/publishedVersio

Toll-like receptor 4 selective inhibition in medullar microenvironment alters multiple myeloma cell growth.

peer reviewedBone marrow (BM) mesenchymal stromal cells (MSCs) are abnormal in multiple myeloma (MM) and play a critical role by promoting growth, survival, and drug resistance of MM cells. We observed higher Toll-like receptor 4 (TLR4) gene expression in MM MSCs than in MSCs from healthy donors. At the clinical level, we highlighted that TLR4 expression in MM MSCs evolves in parallel with the disease stage. Thus, we reasoned that the TLR4 axis is pivotal in MM by increasing the protumor activity of MSCs. Challenging primary MSCs with TLR4 agonists increased the expression of CD54 and interleukin-6 (IL-6), 2 factors directly implicated in MM MSC-MM cell crosstalk. Then, we evaluated the therapeutic efficacy of a TLR4 antagonist combined or not with conventional treatment in vitro with MSC-MM cell coculture and in vivo with the Vk*MYC mouse model. Selective inhibition of TLR4 specifically reduced the MM MSC ability to support the growth of MM cells in an IL-6-dependent manner and delayed the development of MM in the Vk*MYC mouse model by altering the early disease phase in vivo. For the first time, we demonstrate that specific targeting of the pathological BM microenvironment via TLR4 signaling could be an innovative approach to alter MM pathology development

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

Imprinting of Mesenchymal Stromal Cell Transcriptome Persists even after Treatment in Patients with Multiple Myeloma

Introduction. Multiple myeloma (MM) is a B-cell neoplasm characterized by clonal expansion of malignant plasma cells (MM cells) in the bone-marrow (BM) compartment. BM mesenchymal stromal cells (MSC) from newly diagnosed MM patients were shown to be involved in MM pathogenesis and chemoresistance. The patients displayed a distinct transcriptome and were functionally different from healthy donors’ (HD) MSC. Our aim was to determine whether MM–MSC also contributed to relapse. Methods. We obtained and characterized patients’ MSC samples at diagnosis, two years after intensive treatment, without relapse and at relapse. Results. Transcriptomic analysis revealed differences in gene expression between HD and MM-MSC, whatever the stage of the disease. An easier differentiation towards adipogenesis at the expense of osteoblatogeneis was observed, even in patients displaying a complete response to treatment. Although their transcriptome was similar, we found that MSC from relapsed patients had an increased immunosuppressive ability, compared to those from patients in remission. Conclusion. We demonstrated that imprinting of MSC transcriptome demonstrated at diagnosis of MM, persisted even after the apparent disappearance of MM cells induced by treatment, suggesting the maintenance of a local context favorable to relapse

Functional Comparison between Healthy and Multiple Myeloma Adipose Stromal Cells

Multiple myeloma (MM) is an incurable B cell neoplasia characterized by the accumulation of tumor plasma cells within the bone marrow (BM). As a consequence, bone osteolytic lesions develop in 80% of patients and remain even after complete disease remission. We and others had demonstrated that BM-derived mesenchymal stromal cells (MSCs) are abnormal in MM and thus cannot be used for autologous treatment to repair bone damage. Adipose stromal cells (ASCs) represent an interesting alternative to MSCs for cellular therapy. Thus, in this study, we wondered whether they could be a good candidate in repairing MM bone lesions. For the first time, we present a transcriptomic, phenotypic, and functional comparison of ASCs from MM patients and healthy donors (HDs) relying on their autologous MSC counterparts. In contrast to MM MSCs, MM ASCs did not exhibit major abnormalities. However, the changes observed in MM ASCs and the supportive property of ASCs on MM cells question their putative and safety uses at an autologous or allogenic level

Primary plasma cell leukemias displaying t(11;14) have specific genomic, transcriptional, and clinical features

International audiencePrimary plasma cell leukemia (pPCL) is an aggressive form of multiple myeloma (MM) that has not benefited from recent therapeutic advances in the field. Because it is very rare and heterogeneous, it remains poorly understood at the molecular level. To address this issue, we performed DNA and RNA sequencing of sorted plasma cells from a large cohort of 90 newly diagnosed pPCL and compared with MM. We observed that pPCL presents a specific genomic landscape with a high prevalence of t(11;14) (about half) and high-risk genomic features such as del(17p), gain 1q, and del(1p32). In addition, pPCL displays a specific transcriptome when compared with MM. We then wanted to characterize specifically pPCL with t(11;14). We observed that this subentity displayed significantly fewer adverse cytogenetic abnormalities. This translated into better overall survival when compared with pPCL without t(11;14) (39.2 months vs 17.9 months, P 5 .002). Finally, pPCL with t(11;14) displayed a specific transcriptome, including differential expression of BCL2 family members. This study is the largest series of patients with pPCL reported so far

Biallelic deletion of 1p32 defines ultra-high-risk myeloma, but monoallelic del(1p32) remains a strong prognostic factor

Cytogenetics abnormalities (CA) are known to be the preponderant prognostic factor in multiple myeloma (MM). Our team has recently developed a prognostic score based on 6 CA, where del(1p32) appears to be the second worst abnormality after del(17p). The aim of this study was to confirm the adverse impact of 1p32 deletion on newly-diagnosed multiple myeloma (NDMM) patients. Among 2551 NDMM patients, 11% were harboring del(1p32). Their overall survival (OS) was significantly inferior compared to patients without del(1p32) (median OS: 49 months vs. 124 months). Likewise, progression-free survival was significantly shorter. More importantly, biallelic del(1p32) conferred a dramatically poorer prognosis than a monoallelic del(1p32) (median OS: 25 months vs. 60 months). As expected, the OS of del(1p32) patients significantly decreased when this abnormality was associated with other high-risk CA (del(17p), t(4;14) or gain(1q)). In the multivariate analysis, del(1p32) appeared as a negative prognostic factor; after adjustment for age and treatment, the risk of progression was 1.3 times higher among patients harboring del(1p32), and the risk of death was 1.9 times higher. At the dawn of risk-adapted treatment strategies, we have confirmed the adverse impact of del(1p32) in MM and the relevance of its assessment at diagnosis

NEOTROPICAL CARNIVORES: a data set on carnivore distribution in the Neotropics

Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non-detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non-governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer-reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non-detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio-temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large-scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data