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Primary plasma cell leukemia (pPCL) is an infrequent and aggressive plasma cell disorder. The prognosis is still very poor, and the optimal treatment remains to be established. A retrospective, multicentric, international observational study was performed. Patients from 9 countries of Latin America (LATAM) with a diagnosis of pPCL between 2012 and 2020 were included. 72 patients were included. Treatment was based on thalidomide in 15%, proteasome inhibitors (PI)-based triplets in 38% and chemotherapy plus IMIDs and/or PI in 29%. The mortality rate at 3 months was 30%. The median overall survival (OS) was 18 months. In the multivariate analysis, frontline PI-based triplets, chemotherapy plus IMIDs and/or PI therapy, and maintenance were independent factors of better OS. In conclusion, the OS of pPCL is still poor in LATAM, with high early mortality. PI triplets, chemotherapy plus IMIDs, and/or PI and maintenance therapy were associated with improved survival.

Purpose: Waldenstrom Macroglobulinemia (WM) is a rare lymphoma with distinct clinical features, and data from Latin American patients are lacking. Therefore, we aim to investigate the clinical, therapy, and outcome patterns of WM in Latin America., Methods: We retrospectively analyzed patients with WM diagnosed between 1991 and 2019 from 24 centers in seven Latin American countries. The study outcomes were overall survival (OS) and progression-free survival (PFS)., Results: We identified 159 cases (median age 67 years, male 62%). Most patients (95%) were symptomatic at diagnosis. The International Prognostic Scoring System for WM (IPSSWM) at diagnosis was available in 141 (89%) patients (high-risk 40%, intermediate-risk 37%, and low-risk 23%). Twenty-seven (17%) patients were tested for MYD88 L265P , with 89% (n = 24 of 27) carrying the mutation. First-line and second-line therapies were administered to 142 (89%) and 53 (33%) patients, respectively. Chemoimmunotherapy was the most commonly used first-line (66%) and second-line (45%) approach; only 18 (11%) patients received ibrutinib. With a median follow-up of 69 months, the 5-year OS rate was 81%. In treated patients, the 5-year OS and PFS rates were 78% and 59%, respectively. High-risk IPSSWM at treatment initiation was an independent risk factor for OS (adjusted hazard ratio: 4.73, 95% CI, 1.67 to 13.41, P = .003) and PFS (adjusted hazard ratio: 2.43, 95% CI, 1.31 to 4.50, P = .005)., Conclusion: In Latin America, the management of WM is heterogeneous, with limited access to molecular testing and novel agents. However, outcomes were similar to those reported internationally. We validated the IPSSWM score as a prognostic factor for OS and PFS. There is an unmet need to improve access to recommended diagnostic approaches and therapies in Latin America.

Cassava is cultivated due to its drought tolerance and high carbohydrate-containing storage roots. The lack of uniformity and irregular shape of storage roots poses constraints on harvesting and post-harvest processing. Here, we phenotyped the Genetic gain and offspring (C1) populations from the International Institute of Tropical Agriculture (IITA) breeding program using image analysis of storage root photographs taken in the field. In the genome-wide association analysis (GWAS), we detected for most shape and size-related traits, QTL on chromosomes 1 and 12. In a previous study, we found the QTL on chromosome 12 to be associated with cassava mosaic disease (CMD) resistance. Because the root uniformity is important for breeding, we calculated the standard deviation (SD) of individual root measurements per clone. With SD measurements we identified new significant QTL for Perimeter, Feret and Aspect Ratio on chromosomes 6, 9 and 16. Predictive accuracies of root size and shape image-extracted traits were mostly higher than yield trait prediction accuracies. This study aimed to evaluate the feasibility of the image phenotyping protocol and assess GWAS and genomic prediction for size and shape image-extracted traits. The methodology described and the results are promising and open up the opportunity to apply high-throughput methods in cassava.

Cassava (Manihot esculenta Crantz) is an important security crop that faces severe yield loses due to cassava brown streak disease (CBSD). Motivated by the slow progress of conventional breeding, genetic improvement of cassava is undergoing rapid change due to the implementation of quantitative trait loci mapping, Genome-wide association mapping (GWAS), and genomic selection (GS). In this study, two breeding panels were genotyped for SNP markers using genotyping by sequencing and phenotyped for foliar and CBSD root symptoms at five locations in Uganda. Our GWAS study found two regions associated to CBSD, one on chromosome 4 which co-localizes with a Manihot glaziovii introgression segment and one on chromosome 11, which contains a cluster of nucleotide-binding site-leucine-rich repeat (NBS-LRR) genes. We evaluated the potential of GS to improve CBSD resistance by assessing the accuracy of seven prediction models. Predictive accuracy values varied between CBSD foliar severity traits at 3 months after planting (MAP) (0.27-0.32), 6 MAP (0.40-0.42) and root severity (0.31-0.42). For all traits, Random Forest and reproducing kernel Hilbert spaces regression showed the highest predictive accuracies. Our results provide an insight into the genetics of CBSD resistance to guide CBSD marker-assisted breeding and highlight the potential of GS to improve cassava breeding.

Brassica rapa studies towards metabolic variation have largely been focused on the profiling of the diversity of metabolic compounds in specific crop types or regional varieties, but none aimed to identify genes with regulatory function in metabolite composition. Here we followed a genetical genomics approach to identify regulatory genes for six biosynthetic pathways of health-related phytochemicals, i.e carotenoids, tocopherols, folates, glucosinolates, flavonoids and phenylpropanoids. Leaves from six weeks-old plants of a Brassica rapa doubled haploid population, consisting of 92 genotypes, were profiled for their secondary metabolite composition, using both targeted and LC-MS-based untargeted metabolomics approaches. Furthermore, the same population was profiled for transcript variation using a microarray containing EST sequences mainly derived from three Brassica species: B. napus, B. rapa and B. oleracea. The biochemical pathway analysis was based on the network analyses of both metabolite QTLs (mQTLs) and transcript QTLs (eQTLs). Co-localization of mQTLs and eQTLs lead to the identification of candidate regulatory genes involved in the biosynthesis of carotenoids, tocopherols and glucosinolates. We subsequently focused on the well-characterized glucosinolate pathway and revealed two hotspots of co-localization of eQTLs with mQTLs in linkage groups A03 and A09. Our results indicate that such a large-scale genetical genomics approach combining transcriptomics and metabolomics data can provide new insights into the genetic regulation of metabolite composition of Brassica vegetables.

With the recent advances in high throughput profiling techniques the amount of genetic and phenotypic data available has increased dramatically. Although many genetic diversity studies combine morphological and genetic data, metabolite profiling has yet to be integrated into these studies. For our study we selected 168 accessions representing the different morphotypes and geographic origins of Brassica rapa. Metabolite profiling was performed on all plants of this collection in the youngest expanded leaves, 5 weeks after transplanting and the same material was used for molecular marker profiling. During the same season a year later, 26 morphological characteristics were measured on plants that had been vernalized in the seedling stage. The number of groups and composition following a hierarchical clustering with molecular markers was highly correlated to the groups based on morphological traits (r = 0.420) and metabolic profiles (r = 0.476). To reveal the admixture levels in B. rapa, comparison with the results of the programme STRUCTURE was needed to obtain information on population substructure. To analyze 5546 metabolite (LC-MS) signals the groups identified with STRUCTURE were used for random forests classification. When comparing the random forests and STRUCTURE membership probabilities 86% of the accessions were allocated into the same subgroup. Our findings indicate that if extensive phenotypic data (metabolites) are available, classification based on this type of data is very comparable to genetic classification. These multivariate types of data and methodological approaches are valuable for the selection of accessions to study the genetics of selected traits and for genetic improvement programs, and additionally provide information on the evolution of the different morphotypes in B. rapa.

The distinction between IgM monoclonal gammopathy of undetermined significance (MGUS), asymptomatic Waldenstrom's macroglobulinemia (WM; aWM), and symptomatic WM (sWM) relies on two features: the presence of infiltration by lymphoplasmacytic lymphoma in the bone marrow (BM) biopsy and the existence of signs or symptoms attributable to the disease. Nevertheless, some patients lack a BM biopsy or it is not conclusive for diagnosis. In this study we have investigated 94 patients with IgM monoclonal gammopathies, in which a BM trephine biopsy and morphological and flow cytometry (FCM) evaluation of BM aspirate were available at diagnosis. We found a clear correlation between BM infiltration of B-lymphocytes assessed by morphology and by FCM with a Pearson correlation of 0.62 (P<.001). Moreover, in the absence of a BM trephine biopsy, the cut-off points that would help in the differential diagnosis between MGUS, aWM, and sWM would be 20% for morphology and 5% for FCM, both assessed in the BM aspirate.

Background: Association mapping is a statistical approach combining phenotypic traits and genetic diversity in natural populations with the goal of correlating the variation present at phenotypic and allelic levels. It is essential to separate the true effect of genetic variation from other confounding factors, such as adaptation to different uses and geographical locations. The rapid availability of large datasets makes it necessary to explore statistical methods that can be computationally less intensive and more flexible for data exploration., Methodology/principal Findings: A core collection of 168 Brassica rapa accessions of different morphotypes and origins was explored to find genetic association between markers and metabolites: tocopherols, carotenoids, chlorophylls and folate. A widely used linear model with modifications to account for population structure and kinship was followed for association mapping. In addition, a machine learning algorithm called Random Forest (RF) was used as a comparison. Comparison of results across methods resulted in the selection of a set of significant markers as promising candidates for further work. This set of markers associated to the metabolites can potentially be applied for the selection of genotypes with elevated levels of these metabolites., Conclusions/significance: The incorporation of the kinship correction into the association model did not reduce the number of significantly associated markers. However incorporation of the STRUCTURE correction (Q matrix) in the linear regression model greatly reduced the number of significantly associated markers. Additionally, our results demonstrate that RF is an interesting complementary method with added value in association studies in plants, which is illustrated by the overlap in markers identified using RF and a linear mixed model with correction for kinship and population structure. Several markers that were selected in RF and in the models with correction for kinship, but not for population structure, were also identified as QTLs in two bi-parental DH populations.

Glucosinolates and their breakdown products have been recognized for their effects on plant defense, human health, flavor and taste of cruciferous vegetables. Despite this importance, little is known about the regulation of the biosynthesis and degradation in Brassica rapa. Here, the identification of quantitative trait loci (QTL) for glucosinolate accumulation in B. rapa leaves in two novel segregating double haploid (DH) populations is reported: DH38, derived from a cross between yellow sarson R500 and pak choi variety HK Naibaicai; and DH30, from a cross between yellow sarson R500 and Kairyou Hakata, a Japanese vegetable turnip variety. An integrated map of 1068 cM with 10 linkage groups, assigned to the international agreed nomenclature, is developed based on the two individual DH maps with the common parent using amplified fragment length polymorphism (AFLP) and single sequence repeat (SSR) markers. Eight different glucosinolate compounds were detected in parents and F(1)s of the DH populations and found to segregate quantitatively in the DH populations. QTL analysis identified 16 loci controlling aliphatic glucosinolate accumulation, three loci controlling total indolic glucosinolate concentration and three loci regulating aromatic glucosinolate concentrations. Both comparative genomic analyses based on Arabidopsis-Brassica rapa synteny and mapping of candidate orthologous genes in B. rapa allowed the selection of genes involved in the glucosinolate biosynthesis pathway that may account for the identified QTL.

A case of a twenty-one year old woman with Strongyloides stercoralis hyperinfection associated with human immunodeficiency virus infection that represents the fifteen case reported in the literature is here presented. Hypothesis for which this association is infrequent and several therapeutic alternatives are reviewed.