Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Implications of the human microbiome in inflammatory bowel diseases

The study of the human microbiome or community of microbes and collection of genomes found in the human body is one of the fastest growing research areas because many diseases are reported to be associated with microbiome imbalance or dysbiosis. With the improvement in novel sequencing techniques, researchers are now generating millions of sequences of different sites from the human body and evaluating specific differences in microbial communities. The importance of microbiome constituency is so relevant that several consortialike MetaHIT and HMP are focusing mainly on the human microbiome. The aim of this review is to highlight points of research in this field, mainly focusing on particular factors that modulate the microbiome and important insights into its potential impact on our health and well-being.Fil: Bakhtiar, Syeda M.. Universidade Federal de Minas Gerais; BrasilFil: Leblanc, Jean Guy Joseph. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tucumán. Centro de Referencia para Lactobacilos (i); Argentina. Universidad Nacional de Tucuman. Facultad de Medicina; ArgentinaFil: Salvucci, Emiliano Jesus. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Córdoba. Instituto de Ciencia y Tecnología de Alimentos Córdoba; ArgentinaFil: Ali, Amjad. Universidade Federal de Minas Gerais; BrasilFil: Martin, Rebeca. No especifíca;Fil: Langella, Philippe. No especifíca;Fil: Chatel, Jean Marc. No especifíca;Fil: Miyoshi, Anderson. Universidade Federal de Minas Gerais; BrasilFil: Bermudez Humaran, Luis G.. No especifíca;Fil: Azevedo, Vasco. Universidade Federal de Minas Gerais; Brasi

Preaxial polydactyly/triphalangeal thumb is associated with changed transcription factor-binding affinity in a family with a novel point mutation in the long-range cis-regulatory element ZRS

A cis-regulatory sequence also known as zone of polarizing activity (ZPA) regulatory sequence (ZRS) located in intron 5 of LMBR1 is essential for expression of sonic hedgehog (SHH) in the developing posterior limb bud mesenchyme. Even though many point mutations causing preaxial duplication defects have been reported in ZRS, the underlying regulatory mechanism is still unknown. In this study, we analyzed the effect on transcription factor binding of a novel ZRS point mutation (463T>G) in a Pakistani family with preaxial polydactyly and triphalangeal thumb. Electrophoretical mobility shift assay demonstrated a marked difference between wild-type and the mutant probe, which uniquely bound one or several transcription factors extracted from Caco-2 cells. This finding supports a model in which ectopic anterior SHH expression in the developing limb results from abnormal binding of one or more transcription factors to the mutant sequence

Venn diagram representing the core genomes of the <i>C. pseudotuberculosis</i> strains.

<p>All genomes, the number of genes composing the core genome of all the strains; <i>equi</i>, the number of genes of the core genome of the <i>C. pseudotuberculosis</i> biovar <i>equi</i> strains, which were absent in one or more of the <i>C. pseudotuberculosis</i> biovar <i>ovis</i> strains; <i>ovis</i>, the number of genes of the core genome of the <i>C. pseudotuberculosis</i> biovar <i>ovis</i> strains, which were absent in one or more of the <i>C. pseudotuberculosis</i> biovar <i>equi</i> strains.</p

Plasticity of the pilus gene clusters <i>spaA</i> and <i>spaD</i> in <i>C. pseudotuberculosis</i>.

<p>A1 and B1, PiCp15 harboring the <i>spaA</i> cluster of genes; A2 and B2, PiCp7 harboring the <i>spaD</i> cluster of genes. A, all the <i>C. pseudotuberculosis</i> strains were aligned using <i>C. pseudotuberculosis</i> strain 1002 as a reference. From the inner to outer circle on A1 and A2: the biovar <i>equi</i> strains Cp31, Cp1/06-A, CpCp162, Cp258, Cp316, CpCIP52.97; and, the biovar <i>ovis</i> strains CpC231, CpP54B96, Cp267, CpPAT10, CpI19, Cp42/02-A, Cp3/99-5, CpFRC41 and Cp1002. B, all the <i>C. pseudotuberculosis</i> strains were aligned using <i>C. pseudotuberculosis</i> strain CIP52.97 as a reference. From the inner to outer circle on B1 and B2: the biovar <i>ovis</i> strains CpC231, Cp1002, CpPAT10, Cp267, CpP54B96, CpI19, Cp42/02-A, CpFRC41, Cp3/99-5, Cp1/06-A; and, the biovar <i>equi</i> strains Cp31, CpCp162, Cp316, Cp258 and CpCIP52.97. CDS, coding sequences; tRNA, transfer RNA; rRNA, ribosomal RNA; and PAI, pathogenicity island.</p

Phylogenomic tree and heatmap analyses of the genus <i>Corynebacterium</i>.

<p>All the complete genomes from the genus <i>Corynebacterium</i> were retrieved from the NCBI ftp site. Comparisons between the variable content of all the strains were plotted as percentages of similarity on the heatmap using Gegenees (version 1.1.4). The percentage of similarity was used to generate a phylogenomic tree with SplitsTree (version 4.12.6). Numbers from 1 to 39 (upper-left to upper-right corner) represent species from <i>Corynebacterium aurimucosum</i> ATCC 70097 to <i>Corynebacterium variable</i> DSM 44702 (upper-left to lower-left corner). Percentages were plotted with a spectrum ranging from red (low similarity) to green (high similarity). On the heatmap, the upper portion is not symmetrical to the lower portion because the variable contents of all genomes present different sizes. Therefore, considering a scenario where the variable content from genomes A and B are composed of 100 and 80 genes, respectively, with a common repertoire of 40 genes, genome A will present 40% of similarity to genome B and genome B will present 50% of similarity to genome A.</p

Phylogenomic tree and heatmap analyses of the <i>Corynebacterium pseudotuberculosis</i> strains based on pathogenicity island plasticity.

<p>Comparisons between the PAI contents of all the strains were plotted as percentages of similarity on the heatmap using Gegenees (version 1.1.4). The percentages of similarity were used to generate a phylogenomic tree with SplitsTree (version 4.12.6). Numbers from 1 to 15 (upper-left to upper-right corner) represent the strains from Cp1002 to Cp1/06-A (upper-left to lower-left corner). On the heatmap, the upper portion is not symmetrical to the lower portion because the pathogenicity islands contents of all genomes present different sizes. Therefore, considering a scenario where the pathogenicity islands content from genomes A and B are composed of 100 and 80 genes, respectively, with a common repertoire of 40 genes, genome A will present 40% of similarity to genome B and genome B will present 50% of similarity to genome A.</p

General information about the 15 <i>C. pseudotuberculosis</i> strains used in this work.

<p>General information about the 15 <i>C. pseudotuberculosis</i> strains used in this work.</p

Comparative genomic maps of the <i>C. pseudotuberculosis</i> biovar <i>equi</i> and <i>ovis</i> strains.

<p>A, all the <i>C. pseudotuberculosis</i> strains were aligned using <i>C. pseudotuberculosis</i> strain 1002 as a reference. From the inner to outer circle on A: the biovar <i>equi</i> strains Cp31, Cp1/06-A, CpCp162, Cp258, Cp316 and CpCIP52.97; and, the biovar <i>ovis</i> strains CpC231, CpP54B96, Cp267, CpPAT10, CpI19, Cp42/02-A, Cp3/99-5, CpFRC41 and Cp1002. B, all the <i>C. pseudotuberculosis</i> strains were aligned using <i>C. pseudotuberculosis</i> strain CIP52.97 as a reference. From the inner to outer circle on B: the biovar <i>ovis</i> strains CpC231, Cp1002, CpPAT10, Cp267, CpP54B96, CpI19, Cp42/02-A, CpFRC41, Cp3/99-5; and, the biovar <i>equi</i> strains Cp1/06-A Cp31, CpCp162, Cp316, Cp258 and CpCIP52.97. CDS, coding sequences; tRNA, transfer RNA; rRNA, ribosomal RNA; and PAI, pathogenicity island.</p