Samoans are a unique founder population with a high prevalence of obesity1–3, making them well suited for identifying new genetic contributors to obesity4. We conducted a genome-wide association study (GWAS) in 3,072 Samoans, discovered a variant, rs12513649, strongly associated with body mass index (BMI) (P = 5.3 × 10−14), and replicated the association in 2,102 additional Samoans (P = 1.2 × 10−9). Targeted sequencing identified a strongly associated missense variant, rs373863828 (p.Arg457Gln), in CREBRF (meta P = 1.4 × 10−20). Although this variant is extremely rare in other populations, it is common in Samoans (frequency of 0.259), with an effect size much larger than that of any other known common BMI risk variant (1.36–1.45 kg/m2 per copy of the risk-associated allele). In comparison to wild-type CREBRF, the Arg457Gln variant when overexpressed selectively decreased energy use and increased fat storage in an adipocyte cell model. These data, in combination with evidence of positive selection of the allele encoding p.Arg457Gln, support a ‘thrifty’ variant hypothesis as a factor in human obesity.

Background Mutation of a single amino acid residue can cause changes in a protein, which could then lead to a loss of protein function. Predicting the protein stability changes can provide several possible candidates for the novel protein designing. Although many prediction tools are available, the conflicting prediction results from different tools could cause confusion to users. Results We proposed an integrated predictor, iStable, with grid computing architecture constructed by using sequence information and prediction results from different element predictors. In the learning model, several machine learning methods were evaluated and adopted the support vector machine as an integrator, while not just choosing the majority answer given by element predictors. Furthermore, the role of the sequence information played was analyzed in our model, and an 11-window size was determined. On the other hand, iStable is available with two different input types: structural and sequential. After training and cross-validation, iStable has better performance than all of the element predictors on several datasets. Under different classifications and conditions for validation, this study has also shown better overall performance in different types of secondary structures, relative solvent accessibility circumstances, protein memberships in different superfamilies, and experimental conditions. Conclusions The trained and validated version of iStable provides an accurate approach for prediction of protein stability changes. iStable is freely available online at: http://predictor.nchu.edu.tw/iStable.

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Rationale No genome-wide association study (GWAS) of asthma has been conducted in Puerto Ricans. Objective To identify susceptibility genetic variants for asthma in Puerto Ricans. Methods We conducted a meta-analysis of GWAS of asthma, including Puerto Rican participants from: GALA I-II, the Hartford-Puerto Rico Study, and the Hispanic Community Health Study. Moreover, we examined whether susceptibility loci identified in previous meta-analyses of GWAS are associated with asthma in Puerto Ricans. Results The only locus to achieve a genome-wide significant association with asthma in an analysis of 2,144 cases and 2,893 controls was chromosome 17q21, as evidenced by our top SNP, rs907092 (OR = 0.71, P = 1.2 ×10−12) on IKZF3. Similar to findings in non-Puerto Ricans, SNPs in genes in the same LD block as IKZF3 (e.g. ZPBP2, ORMDL3 and GSDMB) were also significantly associated with asthma in Puerto Ricans. With regard to results from a meta-analysis in Europeans, we replicated findings for the SNP at GSDMB, but not for SNPs in any other genes. On the other hand, we replicated results from a meta-analysis of North American populations for SNPs in IL1RL1, TSLP and GSDMB but not for IL33. Conclusions Common variants on chromosome 17q21 have the greatest effects on asthma in Puerto Ricans, a high-risk ethnic group.

The elementary basis of intelligence in organisms with a central nervous system includes neurons and synapses and their complex interconnections forming neural circuits. In non-neural organisms such as slime mold with gel-like media, viscosity modulation enables adaptation to changing environments. At a larger scale, collective intelligence emerges via social interactions and feedback in animal colonies. Learning and memory are therefore multi-scale features that evolve as a result of constant interactions with the environment. There is growing interest in emulating such features of intelligence in computing machines and autonomous systems. Materials that can respond to their environment in a manner similar to organisms (referred to as “organismic materials”) therefore may be of interest as hardware components in artificial intelligence machines. In this brief review, we present a class of semiconductors called correlated oxides as candidates for learning machines. The term “correlated” refers to the fact that electrons in such lattices strongly interact and the ground state is not what is predicted by classical band theory. Such materials can undergo insulator–metal transitions at near ambient conditions under external stimuli such as thermal or electrical fields, strain, and chemical doping. Depending on the mechanism driving the transition, intermediate states can be metastable with different volatilities, and the time scales of phase change can be controlled over many orders of magnitude. The change in electronic properties can be sharp or gradual, leading to digital or analog behavior. These properties enable the realization of artificial neurons and synapses and emulate the associative and non-associative learning characteristics found in various organisms. We examine microscopic properties concerning electronic and structural transitions leading to collective behavior and theoretical treatments of the ground state and dynamical response, showcasing VO2 as a model system. Next, we briefly review algorithms designed from the plasticity demonstrated by phase changing systems. We conclude the brief review with suggestions for future research toward realizing non-von Neumann machines.

Bone Morphogenetic Protein (BMP) patterns the dorsal–ventral (DV) embryonic axis in all vertebrates, but it is unknown how cells along the DV axis interpret and translate the gradient of BMP signaling into differential gene activation that will give rise to distinct cell fates. To determine the mechanism of BMP morphogen interpretation in the zebrafish gastrula, we identified 57 genes that are directly activated by BMP signaling. By using Seurat analysis of single-cell RNA sequencing (scRNA-seq) data, we found that these genes are expressed in at least 3 distinct DV domains of the embryo. We distinguished between 3 models of BMP signal interpretation in which cells activate distinct gene expression through interpretation of thresholds of (1) the BMP signaling gradient slope; (2) the BMP signal duration; or (3) the level of BMP signal activation. We tested these 3 models using quantitative measurements of phosphorylated Smad5 (pSmad5) and by examining the spatial relationship between BMP signaling and activation of different target genes at single-cell resolution across the embryo. We found that BMP signaling gradient slope or BMP exposure duration did not account for the differential target gene expression domains. Instead, we show that cells respond to 3 distinct levels of BMP signaling activity to activate and position target gene expression. Together, we demonstrate that distinct pSmad5 threshold levels activate spatially distinct target genes to pattern the DV axis.

Graphical Abstract Highlights d High-dimensional profiling of CD4 + T cells in HIV-infected lymph nodes d A subset of CXCR5 À CD4 + T cells in lymph nodes are clonally related to T FH cells d CXCR5 À PD-1 + ICOS + CD4 + T cells exhibit T FH -like functional features d T FH -like CXCR5 À T cells contribute to circulating T cells with B cell help function In Brief Follicular helper T (T FH ) cells are critical for antibody production. Del Alcazar et al. showed that T FH cells can lose their characteristic chemokine receptor, giving rise to migratory populations of CXCR5 À T cells that retain B cell help function and are poised for CXCR5 expression.T FH cells B cells CXCR5 S1PR1 CCR2 S1P SUMMARYCXCR5 is a key marker of follicular helper T (T FH ) cells. Using primary lymph nodes (LNs) from HIVinfected patients, we identified a population of CXCR5 À CD4 + T cells with T FH -cell-like features. This CXCR5 À subset becomes expanded in severe HIV infection and is characterized by the upregulation of activation markers and high PD-1 and ICOS surface expression. Integrated analyses on the phenotypic heterogeneity, functional capacity, T cell receptor (TCR) repertoire, transcriptional profile, and epigenetic state of CXCR5 À PD-1 + ICOS + T cells revealed a shared clonal relationship with T FH cells. CXCR5 À PD-1 + ICOS + T cells retained a poised state for CXCR5 expression and exhibited a migratory transcriptional program. TCR sequence overlap revealed a contribution of LN-derived CXCR5 À PD-1 + ICOS + T cells to circulating CXCR5 À CD4 + T cells with B cell help function. These data link LN pathology to circulating T cells and expand the current understanding on the diversity of T cells that regulate B cell responses during chronic inflammation.

BackgroundExpression quantitative trait loci (eQTL) have been identified using tissue or cell samples from diverse human populations, thus enhancing our understanding of regulation of gene expression. However, few studies have attempted to identify eQTL in racially admixed populations such as Hispanics.MethodsWe performed a systematic eQTL study to identify regulatory variants of gene expression in whole blood from 121 Puerto Rican children with (n = 63) and without (n = 58) asthma. Genome-wide genotyping was conducted using the Illumina Omni2.5M Bead Chip, and gene expression was assessed using the Illumina HT-12 microarray. After completing quality control, we performed a pair-wise genome analysis of ~15 K transcripts and ~1.3 M SNPs for both local and distal effects. This analysis was conducted under a regression framework adjusting for age, gender and principal components derived from both genotypic and mRNA data. We used a false discovery rate (FDR) approach to identify significant eQTL signals, which were next compared to top eQTL signals from existing eQTL databases. We then performed a pathway analysis for our top genes.ResultsWe identified 36,720 local pairs in 3,391 unique genes and 1,851 distal pairs in 446 unique genes at FDR <0.05, corresponding to unadjusted P values lower than 1.5x10-4 and 4.5x10-9, respectively. A significant proportion of genes identified in our study overlapped with those identified in previous studies. We also found an enrichment of disease-related genes in our eQTL list.ConclusionsWe present results from the first eQTL study in Puerto Rican children, who are members of a unique Hispanic cohort disproportionately affected with asthma, prematurity, obesity and other common diseases. Our study confirmed eQTL signals identified in other ethnic groups, while also detecting additional eQTLs unique to our study population. The identified eQTLs will help prioritize findings from future genome-wide association studies in Puerto Ricans.