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Lentil, a cool-season food legume, is rich in protein and micronutrients with a range of prebiotic carbohydrates, such as raffinose-family oligosaccharides (RFOs), fructooligosaccharides (FOSs), sugar alcohols (SAs), and resistant starch (RS), which contribute to lentil's health benefits. Beneficial microorganisms ferment prebiotic carbohydrates in the colon, which impart health benefits to the consumer. In addition, these carbohydrates are vital to lentil plant health associated with carbon transport, storage, and abiotic stress tolerance. Thus, lentil prebiotic carbohydrates are a potential nutritional breeding target for increasing crop resilience to climate change with increased global nutritional security. This study phenotyped a total of 143 accessions for prebiotic carbohydrates. A genome-wide association study (GWAS) was then performed to identify associated variants and neighboring candidate genes. All carbohydrates analyzed had broad-sense heritability estimates ( H 2 ) ranging from 0.22 to 0.44, comparable to those reported in the literature. Concentration ranges corresponded to percent recommended daily allowances of 2–9% SAs, 7–31% RFOs, 51–111% RS, and 57–116% total prebiotic carbohydrates. Significant SNPs and associated genes were identified for numerous traits, including a galactosyltransferase ( Lcu.2RBY.1g019390 ) known to aid in RFO synthesis. Further studies in multiple field locations are necessary. Yet, these findings suggest the potential for molecular-assisted breeding for prebiotic carbohydrates in lentil to support human health and crop resilience to increase global food security.

Dysregulation of glucagon secretion in type 1 diabetes (T1D) involves hypersecretion during postprandial states, but insufficient secretion during hypoglycemia. The sympathetic nervous system regulates glucagon secretion. To investigate islet sympathetic innervation in T1D, sympathetic tyrosine hydroxylase (TH) axons were analyzed in control non-diabetic organ donors, non-diabetic islet autoantibody-positive individuals (AAb), and age-matched persons with T1D. Islet TH axon numbers and density were significantly decreased in AAb compared to T1D with no significant differences observed in exocrine TH axon volume or lengths between groups. TH axons were in close approximation to islet α-cells in T1D individuals with long-standing diabetes. Islet RNA-sequencing and qRT-PCR analyses identified significant alterations in noradrenalin degradation, α-adrenergic signaling, cardiac β-adrenergic signaling, catecholamine biosynthesis, and additional neuropathology pathways. The close approximation of TH axons at islet α-cells supports a model for sympathetic efferent neurons directly regulating glucagon secretion. Sympathetic islet innervation and intrinsic adrenergic signaling pathways could be novel targets for improving glucagon secretion in T1D.

Pea (Pisum sativum L.) is an important cool season food legume for sustainable food production and human nutrition due to its nitrogen fixation capabilities and nutrient-dense seed. However, minimal breeding research has been conducted to improve the nutritional quality of the seed for biofortification, and most genomic-assisted breeding studies utilize small populations with few single nucleotide polymorphisms (SNPs). Genomic resources for pea have lagged behind those of other grain crops, but the recent release of the Pea Single Plant Plus Collection (PSPPC) and the pea reference genome provide new tools to study nutritional traits for biofortification. Calcium, phosphorus, potassium, iron, zinc, and phytic acid concentrations were measured in a study population of 299 different accessions grown under greenhouse conditions. Broad phenotypic variation was detected for all parameters except phytic acid. Calcium exhibited moderate broad-sense heritability (H2) estimates, at 50%, while all other minerals exhibited low heritability. Of the accessions used, 267 were previously genotyped in the PSPPC release by the USDA, and we mapped the genotyping data to the pea reference genome for the first time. This study generated 54,344 high-quality SNPs used to investigate the population structure of the PSPPC and perform a genome-wide association study to identify genomic loci associated with mineral concentrations in mature pea seed. Overall, we were able to identify multiple significant SNPs and candidate genes for iron, phosphorus, and zinc. These results can be used for genetic improvement in pea for nutritional traits and biofortification, and the candidate genes provide insight into mineral metabolism.

Chickpea is a nutritionally dense pulse crop with high levels of protein, carbohydrates, micronutrients and low levels of fats. Chickpea fatty acids are associated with a reduced risk of obesity, blood cholesterol, and cardiovascular diseases in humans. We measured four primary chickpea fatty acids; palmitic acid (PA), linoleic acid (LA), alpha-linolenic acid (ALA), and oleic acid (OA), which are crucial for human health and plant stress responses in a chickpea diversity panel with 256 accessions ( Kabuli and desi types). A wide concentration range was found for PA (450.7–912.6 mg/100 g), LA (1605.7–3459.9 mg/100 g), ALA (416.4–864.5 mg/100 g), and OA (1035.5–1907.2 mg/100 g). The percent recommended daily allowances also varied for PA (3.3–6.8%), LA (21.4–46.1%), ALA (34.7–72%), and OA (4.3–7.9%). Weak correlations were found among fatty acids. Genome-wide association studies (GWAS) were conducted using genotyping-by-sequencing data. Five significant single nucleotide polymorphisms (SNPs) were identified for PA. Admixture population structure analysis revealed seven subpopulations based on ancestral diversity in this panel. This is the first reported study to characterize fatty acid profiles across a chickpea diversity panel and perform GWAS to detect associations between genetic markers and concentrations of selected fatty acids. These findings demonstrate biofortification of chickpea fatty acids is possible using conventional and genomic breeding techniques, to develop superior cultivars with better fatty acid profiles for improved human health and plant stress responses.

Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits—amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.

Molecular characterization of diverse germplasm can contribute to breeding programs by increasing genetic gain for sorghum [ Sorghum bicolor (L.) Moench] improvement. Identifying novel marker-trait associations and candidate genes enriches the existing genomic resources and can improve bioenergy-related traits using genomic-assisted breeding. In the current scenario, identifying the genetic loci underlying biomass and carbon partitioning is vital for ongoing efforts to maximize each carbon sink’s yield for bioenergy production. Here, we have processed a high-density genomic marker (22 466 550) data based on whole-genome sequencing (WGS) using a set of 365 accessions from the bioenergy association panel (BAP), which includes ~19.7 million (19 744 726) single nucleotide polymorphism (SNPs) and 2.7 million (~2 721 824) insertion deletions (indels). A set of high-quality filtered SNP (~5.48 million) derived markers facilitated the assessment of population structure, genetic diversity, and genome-wide association studies (GWAS) for various traits related to biomass and its composition using the BAP. The phenotypic traits for GWAS included seed color (SC), plant height (PH), days to harvest (DTH), fresh weight (FW), dry weight (DW), brix content % (BRX), neutral detergent fiber (NDF), acid detergent fiber (ADF), non-fibrous carbohydrate (NFC), and lignin content. Several novel loci and candidate genes were identified for bioenergy-related traits, and some well-characterized genes for plant height ( Dw1 and Dw2 ) and the YELLOW SEED1 locus ( Y1 ) were validated. We further performed a multi-variate adaptive shrinkage analysis to identify pleiotropic QTL, which resulted in several shared marker-trait associations among bioenergy and compositional traits. Significant marker-trait associations with pleiotropic effects can be used to develop molecular markers for trait improvement using a marker-assisted breeding approach. Significant nucleotide diversity and heterozygosity were observed between photoperiod-sensitive and insensitive individuals of the panel. This diverse bioenergy panel with genomic resources will provide an excellent opportunity for further genetic studies, including selecting parental lines for superior hybrid development to improve biomass-related traits in sorghum.

The African spiny mouse, Acomys spp., is capable of scar-free dermal wound healing. Here, we have performed a comprehensive analysis of gene expression throughout wound healing following full-thickness excisional dermal wounds in both Acomys cahirinus and Mus musculus. Additionally, we provide an annotated, de novo transcriptome assembly of A. cahirinus skin and skin wounds. Using a novel computational comparative RNA-Seq approach along with pathway and co-expression analyses, we identify enrichment of regeneration associated genes as well as upregulation of genes directly related to muscle development or function. Our RT-qPCR data reveals induction of the myogenic regulatory factors, as well as upregulation of embryonic myosin, starting between days 14 and 18 post-wounding in A. cahirinus. In contrast, the myogenic regulatory factors remain downregulated, embryonic myosin is only modestly upregulated, and no new muscle fibers of the panniculus carnosus are generated in M. musculus wounds. Additionally, we show that Col6a1, a key component of the satellite cell niche, is upregulated in A. cahirinus compared to M. musculus. Our data also demonstrate that the macrophage profile and inflammatory response is different between species, with A. cahirinus expressing significantly higher levels of Il10. We also demonstrate differential expression of the upstream regulators Wnt7a, Wnt2 and Wnt6 during wound healing. Our analyses demonstrate that A. cahirinus is capable of de novo skeletal muscle regeneration of the panniculus carnosus following removal of the extracellular matrix. We believe this study represents the first detailed analysis of de novo skeletal muscle regeneration observed in an adult mammal.

Carbon partitioning in plants may be viewed as a dynamic process composed of the many interactions between sources and sinks. The accumulation and distribution of fixed carbon is not dictated simply by the sink strength and number but is dependent upon the source, pathways, and interactions of the system. As such, the study of carbon partitioning through perturbations to the system or through focus on individual traits may fail to produce actionable developments or a comprehensive understanding of the mechanisms underlying this complex process. Using the recently published sorghum carbon-partitioning panel, we collected both macroscale phenotypic characteristics such as plant height, above-ground biomass, and dry weight along with microscale compositional traits to deconvolute the carbon-partitioning pathways in this multipurpose crop. Multivariate analyses of traits resulted in the identification of numerous loci associated with several distinct carbon-partitioning traits, which putatively regulate sugar content, manganese homeostasis, and nitrate transportation. Using a multivariate adaptive shrinkage approach, we identified several loci associated with multiple traits suggesting that pleiotropic and/or interactive effects may positively influence multiple carbon-partitioning traits, or these overlaps may represent molecular switches mediating basal carbon allocating or partitioning networks. Conversely, we also identify a carbon tradeoff where reduced lignin content is associated with increased sugar content. The results presented here support previous studies demonstrating the convoluted nature of carbon partitioning in sorghum and emphasize the importance of taking a holistic approach to the study of carbon partitioning by utilizing multiscale phenotypes.

Molecular HIV surveillance is a promising public health strategy for curbing the HIV epidemic. Clustering technologies used by health departments to date are limited in their ability to infer/forecast cluster growth trajectories. Resolution of the spatiotemporal dynamics of clusters, through phylodynamic and phylogeographic modelling, is one potential strategy to develop a forecasting tool; however, the projected utility of this approach needs assessment. Prior to incorporating novel phylodynamic-based molecular surveillance tools, we sought to identify possible issues related to their feasibility, acceptability, interpretation, and utility. Qualitative data were collected via focus groups among field experts (n = 17, 52.9% female) using semi-structured, open-ended questions. Data were coded using an iterative process, first through the development of provisional themes and subthemes, followed by independent line-by-line coding by two coders. Most participants routinely used molecular methods for HIV surveillance. All agreed that linking molecular sequences to epidemiological data is important for improving HIV surveillance. We found that, in addition to methodological challenges, a variety of implementation barriers are expected in relation to the uptake of phylodynamic methods for HIV surveillance. The participants identified several opportunities to enhance current methods, as well as increase the usability and utility of promising works-in-progress.

Polyploidy has played a pivotal and recurring role in angiosperm evolution. Allotetraploids arise from hybridization between species and possess duplicated gene copies (homeologs) that serve redundant roles immediately after polyploidization. Although polyploidization is a major contributor to plant evolution, it remains poorly understood. We describe an analytical approach for assessing homeolog-specific expression that begins with de novo assembly of parental transcriptomes and effectively (i) reduces redundancy in de novo assemblies, (ii) identifies putative orthologs, (iii) isolates common regions between orthologs, and (iv) assesses homeolog-specific expression using a robust Bayesian Poisson-Gamma model to account for sequence bias when mapping polyploid reads back to parental references. Using this novel methodology, we examine differential homeolog contributions to the transcriptome in the recently formed allopolyploids Tragopogon mirus and T. miscellus (Compositae). Notably, we assess a larger Tragopogon gene set than previous studies of this system. Using carefully identified orthologous regions and filtering biased orthologs, we find in both allopolyploids largely balanced expression with no strong parental bias. These new methods can be used to examine homeolog expression in any tetrapolyploid system without requiring a reference genome.