Genetic and Physiological Analysis of Iron Biofortification in Maize Kernels

BACKGROUND: Maize is a major cereal crop widely consumed in developing countries, which have a high prevalence of iron (Fe) deficiency anemia. The major cause of Fe deficiency in these countries is inadequate intake of bioavailable Fe, where poverty is a major factor. Therefore, biofortification of maize by increasing Fe concentration and or bioavailability has great potential to alleviate this deficiency. Maize is also a model system for genomic research and thus allows the opportunity for gene discovery. Here we describe an integrated genetic and physiological analysis of Fe nutrition in maize kernels, to identify loci that influence grain Fe concentration and bioavailability. METHODOLOGY: Quantitative trait locus (QTL) analysis was used to dissect grain Fe concentration (FeGC) and Fe bioavailability (FeGB) from the Intermated B73 × Mo17 (IBM) recombinant inbred (RI) population. FeGC was determined by ion coupled argon plasma emission spectroscopy (ICP). FeGB was determined by an in vitro digestion/Caco-2 cell line bioassay. CONCLUSIONS: Three modest QTL for FeGC were detected, in spite of high heritability. This suggests that FeGC is controlled by many small QTL, which may make it a challenging trait to improve by marker assisted breeding. Ten QTL for FeGB were identified and explained 54% of the variance observed in samples from a single year/location. Three of the largest FeGB QTL were isolated in sister derived lines and their effect was observed in three subsequent seasons in New York. Single season evaluations were also made at six other sites around North America, suggesting the enhancement of FeGB was not specific to our farm site. FeGB was not correlated with FeGC or phytic acid, suggesting that novel regulators of Fe nutrition are responsible for the differences observed. Our results indicate that iron biofortification of maize grain is achievable using specialized phenotyping tools and conventional plant breeding techniques

SARS-CoV-2 seroprevalence in pregnant women in Kilifi, Kenya from March 2020 to March 2022

BackgroundSeroprevalence studies are an alternative approach to estimating the extent of transmission of SARS-CoV-2 and the evolution of the pandemic in different geographical settings. We aimed to determine the SARS-CoV-2 seroprevalence from March 2020 to March 2022 in a rural and urban setting in Kilifi County, Kenya.MethodsWe obtained representative random samples of stored serum from a pregnancy cohort study for the period March 2020 to March 2022 and tested for antibodies against the spike protein using a qualitative SARS-CoV-2 ELISA kit (Wantai, total antibodies). All positive samples were retested for anti-SARS-CoV-2 anti-nucleocapsid antibodies (Euroimmun, ELISA kits, NCP, qualitative, IgG) and anti-spike protein antibodies (Euroimmun, ELISA kits, QuantiVac; quantitative, IgG).ResultsA total of 2,495 (of 4,703 available) samples were tested. There was an overall trend of increasing seropositivity from a low of 0% [95% CI 0–0.06] in March 2020 to a high of 89.4% [95% CI 83.36–93.82] in Feb 2022. Of the Wantai test-positive samples, 59.7% [95% CI 57.06–62.34] tested positive by the Euroimmun anti-SARS-CoV-2 NCP test and 37.4% [95% CI 34.83–40.04] tested positive by the Euroimmun anti-SARS-CoV-2 QuantiVac test. No differences were observed between the urban and rural hospital but villages adjacent to the major highway traversing the study area had a higher seroprevalence.ConclusionAnti-SARS-CoV-2 seroprevalence rose rapidly, with most of the population exposed to SARS-CoV-2 within 23 months of the first cases. The high cumulative seroprevalence suggests greater population exposure to SARS-CoV-2 than that reported from surveillance data

Locations of FeGB QTL detected by GLM Select analysis for 2003 NY field season.

<p>Markers are given in order of inclusion in the trait model according to GLM Select. AIC is the Akaike Information Criterion and estimates the goodness of fit for the model. Significance of the association between marker and trait is demonstrated by F and p values. The t-value estimates the magnitude of the effect; a positive score indicates Mo17 donated the superior allele. Marker locations are reported using IBM v1 coordinates (chromosome; position). Summary statistics for the 10-factor model are presented below.</p

Multi-site evaluation of FeGB in derived lines.

<p>Contrasting BC₂S₄ derivatives from the IBM039 RI line were grown on 8 plots over 2 years, to evaluate the heritability and penetrance of the high FeGB effect across multiple environments. ANOVA were used to assess whether pairs of related high and low-nutritional value derivatives were significantly different and are denoted by letter. Comparisons were made within sites only, where trait data are expressed as a percentage of the control variety from the Caco-2 bioassay. Locations where significant differences were not observed according to our hypotheses appear in italic type.</p

Locations of FeGB QTL detected by composite interval mapping analysis for 2003 field season.

<p>Standardized ferritin protein production values were used for FeGB quantitative trait locus detection by composite interval mapping. Confidence intervals (CI) for each QTL are reported at two different confidence values. Genetic locations refer to IBM v1 map coordinates. Positive values for the additive effect denote B73 provided the superior allele. Multiple Interval Mapping (MIM) was used to estimate the 3-factor model.</p

Comparison between FeGB and phytate content (NY 03).

<p>Comparison between FeGB and phytate content (NY 03).</p

Locations of FeGC QTL detected by composite interval mapping analysis from summary trait data.

<p>BLUPs were estimated from the analysis of variance and used as summaries for quantitative trait locus detection by composite interval mapping. Confidence intervals (CI) for each QTL are reported at two different confidence values. Genetic locations refer to IBM v1 map coordinates. Positive values for the additive effect denote B73 provided the superior allele. Multiple Interval Mapping (MIM) was used to estimate the 3-factor model.</p

FeGC observed for a maize population.

<p>The Intermated B73 × Mo17 recombinant inbred (RI) mapping population was grown in Aurora NY and Clayton NC on research farms owned by Cornell University and North Carolina State University, respectively. Grain Fe concentrations were determined by ion coupled argon plasma emission spectroscopy. The results for the RI lines are organized into bins of 2 µg Fe g⁻¹ grain DW for the histogram. Median population values are reported along with standard deviations for each of the three contributing data sets.</p

Analysis of variance for grain iron concentration (FeGC).

<p>General Linear Model (GLM) and Restricted Maximum Likelihood (REML) analyses of variance (ANOVA) were used to describe the variance in grain iron concentration due to Line, Year (nested within Site), and Site terms from the NY05, NY03 and NC05 data. Heritability (h²_b) was estimated at 0.745.</p

Correlation analysis of grain nutrients and mass.

<p>Pearson's correlation coefficient (left) and p-value (right) are reported for each correlation. Bold entries indicate significant correlations; italic entries indicate non-significant correlations from the NY03 dataset.</p