Allomaternal Investment and Relational Uncertainty among Ngandu Farmers of the Central African Republic

Abstract Several studies have suggested a matrilateral bias in allomaternal (nonmaternal) infant and child caregiving. The bias has been associated with the allomother's certainty of genetic relatedness, where allomothers with high certainty of genetic relatedness will invest more in children because of potential fitness benefits. Using quantitative behavioral observations collected on Ngandu 8-to 12-month-old infants from the Central African Republic, I examine who is caring for infants and test whether certainty of genetic relatedness may influence investment by allomothers. Results indicate a matrilateral bias in caregiving by extended kin members, but this does not affect the total level of care infants receive when fathers and siblings are included in the analysis. These results replicate a previous study done among an adjacent foraging population and emphasize the importance of examining children's complete social environments when addressing caregiving and child development

What's normal? Oligosaccharide concentrations and profiles in milk produced by healthy women vary geographically.

Background: Human milk is a complex fluid comprised of myriad substances, with one of the most abundant substances being a group of complex carbohydrates referred to as human milk oligosaccharides (HMOs). There has been some evidence that HMO profiles differ in populations, but few studies have rigorously explored this variability.Objectives: We tested the hypothesis that HMO profiles differ in diverse populations of healthy women. Next, we examined relations between HMO and maternal anthropometric and reproductive indexes and indirectly examined whether differences were likely related to genetic or environmental variations.Design: In this cross-sectional, observational study, milk was collected from a total of 410 healthy, breastfeeding women in 11 international cohorts and analyzed for HMOs by using high-performance liquid chromatography.Results: There was an effect of the cohort (P 4 times higher in milk collected in Sweden than in milk collected in rural Gambia (mean ± SEM: 473 ± 55 compared with 103 ± 16 nmol/mL, respectively; P < 0.05), and disialyllacto-N-tetraose (DSLNT) concentrations ranged from 216 ± 14 nmol/mL (in Sweden) to 870 ± 68 nmol/mL (in rural Gambia) (P < 0.05). Maternal age, time postpartum, weight, and body mass index were all correlated with several HMOs, and multiple differences in HMOs [e.g., lacto-N-neotetrose and DSLNT] were shown between ethnically similar (and likely genetically similar) populations who were living in different locations, which suggests that the environment may play a role in regulating the synthesis of HMOs.Conclusions: The results of this study support our hypothesis that normal HMO concentrations and profiles vary geographically, even in healthy women. Targeted genomic analyses are required to determine whether these differences are due at least in part to genetic variation. A careful examination of sociocultural, behavioral, and environmental factors is needed to determine their roles in this regard. This study was registered at clinicaltrials.gov as NCT02670278

Key genetic variants associated with variation of milk oligosaccharides from diverse human populations

Human milk oligosaccharides (HMO), the third most abundant component of human milk, are thought to be important contributors to infant health. Studies have provided evidence that geography, stage of lactation, and Lewis and secretor blood groups are associated with HMO profile. However, little is known about how variation across the genome may influence HMO composition among women in various populations. In this study, we performed genome-wide association analyses of 395 women from 8 countries to identify genetic regions associated with 19 different HMO. Our data support FUT2 as the most significantly associated (P < 4.23-9 to P < 4.5-70) gene with seven HMO and provide evidence of balancing selection for FUT2. Although polymorphisms in FUT3 were also associated with variation in lacto-N-fucopentaose II and difucosyllacto-N-tetrose, we found little evidence of selection on FUT3. To our knowledge, this is the first report of the use of genome-wide association analyses on HMO

Comparison of Two Approaches for the Metataxonomic Analysis of the Human Milk Microbiome.

Recent work has demonstrated the existence of large inter-individual and inter-population variability in the microbiota of human milk from healthy women living across variable geographical and socio-cultural settings. However, no studies have evaluated the impact that variable sequencing approaches targeting different 16S rRNA variable regions may have on the human milk microbiota profiling results. This hampers our ability to make meaningful comparisons across studies. In this context, the main purpose of the present study was to re-process and re-sequence the microbiome in a large set of human milk samples (n = 412) collected from healthy women living at diverse international sites (Spain, Sweden, Peru, United States, Ethiopia, Gambia, Ghana and Kenya), by targeting a different 16S rRNA variable region and reaching a larger sequencing depth. Despite some differences between the results obtained from both sequencing approaches were notable (especially regarding alpha and beta diversities and Proteobacteria representation), results indicate that both sequencing approaches revealed a relatively consistent microbiota configurations in the studied cohorts. Our data expand upon the milk microbiota results we previously reported from the INSPIRE cohort and provide, for the first time across globally diverse populations, evidence of the impact that different DNA processing and sequencing approaches have on the microbiota profiles obtained for human milk samples. Overall, our results corroborate some similarities regarding the microbial communities previously reported for the INSPIRE cohort, but some differences were also detected. Understanding the impact of different sequencing approaches on human milk microbiota profiles is essential to enable meaningful comparisons across studies. Clinical Trial Registration: www.clinicaltrials.gov, identifier NCT02670278

Variation in Human Milk Composition Is Related to Differences in Milk and Infant Fecal Microbial Communities.

Previously published data from our group and others demonstrate that human milk oligosaccharide (HMOs), as well as milk and infant fecal microbial profiles, vary by geography. However, little is known about the geographical variation of other milk-borne factors, such as lactose and protein, as well as the associations among these factors and microbial community structures in milk and infant feces. Here, we characterized and contrasted concentrations of milk-borne lactose, protein, and HMOs, and examined their associations with milk and infant fecal microbiomes in samples collected in 11 geographically diverse sites. Although geographical site was strongly associated with milk and infant fecal microbiomes, both sample types assorted into a smaller number of community state types based on shared microbial profiles. Similar to HMOs, concentrations of lactose and protein also varied by geography. Concentrations of HMOs, lactose, and protein were associated with differences in the microbial community structures of milk and infant feces and in the abundance of specific taxa. Taken together, these data suggest that the composition of human milk, even when produced by relatively healthy women, differs based on geographical boundaries and that concentrations of HMOs, lactose, and protein in milk are related to variation in milk and infant fecal microbial communities

Corrigendum: What's Normal? Microbiomes in Human Milk and Infant Feces Are Related to Each Other but Vary Geographically: The INSPIRE Study

A correction has been made to the Materials and Methods section, subsection Extraction of DNA fromMilk, paragraph 2, The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated

What's Normal? Microbiomes in Human Milk and Infant Feces Are Related to Each Other but Vary Geographically: The INSPIRE Study

Background: Microbial communities in human milk and those in feces from breastfed infants vary within and across populations. However, few researchers have conducted cross-cultural comparisons between populations, and little is known about whether certain “core” taxa occur normally within or between populations and whether variation in milk microbiome is related to variation in infant fecal microbiome. The purpose of this study was to describe microbiomes of milk produced by relatively healthy women living at diverse international sites and compare these to the fecal microbiomes of their relatively healthy infants. Methods: We analyzed milk (n = 394) and infant feces (n = 377) collected from mother/infant dyads living in 11 international sites (2 each in Ethiopia, The Gambia, and the US; 1 each in Ghana, Kenya, Peru, Spain, and Sweden). The V1-V3 region of the bacterial 16S rRNA gene was sequenced to characterize and compare microbial communities within and among cohorts. Results: Core genera in feces were Streptococcus, Escherichia/Shigella, and Veillonella, and in milk were Streptococcus and Staphylococcus, although substantial variability existed within and across cohorts. For instance, relative abundance of Lactobacillus was highest in feces from rural Ethiopia and The Gambia, and lowest in feces from Peru, Spain, Sweden, and the US; Rhizobium was relatively more abundant in milk produced by women in rural Ethiopia than all other cohorts. Bacterial diversity also varied among cohorts. For example, Shannon diversity was higher in feces from Kenya than Ghana and US-California, and higher in rural Ethiopian than Ghana, Peru, Spain, Sweden, and US-California. There were limited associations between individual genera in milk and feces, but community-level analyses suggest strong, positive associations between the complex communities in these sample types. Conclusions: Our data provide additional evidence of within- and among-population differences in milk and infant fecal bacterial community membership and diversity and support for a relationship between the bacterial communities in milk and those of the recipient infant's feces. Additional research is needed to understand environmental, behavioral, and genetic factors driving this variation and association, as well as its significance for acute and chronic maternal and infant health

Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

Telomere length is a risk factor in disease and the dynamics of telomere length are crucial to our understanding of cell replication and vitality. The proliferation of whole genome sequencing represents an unprecedented opportunity to glean new insights into telomere biology on a previously unimaginable scale. To this end, a number of approaches for estimating telomere length from whole-genome sequencing data have been proposed. Here we present Telomerecat, a novel approach to the estimation of telomere length. Previous methods have been dependent on the number of telomeres present in a cell being known, which may be problematic when analysing aneuploid cancer data and non-human samples. Telomerecat is designed to be agnostic to the number of telomeres present, making it suited for the purpose of estimating telomere length in cancer studies. Telomerecat also accounts for interstitial telomeric reads and presents a novel approach to dealing with sequencing errors. We show that Telomerecat performs well at telomere length estimation when compared to leading experimental and computational methods. Furthermore, we show that it detects expected patterns in longitudinal data, repeated measurements, and cross-species comparisons. We also apply the method to a cancer cell data, uncovering an interesting relationship with the underlying telomerase genotype

Publisher Correction: Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper