Dynamics of the Infant Microbiome
Maternal Influences on Infant Gut Microbiota
Table of Contents
What is the Microbiome?
There is a community of 38,000,000,000,0000 (38 trillion!) microorganisms living in and on your body at this very moment.
These microorganisms, referred to as microbiota, are primarily bacteria, but also fungi, viruses, and protozoa.
The majority of them live in your gastrointestinal tract, but also in your mouth, on your skin and armpits, and other places.
The microbiome is comprised of every microorganism (bug) in and on you, their products, their genes, and their interactions with you (their host) and one another.
You are a Superorganism!
Good vs. Bad Bacteria?
The bacteria of the microbiome are high in density and diversity, and interact in complex ways. They are found in the highest concentration in the large intestine (colon). The bacteria can be holistically characterized on a scale from antagonistic (harmful to the host) or mutualistic (beneficial to the host).
Within the microbiome, there are two different types of bacteria: those that naturally occur and those only present under abnormal circumstances.
Benefits to Human Health
The microbiome is an essential part of human health because it...
90% of the immune system is located in the gut
Reinforces the walls of the intestine and maintains an acidic pH to protect from pathogens (disease-causing microorganisms)
Supports a healthy weight by producing chemicals that indicate satiety (state of hunger & being fulfilled)
Improves mental health by synthesizing neurotransmitters
Increases energy levels by absorbing nutrients
Promoted cardiovascular health by improving cholesterol levels
Regulates hormones, such as estrogen, B12, folic acid, and vitamin D
Disturbances to the commensal (beneficial or neutral relationship to host) community of microbiota have larger implications for human health.
Introduction to the Mother-Infant Dynamic
🌱 Seeding - the process of receiving your microbes from your mother, who received their microbes from their mother, and your surrounding environment.
As a result, each of us receive the genes of 3.7 billion-year-old bacteria that have been passed down between generations, from mothers to infants.
Image Source: @Seed on Instagram
This process of seeding is thought to begin at birth, where the infant comes in contact with an abundance of various bacteria from the birth canal (if vaginally born), skin, the hospital, etc. This transmission from mother to infant continues with skin-to-skin contact and breastfeeding.
Rapidly these bacteria colonize the bodily surfaces including the intestines of the neonate, an infant within their first month of life. More recently, some studies suggest that there is transmission of bacteria between mother and infant in utero.
But, we are also "seeded" by the environment that we grow up in. Parents, siblings, animals, the soil from our backyards, nature, and more - they all aid in the development of our microbiome.
While the mother to infant microbiome dynamic is very complex, there are a few main factors that influence the transmission of shared microbiota between mother and infant, as well as those that influence the typical composition of the infant microbiome.
In this project, we selected three factors to concentrate on: mode of delivery (how the infant is birthed), the initial infant's diet (what they eat for the first months of life), and mother's diet (what the mother eats).
Investigations of the microbiome have expanded as technology has advanced, but a lot remains unknown about the microbes that live within us. This was an added challenge that we didn't realize at first - we were simply looking for answers to questions that have yet to be found by scientists. As a result, there is more existing literature around mode of delivery and infant diet, but less so related to mother's diet.
Mode of Delivery
→ Cesarean v. Vaginal
The different types of microorganisms that initially colonize the infant gut microbiome depend on the infant’s mode of delivery and some studies suggest that the method of delivery is the greatest determinant of the succession of early gut microbiota.
In a vaginal delivery, the infant travels down the vaginal canal and is exposed to the bacterial communities of the vagina and rectum. For vaginally delivered infants, the very first bacteria to colonize and survivie in the infant’s gut are facultative anaerobes. Following vaginal delivery, infant bacterial communities are most similar to the composition of the maternal vaginal communities suggesting direct transmission of vaginal bacteria to the infant as it passes through the vaginal canal.
In contrast, cesarean delivered infants are most similar to maternal skin communities since they come into immediate contact with the mother’s skin and the surrounding birthing environment (e.g. nurses and equipment). In elective cesarean deliveries, specifically, the infant is not exposed to any vaginal microorganisms.
Since an infant’s delivery is their first exposure to microorganisms, the mode of delivery, therefore plays an instrumental role in orchestrating the composition of the initial gut microbiota.
→ Breast-fed v. Formula-fed
As the microbes that an infant consumes through food seeds the infant's gut microbiome directly, the infant’s diet is linked to many long term effects on microbiome composition and health conditions. This includes the strength of the immune system, body composition, and metabolism.
When an infant is breastfed, the microorganisms within the mother's breast milk and on the areolar skin are ingested and transferred to the gut. The microbial composition of breastmilk has been shown to vary due to gestational age, mode of delivery, stage in lactation, and the mother’s health status. Overall, the microorganisms consistently found in breastmilk, including Bifidobacterium and Lactobacillus, are vital for inhibiting the growth of pathogenic organisms, regulating function of the mucus barrier, and promoting immunological responses. Additionally, breast milk contains prebiotic human milk oligosaccharides (HMOs) that promote the growth of Bifidobacterium, among other microbial communities. The synergistic relationship between the prebiotic and probiotic components provides a relatively stable and uniform gut microbiome which Ho et al. (2018) speculates “may be necessary in the early months of development.”
On the other hand, when formula-fed, the infant’s gut microbiome has been associated with increased bacterial diversity that resembles that of older children and a decreased prevalence of Bifidobacteria. Additionally, while it is not included in our data research and analysis, another factor in the infant’s diet is solid food. The amount and age at which solid food is introduced to the diet plays an important role in determining the relative maturity of the infant's microbiome.
Whether to breastfeed or formula feed (or some of each) is a complex decision, often influenced by environmental factors such as socio-economic status, access to healthcare, or social stigma.
→ Types of diets and influential factors
Diet Affects Your Microbiome
What you eat has an unquestionable impact on your diet and the nutrition that your body receives. This has repercussions on your health in relation to your disposition and immunity to diseases. What you eat alters your gut bacteria which affects host metabolism and the immune system’s production of pro- and anti-inflammatory metabolites (products of metabolism). A change to the immune system affects host disease susceptibility.
There are many decisions as to what one’s diet is composed of and how much one eats of it:
Protein (animal vs. plant)
Fats (saturated vs. unsaturated)
Starch and sugars (artificial vs. natural sweeteners)
Probiotic and fermented foods
Choosing a select diet (vegan, gluten-free, Western, etc.)
The human diet can change in both short and long-term manners. A short-term change may be a switch to a vegan diet, while a long-term dietary change would be from an evolutionary standpoint such as humans shifting from foraging to the modern, “Western” diet. "Diet rapidly and reproducibly alters the human gut microbiome" showed that the human gut microbiota can rapidly shift between two different functional profiles, herbivorous and carnivorous. For human ancestors, diet could have rapidly changed by season or year-to-year, thus evolutionarily, it would have been advantageous to have a microbiome that could make this profile switch.
In summary, it is definite that what you eat affects your gut microbiota.
But How Does It Affect Your Infant?
There is a lot that remains unknown about the effects of diet on an infant’s microbiome and health. However, there are known connections between diet and pregnancy risks which in turn can impact an infant’s microbiota by determining which method of delivery (believed to influence infant microbiome) is viable. See flowchart below.
Overall, variation in the maternal diet has yet to be proven as a significant factor in shaping the infant’s microbiome. Concluded by “Diet during Pregnancy and Infancy and the Infant Intestinal Microbiome,” maternal diet during pregnancy and solid food introduction were less associated with the infant gut microbiome than breastfeeding status (breast vs. formula-fed). Although we were curious about the relationship between mother's nutrition and infant, we decided not to include it as a consideration in our data search and analysis for this reason.
In summary, the effect of maternal diet on the infant microbiome is a budding area of research, thus we were unable to find sufficient literature supporting a connection to pursue it in our evaluation.
Importance of Initial Microorganisms
The initial microorganisms of the gut microbiome are important because they are the first gut microbiota, so they have a large effect on the host. The initial microorganisms also create an anaerobic environment which determines the pattern of succession of the gut microbiota. These microorganisms inform the final composition, complexity, and stability of the adult microbiome because they provide the first information for the microbiome, and first protection for the infant’s immune system.
Our first set of models aims to provide context for our data analysis by (1) looking at the broader influences on the infant microbiome and their relation to their mother and (2) the specific dynamics within the infant's gut connected to maternal influences.
The following definitions are important for understanding computational models.
A system - a collection of parts that interact with each other to function as a whole.
Nodes - the parts of a system.
Edges - the interactions between nodes.
Positive feedback loops - amplify change and growth in systems
- When A causes B to produce more of A
- Example: warmer temperatures lead to more sea ice melting --> less sea ice reflecting sunlight --> results in warmer temperatures
Negative feedback loops - provide stability by canceling out any changes in systems.
- When A causes B to yield less of A
- Example: predator-prey relationships - as predators increase, prey decrease
Neutral interactions - (grey) we were unable to classify these, but are certain that they have influence; neither positive or negative.
1. Generic Model
This model considers the larger factors at play in the formation of the infant microbiome.
We additionally wanted to acknowledge the development of the microbiome from a larger systemic view, incorporating the societal factors that influence its development. This is most prominent in what determines whether someone delivers vaginally or via cesarean (E.g. Do they have the privilege of creating a birthing plan? Is it the result of an emergency? Do they have health care or emotional support? ), and whether someone chooses to breastfeed or formula-feed (E.g. What is there socio-economic status? Do they have access to time to breastfeed/pump?). Our generic model incorporates these questions.
Tutorial to navigate and consider model
The colors of the arrows, and the colors and shapes of the nodes are defined in the key at the top and are essential to understanding our model. For insight into feedback loops read this.
Consider the node "Race" - let's consider that the mother (since it is a purple node) is white. Following the arrow to how this influences medical racism, this means that the mother is not at a lower risk to die during childbirth.
This means that her child will more likely be breast-fed, and therefore less likely to be formula-fed.
Given her race, this also means she is of a higher socioeconomic status, and therefore she has more time with the infant (and therefore more time to breast-feed), and access to affordable tools to pump.
As a result of breast-feeding her infant, the infant's gut microbiota is effected. These specific compositional alterations are depicted in the specific model.
2. Specific Model
This model visualizes the detailed interactions between the mother and the infant's microbiome and health.
Principal Coordinate Analysis
The main goal of our project was to gain insights into the world of computational modeling in biology therefore the centerpiece of our work is our analysis of our data using a Principal Coordinate Analysis.
Why a PCoA?
To analyze our data, we set a goal to use Python, a general purpose coding language that can be used for a wide variety of purposes. Prior to this summer workgroup, we had no experience with Python and little to no experience with coding. With that in mind, we collaboratively taught ourselves the basics of Python using Kaggle and Codecademy.
After meeting with Sean Gibbons, we concluded that the best way to computationally model our data would be a principal coordinates analysis (PCoA). We ran our PCoA using Anaconda's Jupyter Notebooks.
What is it?
A principal coordinate analysis is a form of multidimensional scaling that represents the (dis)similarity between data points in a low-dimensional, Euclidean space. It takes any (dis)similarity matrix as the input.
The dissimilarity matrix we used was a Bray-Curtis, which measures the dissimilarity of the composition of locations, based on the differing counts of species.
A PCoA outputs a set of statistically independent axes that are responsible for the variation of the data. PC1 is responsible for the most variation in the data and PC3 is responsible for the least. Therefore, objects plotted closer are more similar (smaller dissimilarity; as determined by (dis)similarity matrix) to each other than to those further away.
This is a video that we found helpful in understanding what a PCoA is and what it shows about the data it is used for.
We are immensely grateful to Rachel Calder and Dr. Sean Gibbons for helping us problem-solve our many errors. We produced 4 PCoA plots: one for all infant samples, one for vaginally-delivered infants, one for cesarean-delivered infants, and one for breastfed infants.
The objects of each plot are labelled according to their metadata* category. By labelling them we can analyze the distribution of our data points.
*metadata - the characteristics assigned to each sample, specifically mode of delivery and mode of nutrition.
Colors specified to the best of our ability since numbers correspond to a gradient.
1) Vaginally-delivered (VD) + Breastfed
2) VD+ Formula-fed
3) Only in the specified sub-PCoAs, not in the overall PCoA: VD + Mixed/Other
4) VD + Solids
5) Cesarean-delivered (CD) + Breastfed
6) CD + Formula-fed
7) Only in the specified sub-PCoAs, not in the overall PCoA: CD + Mixed/Other
8) CD + Solids
PCoA of all Infant Samples
This PCoA plots all samples, therefore including both cesarean and vaginally delivered infant samples, and all modes of nutrition (MON), except Mixed/Other.
Corresponding Key Values
1) Purple: Vaginally-delivered (VD) + Breastfed
2) VD+ Formula-fed
4) VD + Solids
5) Green: Cesarean-delivered (CD) + Breastfed
6) CD + Formula-fed
8) Yellow: CD + Solids
All infant samples are included, except for infants whose mode of nutrition was Mixed/Other.
This PCoA shows the most variation due to PC1, as any PCoA does.
There is a distinct grouping of objects corresponding to VD + Solids (4) to the left side of PC1 (-0.3) , to the ride of PC2 (0.4), on the bottom of PC3 (-0.2). Indicating an intra-grouping similarity as well as dissimilarity compared to samples of other MOD and MON.
While there is also a small, but not cohesive, grouping of purple objects (VD + Breastfed) along the right of PC1 (0.2) as well as PC2 (0.3), along the bottom of PC3 (-0.2). Indicating a slightly increased intra-grouping similarity as well as inter-grouping dissimilarity compared to samples of other MOD and MON.
PCoA of Samples of Vaginally-Delivered Infants
This PCoA controls for mode of delivery (MOD) so that all of the infant samples shown were delivered vaginally. Therefore, this shows the influence of different modes of nutrition (MON) on vaginally-delivered infant samples.
Corresponding Key Values
1) Purple: VD + Breastfed
2) Blue: VD + Formula-fed
3) Green: VD + Mixed/Other
4) Yellow: VD + Solids
There is a cluster of primarily green objects, meaning VD + Mixed/Other, to the left of center of PC1.
This indicates that the infants of this metadata category are moderately dissimilar from the samples of VD infants of other MON.
There is a small cluster on the right side of PC1, along PC2, in the middle of PC3 of purple objects, meaning VD + Breastfed infant samples are similar to each other, and slightly more dissimilar to other MON.
However, there are purple objects throughout the plot, especially seen in outliers distributed along all PCs, so this conclusion is not fully representative of all VD + Breastfed infant samples.
PCoA of Samples of Cesarean-Delivered Infants
This PCoA controls for MOD so that all of the infants shown were delivered via cesarean. Therefore, this shows the influence of different modes of nutrition (MON) on cesarean-delivered infants.
Corresponding Key Values
1) Purple: CD + Breastfed
5) Blue: CD + Formula-fed
3) Yellow: CD + Mixed/Other
This PCoA controls for mode of delivery (MOD) so that all of the infant samples shown were delivered via cesarean.
CD + Solid were unable to be included because the data needed to be analyzed in square matrices
This PCoA has the most clearly observed difference between metadata-grouped samples compared to our other PCoAs.
PC1 is responsible for the distinct variation between CD + Mixed/Others and both CD + Formula-Fed and CD + Breastfed
CD + Mixed/Others are ordinated to the right side of PC1 (0.1) while CD + Formula-Fed and CD + Breastfed are more to the left.
There is a concentration of CD + Formula-fed objects, ordinated in the upper area of PC3, indicating its intra-grouping similarity
CD + Mixed/Others are distinct from other groupings, and do not overlap, indicating its intra-grouping similarity and dissimilarity from other groups
There is a larger dissimilarity between CD + Mixed/Others and CD + Formula-Fed and CD + Breastfed than between CD + Formula-Fed and CD + Breastfed
PCoA of Samples of Breastfed Infants
This PCoA controls for mode of nutrition so that all of the infants shown were majority (>50%) breast-fed. Therefore, this shows the influence of different modes of delivery (MOD) on breastfed infants.
Corresponding Key Values
1) Purple: VD + Breastfed
5) Yellow: CD + Breastfed
As this PCoA controls for mode of nutrition, showing only breastfed infants, only two differences between samples are included: VD (1) vs CD (5).
There is a slight concentration of yellow objects (CD + Breastfed) in the center of PC1 as well as PC2, and the bottom of PC3 (-0.3), indicating a slight intra-grouping similarity, as well as an inter-grouping similarity because there are purple objects interspersed within this concentration of yellow objects.
There are not completely distinct groupings of objects in this plot, suggesting that the variation of our samples can be better explained by MON than MOD
PCoA Overall Analysis
Both MOD and MON indicate varying degrees of influence on the dissimilarity of objects within our PCoAs. With CD infant samples of different MON demonstrating the most dissimilarity between different groupings.
Altogether there is not a vast dissimilarity between groupings, as seen in the PCoA with all samples, as a result of these two variables, likely due to the multitude of other factors influencing the microbiome as well as the limitations of our data processing (see below).
We were able to successfully label our data points according to their metadata. Without this it would have been extremely difficult to draw any conclusions from our PCoA.
We were able to control and take "snapshot" sub-plot PCoAs where we controlled for one variable at a time. This allowed us to isolate for certain differences and gave us better insight into the effect of each variable.
For some reason, the data could only be plotted if it was sliced into subsets of data that were 1158 rows x 1158 columns (square matrices).
For this reason we did not have enough data points to create a sub-plot PCoA that controlled for just formula-fed infants, as well as forced us to limit the amount of of data we could plot for all our PCoAs.
When we began our project we set out with the goal of using code to analyze our data. Soon after we chose to use Python, a general purpose programming language. Although prior to this project we had very little to no experience with Python or even computational data analysis, we were determined to achieve our goal.
The type of microbiome data we intended to analyze was relative abundance data. We initially searched for this data in open-source microbial ecology platforms. During our search, we were generously given access by Dr. Sean Gibbons to a relative abundance data-set compiled by Sushmita Patwardhan, which had been previously normalized to allow its analysis using Euclidean geometry.
After some research on how best to approach analyzing our data, we thought that a Principal Component Analysis (PCA) would be best. However, after meeting with Dr. Sean Gibbons, we were directed to use a Principal Coordinate Analysis (PCoA) to better represent the trends we intended to analyze. Given that a PCoA uses the difference between data points (known as beta diversity), we needed to select what is known as a distance metric (the equation or type of difference we would run). Originally we set out to use a UniFrac distance metric, which takes into account its phylogeny, to measure beta diversity. However, Sean advised that we use a Bray-Curtis distance metric instead.
In the scheme of our project, our PCoA was the most challenging and rewarding aspect. We ran into numerous errors in the code that left us confused and often frustrated; however, the internet, Rachel, and Sean were incredibly valuable resources in helping to troubleshoot all of our errors.
We compiled a list of resources that we found to be the most informative and interesting in relation to our topic.
If you're interested we encourage looking at our sources (scroll down to "Sources") but the two websites below are incredible resources on the microbiome, including but not limited to the infant and mother dynamic, compiled by Seed. Enjoy!
For Fun 🤓
We could not have completed this project without the guidance and support of following people:
Claudia Ludwig and Rachel Calder - We are deeply appreciative of your continuous kindness and encouragement in helping us explore our passions.
Rachel - We are deeply appreciative of your continued support, patience, and commitment to helping us problem-solve the errors in our programming and for providing mentorship to us.
Dr. Sean Gibbons - We are deeply appreciative of your guidance as we planned and executed our project design, your generosity with providing the data we used, and your willingness to answer our many questions. Your help made our investigation possible.
Dr. Sushmita Patwardhan - Thank you for giving us access to your incredible, extensive, and normalized compilation of data on the infant-mother microbiome dynamic.
Dr. Meghan Koch - We are deeply appreciative that you took the time to meet with us, as well as for inspiring us with your work, and providing feedback on our project.
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