main.typ

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#import "@preview/tiaoma:0.3.0"

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      This is your brain on bugs #h(6em)
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    ],
    subtitle: [Co-development of the infant gut microbiome and the brain],
    author: [Kevin Bonham, PhD],
    date: datetime(year: 2025, month: 9, day: 18),
    institution: [GSBS MMB Seminar],
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#title-slide()

== Outline

#slide[
  #set text(24pt)
  - My Background - Bench to desk
  - Story 1: Gut microbiome and brain development
  - Story 2: Salt tolerance in _B. infantis_
]

= All about me

#figure(image("assets/lab_general/narcissus.jpg",), numbering: none, caption:[
  #text(15pt, fill: gray)[https://en.wikipedia.org/wiki/Narcissus_(Caravaggio)]
])

== At the bench - Biochemistry and cell-biology of the immune system

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== Transition to computing - Cheese HGT

#slide[
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== Expanding computing - Human microbiome epidemiology

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    content((14,3.4),[2017-2019])
    content((6,-9.2), text(14pt, [@thompsonAlterationsGutMicrobiomeImplicateKey2023]))
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== Outline

#slide[
  #set text(24pt)
  - My Background - Bench to desk
  - *Story 1: Gut microbiome and brain development*
  - Story 2: Salt tolerance in _B. infantis_
]

= Introduction - bugs & brains


== The gut-brain-microbiome "axis"

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    (pause,)
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    }))
]

== The RESONANCE cohort of Child Development

#slide[
    #v(1em)
    #figure(
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        import cetz.draw: *
        content((-10,-5), text(14pt)[@bonhamGutresidentMicroorganismsTheirGenesAre2023])
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    #v(1em)
]

#focus-slide[
  Only 7 species were significantly associated with cognitive development
  using "traditional" linear models
]

== Random Forest models use decision trees

#slide[
   
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        content((0, 0), [#image("assets/stats_ML/decision-tree_1.png", width: 600pt)], name:"fig")
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#slide[
   
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        content((0, 0), [#image("assets/stats_ML/decision-tree-train.svg", width: 600pt)], name:"fig")
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]

== RF models find many more microbes than linear models

#slide[
   
    #figure(
        cetz-canvas({
        import cetz.draw: *
        content((-10,-5), text(14pt)[@bonhamGutresidentMicroorganismsTheirGenesAre2023])
        content((0, 0), [#image("assets/child_brain_microbiome/resonance-fig3.jpg", width: 760pt)], name:"fig")
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        (pause,)
        content((0, 0), [#image("assets/child_brain_microbiome/resonance-fig3.jpg", width: 760pt)], name:"fig")
        })
    )
]


== ML also identifies microbes associated with brain structure

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#focus-slide[
The accelerated longitudinal study design makes stats complicated
#pause

The "cognitive score" measure is imprecise
]

== The Khula study of child development

#slide[
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        content((-3.5,-6), text(14pt)[@zieffCharacterizingDevelopingExecutiveFunctionsFirst2024], name:"cite")
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        content((10.5,-6), text(14pt)[@bonhamCodevelopmentGutMicrobialMetabolismVisual2025], name:"cite")
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    #v(-1em)
]

== The visual evoked potential (VEP) measures brain development

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]

== VEP peaks get faster and smaller as the brain develops

#slide[
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        })
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]

#focus-slide[
  NO species were significantly associated with VEP
  using "traditional" linear models
  #pause
  
  RF models had poor performance at predicting VEP
  
]

== Gene set enrichment analysis makes use of full metagenome

#slide[
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]

== Microbial genes are associated with VEP development

#slide[
#v(1em)
#figure(image("assets/child_brain_microbiome/eeg-enrichments.png", width: 600pt))
#text(14pt)[@bonhamCodevelopmentGutMicrobialMetabolismVisual2025]
]

== More microbial genes are associated with _future_ VEP development

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        content((-9.5,-8), text(14pt)[@bonhamCodevelopmentGutMicrobialMetabolismVisual2025], name:"cite")

        content((0, 0), [#image("assets/child_brain_microbiome/concurrent_volcano.png", width: 673pt)], name:"fig")
        content((0, -6), [#image("assets/child_brain_microbiome/geneset-keys.png", width: 250pt)], name:"fig")

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]

#slide[

#figure(image("assets/child_brain_microbiome/fsea-futures.png", width: 600pt))
#v(-1em)
#text(14pt)[@bonhamCodevelopmentGutMicrobialMetabolismVisual2025]
]

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]

== Outline

#slide[
  #set text(24pt)
  - My Background - Bench to desk
  - Story 1: Gut microbiome and brain development
  - *Story 2: Salt tolerance in _B. infantis_*
]

= Salt tolerance in _Bifidobacterium longum_ subsp. _infantis_

== Humans have evolved to feed the infant microbiome

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    content((0,0),
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        Humans can't digest HMOs!
      ]), angle:20deg)
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== _B. infantis_ is a specialized HMO metabolizer

#slide[
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== _B. infantis_ is in decline in Industrialized societies

#slide[
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    content((0, 0), image("assets/binfantis/sonnenberg-2.png", width: 400pt))
    content((5,-5), text(14pt)[@olmRobustVariationInfantGutMicrobiome2022])
    }))
]

== _B. infantis_ is sensitive to salt concentration

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    (pause,)
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]

== How does _B. infantis_ respond to salt stress?

#slide[
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    (pause,)
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    (pause,)
    content((0, -8.6), image("assets/binfantis/transfers-375mM.svg", width: 500pt))
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]

== Lots of open questions

#text(24pt)[
- What genes are enabling adaptation to salt? 
  - RNAseq during growth / transfer experiments
  - Microbial genetics experiments #pause
- Are there variants in salt-tolerance genes in global human-associated _B. infantis_ stains?
  - We have thousands of infant metagenomes to explore
- Do other gut-associated Bifidos have different salt tolerance? (competition?)
]

== Other lab Projects

#text(24pt)[
- Microbial neuroactive gene discovery (using graph-convolutional neural networks)
- Software development - Gaussian Process models
- Spatial transcriptomics data processing / modeling (with Matt Woodruff \@ Emory)
]


== Contact info


#slide(composer: (2fr,3fr))[
#figure(image("assets/lab_general/narcissus.jpg", width:75%), numbering: none, caption:[
    #text(15pt, fill: gray)[https://en.wikipedia.org/wiki/Narcissus_(Caravaggio)]
])
][
  #align(center, image("assets/lab_general/lab-logo-banner.png", width: 50%))

  - #fa-icon("github", font: "Font Awesome 6 Pro Solid")#h(0.5em)
    \/ #fa-icon("gitlab", font:  "Font Awesome 6 Pro Solid"): `@kescobo`
  - 🔬(WIP) https://lab.bonham.ch
  - 🎙️ https://audiommunity.org
  - 🦋 https://bsky.app/\@kevinbonham.com

  #align(center, [
    This Presentation: #linebreak()
    #text(12pt,blue)[https://github.com/BonhamLab/presentation_gsbs_micro]
    #tiaoma.qrcode("https://github.com/BonhamLab/presentation_gsbs_micro")
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]

== References

#set text(11pt)

#bibliography("2025-gsbs.bib", style: "annual-reviews-author-date", title:none)

== Thanks! Questions?

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