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Principal Investigator: Tatiana T. Marquez-Lago
Organization: UNIVERSITY OF ALABAMA AT BIRMINGHAM
Fiscal Year: 2024
Award: $336,295
Funding agency: Eunice Kennedy Shriver National Institute of Child Health and Human Development
Despite decades-long research and multiple trials, there is no licensed vaccine against Cytomegalovirus (CMV)
yet, urging efforts to better understand its transmission dynamics. CMV is a frequent cause of tissue-invasive
disease in infants and immunocompromised individuals, and transmission happens easily and through contact
with various body fluids. Among transmission modes, CMV transmission via human milk (HM) is recognized to
have the largest global impact on population prevalence. Factors determining CMV transmission remain largely
unknown, including CMV interactions with HM microbiota and metabolites. In general, commensal microbiota
can greatly impact sensitivity to viral infections while, metabolites, such as human milk oligosaccharides (HMOs),
not only feed human microbiota but can also act as soluble decoy receptors, blocking the attachment of viral
pathogens to epithelial cells. Additionally, short chain and medium chain fatty acids are products of microbial
fermentation, known to influence immune responses, and microbiota can also produce antivirals through
secondary metabolism. Therefore, the objective of this proposal is to better define CMV transmission dynamics,
considering different factors and timescales, and to systematically and quantitatively study the role and
interactions of the HM metabolome and microbiome influencing CMV transmission from women to their infants.
Our preliminary data readily shows that CMV seronegative and seropositive mothers have distinct HM
microbiome and metabolome ecologies. In particular, we found clear differences distinguishing seropositive
mothers that are non-shedding, shedding but not transmitting, and shedding and transmitting CMV. These results
led to the central hypothesis, which is that certain combinations of HM microbiota and metabolites prevent CMV
transmissions, and that these combinations vary among individual dyads, but follow traceable and reproducible
patterns. We propose to test the central hypothesis by pursuing the following three specific aims: (1) Determine
CMV transmission dynamics with high sample density and HM microbiome ecologies underlying CMV
transmission versus non-transmission; (2) Identify and validate key metabolites involved in CMV transmission
and non-transmission; and (3) Define causality and identify molecular mechanisms of CMV transmission
inhibition assisted by mathematical modeling and artificial intelligence. Collectively, our proposed research will
broadly impact the field by elucidating CMV-host interactions and CMV transmission dynamics in various time
scales, validating factors blocking CMV transmission, and providing models and tools to help advance the arrival
of clinical resource. These studies also have the potential to lay the groundwork for, and translate into, rational
design of personalized HM and microbiome-metabolome interventions without replacing HM.
Terms: <0-11 years old><0-4 weeks old><AI system><Age Months><Anti-viral Agents><Antibodies><Artificial Intelligence><Bile Acids><Biomedical Engineering><Bionomics><Blood Grouping><Blood Typing><Blood typing procedure><Body Fluids><Body Tissues><Breast Milk><Breastmilk><CMV><Caring><Causality><Cell Body><Cells><Child><Child Youth><Children (0-21)><Chromatography><Chronic><Clinical><Co-culture><Cocultivation><Coculture><Coculture Techniques><Communities><Computer Reasoning><Cytomegalovirus><Data><Dimensions><Disease><Disorder><Ecology><Epithelial Cells><Etiology><Exposure to><Fatty Acids><Fermentation><Foundations><Functional Metagenomics><GI microbiota><Gastrointestinal microbiota><Gender><Goals><HCMV><Human><Human Microbiome><Human Milk><Human Mother's Milk><Immune response><Immune system><Immunocompromised><Immunocompromised Host><Immunocompromised Patient><Immunological response><Immunosuppressed Host><Individual><Infant><Infant Development><Infection prevention><Interdisciplinary Research><Interdisciplinary Study><Intermediary Metabolism><Intervention><Intervention Strategies><Investigation><Lactation><Licensing><Life><Machine Intelligence><Mammary Gland Milk><Maps><Maternal Age><Math Models><Measures><Medium chain fatty acid><Metabolic Processes><Metabolism><Metagenomics><Modeling><Modern Man><Molecular><Mother's Milk><Mothers><Multidisciplinary Collaboration><Multidisciplinary Research><Newborn Infant><Newborns><Passive Immunization><Pattern><Population><Prevalence><Prevent infection><Public Health><Receptor Protein><Reproducibility><Research><Research Resources><Resources><Risk><Role><Saliva><Salivary Gland Viruses><Sampling><Shotguns><Site><Supplementation><Testing><Time><Tissues><Translating><Translations><Transmission><Urine><Vaccines><Viral Antibodies><Viral Burden><Viral Diseases><Viral Load><Viral Load result><Viral Shedding><Virus Diseases><Virus Shedding><Woman><age at pregnancy><anti-viral antibody><anti-viral compound><anti-viral drugs><anti-viral medication><anti-viral therapeutic><anti-virals><bio-engineered><bio-engineers><bioengineering><biological engineering><blocking factor><blood group><causation><cofactor><commensal flora><commensal microbes><commensal microbiota><commensal microflora><comparative><computer based prediction><congenital infection><cytomegalovirus group><density><disability><disease causation><enteric microbial community><enteric microbiota><fecal sample><gastrointestinal microbial flora><gut commensal><gut community><gut flora><gut microbe community><gut microbial community><gut microbial composition><gut microbial consortia><gut microbiota><gut microbiotic><gut microflora><host response><human flora><human microbial communities><human microbiota><human microflora><human milk oligosaccharides><human-associated microbial communities><human-associated microbiome><human-associated microbiota><immune system response><immunoresponse><immunosuppressed patient><improved><insight><interventional strategy><intestinal flora><intestinal microbiota><intestinal microflora><intestinal tract microflora><kids><lactating><lactational><machine learning based prediction model><machine learning based predictive model><machine learning prediction><machine learning prediction model><maternal milk><mathematic model><mathematical model><mathematical modeling><metabolism measurement><metabolome><metabolomics><metabonome><metabonomics><metatranscriptomics><microbial><microbial consortia><microbial flora><microbial products><microbiome><microbiota><microflora><milk microbiome><multiomics><multiple omics><multispecies consortia><newborn child><newborn children><non-genetic><nongenetic><offspring><opportunistic pathogen><panomics><passive vaccination><pathogenic virus><personalized health intervention><personalized intervention><precision interventions><predictive modeling><prevent><preventing><programs><rational design><receptor><risk mitigation><saliva sample><salivary sample><seropositive><shot gun><social role><stool sample><stool specimen><tandem mass spectrometry><tool><translation><translational framework><transmission process><viral infection><viral pathogen><viral transmission><virus infection><virus pathogen><virus transmission><virus-induced disease><youngster>