Biology subjects

Krementsov, D. N.

Publications and source records attributed to Krementsov, D. N..

6 recordsLinked to original sources

bioRxiv2026

A novel wild-derived MHC-linked locus regulates host immunity to gammaherpesvirus infection

Inefficient host control of infection by gammaherpesviruses is a risk factor for lymphoproliferative disease, autoimmunity, and cancer, yet naturally occurring genetic determinants of viral control remain poorly defined. Previously, we discovered that wild-derived PWD/PhJ (PWD) mice exhibit markedly improved control of murine gammaherpesvirus 68 (MHV-68) replication compared with C57BL/6J (B6) mice. This elite control of viral replication was linked to muted T cell responses, but partially dependent on NK cells. Here, we used genetic approaches to identify host determinants controlling MHV-68 resistance in PWD mice. Analysis of B6PWDF1 and B6PWDF2 populations suggested the existence of a major locus in the PWD genome contributing to MHV-68 resistance. Using B6.ChrPWD chromosome substitution (consomic) mice, we mapped a major resistance locus to Chr 17 and refined the required interval to 27.6-49.4 Mb, including the MHC locus. While B6.Chr17PWD mice recapitulated muted T cell responses observed in PWD mice, they controlled viral burden independent of NK cells and without reduced frequencies of infected germinal center B cells, indicating that resistance of PWD mice comprises multiple genetically and mechanistically distinct pathways. While the NK cell receptor complex on PWD chromosome 6 did not provide protection, genotype-phenotype analysis of the B6PWDF2 cohort revealed additional non-Chr 17-linked loci contributing to viral control. Together, our results identify a novel MHC-linked locus regulating gammaherpesvirus burden with minimal cytotoxic T cell expansion, demonstrating that effective host control of gammaherpesviruses can be achieved by diverse immune mechanisms. ImportancePoor host control of human gammaherpesvirus infection is linked to cancer and autoimmunity, but how natural genetic variation impacts immune mechanism of control is poorly defined. In this study, we examined genetic mechanisms linked to superior control of gammaherpesvirus infection. We identified a novel MHC-linked locus that regulates gammaherpesvirus burden without a large expansion of cytotoxic T cells and independent of NK cells. These findings show that host control of gammaherpesviruses can be achieved through diverse immune mechanisms that are driven by variation in host genetic background.

immunology↗

bioRxiv2026

Integrated multi-omics analysis identifies microbial and metabolic signatures and drivers of CNS autoimmunity

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) driven by genetic and environmental determinants. The gut microbiome of people with MS (pwMS) is distinct and influences disease through immunomodulatory metabolite production. Circulating metabolites are altered in pwMS, but identifying microbial-metabolic drivers remains challenging. We previously showed that colonization by the gut commensal Limosilactobacillus reuteri (L. reuteri) exacerbates disease in the experimental autoimmune encephalomyelitis (EAE) model of MS, in a tryptophan-dependent manner. Here, we integrated microbiomic and metabolomic datasets from a longitudinal EAE study utilizing high and low tryptophan diets in mice colonized or not with L. reuteri. Gut microbiome dynamics under short- and long-term alterations in tryptophan bioavailability, were affected by diet, microbiome context, or disease. During short-term dietary intervention, L. reuteri colonization exerted a greater impact on microbiome composition than tryptophan bioavailability. With longer dietary exposure and EAE progression, high dietary tryptophan and L. reuteri colonization synergized to elicit profound microbiota changes, including alterations in Lachnospiraceae, Blautia, and Akkermansia. Integration of metabolomic and microbiomic datasets using joint Robust Aitchison PCA revealed clusters of associated metabolites and microbiota enriched for functional pathways, including bile acid and tryptophan metabolism. Metabolites outperformed microbiota in predicting EAE severity, identifying p-cresols and indoles as top disease-associated metabolites. Treatment with p-cresol or 3-indoleglyoxylic acid exacerbated EAE, enhanced proinflammatory T cell responses, and increased cerebellar pathology. These data demonstrate that dietary responses are shaped by gut microbiome composition and that integrated microbiomic-metabolomic analyses can identify drivers of disease worsening in MS. IMPORTANCEMS is a multifactorial disease influenced not only by genetics but also by environmental factors, potentially including diet and the composition of the gut microbiome. We show that interactions between diet and commensal gut microbiota profoundly impact levels of immunomodulatory systemic metabolites, including several that are associated with disease in pwMS. Importantly, we demonstrate that individual gut microbiota produced metabolites are sufficient to worsen disease in a mouse model of MS. Integration of gut microbiome and blood metabolite datasets combined with subsequent predictive modeling, may bolster biomarker identification and the capacity to predict disease severity in pwMS, as compared to performance of individual datasets alone. These findings highlight metabolites as key mediators linking diet and the gut microbiota to neuroinflammation. Importantly, this work suggests that targeting microbial metabolites or modifying diet-microbiome interactions may represent new strategies to reduce disease activity in MS and related autoimmune disorders.

bioinformatics↗

bioRxiv2025

Single cell analysis of ovarian immune cells during homeostasis and hormonal flux reveals dynamic changes in NK and B cell populations in the periovulatory phase

The mammalian ovary is the dynamic end-organ of the hypothalamic pituitary ovarian axis. In this coordinated system, ovarian cells undergo continuous cycles of apoptosis, proliferation, and differentiation. These changes parallel fluctuations in ovarian hormones such as estradiol (E2); however, the ovarian immune microenvironment during high-E2 and low-E2 states is not fully understood. We induced a high-E2 state in the mouse ovary by stimulation via gonadotropin treatment. Single-cell RNA sequencing and flow cytometry analysis of ovarian leukocytes revealed abundant mature NK cells, B1 and B2 cells, CD8+ T cells, CD4+ T cells, mature CD4-CD8-T cells, T regulatory cells, and distinct myeloid subsets, including Trem2+ and Apoe+ macrophages. In vivo labeling of circulating cells determined that the vast majority of ovarian leukocytes were tissue-resident. Following gonadotropin treatment, the frequency of NK cells increased two-fold, while B1 cell frequency was reduced by half. Consistently, flow cytometric analysis revealed an increase in mature CD11b+ NK cells following gonadotropin treatment. Gonadotropin treatment also increased cell-cell signaling by myeloid cells at the expense of NK cells. Our findings reveal a diverse resident immune landscape in the ovary that responds robustly to hormonal changes. These findings have implications for a role for immune cells in ovarian physiology and functional dysregulation.

immunology↗

bioRxiv2024

Probing the basis of disease heterogeneity in multiple sclerosis using genetically diverse mice

Multiple sclerosis (MS) is a complex disease with significant heterogeneity in disease course and progression. Genetic studies have identified numerous loci associated with MS risk, but the genetic basis of disease progression remains elusive. To address this, we leveraged the Collaborative Cross (CC), a genetically diverse mouse strain panel, and experimental autoimmune encephalomyelitis (EAE). The thirty-two CC strains studied captured a wide spectrum of EAE severity, trajectory, and presentation, including severe-progressive, monophasic, relapsing remitting, and axial rotary (AR)-EAE, accompanied by distinct immunopathology. Sex differences in EAE severity were observed in six strains. Quantitative trait locus analysis revealed distinct genetic linkage patterns for different EAE phenotypes, including EAE severity and incidence of AR-EAE. Machine learning-based approaches prioritized candidate genes for loci underlying EAE severity (Abcc4 and Gpc6) and AR-EAE (Yap1 and Dync2h1). This work expands the EAE phenotypic repertoire and identifies novel loci controlling unique EAE phenotypes, supporting the hypothesis that heterogeneity in MS disease course is driven by genetic variation. SummaryThe genetic basis of disease heterogeneity in multiple sclerosis (MS) remains elusive. We leveraged the Collaborative Cross to expand the phenotypic repertoire of the experimental autoimmune encephalomyelitis (EAE) model of MS and identify loci controlling EAE severity, trajectory, and presentation.

immunology↗

bioRxiv2021

Genetic Analysis of Bphse: a Novel Gene Complementing Resistance to Bordetella pertussis-Induced Histamine Sensitization

AO_SCPLOWBSTRACTC_SCPLOWHistamine is a bioactive amine associated with a plethora of normal and pathophysiological processes, with the latter being dependent on both genetic and environmental factors including infectious agents. Previously, we showed in mice that susceptibility to Bordetella pertussis and pertussis toxin (PTX) induced histamine sensitization (Bphs) is controlled by histamine receptor H1 (Hrh1/HRH1) alleles. Bphs susceptible and resistant alleles (Bphss/Bphsr) encode for two-conserved protein haplotypes. Given the importance of HRH1 signaling in health and disease, we sequenced Hrh1 across an extended panel of laboratory and wild-derived inbred strains and phenotyped them for Bphs. Unexpectedly, eight strains homozygous for the Bphsr allele phenotyped as Bphss, suggesting the existence of a modifying locus segregating among the strains capable of complementing Bphsr. Genetic analyses mapped this modifier locus to mouse chromosome 6; designated Bphs-enhancer (Bphse), within a functional linkage disequilibrium domain encoding multiple loci controlling responsiveness to histamine (Bphs/Hrh1 and Histh1-4). Interval-specific single-nucleotide polymorphism (SNP) based association testing across 50 laboratory and wild-derived inbred mouse strains and functional prioritization analyses resulted in the identification of candidate genes for Bphse within a [~]5.5 Mb interval (Chr6:111.0-116.4 Mb), including Atg7, Plxnd1, Tmcc1, Mkrn2, Il17re, Pparg, Lhfpl4, Vgll4, Rho and Syn2. Taken together, these results demonstrate the power of combining network-based computational methods with the evolutionarily significant diversity of wild-derived inbred mice to identify novel genetic mechanisms controlling susceptibility and resistance to histamine shock.

genetics↗

bioRxiv2019

Network-based functional prediction augments genetic association to predict candidate genes for histamine hypersensitivity in mice

Genetic mapping is a primary tool of genetics in model organisms; however, many quantitative trait loci (QTL) contain tens or hundreds of positional candidate genes. Prioritizing these genes for validation is often ad hoc and biased by previous findings. Here we present a technique for computationally prioritizing positional candidates based on computationally inferred gene function. Our method uses machine learning with functional genomic networks, whose links encode functional associations among genes, to identify network-based signatures of functional association to a trait of interest. We demonstrate the method by functionally ranking positional candidates in a large locus on mouse Chr 6 (45.9 Mb to 127.8 Mb) associated with histamine hypersensitivity (Hhs). Hhs is characterized by systemic vascular leakage and edema in response to histamine challenge, which can lead to multiple organ failure and death. Although Hhs risk is strongly influenced by genetics, little is known about its underlying molecular or genetic causes, due to genetic and physiological complexity of the trait. To dissect this complexity, we ranked genes in the Hhs locus by predicting functional association with multiple Hhs-related processes. We integrated these predictions with new single nucleotide polymorphism (SNP) association data derived from a survey of 23 inbred mouse strains and congenic mapping data. The top-ranked genes included Cxcl12, Ret, Cacna1c, and Cntn3, all of which had strong functional associations and were proximal to SNPs segregating with Hhs. These results demonstrate the power of network-based computational methods to nominate highly plausible quantitative trait genes even in highly challenging cases involving large QTLs and extreme trait complexity.

genetics↗