Maternal genetic variants in kinesin motor domains prematurely increase egg aneuploidy.

The female reproductive lifespan is highly dependent on egg quality, especially the presence of a normal number of chromosomes in an egg, known as euploidy. Mistakes in meiosis leading to egg aneuploidy are frequent in humans. Yet, knowledge of the precise genetic landscape that causes egg aneuploidy in women is limited, as phenotypic data on the frequency of human egg aneuploidy are difficult to obtain and therefore absent in public genetic datasets. Here, we identify genetic determinants of reproductive aging via egg aneuploidy in women using a biobank of individual maternal exomes linked with maternal age and embryonic aneuploidy data. Using the exome data, we identified 404 genes bearing variants enriched in individuals with pathologically elevated egg aneuploidy rates. Analysis of the gene ontology and protein-protein interaction network implicated genes encoding the kinesin protein family in egg aneuploidy. We interrogate the causal relationship of the human variants within candidate kinesin genes via experimental perturbations and demonstrate that motor domain variants increase aneuploidy in mouse oocytes. Finally, using a knock-in mouse model, we validate that a specific variant in kinesin KIF18A accelerates reproductive aging and diminishes fertility. These findings reveal additional functional mechanisms of reproductive aging and shed light on how genetic variation underlies individual heterogeneity in the female reproductive lifespan, which might be leveraged to predict reproductive longevity. Together, these results lay the groundwork for the noninvasive biomarkers for egg quality, a first step toward personalized fertility medicine.

C. elegans ADARs antagonize silencing of cellular dsRNAs by the antiviral RNAi pathway.

Cellular dsRNAs are edited by adenosine deaminases that act on RNA (ADARs). While editing can alter mRNA-coding potential, most editing occurs in noncoding sequences, the function of which is poorly understood. Using dsRNA immunoprecipitation (dsRIP) and RNA sequencing (RNA-seq), we identified 1523 regions of clustered A-to-I editing, termed editing-enriched regions (EERs), in four stages of Caenorhabditis elegans development, often with highest expression in embryos. Analyses of small RNA-seq data revealed 22- to 23-nucleotide (nt) siRNAs, reminiscent of viral siRNAs, that mapped to EERs and were abundant in adr-1;adr-2 mutant animals. Consistent with roles for these siRNAs in silencing, EER-associated genes (EAGs) were down-regulated in adr-1;adr-2 embryos, and this was dependent on associated EERs and the RNAi factor RDE-4. We observed that ADARs genetically interact with the 26G endogenous siRNA (endo-siRNA) pathway, which likely competes for RNAi components; deletion of factors required for this pathway (rrf-3 or ergo-1) in adr-1;adr-2 mutant strains caused a synthetic phenotype that was rescued by deleting antiviral RNAi factors. Poly(A)+ RNA-seq revealed EAG down-regulation and antiviral gene induction in adr-1;adr-2;rrf-3 embryos, and these expression changes were dependent on rde-1 and rde-4 Our data suggest that ADARs restrict antiviral silencing of cellular dsRNAs.

Yin Yang 1 controls cerebellar astrocyte maturation.

Diverse subpopulations of astrocytes tile different brain regions to accommodate local requirements of neurons and associated neuronal circuits. Nevertheless, molecular mechanisms governing astrocyte diversity remain mostly unknown. We explored the role of a zinc finger transcription factor Yin Yang 1 (YY1) that is expressed in astrocytes. We found that specific deletion of YY1 from astrocytes causes severe motor deficits in mice, induces Bergmann gliosis, and results in simultaneous loss of GFAP expression in velate and fibrous cerebellar astrocytes. Single cell RNA-seq analysis showed that YY1 exerts specific effects on gene expression in subpopulations of cerebellar astrocytes. We found that although YY1 is dispensable for the initial stages of astrocyte development, it regulates subtype-specific gene expression during astrocyte maturation. Moreover, YY1 is continuously needed to maintain mature astrocytes in the adult cerebellum. Our findings suggest that YY1 plays critical roles regulating cerebellar astrocyte maturation during development and maintaining a mature phenotype of astrocytes in the adult cerebellum.

Mathematical modeling of human oocyte aneuploidy.

Aneuploidy is the leading contributor to pregnancy loss, congenital anomalies, and in vitro fertilization (IVF) failure in humans. Although most aneuploid conceptions are thought to originate from meiotic division errors in the female germline, quantitative studies that link the observed phenotypes to underlying error mechanisms are lacking. In this study, we developed a mathematical modeling framework to quantify the contribution of different mechanisms of erroneous chromosome segregation to the production of aneuploid eggs. Our model considers the probabilities of all possible chromosome gain/loss outcomes that arise from meiotic errors, such as nondisjunction (NDJ) in meiosis I and meiosis II, and premature separation of sister chromatids (PSSC) and reverse segregation (RS) in meiosis I. To understand the contributions of different meiotic errors, we fit our model to aneuploidy data from 11,157 blastocyst-stage embryos. Our best-fitting model captures several known features of female meiosis, for instance, the maternal age effect on PSSC. More importantly, our model reveals previously undescribed patterns, including an increased frequency of meiosis II errors among eggs affected by errors in meiosis I. This observation suggests that the occurrence of NDJ in meiosis II is associated with the ploidy status of an egg. We further demonstrate that the model can be used to identify IVF patients who produce an extreme number of aneuploid embryos. The dynamic nature of our mathematical model makes it a powerful tool both for understanding the relative contributions of mechanisms of chromosome missegregation in human female meiosis and for predicting the outcomes of assisted reproduction.

Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing.

Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother's genome. In this study, we evaluated machine learning-based classifiers for predicting the embryonic aneuploidy risk in female IVF patients using whole-exome sequencing data. Using two exome datasets, we obtained an area under the receiver operating curve of 0.77 and 0.68, respectively. High precision could be traded off for high specificity in classifying patients by selecting different prediction score cutoffs. For example, a strict prediction score cutoff of 0.7 identified 29% of patients as high-risk with 94% precision. In addition, we identified MCM5, FGGY, and DDX60L as potential aneuploidy risk genes that contribute the most to the predictive power of the model. These candidate genes and their molecular interaction partners are enriched for meiotic-related gene ontology categories and pathways, such as microtubule organizing center and DNA recombination. In summary, we demonstrate that sequencing data can be mined to predict patients' aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways we identified are promising targets for future aneuploidy studies.

Analysis of DNA variants in miRNAs and miRNA 3'UTR binding sites in female infertility patients.

Early human embryogenesis relies on maternal gene products accumulated during oocyte growth and maturation, until around day-3 post-fertilization when human zygotic genome activation occurs. The maternal-to-zygotic transition (MZT) is a tightly coordinated process of selective maternal transcript clearance and new zygotic transcript production. If MZT is disrupted, it will lead to developmental arrest and pregnancy loss. It is well established that microRNA (miRNA) mutations disrupt regulation of their target transcripts. We hypothesize that some cases of embryonic arrest and pregnancy loss could be explained by the mutations in the maternal genome that affect miRNA-target transcript pairs. To this end, we examined mutations within miRNAs or miRNA binding sites in the 3' untranslated regions (3'UTR) of target transcripts. Using whole-exome sequencing data from 178 women undergoing in vitro fertilization (IVF) procedures, we identified 1197 variants in miRNA genes, including 93 single nucleotide variants (SNVs) and 19 small insertions/deletions (INDELs) within the seed region of 100 miRNAs. Eight miRNA seed-region variants were significantly enriched among our patients when compared to a normal population. Within predicted 3'UTR miRNA binding sites, we identified 7393 SNVs and 1488 INDELs. Between our patients and a normal population, 52 SNVs and 30 INDELs showed significant association in the single-variant testing, whereas 51 genes showed significant association in the gene-burden analysis for genes that are expressed in preimplantation embryos. Interestingly, we found that many genes with disrupted 3'UTR miRNA binding sites follow gene expression patterns resembling MZT. In addition, some of these variants showed dramatic allele frequency difference between the patient and the normal group, offering potential utility as biomarkers for screening patients prior to IVF procedures.

Exome sequencing links CEP120 mutation to maternally derived aneuploid conception risk.

STUDY QUESTION: What are the genetic factors that increase the risk of aneuploid egg production? SUMMARY ANSWER: A non-synonymous variant rs2303720 within centrosomal protein 120 (CEP120) disrupts female meiosis in vitro in mouse. WHAT IS KNOWN ALREADY: The production of aneuploid eggs, with an advanced maternal age as an established contributing factor, is the major cause of IVF failure, early miscarriage and developmental anomalies. The identity of maternal genetic variants contributing to egg aneuploidy irrespective of age is missing. STUDY DESIGN, SIZE, DURATION: Patients undergoing fertility treatment (n = 166) were deidentified and selected for whole-exome sequencing. PARTICIPANTS/MATERIALS, SETTING, METHODS: Patients self-identified their ethnic groups and their ages ranged from 22 to 49 years old. The study was performed using genomes from White, non-Hispanic patients divided into controls (97) and cases (69) according to the number of aneuploid blastocysts derived during each IVF procedure. Following a gene prioritization strategy, a mouse oocyte system was used to validate the functional significance of the discovered associated genetic variants. MAIN RESULTS AND THE ROLE OF CHANCE: Patients producing a high proportion of aneuploid blastocysts (considered aneuploid if they missed any of the 40 chromatids or had extra copies) were found to carry a higher mutational burden in genes functioning in cytoskeleton and microtubule pathways. Validation of the functional significance of a non-synonymous variant rs2303720 within Cep120 on mouse oocyte meiotic maturation revealed that ectopic expression of CEP120:p.Arg947His caused decreased spindle microtubule nucleation efficiency and increased incidence of aneuploidy. LIMITATIONS, REASONS FOR CAUTION: Functional validation was performed using the mouse oocyte system. Because spindle building pathways differ between mouse and human oocytes, the defects we observed upon ectopic expression of the Cep120 variant may alter mouse oocyte meiosis differently than human oocyte meiosis. Further studies using knock-in 'humanized' mouse models and in human oocytes will be needed to translate our findings to human system. Possible functional differences of the variant between ethnic groups also need to be investigated. WIDER IMPLICATIONS OF THE FINDINGS: Variants in centrosomal genes appear to be important contributors to the risk of maternal aneuploidy. Functional validation of these variants will eventually allow prescreening to select patients that have better chances to benefit from preimplantation genetic testing. STUDY FUNDING/COMPETING INTEREST(S): This study was funded through R01-HD091331 to K.S. and J.X. and EMD Serono Grant for Fertility Innovation to N.R.T. N.R.T. is a shareholder and an employee of Genomic Prediction. TRIAL REGISTRATION NUMBER: N/A.

Maternal genetic variants in kinesin motor domains prematurely increase egg aneuploidy.

The female reproductive lifespan depends on egg quality, particularly euploidy. Mistakes in meiosis leading to egg aneuploidy are common, but the genetic landscape causing this is not well understood due to limited phenotypic data. We identify genetic determinants of reproductive aging via egg aneuploidy using a biobank of maternal exomes linked with maternal age and embryonic aneuploidy data. We found 404 genes with variants enriched in individuals with high egg aneuploidy rates and implicate kinesin protein family genes in aneuploidy risk. Experimental perturbations showed that motor domain variants in these genes increase aneuploidy in mouse oocytes. A knock-in mouse model validated that a specific variant in kinesin KIF18A accelerates reproductive aging and diminishes fertility. These findings suggest potential non-invasive biomarkers for egg quality, aiding personalized fertility medicine. One sentence summary: The study identifies novel genetic determinants of reproductive aging linked to egg aneuploidy by analyzing maternal exomes and demonstrates that variants in kinesin genes, specifically KIF18A , contribute to increased aneuploidy and accelerated reproductive aging, offering potential for personalized fertility medicine.

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT.

Identifying cell types and states remains a time-consuming and error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data, using unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integration of TACIT-identified cell with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discover under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

Spatial Deconvolution of Cell Types and Cell States at Scale Utilizing TACIT.

Identifying cell types and states remains a time-consuming, error-prone challenge for spatial biology. While deep learning is increasingly used, it is difficult to generalize due to variability at the level of cells, neighborhoods, and niches in health and disease. To address this, we developed TACIT, an unsupervised algorithm for cell annotation using predefined signatures that operates without training data. TACIT uses unbiased thresholding to distinguish positive cells from background, focusing on relevant markers to identify ambiguous cells in multiomic assays. Using five datasets (5,000,000-cells; 51-cell types) from three niches (brain, intestine, gland), TACIT outperformed existing unsupervised methods in accuracy and scalability. Integrating TACIT-identified cell types with a novel Shiny app revealed new phenotypes in two inflammatory gland diseases. Finally, using combined spatial transcriptomics and proteomics, we discovered under- and overrepresented immune cell types and states in regions of interest, suggesting multimodality is essential for translating spatial biology to clinical applications.

Single-cell and spatially resolved interactomics of tooth-associated keratinocytes in periodontitis.

Periodontitis affects billions of people worldwide. To address relationships of periodontal niche cell types and microbes in periodontitis, we generated an integrated single-cell RNA sequencing (scRNAseq) atlas of human periodontium (34-sample, 105918-cell), including sulcular and junctional keratinocytes (SK/JKs). SK/JKs displayed altered differentiation states and were enriched for effector cytokines in periodontitis. Single-cell metagenomics revealed 37 bacterial species with cell-specific tropism. Fluorescence in situ hybridization detected intracellular 16 S and mRNA signals of multiple species and correlated with SK/JK proinflammatory phenotypes in situ. Cell-cell communication analysis predicted keratinocyte-specific innate and adaptive immune interactions. Highly multiplexed immunofluorescence (33-antibody) revealed peri-epithelial immune foci, with innate cells often spatially constrained around JKs. Spatial phenotyping revealed immunosuppressed JK-microniches and SK-localized tertiary lymphoid structures in periodontitis. Here, we demonstrate impacts on and predicted interactomics of SK and JK cells in health and periodontitis, which requires further investigation to support precision periodontal interventions in states of chronic inflammation.

GZMK+CD8+ T cells Target A Specific Acinar Cell Type in Sjögren's Disease.

Sjögren's Disease (SjD) is a systemic autoimmune disease without a clear etiology or effective therapy. Utilizing unbiased single-cell and spatial transcriptomics to analyze human minor salivary glands in health and disease we developed a comprehensive understanding of the cellular landscape of healthy salivary glands and how that landscape changes in SjD patients. We identified novel seromucous acinar cell types and identified a population of PRR4+CST3+WFDC2- seromucous acinar cells that are particularly targeted in SjD. Notably, GZMK+CD8 T cells, enriched in SjD, exhibited a cytotoxic phenotype and were physically associated with immune-engaged epithelial cells in disease. These findings shed light on the immune response's impact on transitioning acinar cells with high levels of secretion and explain the loss of this specific cell population in SjD. This study explores the complex interplay of varied cell types in the salivary glands and their role in the pathology of Sjögren's Disease.

Novel mutant KRAS addiction signature predicts response to the combination of ERBB and MEK inhibitors in lung and pancreatic cancers.

KRAS mutations are prevalent in pancreatic and lung cancers, but not all mutant (mt) KRAS tumors are addicted to mt KRAS. Here, we discovered a 30-gene transcriptome signature "KDS30" that encodes a novel EGFR/ERBB2-driven signaling network and predicts mt KRAS, but not NRAS or HRAS, oncogene addiction. High KDS30 tumors from mt KRAS lung and pancreatic cancer patients are enriched in genes upregulated by EGFR, ERBB2, mt KRAS or MEK. EGFR/ERBB2 (neratinib) and MEK (cobimetinib) inhibitor combination inhibits tumor growth and prolongs mouse survival in high, but not low, KDS30 mt KRAS lung and pancreatic xenografts, and is synergistic only in high KDS30 mt KRAS patient-derived organoids. Furthermore, mt KRAS high KDS30 lung and pancreatic cancer patients live significantly shorter lives than those with low KDS30. Thus, KDS30 can identify lung and pancreatic cancer patients whose tumors are addicted to mt KRAS, and predicts EGFR/ERBB2 and MEK inhibitor combination response.

Chromatin conformation capture (Hi-C) sequencing of patient-derived xenografts: analysis guidelines.

BACKGROUND: Sequencing of patient-derived xenograft (PDX) mouse models allows investigation of the molecular mechanisms of human tumor samples engrafted in a mouse host. Thus, both human and mouse genetic material is sequenced. Several methods have been developed to remove mouse sequencing reads from RNA-seq or exome sequencing PDX data and improve the downstream signal. However, for more recent chromatin conformation capture technologies (Hi-C), the effect of mouse reads remains undefined. RESULTS: We evaluated the effect of mouse read removal on the quality of Hi-C data using in silico created PDX Hi-C data with 10% and 30% mouse reads. Additionally, we generated 2 experimental PDX Hi-C datasets using different library preparation strategies. We evaluated 3 alignment strategies (Direct, Xenome, Combined) and 3 pipelines (Juicer, HiC-Pro, HiCExplorer) on Hi-C data quality. CONCLUSIONS: Removal of mouse reads had little-to-no effect on data quality as compared with the results obtained with the Direct alignment strategy. Juicer extracted more valid chromatin interactions for Hi-C matrices, regardless of the mouse read removal strategy. However, the pipeline effect was minimal, while the library preparation strategy had the largest effect on all quality metrics. Together, our study presents comprehensive guidelines on PDX Hi-C data processing.

Development and Characterization of a Modular CRISPR and RNA Aptamer Mediated Base Editing System.

Conventional CRISPR approaches for precision genome editing rely on the introduction of DNA double-strand breaks (DSB) and activation of homology-directed repair (HDR), which is inherently genotoxic and inefficient in somatic cells. The development of base editing (BE) systems that edit a target base without requiring generation of DSB or HDR offers an alternative. Here, we describe a novel BE system called Pin-pointTM that recruits a DNA base-modifying enzyme through an RNA aptamer within the gRNA molecule. Pin-point is capable of efficiently modifying base pairs in the human genome with precision and low on-target indel formation. This system can potentially be applied for correcting pathogenic mutations, installing premature stop codons in pathological genes, and introducing other types of genetic changes for basic research and therapeutic development.

The Conserved Intron Binding Protein EMB-4 Plays Differential Roles in Germline Small RNA Pathways of C. elegans.

Proper regulation of the germline transcriptome is essential for fertility. In C. elegans, germline homeostasis hinges on a complex repertoire of both silencing and activating small RNA pathways, along with RNA processing. However, our understanding of how fundamental RNA processing steps intersect with small RNA machineries in the germline remains limited. Here, we link the conserved intron binding protein, EMB-4/AQR/IBP160, to the CSR-1 and HRDE-1 nuclear 22G-RNA pathways in the C. elegans germline. Loss of emb-4 leads to distinct alterations in CSR-1- versus HRDE-1-associated small RNA and mRNA transcriptomes. Our transcriptome-wide analysis shows that EMB-4 is enriched along pre-mRNAs of nearly 8,000 transcripts. While EMB-4 complexes are enriched for both intronic and exonic sequences of HRDE-1 targets, CSR-1 pathway targets are enriched for intronic, but not exonic, sequences. These data suggest that EMB-4 could contribute to a molecular signature that distinguishes the targets of these two germline small RNA pathways.

The game theory of Candida albicans colonization dynamics reveals host status-responsive gene expression.

BACKGROUND: The fungal pathogen Candida albicans colonizes the gastrointestinal (GI) tract of mammalian hosts as a benign commensal. However, in an immunocompromised host, the fungus is capable of causing life-threatening infection. We previously showed that the major transcription factor Efg1p is differentially expressed in GI-colonizing C. albicans cells dependent on the host immune status. To understand the mechanisms that underlie this host-dependent differential gene expression, we utilized mathematical modeling to dissect host-pathogen interactions. Specifically, we used principles of evolutionary game theory to study the mechanism that governs dynamics of EFG1 expression during C. albicans colonization. RESULTS: Mathematical modeling predicted that down-regulation of EFG1 expression within individual fungal cells occurred at different average rates in different hosts. Rather than using relatively transient signaling pathways to adapt to a new environment, we demonstrate that C. albicans overcomes the host defense strategy by modulating the activity of diverse fungal histone modifying enzymes that control EFG1 expression. CONCLUSION: Based on our modeling and experimental results we conclude that C. albicans cells sense the local environment of the GI tract and respond to differences by altering EFG1 expression to establish optimal survival strategies. We show that the overall process is governed via modulation of epigenetic regulators of chromatin structure.

Dosage and Dose Schedule Screening of Drug Combinations in Agent-Based Models Reveals Hidden Synergies.

The fungus Candida albicans is the most common causative agent of human fungal infections and better drugs or drug combination strategies are urgently needed. Here, we present an agent-based model of the interplay of C. albicans with the host immune system and with the microflora of the host. We took into account the morphological change of C. albicans from the yeast to hyphae form and its dynamics during infection. The model allowed us to follow the dynamics of fungal growth and morphology, of the immune cells and of microflora in different perturbing situations. We specifically focused on the consequences of microflora reduction following antibiotic treatment. Using the agent-based model, different drug types have been tested for their effectiveness, namely drugs that inhibit cell division and drugs that constrain the yeast-to-hyphae transition. Applied individually, the division drug turned out to successfully decrease hyphae while the transition drug leads to a burst in hyphae after the end of the treatment. To evaluate the effect of different drug combinations, doses, and schedules, we introduced a measure for the return to a healthy state, the infection score. Using this measure, we found that the addition of a transition drug to a division drug treatment can improve the treatment reliability while minimizing treatment duration and drug dosage. In this work we present a theoretical study. Although our model has not been calibrated to quantitative experimental data, the technique of computationally identifying synergistic treatment combinations in an agent based model exemplifies the importance of computational techniques in translational research.

Assessing the advantage of morphological changes in Candida albicans: a game theoretical study.

A range of attributes determines the virulence of human pathogens. During interactions with their hosts, pathogenic microbes often undergo transitions between distinct stages, and the ability to switch between these can be directly related to the disease process. Understanding the mechanisms and dynamics of these transitions is a key factor in understanding and combating infectious diseases. The human fungal pathogen Candida albicans exhibits different morphotypes at different stages during the course of infection (candidiasis). For example, hyphae are considered to be the invasive form, which causes tissue damage, while yeast cells are predominant in the commensal stage. Here, we described interactions of C. albicans with its human host in a game theoretic model. In the game, players are fungal cells. Each fungal cell can adopt one of the two strategies: to exist as a yeast or hyphal cell. We characterized the ranges of model parameters in which the coexistence of both yeast and hyphal forms is plausible. Stability analysis of the system showed that, in theory, a reduced ability of the host to specifically recognize yeast and hyphal cells can result in bi-stability of the microbial populations' profile. Inspired by the model analysis we reasoned that the types of microbial interactions can change during invasive candidiasis. We found that positive cooperation among fungal cells occurs in mild infections and an enhanced tendency to invade the host is associated with negative cooperation. The model can easily be extended to multi-player systems with direct application to identifying individuals that enhance either positive or negative cooperation. Results of the modeling approach have potential application in developing treatment strategies.

Modelling the regulation of thermal adaptation in Candida albicans, a major fungal pathogen of humans.

Eukaryotic cells have evolved mechanisms to sense and adapt to dynamic environmental changes. Adaptation to thermal insults, in particular, is essential for their survival. The major fungal pathogen of humans, Candida albicans, is obligately associated with warm-blooded animals and hence occupies thermally buffered niches. Yet during its evolution in the host it has retained a bona fide heat shock response whilst other stress responses have diverged significantly. Furthermore the heat shock response is essential for the virulence of C. albicans. With a view to understanding the relevance of this response to infection we have explored the dynamic regulation of thermal adaptation using an integrative systems biology approach. Our mathematical model of thermal regulation, which has been validated experimentally in C. albicans, describes the dynamic autoregulation of the heat shock transcription factor Hsf1 and the essential chaperone protein Hsp90. We have used this model to show that the thermal adaptation system displays perfect adaptation, that it retains a transient molecular memory, and that Hsf1 is activated during thermal transitions that mimic fever. In addition to providing explanations for the evolutionary conservation of the heat shock response in this pathogen and the relevant of this response to infection, our model provides a platform for the analysis of thermal adaptation in other eukaryotic cells.