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The ability to generate genetic variation facilitates rapid adaptation in stressful environments. The opportunistic fungal pathogen Candida albicans frequently undergoes large-scale genomic changes, including aneuploidy and loss-of heterozygosity (LOH), following exposure to host environments. However, the specific host factors inducing C. albicans genome instability remain largely unknown. Here, we leveraged the genetic tractability of nematode hosts to investigate whether innate immune components, including antimicrobial peptides (AMPs) and reactive oxygen species (ROS), induced host-associated C. albicans genome instability. C. albicans associated with immunocompetent hosts carried multiple large-scale genomic changes including LOH, whole chromosome, and segmental aneuploidies. In contrast, C. albicans associated with immunocompromised hosts deficient in AMPs or ROS production had reduced LOH frequencies and fewer, if any, additional genomic changes. To evaluate if extensive host-induced genomic changes had long-term consequences for C. albicans adaptation, we experimentally evolved C. albicans in either immunocompetent or immunocompromised hosts and selected for increased virulence. C. albicans evolved in immunocompetent hosts rapidly increased virulence, but not in immunocompromised hosts. Taken together, this work suggests that host-produced ROS and AMPs induces genotypic plasticity in C. albicans which facilitates rapid evolution. 

ABSTRACT Candida albicans is an opportunistic fungal pathogen of humans that is typically diploid yet has a highly labile genome tolerant of large-scale perturbations including chromosomal aneuploidy and loss-of-heterozygosity events. The ability to rapidly generate genetic variation is crucial for C. albicans to adapt to changing or stressful environments, like those encountered in the host. Genetic variation occurs via stress-induced mutagenesis or can be generated through its parasexual cycle, in which tetraploids arise via diploid mating or stress-induced mitotic defects and undergo nonmeiotic ploidy reduction. However, it remains largely unknown how genetic background contributes to C. albicans genome instability in vitro or in the host environment. Here, we tested how genetic background, ploidy, and the host environment impacts C. albicans genome stability. We found that host association induced both loss-of-heterozygosity events and genome size changes, regardless of genetic background or ploidy. However, the magnitude and types of genome changes varied across C. albicans strain background and ploidy state. We then assessed if host-induced genomic changes resulted in fitness consequences on growth rate and nonlethal virulence phenotypes and found that many host-derived isolates significantly changed relative to their parental strain. Interestingly, diploid host-associated C. albicans predominantly decreased host reproductive fitness, whereas tetraploid host-associated C. albicans increased host reproductive fitness. Together, these results are important for understanding how host-induced genomic changes in C. albicans alter its relationship with the host. IMPORTANCE Candida albicans is an opportunistic fungal pathogen of humans. The ability to generate genetic variation is essential for adaptation and is a strategy that C. albicans and other fungal pathogens use to change their genome size. Stressful environments, including the host, induce C. albicans genome instability. Here, we investigated how C. albicans genetic background and ploidy state impact genome instability, both in vitro and in a host environment. We show that the host environment induces genome instability, but the magnitude depends on C. albicans genetic background. Furthermore, we show that tetraploid C. albicans is highly unstable in host environments and rapidly reduces in genome size. These reductions in genome size often resulted in reduced virulence. In contrast, diploid C. albicans displayed modest host-induced genome size changes, yet these frequently resulted in increased virulence. Such studies are essential for understanding how opportunistic pathogens respond and potentially adapt to the host environment. 

Crowdsourcing has become a popular means to solicit assistance for scientific research. From classifying images or texts to responding to surveys, tapping into the knowledge of crowds to complete complex tasks has become a common strategy in social and information sciences. Although the timeliness and cost-effectiveness of crowdsourcing may provide desirable advantages to researchers, the data it generates may be of lower quality for some scientific purposes. The quality control mechanisms, if any, offered by common crowdsourcing platforms may not provide robust measures of data quality. This study explores whether research task participants may engage in motivated misreporting whereby participants tend to cut corners to reduce their workload while performing various scientific tasks online. We conducted an experiment with three common crowdsourcing tasks: answering surveys, coding images, and classifying online social media content. The experiment recruited workers from three sources: a crowdsourcing platform (Amazon Mechanical Turk) and a commercial online survey panel. The analysis seeks to address the following two questions: (1) whether online panelists or crowd workers may engage in motivated misreporting differently and (2) whether the patterns of misreporting vary by different task types. The study focuses on the analysis of the experiment in answering surveys and offers quality assurance practice guideline of using crowdsourcing for social science research. 

Observations of the Cosmic Microwave Background rely on cryogenic instrumentation with cold detectors, readout, and optics providing the low noise performance and instrumental stability required to make more sensitive measurements. It is therefore critical to optimize all aspects of the cryogenic design to achieve the necessary performance, with low temperature components and acceptable system cooling requirements. In particular, we will focus on our use of thermal filters and cold optics, which reduce the thermal load passed along to the cryogenic stages. To test their performance, we have made a series of in situ measurements while integrating the third receiver for the BICEP Array telescope. In addition to characterizing the behavior of this receiver, these measurements continue to refine the models that are being used to inform design choices being made for future instruments. 

The gap between chronological age (CA) and biological brain age, as estimated from magnetic resonance images (MRIs), reflects how individual patterns of neuroanatomic aging deviate from their typical trajectories. MRI-derived brain age (BA) estimates are often obtained using deep learning models that may perform relatively poorly on new data or that lack neuroanatomic interpretability. This study introduces a convolutional neural network (CNN) to estimate BA after training on the MRIs of 4,681 cognitively normal (CN) participants and testing on 1,170 CN participants from an independent sample. BA estimation errors are notably lower than those of previous studies. At both individual and cohort levels, the CNN provides detailed anatomic maps of brain aging patterns that reveal sex dimorphisms and neurocognitive trajectories in adults with mild cognitive impairment (MCI, N  = 351) and Alzheimer’s disease (AD, N  = 359). In individuals with MCI (54% of whom were diagnosed with dementia within 10.9 y from MRI acquisition), BA is significantly better than CA in capturing dementia symptom severity, functional disability, and executive function. Profiles of sex dimorphism and lateralization in brain aging also map onto patterns of neuroanatomic change that reflect cognitive decline. Significant associations between BA and neurocognitive measures suggest that the proposed framework can map, systematically, the relationship between aging-related neuroanatomy changes in CN individuals and in participants with MCI or AD. Early identification of such neuroanatomy changes can help to screen individuals according to their AD risk. 

In the Alzheimer’s disease (AD) continuum, the prodromal state of mild cognitive impairment (MCI) precedes AD dementia and identifying MCI individuals at risk of progression is important for clinical management. Our goal was to develop generalizable multivariate models that integrate high-dimensional data (multimodal neuroimaging and cerebrospinal fluid biomarkers, genetic factors, and measures of cognitive resilience) for identification of MCI individuals who progress to AD within 3 years. Our main findings were i) we were able to build generalizable models with clinically relevant accuracy (~93%) for identifying MCI individuals who progress to AD within 3 years; ii) markers of AD pathophysiology (amyloid, tau, neuronal injury) accounted for large shares of the variance in predicting progression; iii) our methodology allowed us to discover that expression ofCR1(complement receptor 1), an AD susceptibility gene involved in immune pathways, uniquely added independent predictive value. This work highlights the value of optimized machine learning approaches for analyzing multimodal patient information for making predictive assessments.