Autophagy and Inflammation.

The cellular degradative pathway of autophagy has a fundamental role in immunity. Here, we review the function of autophagy and autophagy proteins in inflammation. We discuss how the autophagy machinery controls the burden of infectious agents while simultaneously limiting inflammatory pathologies, which often involves processes that are distinct from conventional autophagy. Among the newly emerging processes we describe are LC3-associated phagocytosis and targeting by autophagy proteins, both of which require many of the same proteins that mediate conventional autophagy. We also discuss how autophagy contributes to differentiation of myeloid and lymphoid cell types, coordinates multicellular immunity, and facilitates memory responses. Together, these functions establish an intimate link between autophagy, mucosal immunity, and chronic inflammatory diseases. Finally, we offer our perspective on current challenges and barriers to translation.

An atlas of human vector-borne microbe interactions reveals pathogenicity mechanisms.

Vector-borne diseases are a leading cause of death worldwide and pose a substantial unmet medical need. Pathogens binding to host extracellular proteins (the "exoproteome") represents a crucial interface in the etiology of vector-borne disease. Here, we used bacterial selection to elucidate host-microbe interactions in high throughput (BASEHIT)-a technique enabling interrogation of microbial interactions with 3,324 human exoproteins-to profile the interactomes of 82 human-pathogen samples, including 30 strains of arthropod-borne pathogens and 8 strains of related non-vector-borne pathogens. The resulting atlas revealed 1,303 putative interactions, including hundreds of pairings with potential roles in pathogenesis, including cell invasion, tissue colonization, immune evasion, and host sensing. Subsequent functional investigations uncovered that Lyme disease spirochetes recognize epidermal growth factor as an environmental cue of transcriptional regulation and that conserved interactions between intracellular pathogens and thioredoxins facilitate cell invasion. In summary, this interactome atlas provides molecular-level insights into microbial pathogenesis and reveals potential host-directed targets for next-generation therapeutics.

The immunobiology of interleukin-27.

Interleukin-27 (IL-27) is a cytokine with strikingly diverse influences on the immune response. Although it was initially linked with the development of Th1 responses, it is now recognized as a potent antagonist of different classes of inflammation through its ability to directly modify CD4(+) and CD8(+) T cell effector functions, to induce IL-10, and to promote specialized T regulatory cell responses. Although this aspect of IL-27 biology has provided insights into how the immune system prevents hyperactivity in the setting of infectious and autoimmune inflammation, in vaccination and cancer models the stimulatory effects of IL-27 on CD8(+) T cell function appear prominent. Additionally, associations between IL-27 and antibody-mediated disease have led to an interest in defining the impact of IL-27 on innate immunity and humoral responses in different disease states. The maturation of this literature has been accompanied by attempts to translate these findings from experimental models into human diseases and by efforts to define where IL-27 might represent a viable therapeutic target.

Mortality Risk Profiling of Staphylococcus aureus Bacteremia by Multi-omic Serum Analysis Reveals Early Predictive and Pathogenic Signatures.

Staphylococcus aureus bacteremia (SaB) causes significant disease in humans, carrying mortality rates of ∼25%. The ability to rapidly predict SaB patient responses and guide personalized treatment regimens could reduce mortality. Here, we present a resource of SaB prognostic biomarkers. Integrating proteomic and metabolomic techniques enabled the identification of >10,000 features from >200 serum samples collected upon clinical presentation. We interrogated the complexity of serum using multiple computational strategies, which provided a comprehensive view of the early host response to infection. Our biomarkers exceed the predictive capabilities of those previously reported, particularly when used in combination. Last, we validated the biological contribution of mortality-associated pathways using a murine model of SaB. Our findings represent a starting point for the development of a prognostic test for identifying high-risk patients at a time early enough to trigger intensive monitoring and interventions.

Travel Surveillance and Genomics Uncover a Hidden Zika Outbreak during the Waning Epidemic.

The Zika epidemic in the Americas has challenged surveillance and control. As the epidemic appears to be waning, it is unclear whether transmission is still ongoing, which is exacerbated by discrepancies in reporting. To uncover locations with lingering outbreaks, we investigated travel-associated Zika cases to identify transmission not captured by reporting. We uncovered an unreported outbreak in Cuba during 2017, a year after peak transmission in neighboring islands. By sequencing Zika virus, we show that the establishment of the virus was delayed by a year and that the ensuing outbreak was sparked by long-lived lineages of Zika virus from other Caribbean islands. Our data suggest that, although mosquito control in Cuba may initially have been effective at mitigating Zika virus transmission, such measures need to be maintained to be effective. Our study highlights how Zika virus may still be "silently" spreading and provides a framework for understanding outbreak dynamics. VIDEO ABSTRACT.

Differential plasticity and fate of brain-resident and recruited macrophages during the onset and resolution of neuroinflammation.

Microglia and border-associated macrophages (BAMs) are brain-resident self-renewing cells. Here, we examined the fate of microglia, BAMs, and recruited macrophages upon neuroinflammation and through resolution. Upon infection, Trypanosoma brucei parasites invaded the brain via its border regions, triggering brain barrier disruption and monocyte infiltration. Fate mapping combined with single-cell sequencing revealed microglia accumulation around the ventricles and expansion of epiplexus cells. Depletion experiments using genetic targeting revealed that resident macrophages promoted initial parasite defense and subsequently facilitated monocyte infiltration across brain barriers. These recruited monocyte-derived macrophages outnumbered resident macrophages and exhibited more transcriptional plasticity, adopting antimicrobial gene expression profiles. Recruited macrophages were rapidly removed upon disease resolution, leaving no engrafted monocyte-derived cells in the parenchyma, while resident macrophages progressively reverted toward a homeostatic state. Long-term transcriptional alterations were limited for microglia but more pronounced in BAMs. Thus, brain-resident and recruited macrophages exhibit diverging responses and dynamics during infection and resolution.

Deubiquitinases: From mechanisms to their inhibition by small molecules.

Deubiquitinases (DUBs) are specialized proteases that remove ubiquitin from substrates or cleave within ubiquitin chains to regulate ubiquitylation and therefore play important roles in eukaryotic biology. Dysregulation of DUBs is implicated in several human diseases, highlighting the importance of DUB function. In addition, many pathogenic bacteria and viruses encode and deploy DUBs to manipulate host immune responses and establish infectious diseases in humans and animals. Hence, therapeutic targeting of DUBs is an increasingly explored area that requires an in-depth mechanistic understanding of human and pathogenic DUBs. In this review, we summarize the multiple layers of regulation that control autoinhibition, activation, and substrate specificity of DUBs. We discuss different strategies to inhibit DUBs and the progress in developing selective small-molecule DUB inhibitors. Finally, we propose a classification system of DUB inhibitors based on their mode of action.

Longitudinal Multi-omics Analyses Identify Responses of Megakaryocytes, Erythroid Cells, and Plasmablasts as Hallmarks of Severe COVID-19.

Temporal resolution of cellular features associated with a severe COVID-19 disease trajectory is needed for understanding skewed immune responses and defining predictors of outcome. Here, we performed a longitudinal multi-omics study using a two-center cohort of 14 patients. We analyzed the bulk transcriptome, bulk DNA methylome, and single-cell transcriptome (>358,000 cells, including BCR profiles) of peripheral blood samples harvested from up to 5 time points. Validation was performed in two independent cohorts of COVID-19 patients. Severe COVID-19 was characterized by an increase of proliferating, metabolically hyperactive plasmablasts. Coinciding with critical illness, we also identified an expansion of interferon-activated circulating megakaryocytes and increased erythropoiesis with features of hypoxic signaling. Megakaryocyte- and erythroid-cell-derived co-expression modules were predictive of fatal disease outcome. The study demonstrates broad cellular effects of SARS-CoV-2 infection beyond adaptive immune cells and provides an entry point toward developing biomarkers and targeted treatments of patients with COVID-19.

Liquid-Liquid Phase Separation in Disease.

We have made rapid progress in recent years in identifying the genetic causes of many human diseases. However, despite this recent progress, our mechanistic understanding of these diseases is often incomplete. This is a problem because it limits our ability to develop effective disease treatments. To overcome this limitation, we need new concepts to describe and comprehend the complex mechanisms underlying human diseases. Condensate formation by phase separation emerges as a new principle to explain the organization of living cells. In this review, we present emerging evidence that aberrant forms of condensates are associated with many human diseases, including cancer, neurodegeneration, and infectious diseases. We examine disease mechanisms driven by aberrant condensates, and we point out opportunities for therapeutic interventions. We conclude that phase separation provides a useful new framework to understand and fight some of the most severe human diseases.

Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism Mycobacterium tuberculosis (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Acceleration of infectious disease drug discovery and development using a humanized model of drug metabolism.

A key step in drug discovery, common to many disease areas, is preclinical demonstration of efficacy in a mouse model of disease. However, this demonstration and its translation to the clinic can be impeded by mouse-specific pathways of drug metabolism. Here, we show that a mouse line extensively humanized for the cytochrome P450 gene superfamily ("8HUM") can circumvent these problems. The pharmacokinetics, metabolite profiles, and magnitude of drug-drug interactions of a test set of approved medicines were in much closer alignment with clinical observations than in wild-type mice. Infection with Mycobacterium tuberculosis, Leishmania donovani, and Trypanosoma cruzi was well tolerated in 8HUM, permitting efficacy assessment. During such assessments, mouse-specific metabolic liabilities were bypassed while the impact of clinically relevant active metabolites and DDI on efficacy were well captured. Removal of species differences in metabolism by replacement of wild-type mice with 8HUM therefore reduces compound attrition while improving clinical translation, accelerating drug discovery.

Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects.

Genome-wide association studies (GWASs) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common control subjects from biobanks and extensive consortia is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the control subjects are not well characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of affected subjects to population-based common control subjects regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen-exposed cases and population-based common control subjects, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well-characterized control subjects and population-based common control subjects from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance associations. These findings suggest that the choice of control subjects is especially important for infectious diseases or outcomes that are conditional upon environmental exposures.

Phylogenetic inference of inter-population transmission rates for infectious diseases.

Estimating transmission rates is a challenging yet essential aspect of comprehending and controlling the spread of infectious diseases. Various methods exist for estimating transmission rates, each with distinct assumptions, data needs, and constraints. This study introduces a novel phylogenetic approach called transRate, which integrates genetic information with traditional epidemiological approaches to estimate inter-population transmission rates. The phylogenetic method is statistically consistent as the sample size (i.e. the number of pathogen genomes) approaches infinity under the multi-population susceptible-infected-recovered model. Simulation analyses indicate that transRate can accurately estimate the transmission rate with a sample size of 200 ~ 400 pathogen genomes. Using transRate, we analyzed 40,028 high-quality sequences of SARS-CoV-2 in human hosts during the early pandemic. Our analysis uncovered significant transmission between populations even before widespread travel restrictions were implemented. The development of transRate provides valuable insights for scientists and public health officials to enhance their understanding of the pandemic's progression and aiding in preparedness for future viral outbreaks. As public databases for genomic sequences continue to expand, transRate is increasingly vital for tracking and mitigating the spread of infectious diseases.

Virulence and Pathogenicity of Chytrid Fungi Causing Amphibian Extinctions.

Ancient enzootic associations between wildlife and their infections allow evolution to innovate mechanisms of pathogenicity that are counterbalanced by host responses. However, erosion of barriers to pathogen dispersal by globalization leads to the infection of hosts that have not evolved effective resistance and the emergence of highly virulent infections. Global amphibian declines driven by the rise of chytrid fungi and chytridiomycosis are emblematic of emerging infections. Here, we review how modern biological methods have been used to understand the adaptations and counteradaptations that these fungi and their amphibian hosts have evolved. We explore the interplay of biotic and abiotic factors that modify the virulence of these infections and dissect the complexity of this disease system. We highlight progress that has led to insights into how we might in the future lessen the impact of these emerging infections.

Three common assumptions about inflammation, aging, and health that are probably wrong.

Chronic inflammation contributes to the onset and progression of cardiovascular disease and other degenerative diseases of aging. But does it have to? This article considers the associations among inflammation, aging, and health through the lens of human population biology and suggests that chronic inflammation is not a normal nor inevitable component of aging. It is commonly assumed that conclusions drawn from research in affluent, industrialized countries can be applied globally; that aging processes leading to morbidity and mortality begin in middle age; and that inflammation is pathological. These foundational assumptions have shifted focus away from inflammation as a beneficial response to infection or injury and toward an understanding of inflammation as chronic, dysregulated, and dangerous. Findings from community-based studies around the world-many conducted in areas with relatively high burdens of infectious disease-challenge these assumptions by documenting substantial variation in levels of inflammation and patterns of association with disease. They also indicate that nutritional, microbial, and psychosocial environments in infancy and childhood play important roles in shaping inflammatory phenotypes and their contributions to diseases of aging. A comparative, developmental, and ecological approach has the potential to generate novel insights into the regulation of inflammation and how it relates to human health over the life course.

Multimodeling approach to evaluating the efficacy of layering pharmaceutical and nonpharmaceutical interventions for influenza pandemics.

When an influenza pandemic emerges, temporary school closures and antiviral treatment may slow virus spread, reduce the overall disease burden, and provide time for vaccine development, distribution, and administration while keeping a larger portion of the general population infection free. The impact of such measures will depend on the transmissibility and severity of the virus and the timing and extent of their implementation. To provide robust assessments of layered pandemic intervention strategies, the Centers for Disease Control and Prevention (CDC) funded a network of academic groups to build a framework for the development and comparison of multiple pandemic influenza models. Research teams from Columbia University, Imperial College London/Princeton University, Northeastern University, the University of Texas at Austin/Yale University, and the University of Virginia independently modeled three prescribed sets of pandemic influenza scenarios developed collaboratively by the CDC and network members. Results provided by the groups were aggregated into a mean-based ensemble. The ensemble and most component models agreed on the ranking of the most and least effective intervention strategies by impact but not on the magnitude of those impacts. In the scenarios evaluated, vaccination alone, due to the time needed for development, approval, and deployment, would not be expected to substantially reduce the numbers of illnesses, hospitalizations, and deaths that would occur. Only strategies that included early implementation of school closure were found to substantially mitigate early spread and allow time for vaccines to be developed and administered, especially under a highly transmissible pandemic scenario.

Two-speed genome evolution drives pathogenicity in fungal pathogens of animals.

The origins and evolution of virulence in amphibian-infecting chytrids Batrachochytrium dendrobatidis (Bd) and Batrachochytrium salamandrivorans (Bsal) are largely unknown. Here, we use deep nanopore sequencing of Bsal and comparative genomics against 21 high-quality genome assemblies that span the fungal Chytridiomycota. We discover that Bsal has the most repeat-rich genome of the Chytridiomycota, comprising 40.9% repetitive elements; this genome has expanded to more than 3× the length of its conspecific Bd, with autonomous and fully functional LTR/Gypsy elements contributing significantly to the expansion. The M36 metalloprotease virulence factors are highly expanded (n = 177) in Bsal, most of which (53%) are flanked by transposable elements, suggesting they have a repeat-associated expansion. We find enrichment upstream of M36 metalloprotease genes of three novel repeat families belonging to the repeat superfamily of LINEs that are implicated with gene copy number variations. Additionally, Bsal has a highly compartmentalized genome architecture, with virulence factors enriched in gene-sparse/repeat-rich compartments, while core conserved genes are enriched in gene-rich/repeat-poor compartments. Genes upregulated during infection are primarily found in the gene-sparse/repeat-rich compartment in both Bd and Bsal. Furthermore, genes with signatures of positive selection in Bd are enriched in repeat-rich regions, suggesting these regions are a cradle for the evolution of chytrid pathogenicity. These are the hallmarks of two-speed genome evolution, and this study provides evidence of two-speed genomes in an animal pathogen, shedding light on the evolution of fungal pathogens of vertebrates driving global declines and extinctions.

Upregulation of Robo4 expression by SMAD signaling suppresses vascular permeability and mortality in endotoxemia and COVID-19 models.

There is an urgent need to develop novel drugs to reduce the mortality from severe infectious diseases with the emergence of new pathogens, including Coronavirus disease 2019 (COVID-19). Although current drugs effectively suppress the proliferation of pathogens, immune cell activation, and inflammatory cytokine functions, they cannot completely reduce mortality from severe infections and sepsis. In this study, we focused on the endothelial cell-specific protein, Roundabout 4 (Robo4), which suppresses vascular permeability by stabilizing endothelial cells, and investigated whether enhanced Robo4 expression could be a novel therapeutic strategy against severe infectious diseases. Endothelial-specific overexpression of Robo4 suppresses vascular permeability and reduces mortality in lipopolysaccharide (LPS)-treated mice. Screening of small molecules that regulate Robo4 expression and subsequent analysis revealed that two competitive small mothers against decapentaplegic (SMAD) signaling pathways, activin receptor-like kinase 5 (ALK5)-SMAD2/3 and ALK1-SMAD1/5, positively and negatively regulate Robo4 expression, respectively. An ALK1 inhibitor was found to increase Robo4 expression in mouse lungs, suppress vascular permeability, prevent extravasation of melanoma cells, and decrease mortality in LPS-treated mice. The inhibitor suppressed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-induced endothelial barrier disruption and decreased mortality in mice infected with SARS-CoV-2. These results indicate that enhancing Robo4 expression is an efficient strategy to suppress vascular permeability and mortality in severe infectious diseases, including COVID-19, and that small molecules that upregulate Robo4 can be potential therapeutic agents against these diseases.

Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations.

Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and have often lacked transparency in terms of prioritization of false-positive versus false-negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false-negative versus false-positive signals. This approach allows policymakers to optimize metrics for specific preferences and interventions. Second, we propose a method to update risk thresholds in real time. We show that this adaptive approach to designating areas as "high risk" improves performance over static metrics in predicting 3-wk-ahead mortality and intensive care usage at both state and county levels. We also demonstrate that with our approach, using only new hospital admissions to predict 3-wk-ahead mortality and intensive care usage has performed consistently as well as metrics that also include cases and inpatient bed usage. Our results highlight that a key challenge for COVID-19 risk prediction is the changing relationship between indicators and outcomes of policy interest. Adaptive metrics therefore have a unique advantage in a rapidly evolving pandemic context.

Mutation rates and adaptive variation among the clinically dominant clusters of Mycobacterium abscessus.

Mycobacterium abscessus (Mab) is a multidrug-resistant pathogen increasingly responsible for severe pulmonary infections. Analysis of whole-genome sequences (WGS) of Mab demonstrates dense genetic clustering of clinical isolates collected from disparate geographic locations. This has been interpreted as supporting patient-to-patient transmission, but epidemiological studies have contradicted this interpretation. Here, we present evidence for a slowing of the Mab molecular clock rate coincident with the emergence of phylogenetic clusters. We performed phylogenetic inference using publicly available WGS from 483 Mab patient isolates. We implement a subsampling approach in combination with coalescent analysis to estimate the molecular clock rate along the long internal branches of the tree, indicating a faster long-term molecular clock rate compared to branches within phylogenetic clusters. We used ancestry simulation to predict the effects of clock rate variation on phylogenetic clustering and found that the degree of clustering in the observed phylogeny is more easily explained by a clock rate slowdown than by transmission. We also find that phylogenetic clusters are enriched in mutations affecting DNA repair machinery and report that clustered isolates have lower spontaneous mutation rates in vitro. We propose that Mab adaptation to the host environment through variation in DNA repair genes affects the organism's mutation rate and that this manifests as phylogenetic clustering. These results challenge the model that phylogenetic clustering in Mab is explained by person-to-person transmission and inform our understanding of transmission inference in emerging, facultative pathogens.