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Serratia marcescens is a healthcare-associated pathogen that can cause severe infections including bacteremia and pneumonia. The capsule polysaccharide of S. marcescens is a bacteremia fitness determinant and previous work defined capsule locus (KL) diversity within the species. Strains belonging to KL1 and KL2 capsule clades produce sialylated polysaccharides and represent the largest subpopulation of isolates from clinical origin. In this study, the contribution of these and other S. marcescens capsules to infection was determined in animal and cellular models. Using a murine model of primary bacteremia, clinical isolates of multiple KL types demonstrated capsule-dependent colonization of spleen, liver, and kidney following tail vein inoculation. Similar results were observed using a bacteremic pneumonia model, in that all tested strains of clinical origin demonstrated a requirement for capsule in both the primary lung infection site and for bloodstream dissemination to secondary organs. Finally, capsule from each KL clade was examined for the ability to resist internalization by bone marrow-derived macrophages. Only the sialylated KL1 and KL2 clade strains exhibited capsule-dependent inhibition of internalization, including KL2 capsule produced in a heterologous background. Together these findings indicate that lineage-specific resistance to macrophage phagocytosis may enhance survival and antibacterial defenses of clinically-adapted S. marcescens.

Isolation-by-distance patterns in genetic variation are a widespread feature of the geographic structure of genetic variation in many species, and many methods have been developed to illuminate such patterns in genetic data. However, long-range genetic similarities also exist, often as a result of rare or episodic long-range gene flow. Jointly characterizing patterns of isolation-by-distance and long-range genetic similarity in genetic data is an open data analysis challenge that, if resolved, could help produce more complete representations of the geographic structure of genetic data in any given species. Here, we present a computationally tractable method that identifies long-range genetic similarities in a background of spatially heterogeneous isolation-by-distance variation. The method uses a coalescent-based framework, and models long-range genetic similarity in terms of directional events with source fractions describing the fraction of ancestry at a location tracing back to a remote source. The method produces geographic maps annotated with inferred long-range edges, as well as maps of uncertainty in the geographic location of each source of long-range gene flow. We have implemented the method in a package called FEEMSmix (an extension to FEEMS from Marcus et al 2021), and validated its implementation using simulations representative of typical data applications. We also apply this method to two empirical data sets. In a data set of over 4,000 humans (Homo sapiens) across Afro-Eurasia, we recover many known signals of long-distance dispersal from recent centuries. Similarly, in a data set of over 100 gray wolves (Canis lupus) across North America, we identify several previously unknown long-range connections, some of which were attributable to recording errors in sampling locations. Therefore, beyond identifying genuine long-range dispersals, our approach also serves as a useful tool for quality control in spatial genetic studies.

The gut microbiota, an extensive ecosystem harboring trillions of bacteria, plays a pivotal role in human health and disease, influencing diverse conditions from obesity to cancer. Among the microbiota's myriad functions, the capacity to metabolize drugs remains relatively unexplored despite its potential implications for drug efficacy and toxicity. Experimental methods are resource-intensive, prompting the need for innovative computational approaches. We present a computational analysis, termed MDM, aimed at predicting gut microbiota-mediated drug metabolism. This computational analysis incorporates data from diverse sources, e.g., UHGG, MagMD, MASI, KEGG, and RetroRules. An existing tool, PROXIMAL2, is used iteratively over all drug candidates from experimental databases queried against biotransformation rules from RetroRules to predict potential drug metabolites along with the enzyme commission number responsible for that biotransformation. These potential metabolites are then categorized into gut MDM metabolites by cross referencing UHGG. The analysis' efficacy is validated by its coverage on each of the experimental databases in the gut microbial context, being able to recall up to 74% of experimental data and producing a list of potential metabolites, of which an average of about 65% are relevant to the gut microbial context. Moreover, explorations into ranking metabolites, iterative applications to account for multi-step metabolic pathways, and potential applications in experimental studies showcase its versatility and potential impact beyond raw predictions. Overall, this study presents a promising computational framework for further research and applications gut MDM, drug development and human health.

Peptidoglycan synthesis is an essential driver of bacterial growth and division. The final steps of this crucial process involve the activity of the SEDS family glycosyltransferases that polymerize glycan strands and the class B penicillin-binding protein (bPBP) transpeptidases that cross-link them. While many bacteria encode multiple bPBPs to perform specialized roles during specific cellular processes, some bPBPs can play redundant roles that are important for resistance against certain cell wall stresses. Our understanding of these compensatory mechanisms, however, remains incomplete. Endospore-forming bacteria typically encode multiple bPBPs that drive morphological changes required for sporulation. The sporulation-specific bPBP, SpoVD, is important for synthesizing the asymmetric division septum and spore cortex peptidoglycan during sporulation in the pathogen Clostridioides difficile . Although SpoVD catalytic activity is essential for cortex synthesis, we show that it is unexpectedly dispensable for SpoVD to mediate asymmetric division. The dispensability of SpoVD's catalytic activity requires the presence of its SEDS partner, SpoVE, and is facilitated by another sporulation-induced bPBP, PBP3. Our data further suggest that PBP3 interacts with components of the asymmetric division machinery, including SpoVD. These findings suggest a possible mechanism by which bPBPs can be functionally redundant in diverse bacteria and facilitate antibiotic resistance.

A glaucoma polygenic risk score (PRS) can effectively identify disease risk, but some individuals with high PRS do not develop glaucoma. Factors contributing to this resilience remain unclear. Using 4,658 glaucoma cases and 113,040 controls in a cross-sectional study in the UK Biobank, we investigated whether plasma metabolites enhanced glaucoma prediction and if a metabolomic signature of resilience in high-genetic risk individuals existed. Logistic regression models incorporating 168 NMR-based metabolites into PRS-based glaucoma assessments were developed, with multiple comparison corrections applied. While metabolites weakly predicted glaucoma (Area Under the Curve=0.579), they offered modest prediction improvement in PRS-only-based models (P=0.004). We identified a metabolomic signature associated with resilience in the top PRS decile, with elevated glycolysis-related metabolites- lactate (P=8.8E-12), pyruvate (P=1.9E-10), and citrate (P=0.02)- linked to reduced glaucoma prevalence. These metabolites combined significantly modified the PRS-glaucoma relationship (P _interaction =0.011). Higher total resilience metabolite levels within the highest PRS quartile corresponded to lower glaucoma prevalence (Odds Ratio _{highest vs. lowest total resilience metabolite quartile} =0.71, 95% Confidence Interval =0.64-0.80). As pyruvate is a foundational metabolite linking glycolysis to tricarboxylic acid cycle metabolism and ATP generation, we pursued experimental validation for this putative resilience biomarker in a human-relevant Mus musculus glaucoma model. Dietary pyruvate mitigated elevated intraocular pressure (P=0.002) and optic nerve damage (P<0.0003) in Lmx1b ^V265D mice. These findings highlight the protective role of pyruvate-related metabolism against glaucoma and suggest potential avenues for therapeutic intervention.

Neuroendocrine prostate cancer (NEPC) arises primarily through neuroendocrine transdifferentiation (NEtD) as an adaptive mechanism of therapeutic resistance. Models to define the functional effects of putative drivers of this process on androgen receptor (AR) signaling and NE cancer lineage programs are lacking. We adapted a genetically defined strategy from the field of cellular reprogramming to directly convert AR-active prostate cancer (ARPC) to AR-independent NEPC using candidate factors. We delineated critical roles of the pioneer factors ASCL1 and NeuroD1 in NEtD and uncovered their abilities to silence AR expression and signaling by remodeling chromatin at the somatically acquired AR enhancer and global AR binding sites with enhancer activity. We also elucidated the dynamic temporal changes in the transcriptomic and epigenomic landscapes of cells undergoing acute lineage conversion from ARPC to NEPC which should inform future therapeutic development. Further, we distinguished the activities of ASCL1 and NeuroD1 from the inactivation of RE-1 silencing transcription factor (REST), a master suppressor of a major neuronal gene program, in establishing a NEPC lineage state and in modulating the expression of genes associated with major histocompatibility complex class I (MHC I) antigen processing and presentation. These findings provide important, clinically relevant insights into the biological processes driving NEtD of prostate cancer.

Understanding the effects of missense mutations or single amino acid variants (SAVs) on protein function is crucial for elucidating the molecular basis of diseases/disorders and designing rational therapies. We introduce here Rhapsody-2, a machine learning tool for discriminating pathogenic and neutral SAVs, significantly expanding on a precursor limited by the availability of structural data. With the advent of AlphaFold2 as a powerful tool for structure prediction, Rhapsody-2 is trained on a significantly expanded dataset of 117,525 SAVs corresponding to 12,094 human proteins reported in the ClinVar database. Adopting a broad set of descriptors composed of sequence evolutionary, structural, dynamic, and energetics features in the training algorithm, Rhapsody-2 achieved an AUROC of 0.94 in 10-fold cross-validation when all SAVs of a particular test protein (mutant) were excluded from the training set. Benchmarking against a variety of testing datasets demonstrated the high performance of Rhapsody-2. While sequence evolutionary descriptors play a dominant role in pathogenicity prediction, those based on structural dynamics provide a mechanistic interpretation. Notably, residues involved in allosteric communication, and those distinguished by pronounced fluctuations in the high frequency modes of motion or subject to spatial constraints in soft modes usually give rise to pathogenicity when mutated. Overall, Rhapsody-2 provides an efficient and transparent tool for accurately predicting the pathogenicity of SAVs and unraveling the mechanistic basis of the observed behavior, thus advancing our understanding of genotype-to-phenotype relations.

Rhesus macaques (RMs) are a vital model for studying human disease and invaluable to pre-clinical vaccine research, particularly for the study of broadly neutralizing antibody responses. Such studies require robust genetic resources for antibody-encoding genes within the immunoglobulin (IG) loci. The complexity of the IG loci has historically made them challenging to characterize accurately. To address this, we developed novel experimental and computational methodologies to generate the largest collection to date of integrated antibody repertoire and long-read genomic sequencing data in 106 Indian origin RMs. We created a comprehensive resource of IG heavy and light chain variable (V), diversity (D), and joining (J) alleles, as well as leader, intronic, and recombination signal sequences (RSSs), including the curation of 1474 novel alleles, unveiling tremendous diversity, and expanding existing IG allele sets by 60%. This publicly available, continually updated resource (https://vdjbase.org/reference_book/Rhesus_Macaque) provides the foundation for advancing RM immunogenomics, vaccine discovery, and translational research.

Rationale: IL-33 is a key driver of type 2 inflammation relevant to airway epithelial biology. However, the mechanisms for IL-33 secretion and regulation in the context of chronic airway disease is poorly understood.

Objectives: We sought to define how a disease associated isoform IL-33d34 that escapes nuclear sequestration and is tonically secreted from epithelial cells can be recruited to non-canonical secretory pathways.

Methods: IL-33d34 interaction with HSP70 was assessed and validated by affinity purification, mass-spectrometry and miniTurboID proximity labeling. Secretion and activity reporter assays were used to probe the effect of HSP70 on epithelial IL-33d34 secretion and receptor binding. Human airway disease biospecimens were analyzed for dysregulation of heat shock pathways revealing modulation of TCP1 complex intermediates.

Measurements and main results: We confirmed that HSP70 interacts directly with IL-33d34, recruits the cytokine to a vesicular compartment and enhances stability upon secretion. IL-33, HSP70 and other key mediators of proteostasis were found to be dysregulated in airway disease biospecimens and secreted extracellular vesicles. The IL-33d34 interactome was characterized and novel secretion modulators were identified.

Conclusions: This study confirms a role for HSP70 in non-canonical IL-33d34 secretion and function that may be amenable for therapeutic targeting in airway diseases.

Sleep and pregnancy are contentious bedfellows; sleep disorders and disturbances are associated with adverse pregnancy outcomes, although much is still unknown about this relationship. Sleep and pregnancy have been studied in many models, but most focus heavily on mammals. However, pregnancy is ubiquitous across the animal kingdom - a hallmark of convergent evolution; similarly sleep is a shared feature across diverse species. Here, we present an ideal model in which to study the dynamics between sleep and pregnancy in invertebrates. The Pacific beetle mimic cockroach, Diploptera punctata, is a viviparous cockroach species that uses milk proteins to nourish its young with a broodsac over a three month pregnancy. However, little is known about the relationship between this unique reproductive biology and daily rhythms of activity and sleep. We established that D. punctata displayed a peak in activity shortly following sunset, with males significantly more active than females. When scavenging behavior was examined, males and non-pregnant females emerged more often and traveled further from a shelter compared to pregnant females, suggesting reduced risk-taking behavior in late pregnancy. Chronic disturbance of sleep during pregnancy negatively impacted embryo development by increasing gestational duration and decreasing the transcription of milk proteins. These findings indicate that sleep is key to embryo development and that pregnancy has a significant impact on the daily rhythms of activity in Diploptera punctata. More broadly, we present a tractable invertebrate model for understanding the relationship between sleep and pregnancy, which will aid in the exploration of the poorly understood interface between these two ubiquitous and highly conserved traits.