School of Medicine

The high-temperature requirement A1 (HTRA1), a serine protease, has been demonstrated to play a pivotal role in the extracellular matrix (ECM) and has been reported to be associated with the pathogenesis of age-related macular degeneration (AMD). To delineate its role in the retina, the phenotype of homozygous Htra1-KO (Htra1-/-) mice was characterized to examine the effect of Htra1 loss on the retina and retinal pigment epithelium (RPE) with age. The ablation of Htra1 led to a significant reduction in rod and cone photoreceptor function, primary cone abnormalities followed by rods, and atrophy in the RPE compared with WT mice. Ultrastructural analysis of Htra1-/- mice revealed RPE and Bruch's membrane (BM) abnormalities, including the presence of sub-RPE deposits at 5 months (m) that progressed with age accompanied by increased severity of pathology. Htra1-/- mice also displayed alterations in key markers for inflammation, autophagy, and lipid metabolism in the retina. These results highlight the crucial role of HTRA1 in the retina and RPE. Furthermore, this study allows for the Htra1-/- mouse model to be utilized for deciphering mechanisms that lead to sub-RPE deposit phenotypes including AMD.

Smoking plays an underappreciated role in breast cancer progression, increasing recurrence and mortality in patients. Here, we show that S100A8/A9 innate immune signaling is a molecular mechanism that identifies smoking-related breast cancers and underlies their enhanced malignancy. In contrast to acute exposure, chronic nicotine increased tumorigenicity and reprogrammed breast cancer cells to express innate immune response genes. This required the α7 nicotinic acetylcholine receptor, which elicited dynamic changes in cell differentiation, proliferation, and expression of secreted cytokines, such as S100A8 and S100A9, as assessed by unbiased scRNA-seq. Indeed, pharmacologic or genetic inhibition of S100A8/A9-RAGE receptor signaling blocked nicotine's tumor-promoting effects. We also discovered Syntaphilin (SNPH) as an S100A8/A9-dependent gene enriched specifically in estrogen receptor-negative (ER^-) cancers from former smokers, linking this response to patient disease. Together, our findings describe a new α7 nAChR-S100A8/A9-Syntaphilin immune signaling module that drives nicotine-induced tumor progression and distinguishes smoking-related patient disease as a distinct subset of aggressive breast cancers.

Measuring virus in biofluids is complicated by confounding biomolecules coisolated with viral nucleic acids. To address this, we developed an affinity-based microfluidic device for specific capture of intact severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our approach used an engineered angiotensin-converting enzyme 2 to capture intact virus from plasma and other complex biofluids. Our device leverages a staggered herringbone pattern, nanoparticle surface coating, and processing conditions to achieve detection of as few as 3 viral copies per milliliter. We further validated our microfluidic assay on 103 plasma, 36 saliva, and 29 stool samples collected from unique patients with COVID-19, showing SARS-CoV-2 detection in 72% of plasma samples. Longitudinal monitoring in the plasma revealed our device's capacity for ultrasensitive detection of active viral infections over time. Our technology can be adapted to target other viruses using relevant cell entry molecules for affinity capture. This versatility underscores the potential for widespread application in viral load monitoring and disease management.

Significance

Measurement of the rate-dependent depression (RDD) of the Hoffmann (H) reflex, a technique developed over half a century ago, is founded on repeated stimulation of the H-reflex with tracking of sequentially evoked H-wave amplitudes in the resulting electromyogram. RDD offers insight into the integrity of spinal reflex pathways and spinal inhibitory regulation. Initially, RDD was predominantly utilized in the mechanistic exploration and evaluation of movement disorders characterized by spasticity symptoms, as may occur following spinal cord injury. However, there is increasing recognition that sensory input from the periphery is modified at the spinal level before ascending to the higher central nervous system and that some pain states can arise from, or be exaggerated by, disruption of spinal processing via a mechanism termed spinal disinhibition. This, along with the urgent clinical need to identify biological markers of pain generator and/or amplifier sites to facilitate targeted pain therapies, has prompted interest in RDD as a biomarker for the contribution of spinal disinhibition to neuropathic pain states. Current research in animals and humans with diabetes has revealed specific disorders of spinal GABAergic function associated with impaired RDD. Future investigations on RDD aim to further elucidate its underlying pathways and enhance its clinical applications.

Extracellular vesicles (EVs) mediate intercellular communication in many physiologic processes and can modulate immune responses in individuals with cancer. Most studies of EVs in cancer have focused on their tumour promoting properties. Whether and how EVs might mediate tumour regression besides carrying antigens has not been well characterized. Using a mouse model of highly immunogenic regressor versus poorly immunogenic progressor tumour cells, we have characterized the role of EVs in activating macrophages and promoting tumour rejection. We found that the signalling molecule MAP2K1 (MEK1) is enriched in EVs secreted by regressor relative to progressor cells. Progressor EVs engineered to have levels of MEK1 similar to regressor EVs could inhibit tumour growth by indirectly promoting adaptive immunity in both syngeneic and 3rd party tumours. This effect required MEK1 activity and could occur by activating macrophages to promote adaptive immune responses against the tumour via the cytokine interferon-gamma. Our results suggest that MEK inhibition may be deleterious to cancer treatment, since MEK1 plays an important cell-extrinsic, tumour-suppressive role within EVs. Moreover, the delivery of MEK1 to tumour-associated macrophages, either by EVs, nanoparticles, or some other means, could be a useful strategy to treat cancer via the activation of anti-tumour immunity.

Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has yielded an abundance of candidate biomarkers. However, translating such findings into bona fide clinical biomarkers remains challenging. To facilitate this process, we introduce Stabl, a general machine learning method that identifies a sparse, reliable set of biomarkers by integrating noise injection and a data-driven signal-to-noise threshold into multivariable predictive modeling. Evaluation of Stabl on synthetic datasets and five independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used sparsity-promoting regularization methods while maintaining predictive performance; it distills datasets containing 1,400-35,000 features down to 4-34 candidate biomarkers. Stabl extends to multi-omic integration tasks, enabling biological interpretation of complex predictive models, as it hones in on a shortlist of proteomic, metabolomic and cytometric events predicting labor onset, microbial biomarkers of pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https://github.com/gregbellan/Stabl .

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Despite a critical role for tumor-initiating cancer stem cells (CSCs) in breast cancer progression, major questions remain about the properties and signaling pathways essential for their function. Recent discoveries highlighting mechanisms of CSC-resistance to the stress caused by chromosomal instability (CIN) may provide valuable new insight into the underlying forces driving stemness properties. While stress tolerance is a well-known attribute of CSCs, CIN-induced stress is distinctive since levels appear to increase during tumor initiation and metastasis. These dynamic changes in CIN levels may serve as a barrier constraining the effects of non-CSCs and shaping the stemness landscape during the early stages of disease progression. In contrast to most other stresses, CIN can also paradoxically activate pro-tumorigenic antiviral signaling. Though seemingly contradictory, this may indicate that mechanisms of CIN tolerance and pro-tumorigenic inflammatory signaling closely collaborate to define the CSC state. Together, these unique features may form the basis for a critical relationship between CIN and stemness properties.

Purpose To develop and validate a deep learning (DL) method to detect and segment enhancing and nonenhancing cellular tumor on pre- and posttreatment MRI scans in patients with glioblastoma and to predict overall survival (OS) and progression-free survival (PFS). Materials and Methods This retrospective study included 1397 MRI scans in 1297 patients with glioblastoma, including an internal set of 243 MRI scans (January 2010 to June 2022) for model training and cross-validation and four external test cohorts. Cellular tumor maps were segmented by two radiologists on the basis of imaging, clinical history, and pathologic findings. Multimodal MRI data with perfusion and multishell diffusion imaging were inputted into a nnU-Net DL model to segment cellular tumor. Segmentation performance (Dice score) and performance in distinguishing recurrent tumor from posttreatment changes (area under the receiver operating characteristic curve [AUC]) were quantified. Model performance in predicting OS and PFS was assessed using Cox multivariable analysis. Results A cohort of 178 patients (mean age, 56 years ± 13 [SD]; 116 male, 62 female) with 243 MRI timepoints, as well as four external datasets with 55, 70, 610, and 419 MRI timepoints, respectively, were evaluated. The median Dice score was 0.79 (IQR, 0.53-0.89), and the AUC for detecting residual or recurrent tumor was 0.84 (95% CI: 0.79, 0.89). In the internal test set, estimated cellular tumor volume was significantly associated with OS (hazard ratio [HR] = 1.04 per milliliter; P < .001) and PFS (HR = 1.04 per milliliter; P < .001) after adjustment for age, sex, and gross total resection (GTR) status. In the external test sets, estimated cellular tumor volume was significantly associated with OS (HR = 1.01 per milliliter; P < .001) after adjustment for age, sex, and GTR status. Conclusion A DL model incorporating advanced imaging could accurately segment enhancing and nonenhancing cellular tumor, distinguish recurrent or residual tumor from posttreatment changes, and predict OS and PFS in patients with glioblastoma. Keywords: Segmentation, Glioblastoma, Multishell Diffusion MRI Supplemental material is available for this article. © RSNA, 2024.