School of Medicine

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.

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UNLABELLED: Gastrointestinal diseases are the most frequently reported clinical problems in captive common marmosets (Callithrix jacchus), often affecting the health and welfare of the animal and ultimately their use as a research subject. The microbiome has been shown to be intimately connected to diet and gastrointestinal health. Here, we use shotgun metagenomics and untargeted metabolomics in fecal samples of common marmosets collected before, during, and after a dietary transition from a biscuit to a gel diet. The overall health of marmosets, measured as weight recovery and reproductive outcome, improved after the diet transition. Moreover, each marmoset pair had significant shifts in the microbiome and metabolome after the diet transition. In general, we saw a decrease in Escherichia coli and Prevotella species and an increase in Bifidobacterium species. Untargeted metabolic profiles indicated that polyamine levels, specifically cadaverine and putrescine, were high after diet transition, suggesting either an increase in excretion or a decrease in intestinal reabsorption at the intestinal level. In conclusion, our data suggest that Bifidobacterium species could potentially be useful as probiotic supplements to the laboratory marmoset diet. Future studies with a larger sample size will be beneficial to show that this is consistent with the diet change. IMPORTANCE: Appropriate diet and health of the common marmoset in captivity are essential both for the welfare of the animal and to improve experimental outcomes. Our study shows that a gel diet compared to a biscuit diet improves the health of a marmoset colony, is linked to increases in Bifidobacterium species, and increases the removal of molecules associated with disease. The diet transition had an influence on the molecular changes at both the pair and time point group levels, but only at the pair level for the microbial changes. It appears to be more important which genes and functions present changed rather than specific microbes. Further studies are needed to identify specific components that should be considered when choosing an appropriate diet and additional supplementary foods, as well as to validate the benefits of providing probiotics. Probiotics containing Bifidobacterium species appear to be useful as probiotic supplements to the laboratory marmoset diet, but additional work is needed to validate these findings.

Genome analysis of individuals affected by retinitis pigmentosa (RP) identified two rare nucleotide substitutions at the same genomic location on chromosome 11 (g.61392563 [GRCh38]), 69 base pairs upstream of the start codon of the ciliopathy gene TMEM216 (c.-69G>A, c.-69G>T [GenBank: NM_001173991.3]), in individuals of South Asian and African ancestry, respectively. Genotypes included 71 homozygotes and 3 mixed heterozygotes in trans with a predicted loss-of-function allele. Haplotype analysis showed single-nucleotide variants (SNVs) common across families, suggesting ancestral alleles within the two distinct ethnic populations. Clinical phenotype analysis of 62 available individuals from 49 families indicated a similar clinical presentation with night blindness in the first decade and progressive peripheral field loss thereafter. No evident systemic ciliopathy features were noted. Functional characterization of these variants by luciferase reporter gene assay showed reduced promotor activity. Nanopore sequencing confirmed the lower transcription of the TMEM216 c.-69G>T allele in blood-derived RNA from a heterozygous carrier, and reduced expression was further recapitulated by qPCR, using both leukocytes-derived RNA of c.-69G>T homozygotes and total RNA from genome-edited hTERT-RPE1 cells carrying homozygous TMEM216 c.-69G>A. In conclusion, these variants explain a significant proportion of unsolved cases, specifically in individuals of African ancestry, suggesting that reduced TMEM216 expression might lead to abnormal ciliogenesis and photoreceptor degeneration.

Patterns of transcriptional activity are encoded in our genome through regulatory elements such as promoters or enhancers that, paradoxically, contain similar assortments of sequence-specific transcription factor (TF) binding sites^1-3. Knowledge of how these sequence motifs encode multiple, often overlapping, gene expression programs is central to understanding gene regulation and how mutations in non-coding DNA manifest in disease^4,5. Here, by studying gene regulation from the perspective of individual transcription start sites (TSSs), using natural genetic variation, perturbation of endogenous TF protein levels and massively parallel analysis of natural and synthetic regulatory elements, we show that the effect of TF binding on transcription initiation is position dependent. Analysing TF-binding-site occurrences relative to the TSS, we identified several motifs with highly preferential positioning. We show that these patterns are a combination of a TF's distinct functional profiles-many TFs, including canonical activators such as NRF1, NFY and Sp1, activate or repress transcription initiation depending on their precise position relative to the TSS. As such, TFs and their spacing collectively guide the site and frequency of transcription initiation. More broadly, these findings reveal how similar assortments of TF binding sites can generate distinct gene regulatory outcomes depending on their spatial configuration and how DNA sequence polymorphisms may contribute to transcription variation and disease and underscore a critical role for TSS data in decoding the regulatory information of our genome.