Biology subjects

Lopez, J. C.

Publications and source records attributed to Lopez, J. C..

3 recordsLinked to original sources

bioRxiv2025

MechanoAge, a machine learning platform to identify individuals susceptible to breast cancer based on mechanical properties of single cells

BackgroundExisting breast cancer risk models inadequately identify individuals at latent risk, particularly among women without known genetic mutations or family history. Risk is often underestimated or overestimated due to reliance on population-level data and neglect of cellular aging and mechanobiological alterations. MethodsWe profiled primary human mammary epithelial cells (HMECs) from women of varying ages and risk backgrounds using mechano-node-pore sensing (mechano-NPS), a high-throughput microfluidic platform that captures single-cell mechanical properties. Using machine learning, we developed a classifier, MechanoAge, to predict age-related mechanical phenotypes and introduce a novel index, mechano-RISQ, to quantify deviations linked to breast cancer risk. We further assessed the cytoskeletal protein keratin 14 (KRT14) as a molecular mediator of these mechanical states through overexpression and knockdown experiments. FindingsCells from younger women carrying BRCA1/2 mutations or with a family history of breast cancer exhibited accelerated mechanical aging compared to age-matched controls. Elevated mechano-RISQ scores reflected an increased proportion of cells with "older" mechanical profiles. KRT14 overexpression induced an aged mechanical phenotype in younger cells, while knockdown partially reversed this state in older cells. CyTOF profiling and modeling showed KRT14 modulation impacted protein expression signatures associated with aging and risk, particularly in luminal cells. InterpretationMechanical properties of breast epithelial cells reflect biologic aging and cancer susceptibility. Mechano-RISQ offers a new approach for identifying individuals at elevated risk, especially among average-risk populations, and may complement existing risk models by incorporating biophysical measures of epithelial aging.

cancer biology↗

bioRxiv2021

Surprise-induced enhancements in the associability of Pavlovian cues facilitate learning across behavior systems

Surprising violations of outcome expectancies have long been known to enhance the associability of Pavlovian cues; that is, the rate at which the cue enters into further associations. The adaptive value of such enhancements resides in promoting new learning in the face of uncertainty. However, it is unclear whether associability enhancements reflect increased associative plasticity within a particular behavior system, or whether they can facilitate learning between a cue and any arbitrary outcome, as suggested by attentional models of conditioning. Here, we show evidence consistent with the latter hypothesis. Violating the outcome expectancies generated by a cue in an appetitive setting (feeding behavior system) facilitated subsequent learning about the cue in an aversive setting (defense behavior system). In addition to shedding light on the nature of associability enhancements, our findings offer the neuroscientist a behavioral tool to dissociate their neural substrates from those of other, behavior system- or valence-specific changes. Moreover, our results present an opportunity to utilize associability enhancements to the advantage of counterconditioning procedures in therapeutic contexts.

animal behavior and cognition↗

bioRxiv2021

Deep Proteome Profiling of Human Mammary Epithelia at Lineage and Age Resolution

Age is the major risk factor in most carcinomas, yet little is known about how proteomes change with age in any human epithelium. We present comprehensive proteomes comprised of >9,000 total proteins, and >15,000 phosphopeptides, from normal primary human mammary epithelia at lineage resolution from ten women ranging in age from 19 to 68. Data were quality controlled, and results were biologically validated with cell-based assays. Age-dependent protein signatures were identified using differential expression analyses and weighted protein co-expression network analyses. Up-regulation of basal markers in luminal cells, including KRT14 and AXL, were a prominent consequence of aging. PEAK1 was identified as an age-dependent signaling kinase in luminal cells, which revealed a potential age-dependent vulnerability for targeted ablation. Correlation analyses between transcriptome and proteome revealed age-associated loss of proteostasis regulation. Protein expression and phosphorylation changes in the aging breast epithelium identify potential therapeutic targets for reducing breast cancer susceptibility.

cell biology↗