Diverse Subpopulations Research Articles

A physical mechanism of heterogeneity and micro-metastasis in stem cells, cancer cells, and cancer stem cells.

Heterogeneity is ubiquitous in stem cells (SC), cancer cells (CS), and cancer SC (CSC). This heterogeneity manifests in the form of diverse sub-populations of SC, CS, and CSC with self-renewal and unique regeneration capacity. Moreover, the progeny of CSC possess multiple plasticity and cancerous characteristics. Many studies have demonstrated that cancer heterogeneity is among the greatest obstacles to successful anti-cancer therapy. This leads to incomplete therapy and transitory efficacy. Furthermore, numerous micro-metastases lead to the spread of tumor cells widely across the body; this is the beginning of metastasis. Epigenetic processes (DNA methylation and histone remodification) represent sources of heterogeneity. In this study, we develop a mathematical model to quantify the heterogeneity of SC, CS, and CSC, taking into consideration both genetic and epigenetic effects. We reveal the roles and physical mechanisms of heterogeneity in SC, CSC, and cancer cells. Under the adiabatic regime (relatively fast regulatory binding and effective coupling among genes), seven native states (SC, CSC, cancer, premalignant, normal, lesion, and hyperplasia) emerge. Under the non-adiabatic regime (relatively slow regulatory binding and effective weak coupling among genes), SC, CS, CSC, and differentiated states emerge and become diffusive, partially explaining the origin of heterogeneity. In other words, slow regulatory binding that mimics epigenetic effects can give rise to heterogeneity. Moreover, we calculated the entropy production rate and Fano factor, which can be used to quantify the thermodynamic cost and the degrees of the variations or the fluctuations as the parameter ω (representing the speed of regulatory binding/unbinding relative to the synthesis/degradation) changes. Elucidating the origins of heterogeneity and the dynamical relationships between intra-tumoral cells has a clear clinical significance and will improve the understanding of the cellular basis of treatment response, therapeutic resistance, and cancer metastasis.

Natural Killer Cells: the Missing Link in Effective Treatment for High-Grade Serous Ovarian Carcinoma.

Ovarian cancer (OC), especially high-grade serous cancer (HGSC), is a highly heterogeneous malignancy with limited options for curative treatment and a high frequency of relapse. Interactions between OC and the immune system may permit immunoediting and immune escape, and current standard of care therapies can influence immune cell infiltration and function within the tumor microenvironment. Natural killer (NK) cells are involved in cancer immunosurveillance and immunoediting and can be activated by therapy, but deliberate approaches to maximize NK cell reactivity for treatment of HGSC are in their infancy. NK cells may be the ideal target for immunotherapy of HGSC. The diverse functions of NK cells, and their established roles in immunosurveillance, make them attractive candidates for more precise and effective HGSC treatment. NK cells' functional capabilities differ because of variation in receptor expression and genetics, with meaningful impacts on their anticancer activity. Studying HGSC:NK cell interactions will define the features that predict the best outcomes for patients with the disease, but the highly diverse nature of HGSC will likely require combination therapies or approaches to simultaneously target multiple, co-existing features of the tumor to avoid tumor escape and relapse. We expect that the ideal therapy will enable NK cell infiltration and activity, reverse immunosuppression within the tumor microenvironment, and enable effector functions against the diverse subpopulations that comprise HGSC.

Association between Phenotypic Age and Mortality in Patients with Multivessel Coronary Artery Disease.

Background Chronological age (CA) is not a perfect proxy for the true biological aging status of the body. A new biological aging measure, phenotypic age (PhenoAge), has been shown to capture morbidity and mortality risk in the general US population and diverse subpopulations. This study was aimed at evaluating the association between PhenoAge and long-term outcome of patients with multivessel coronary artery disease (CAD). Methods A total of 609 multivessel CAD patients who received PCI attempt and with follow-up were enrolled. The clinical outcome was all-cause mortality on follow-up. PhenoAge was calculated using an equation constructed from CA and 9 clinical biomarkers. Cox proportional hazards regression models and receiver operating characteristic (ROC) curves were performed to evaluate the association between PhenoAge and mortality. Results Overall, patients with more diseases had older PhenoAge and phenotypic age acceleration (PhenoAgeAccel). After a median follow-up of 33.5 months, those with positive PhenoAgeAccel had a significantly higher incidence of all-cause mortality (P = 0.001). After adjusting for CA, Cox proportional hazards models showed that both PhenoAge and PhenoAgeAccel were significantly associated with all-cause mortality. Even after further adjusting for confounding factors, each 10-year increase in PhenoAge was also associated with a 51% increased mortality risk. ROC curves revealed that PhenoAge, with an area under the curve of 0.705, significantly outperformed CA, the individual clinical chemistry measure, and other risk factors. When reexamining the ROC curves using various combinations of variables, we found that PhenoAge provides additional predictive power to all models. Conclusions In conclusion, PhenoAge was strongly associated with all-cause mortality even after adjusting for CA. Our findings suggest that PhenoAge measure may be complementary in predicting mortality risk for patients with multivessel CAD.

A Network Analysis Investigation of Disordered Eating Across Demographic and Developmental Subpopulations Using a National Epidemiological Sample of High School Students

Disordered eating (DE) poses a large societal burden, yet limited research has examined DE from a developmental epidemiological perspective. It is important to consider how demographics influence DE symptoms to inform prevention and early intervention programs across diverse subpopulations. Therefore, we conducted network analyses using a large nationally representative epidemiological sample of high school students (Youth Risk Behavior Survey, United States; n = 59,582) to identify the most important symptoms and symptom relationships among six DE behaviors. We compared networks by sex, grade, and race to identify differences in symptom networks. Dieting for weight loss was highly central across networks. Networks significantly differed across sex, grade, and race. Our results suggest that dieting for weight loss may be an early intervention target for eating disorders, regardless of demographic and developmental factors. In addition, sex, race, and age should be accounted for when researching and developing prevention programs for DE and eating disorders. Public health officials, as well as mental health professionals, should present a more balanced message about dieting and weight loss to high school students to prevent the detrimental impact of DE on physical and mental health. Notably, this study is the first large, nationwide epidemiological sample using DE symptoms in network analysis.

Telehealth use in cystic fibrosis during COVID-19: Association with race, ethnicity, and socioeconomic factors

BackgroundDue to the COVID-19 pandemic, there was an uptake of telehealth in cystic fibrosis care. Previous studies show disparities in telehealth use based on socioeconomic status (SES). We aimed to: (1) understand telehealth use and perceptions and (2) identify the facilitators and barriers to telehealth use among people with CF and their families (PwCF) from diverse racial/ethnic and socioeconomic backgrounds. MethodsWe conducted an analysis of the 2020 Cystic Fibrosis State of Care surveys completed by PwCF (PFSoC), CF Care Programs (SoC1) and the CF Foundation Patient Registry (CFFPR). ResultsA total of 424 PwCF and 286 programs responded to the PFSoC and SoC1. Among PwCF, 90% self-identified as White, 6% as Hispanic/Latino, and 2% as Black. Racial/ethnic minorities were less likely to have had a telehealth visit (p=.015). This difference was pronounced among the Hispanic/Latino population (p<.01). Telehealth use did not differ by health insurance and was similarly offered independent of financial status. Compared to PwCF who denied financial constraints, those who reported financial difficulties found telehealth more difficult to use (p=.018) and were less likely to think that their concerns (p=.010) or issues that mattered most to them (p=.020) were addressed during telehealth. Programs perceived lack of technology, language barriers, and home conditions as barriers to telehealth in vulnerable populations. ConclusionPFSoC and SoC1 identified differences in telehealth use and care perceptions by ethnicity, race, and socioeconomic characteristics. Further studies are needed to understand how telehealth can change access to CF care in diverse subpopulations.

Phenotype correlations reveal the relationships of physiological systems underlying human ageing.

Ageing is characterized by degeneration and loss of function across multiple physiological systems. To study the mechanisms and consequences of ageing, several metrics have been proposed in a hierarchical model, including biological, phenotypic and functional ageing. In particular, phenotypic ageing and interconnected changes in multiple physiological systems occur in all ageing individuals over time. Recently, phenotypic age, a new ageing measure, was proposed to capture morbidity and mortality risk across diverse subpopulations in US cohort studies. Although phenotypic age has been widely used, it may overlook the complex relationships among phenotypic biomarkers. Considering the correlation structure of these phenotypic biomarkers, we proposed a composite phenotype analysis (CPA) strategy to analyse 71 biomarkers from 2074 individuals in the Rugao Longitudinal Ageing Study. CPA grouped these biomarkers into 18 composite phenotypes according to their internal correlation, and these composite phenotypes were mostly consistent with prior findings. In addition, compared with prior findings, this strategy exhibited some different yet important implications. For example, the indicators of kidney and cardiovascular functions were tightly connected, implying internal interactions. The composite phenotypes were further verified through associations with functional metrics of ageing, including disability, depression, cognitive function and frailty. Compared to age alone, these composite phenotypes had better predictive performances for functional metrics of ageing. In summary, CPA could reveal the hidden relationships of physiological systems and identify the links between physiological systems and functional ageing metrics, thereby providing novel insights into potential mechanisms underlying human ageing.

Heterogeneity of Cancer Stem Cells in Tumorigenesis, Metastasis, and Resistance to Antineoplastic Treatment of Head and Neck Tumours.

The discovery of a small subset of cancer cells with self-renewal properties that can give rise to phenotypically diverse tumour populations has shifted our understanding of cancer biology. Targeting cancer stem cells (CSCs) is becoming a promising therapeutic strategy in various malignancies, including head and neck squamous cell carcinoma (HNSCC). Diverse sub-populations of head and neck cancer stem cells (HNCSCs) have been identified previously using CSC specific markers, the most common being CD44, Aldehyde Dehydrogenase 1 (ALDH1), and CD133, or by side population assays. Interestingly, distinct HNCSC subsets play different roles in the generation and progression of tumours. This article aims to review the evidence for a role of specific CSCs in HNSCC tumorigenesis, invasion, and metastasis, together with resistance to treatment.

Single-Cell RNA Sequencing of Bone Marrow Mesenchymal Stem Cells from the Elderly People

Background and ObjectivesBone marrow mesenchymal stem cells (BMSCs) show considerable promise in regenerative medicine. Many studies demonstrated that BMSCs cultured in vitro were highly heterogeneous and composed of diverse cell subpopulations, which may be the basis of their multiple biological characteristics. However, the exact cell subpopulations that make up BMSCs are still unknown.Methods and ResultsIn this study, we used single-cell RNA sequencing (scRNA-Seq) to divide 6,514 BMSCs into three clusters. The number and corresponding proportion of cells in clusters 1 to 3 were 3,766 (57.81%), 1,720 (26.40%), and 1,028 (15.78%). The gene expression profile and function of the cells in the same cluster were similar. The vast majority of cells expressed the markers defining BMSCs by flow cytometry and gene expression analysis. Each cluster had at least 20 differentially expressed genes (DEGs). We conducted Gene Ontology enrichment analysis on the top 20 DEGs of each cluster and found that the three clusters had different functions, which were related to self-renewal, multilineage differentiation and cytokine secretion, respectively. In addition, the function of the top 20 DEGs of each cluster was checked by the National Center for Biotechnology Information gene database to further verify our hypothesis.ConclusionsThis study indicated that scRNA-Seq can be used to divide BMSCs into different subpopulations, demonstrating the heterogeneity of BMSCs.

Multi-omic approach identifies a transcriptional network coupling innate immune response to proliferation in the blood of COVID-19 cancer patients

Clinical outcomes of COVID-19 patients are worsened by the presence of co-morbidities, especially cancer leading to elevated mortality rates. SARS-CoV-2 infection is known to alter immune system homeostasis. Whether cancer patients developing COVID-19 present alterations of immune functions which might contribute to worse outcomes have so far been poorly investigated. We conducted a multi-omic analysis of immunological parameters in peripheral blood mononuclear cells (PBMCs) of COVID-19 patients with and without cancer. Healthy donors and SARS-CoV-2-negative cancer patients were also included as controls. At the infection peak, cytokine multiplex analysis of blood samples, cytometry by time of flight (CyTOF) cell population analyses, and Nanostring gene expression using Pancancer array on PBMCs were performed. We found that eight pro-inflammatory factors (IL-6, IL-8, IL-13, IL-1ra, MIP-1a, IP-10) out of 27 analyzed serum cytokines were modulated in COVID-19 patients irrespective of cancer status. Diverse subpopulations of T lymphocytes such as CD8+T, CD4+T central memory, Mucosal-associated invariant T (MAIT), natural killer (NK), and γδ T cells were reduced, while B plasmablasts were expanded in COVID-19 cancer patients. Our findings illustrate a repertoire of aberrant alterations of gene expression in circulating immune cells of COVID-19 cancer patients. A 19-gene expression signature of PBMCs is able to discriminate COVID-19 patients with and without solid cancers. Gene set enrichment analysis highlights an increased gene expression linked to Interferon α, γ, α/β response and signaling which paired with aberrant cell cycle regulation in cancer patients. Ten out of the 19 genes, validated in a real-world consecutive cohort, were specific of COVID-19 cancer patients independently from different cancer types and stages of the diseases, and useful to stratify patients in a COVID-19 disease severity-manner. We also unveil a transcriptional network involving gene regulators of both inflammation response and proliferation in PBMCs of COVID-19 cancer patients.

Nucleus Reuniens Afferents in Hippocampus Modulate CA1 Network Function via Monosynaptic Excitation and Polysynaptic Inhibition.

The thalamic midline nucleus reuniens modulates hippocampal CA1 and subiculum function via dense projections to the stratum lacunosum-moleculare (SLM). Previously, anatomical data has shown that reuniens inputs in the SLM form synapses with dendrites of both CA1 principal cells and inhibitory interneurons. However, the ability of thalamic inputs to excite the CA1 principal cells remains controversial. In addition, nothing is known about the impact of reuniens inputs on diverse subpopulations of interneurons in CA1. Therefore, using whole cell patch-clamp electrophysiology in ex vivo hippocampal slices of wild-type and transgenic mice, we measured synaptic responses in different CA1 neuronal subtypes to optogenetic stimulation of reuniens afferents. Our data shows that reuniens inputs mediate both excitation and inhibition of the CA1 principal cells. However, the optogenetic excitation of the reuniens inputs failed to drive action potential firing in the majority of the principal cells. While the excitatory postsynaptic currents were mediated via direct monosynaptic activation of the CA1 principal cells, the inhibitory postsynaptic currents were generated polysynaptically via activation of local GABAergic interneurons. Moreover, we demonstrate that optogenetic stimulation of reuniens inputs differentially recruit at least two distinct and non-overlapping subpopulations of local GABAergic interneurons in CA1. We show that neurogliaform cells located in SLM, and calretinin-containing interneuron-selective interneurons at the SLM/stratum radiatum border can be excited by stimulation of reuniens inputs. Together, our data demonstrate that optogenetic stimulation of reuniens afferents can mediate excitation, feedforward inhibition, and disinhibition of the postsynaptic CA1 principal cells via multiple direct and indirect mechanisms.

Genetic Risk Prediction of COVID-19 Susceptibility and Severity in the Indian Population.

Host genetic variants can determine their susceptibility to COVID-19 infection and severity as noted in a recent Genome-wide Association Study (GWAS). Given the prominent genetic differences in Indian sub-populations as well as differential prevalence of COVID-19, here, we compute genetic risk scores in diverse Indian sub-populations that may predict differences in the severity of COVID-19 outcomes. We utilized the top 100 most significantly associated single-nucleotide polymorphisms (SNPs) from a GWAS by Pairo-Castineira et al. determining the genetic susceptibility to severe COVID-19 infection, to compute population-wise polygenic risk scores (PRS) for populations represented in the Indian Genome Variation Consortium (IGVC) database. Using a generalized linear model accounting for confounding variables, we found that median PRS was significantly associated (p < 2 x 10−16) with COVID-19 mortality in each district corresponding to the population studied and had the largest effect on mortality (regression coefficient = 10.25). As a control we repeated our analysis on randomly selected 100 non-associated SNPs several times and did not find significant association. Therefore, we conclude that genetic susceptibility may play a major role in determining the differences in COVID-19 outcomes and mortality across the Indian sub-continent. We suggest that combining PRS with other observed risk-factors in a Bayesian framework may provide a better prediction model for ascertaining high COVID-19 risk groups and to design more effective public health resource allocation and vaccine distribution schemes.

Do naloxone access laws affect perceived risk of heroin use? Evidence from national US data.

Whether expanded access to naloxone reduces perceptions of risk about opioid use has been subject to debate. Our aim was to assess how implementation of naloxone access laws shapes perceived risk of heroin use. Using data from the restricted-access National Survey on Drug Use and Health, Prescription Drug Abuse Policy System and the US Census, we applied two-way fixed-effects models to determine whether naloxone access laws decreased perceived risk of any heroin use or regular heroin use. We used Bayes factors (BFs) to confirm evidence for null findings. United States. A total of 884 800 respondents aged 12 and older from 2004 to 2016. A binary indicator of whether a state implemented naloxone access laws was regressed on respondent-perceived risk of (1) any heroin use and (2) regular heroin use. Ratings of perceived risk were assessed on a scale of 1 (none) to 4 (great risk). In all instances, the BFs support evidence for the null hypothesis. Across models with three distinct specifications of naloxone access laws, we found no evidence of decreased risk perceptions, as confirmed by BFs ranging from 0.009 to 0.057. Across models of specific vulnerable subgroups, such as people who use opioids (BFs = 0.039-0.225) or young people (BFs = 0.009-0.158), we found no evidence of decreased risk perceptions. Across diverse subpopulations by gender (BFs = 0.011-0.083), socio-economic status (BFs = 0.015-0.168) or race/ethnicity (BFs = 0.016-0.094), we found no evidence of decreased risk perceptions. There appears to be no empirical evidence that implementation of naloxone access laws has adversely affected perceptions of risk of heroin in the broader US population or within vulnerable subgroups or diverse subpopulations.

Social Support, Isolation, Loneliness, and Health Among Older Adults in the PRISM Randomized Controlled Trial.

Objectives: Social isolation and loneliness are serious public health issues given the association with negative physical, mental; and cognitive health outcomes and increased risk for mortality. Due to changes in life circumstances many aging adults are socially isolated and experience loneliness. We examined the relationships among four correlated but distinct constructs: social network size, social support, social isolation, and loneliness as they relate to indices of health and wellbeing among diverse subpopulations of older adults. Guided by WHO’s International Classification of Functioning, Disability and Health (ICF) we also examined factors that predict loneliness and social isolation.Methods: Analyses of baseline data from sample of older adults who participated in an intervention trial that examined the beneficial effects of a software system designed to support access to resources and information, and social connectivity. Participants included 300 individuals aged 65–98, who lived alone, were primarily of lower socio-economic status and ethnically diverse. Participants completed a demographics questionnaire, self-report measures of health, depression, social network size, social support, and loneliness.Results: Loneliness was strongly associated with depression and self-ratings of health. In turn, greater social isolation and less social support were associated with greater loneliness. Social isolation was associated with depression and lower self-ratings of health. The association between social isolation and health was mediated by loneliness. Individuals in the older cohorts (80+) reported less social support. With respect to loneliness, having a smaller social network, more functional limitations, and limitations in engaging meaningful activities was associated with higher levels of loneliness and greater social isolation.Conclusion: The findings underscore the importance of social connectively to wellbeing for older adults and suggest that those in the older cohorts, who have a small social network, and with greater physical and functional impairments may be particularly vulnerable to being socially isolated and lonely. The findings provide guidance for future interventions. In this regard, we discuss how Information and Communication Technologies (ICTs) may be used to promote social connectivity and engagement. Strategies to make the usability and availability of these applications for aging adults are highlighted.

Predicting the Evolution of Pain Relief

Modeling from data usually has two distinct facets: building sound explanatory models or creating powerful predictive models for a system or phenomenon. Most of recent literature does not exploit the relationship between explanation and prediction while learning models from data. Recent algorithms are not taking advantage of the fact that many phenomena are actually defined by diverse sub-populations and local structures, and thus there are many possible predictive models providing contrasting interpretations or competing explanations for the same phenomenon. In this article, we propose to explore a complementary link between explanation and prediction. Our main intuition is that models having their decisions explained by the same factors are likely to perform better predictions for data points within the same local structures. We evaluate our methodology to model the evolution of pain relief in patients suffering from chronic pain under usual guideline-based treatment. The ensembles generated using our framework are compared with all-in-one approaches of robust algorithms to high-dimensional data, such as Random Forests and XGBoost. Chronic pain can be primary or secondary to diseases. Its symptomatology can be classified as nociceptive, nociplastic, or neuropathic, and is generally associated with many different causal structures, challenging typical modeling methodologies. Our data includes 631 patients receiving pain treatment. We considered 338 features providing information about pain sensation, socioeconomic status, and prescribed treatments. Our goal is to predict, using data from the first consultation only, if the patient will be successful in treatment for chronic pain relief. As a result of this work, we were able to build ensembles that are able to consistently improve performance by up to 33% when compared to models trained using all the available features. We also obtained relevant gains in interpretability, with resulting ensembles using only 15% of the total number of features. We show we can effectively generate ensembles from competing explanations, promoting diversity in ensemble learning and leading to significant gains in accuracy by enforcing a stable scenario in which models that are dissimilar in terms of their predictions are also dissimilar in terms of their explanation factors.

Mutational Landscape of the Proglucagon-Derived Peptides.

Strong efforts have been placed on understanding the physiological roles and therapeutic potential of the proglucagon peptide hormones including glucagon, GLP-1 and GLP-2. However, little is known about the extent and magnitude of variability in the amino acid composition of the proglucagon precursor and its mature peptides. Here, we identified 184 unique missense variants in the human proglucagon gene GCG obtained from exome and whole-genome sequencing of more than 450,000 individuals across diverse sub-populations. This provides an unprecedented source of population-wide genetic variation data on missense mutations and insights into the evolutionary constraint spectrum of proglucagon-derived peptides. We show that the stereotypical peptides glucagon, GLP-1 and GLP-2 display fewer evolutionary alterations and are more likely to be functionally affected by genetic variation compared to the rest of the gene products. Elucidating the spectrum of genetic variations and estimating the impact of how a peptide variant may influence human physiology and pathophysiology through changes in ligand binding and/or receptor signalling, are vital and serve as the first important step in understanding variability in glucose homeostasis, amino acid metabolism, intestinal epithelial growth, bone strength, appetite regulation, and other key physiological parameters controlled by these hormones.

Neoplastic and immune single-cell transcriptomics define subgroup-specific intra-tumoral heterogeneity of childhood medulloblastoma.

Medulloblastoma (MB) is a heterogeneous disease in which neoplastic cells and associated immune cells contribute to disease progression. We aimed to determine the influence of neoplastic and immune cell diversity on MB biology in patient samples and animal models. To better characterize cellular heterogeneity in MB we used single-cell RNA sequencing, immunohistochemistry, and deconvolution of transcriptomic data to profile neoplastic and immune populations in patient samples and animal models across childhood MB subgroups. Neoplastic cells cluster primarily according to individual sample of origin which is influenced by chromosomal copy number variance. Harmony alignment reveals novel MB subgroup/subtype-associated subpopulations that recapitulate neurodevelopmental processes, including photoreceptor and glutamatergic neuron-like cells in molecular subgroups GP3 and GP4, and a specific nodule-associated neuronally differentiated subpopulation in the sonic hedgehog subgroup. We definitively chart the spectrum of MB immune cell infiltrates, which include subpopulations that recapitulate developmentally related neuron-pruning and antigen-presenting myeloid cells. MB cellular diversity matching human samples is mirrored in subgroup-specific mouse models of MB. These findings provide a clearer understanding of the diverse neoplastic and immune cell subpopulations that constitute the MB microenvironment.

Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulations.

Cancer-associated fibroblasts (CAF) are a poorly characterized cell population in the context of liver cancer. Our study investigates CAF functions in intrahepatic cholangiocarcinoma (ICC), a highly desmoplastic liver tumor. Genetic tracing, single-cell RNA sequencing, and ligand-receptor analyses uncovered hepatic stellate cells (HSC) as the main source of CAF and HSC-derived CAF as the dominant population interacting with tumor cells. In mice, CAF promotes ICC progression, as revealed by HSC-selective CAF depletion. In patients, a high panCAF signature is associated with decreased survival and increased recurrence. Single-cell RNA sequencing segregates CAF into inflammatory and growth factor-enriched (iCAF) and myofibroblastic (myCAF) subpopulations, displaying distinct ligand-receptor interactions. myCAF-expressed hyaluronan synthase 2, but not type I collagen, promotes ICC. iCAF-expressed hepatocyte growth factor enhances ICC growth via tumor-expressed MET, thus directly linking CAF to tumor cells. In summary, our data demonstrate promotion of desmoplastic ICC growth by therapeutically targetable CAF subtype-specific mediators, but not by type I collagen.

Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulations

(Cancer Cell 39, 866–882.e1–e11; June 14, 2021) In this study, Affo et al. determine origin and functions of cancer-associated fibroblasts in the liver and uncover distinct subsets that promote the growth of intrahepatic cholangiocarcinoma via different therapeutically targetable mediators. Upon publication of the original article, we realized that we had inadvertently omitted Dr. Raghu Kalluri from the author list. This has been corrected online, and the correct author list appears in print. Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulationsAffo et al.Cancer CellApril 29, 2021In BriefIntrahepatic cholangiocarcinoma (ICC) is an extraordinarily stiff liver tumor due to abundant scar-forming cancer-associated fibroblasts (CAF). Here, Affo et al. determine the origin and functions of CAF, and uncover distinct CAF subsets, promoting ICC growth via different therapeutically targetable mediators. Thus, CAF and their mediators may serve as therapeutic targets for ICC. Full-Text PDF

Healthcare Needs of U.S. Rural Latinos: A Growing, Multicultural Population.

From 2000 to 2050, the Latino population in the United States (U.S.) is expected to grow by 273%. Health outcomes vary widely among Latino subgroups and health disparities more adversely affect rural residents. The commonly used "one-size-fits-all" approach assumes that the U.S. Latino population is homogeneous. Rural Latinos in four study states: Arizona (AZ), California (CA), Florida (FL) and Texas (TX) were the focus of this study. This research describes changes in the Latino population in rural counties of the U.S. in two dimensions: 1) change in population by number, and 2) change in population by country of origin using data from 2000-2017. The following themes emerged: 1) the overall Latino population grew in each state; 2) rural Latino populations in each state also increased but at a higher rate; 3) there is a variety of diversity in the countries of origin of rural Latinos based in each state; and 4) a considerable proportion of Latinos living in rural areas are of unknown Latino origins. As the largest racial or ethnic minority in rural populations and as the second largest group in the nation, Latino health has a significant influence on the U.S. healthcare system. For nurses, evidence-based strategies can be tailored to address diverse Latino subpopulations to reduce specific disparities for various ethnic populations.

TLR4 Transactivates CD8+ T Lymphocytes upon Acute Sterile Tissue Injury.

Acute major tissue injury induces immune dysregulation that is characterized by the development of systemic sterile inflammation and an increased risk for opportunistic infections. Although the contribution of the innate immune system has been examined in detail, research on the impact of acute sterile tissue damage on the T cell compartment remains limited. In the current study, we used a clinically relevant mouse model for traumatic skeletal muscle injury to investigate the impact of sterile tissue damage on diverse subpopulations of CD4+ Th and CD8+ cytotoxic T cells in systemic and local lymphoid organs. For the first time, to our knowledge, we provide evidence that injury selectively induced the expression of the activation marker CD69 on naive and central/virtual memory CD8+ T cells in the lymph nodes but not in the spleen of male mice. CD4+ Th cells remained unaffected in both organs. The activation of CD8+ T cells was dependent on signaling through TLR4. Within a few hours, injury triggered the expression of IL-12 in the lymph nodes in a TLR4-dependent manner. Blocking of IL-12 prevented the activation of naive and central memory CD8+ T cells after injury. Thus, early after traumatic tissue damage, TLR4 transactivates naive and central/virtual memory CD8+ T cells through innate cytokines in local lymph nodes, where they might modulate forthcoming local immune responses.