Failed Induction of the TH1 System in TH2 Dominant Patients: The Cancer-Permissive Immune Macroenvironment.

Tumor microenvironment infiltration by cells of the T helper cell type 1 (TH1) system, including TH1 cells, M1 macrophages, natural killer cells, and CD8+ T cells, is associated with better cancer prognosis. In contrast, tumor microenvironment infiltration by cells of the TH2 system, including TH2 cells, M2 macrophages, and innate lymphoid cells type 2, as well as immune suppressive myeloid-derived suppressor cells and regulatory T cells, is associated with poorer cancer prognosis. Beyond the tumor itself and a myriad of other modifying factors, such as genetic and epigenetic influences on tumorigenesis, the overall immune state of the patient, termed the macroenvironment, has also been shown to significantly influence cancer outcomes. Alterations in the tricarboxylic acid (TCA) cycle (TCA cycle breaks) involving loss of function of succinate dehydrogenase, isocitrate dehydrogenase, and fumarate hydratase have been shown to be associated with an intracellular metabolic shift away from oxidative phosphorylation and into glycolysis in cells that are transforming into cancer cells. The same loss of function of succinate dehydrogenase and isocitrate dehydrogenase has also been identified as inducing a shift in macrophages toward glycolysis that is associated with M1 macrophage polarization. M1 macrophages make interleukin 12, which stimulates TH1 cells and natural killer cells to produce interferon gamma (IFN-γ), which in turn stimulates M1 macrophage activity, forming an activation loop. IFN-γ also drives activation of CD8+ T cells. Thus, M1 macrophage activation initiates and sustains activation of the TH1 system of cells. In this fashion, TCA cycle breaks at succinate dehydrogenase and isocitrate dehydrogenase that promote cellular transformation into cancer cells are also associated with upregulation of the TH1 system that provides anti-cancer immune surveillance. The TH1 and TH2 systems are known to inhibit each other's activation. It is this author's hypothesis that, in patients whose macroenvironment is sufficiently TH2-dominant, the metabolic shift toward glycolysis induced by TCA cycle breaks that gives rise to mutagenic changes in tissue parenchymal cells is not counterbalanced by adequate activation of M1 macrophages, thus giving rise to cancer cell development. For instance, the atopic TH2-high asthma phenotype, a TH2 dominance-based comorbidity, is associated with a more than doubled incidence of colon, breast, lung, and prostate cancer, compared with non-asthmatics. Failure of TCA cycle breaks to induce M1 polarization of tissue-resident macrophages yields a tissue environment in which the tissue-resident macrophages fail to routinely perform M1-associated functions such as phagocytizing newly developing cancer cells. Failure of M1 phenotypic expression in both tissue-resident macrophages and monocyte-derived macrophages recruited to the tumor microenvironment yields both a loss of direct antitumor M1 macrophage actions and failure of TH1 system activation in general, including failure of CD8+ T cell activation, yielding a cancer-permissive tumor microenvironment and a poorer prognosis in patients with existing cancers. This paper proposes a conceptual framework that connects established elements in the existing research and points to the utility of a patient profiling process, aimed at personalization of treatment through identification and targeting of elements in each patient's tumor microenvironment and macroenvironment that contribute to unfavorable prognosis.

Human laboratory models of reward in substance use disorder.

Human laboratory models in substance use disorder provide a key intermediary step between highly controlled and mechanistically informative non-human preclinical methods and clinical trials conducted in human populations. Much like preclinical models, the variety of human laboratory methods provide insights into specific features of substance use disorder rather than modelling the diverse causes and consequences simultaneously in a single model. This narrative review provides a discussion of popular models of reward used in human laboratory research on substance use disorder with a focus on the specific contributions that each model has towards informing clinical outcomes (forward translation) and analogs within preclinical models (backward translation). Four core areas of human laboratory research are discussed: drug self-administration, subjective effects, behavioral economics, and cognitive and executive function. Discussion of common measures and models used, the features of substance use disorder that these methods are purported to evaluate, unique issues for measure validity and application, and translational links to preclinical models and special considerations for studies wishing to evaluate homology across species is provided.

Contextual and psychosocial factors influencing drug reward in humans: The importance of non-drug reinforcement.

The reinforcing efficacy, or behavior-strengthening effect, of a substance is a critical determinant of substance use typically quantified by measuring behavioral allocation to the substance under schedules of reinforcement with escalating response requirements. Although responses on these tasks are often used to indicate stable reinforcing effects or trait-level abuse potential for an individual, task designs often demonstrate within-person variability across varying degrees of a constraint within experimental procedures. As a result, quantifying behavioral allocation is an effective approach for measuring the impact of contextual and psychosocial factors on substance reward. We review studies using laboratory self-administration, behavioral economic purchase tasks, and ambulatory assessments to quantify the impact of various contextual and psychosocial factors on behavioral allocation toward consumption of a substance. We selected these assessment approaches because they cover the translational spectrum from experimental control to ecological relevance, with consistent support across these approaches representing greater confidence in the effect. Conceptually, we organized factors that influence substance value into two broad categories: factors that influence the cost/benefit ratio of the substance (social context, stress and affect, cue exposure), and factors that influence the cost/benefit ratio of an alternative (alternative non-drug reinforcers, alternative drug reinforcers, and opportunity costs). We conclude with an overview of future research directions and considerations for clinical application.

The link between ancient whole-genome duplications and cold adaptations in the Caryophyllaceae.

PREMISE: The Caryophyllaceae (the carnation family) have undergone multiple transitions into colder climates and convergence on cushion plant adaptation, indicating that they may provide a natural system for cold adaptation research. Previous research has suggested that putative ancient whole-genome duplications (WGDs) are correlated with niche shifts into colder climates across the Caryophyllales. Here, we explored the genomic changes potentially involved in one of these discovered shifts in the Caryophyllaceae. METHODS: We constructed a data set combining 26 newly generated transcriptomes with 45 published transcriptomes, including 11 cushion plant species across seven genera. With this data set, we inferred a dated phylogeny for the Caryophyllaceae and mapped ancient WGDs and gene duplications onto the phylogeny. We also examined functional groups enriched for gene duplications related to the climatic shift. RESULTS: The ASTRAL topology was mostly congruent with the current consensus of relationships within the family. We inferred 15 putative ancient WGDs in the family, including eight that have not been previously published. The oldest ancient WGD (ca. 64.4-56.7 million years ago), WGD1, was found to be associated with a shift into colder climates by previous research. Gene regions associated with ubiquitination were overrepresented in gene duplications retained after WGD1 and those convergently retained by cushion plants in Colobanthus and Eremogone, along with other functional annotations. CONCLUSIONS: Gene family expansions induced by ancient WGDs may have contributed to the shifts to cold climatic niches in the Caryophyllaceae. Transcriptomic data are crucial resources that help unravel heterogeneity in deep-time evolutionary patterns in plants.

Correspondence between the alcohol-P3 event-related potential and alcohol reward phenotypes among young adults.

BACKGROUND: Behavioral economic theory suggests that the value of alcohol depends upon elements of the choice context, such that increasing constraints on alternatives (e.g., price) or increasing the benefits of alcohol (e.g., social context) may result in greater likelihood of heavy drinking. The P3 event-related potential elicited by alcohol-related cues, a proposed marker of incentive salience, may be an electrophysiological parallel for behavioral economic alcohol demand. However, these indices have not been connected in prior research, and studies typically do not disaggregate social influences in the context of alcohol cue reactivity. METHOD: The current study recruited heavy drinking young adults (N = 81) who completed measures of alcohol use and alcohol demand, in addition to a 2 (social/nonsocial) × 2 (alcohol/nonalcohol) visual oddball task to elicit the P3. RESULTS: In multilevel models controlling for demographic characteristics, P3 reactivity was greater to alcohol (p < 0.001) and social (p < 0.001) cues than to nonalcohol and nonsocial cues, but without a significant interaction. Higher alcohol consumption (p = 0.02) and lower elasticity of demand (p = 0.01) were associated with greater P3 response to alcohol than nonalcohol cues. CONCLUSIONS: The results highlight a brain-behavior connection that may be an important marker for alcohol reward across units of analysis and may be sensitive to changes in the economic choice contexts that influence the likelihood of alcohol use.

Glucose-fed microbiota alters C. elegans intestinal epithelium and increases susceptibility to multiple bacterial pathogens.

Overconsumption of dietary sugar can lead to many negative health effects including the development of Type 2 diabetes, metabolic syndrome, cardiovascular disease, and neurodegenerative disorders. Recently, the human intestinal microbiota, strongly associated with our overall health, has also been known to be affected by diet. However, mechanistic insight into the importance of the human intestinal microbiota and the effects of chronic sugar ingestion has not been possible largely due to the complexity of the human microbiome which contains hundreds of types of organisms. Here, we use an interspecies C. elegans/E. coli system, where E. coli are subjected to high sugar, then consumed by the bacterivore host C. elegans to become the microbiota. This glucose-fed microbiota results in a significant lifespan reduction accompanied by reduced healthspan (locomotion), reduced stress resistance, and changes in behavior and feeding. Lifespan reduction is also accompanied by two potential major contributors: increased intestinal bacterial density and increased concentration of reactive oxygen species. The glucose-fed microbiota accelerated the age-related development of intestinal cell permeability, intestinal distention, and dysregulation of immune effectors. Ultimately, the changes in the intestinal epithelium due to aging with the glucose-fed microbiota results in increased susceptibility to multiple bacterial pathogens. Taken together, our data reveal that chronic ingestion of sugar, such as a Western diet, has profound health effects on the host due to changes in the microbiota and may contribute to the current increased incidence of ailments including inflammatory bowel diseases as well as multiple age-related diseases.

Corticosteroids in critically ill patients: A narrative review.

Corticosteroids have been utilized in modern medicine for decades. Many indications have been investigated across various treatment settings with both benefit and harm observed. Given the instability of critically ill patients, the increased risk of corticosteroid-related complications, and the pervasive comorbidities, patients who receive corticosteroids must be carefully managed. Common critical care disease states in which corticosteroids have been studied and are routinely utilized include acute respiratory distress syndrome, adrenal insufficiency, angioedema, asthma, chronic obstructive pulmonary disease, community-acquired pneumonia, coronavirus disease 2019, septic shock, and spinal cord injury. Benefits of corticosteroids include an improvement in disease state-specific outcomes, decreased hospital length of stay, decreased mechanical ventilatory support, and decreased mortality. The harm of corticosteroids is well documented through adverse effects that include, but are not limited to, hyperglycemia, tachycardia, hypertension, agitation, delirium, anxiety, immunosuppression, gastrointestinal bleeding, fluid retention, and muscle weakness. Furthermore, corticosteroids are associated with increased health care costs through adverse effects as well as drug acquisition and administration costs. Given the assortment of agents, dosing, benefits, risks, and utilization in the critical care setting, there may be difficulty with identifying the appropriate places for use of corticosteroids in therapy. There currently exists no comprehensive report detailing the use of corticosteroids in the aforementioned disease states within the critical care setting. This narrative review sets out to describe these in detail.

Diroximel Fumarate in Patients with Relapsing-Remitting Multiple Sclerosis: NEDA-3 After Re-Baselining in the Phase 3 EVOLVE-MS-1 Study.

INTRODUCTION: Diroximel fumarate (DRF) and dimethyl fumarate (DMF) are orally administered fumarate disease-modifying therapies (DMTs) for multiple sclerosis (MS). The safety, tolerability, and exploratory efficacy of DRF were evaluated in the phase 3 EVOLVE-MS-1 study. No Evidence of Disease Activity (NEDA-3) is a composite efficacy endpoint used in clinical trials for MS defined as no relapse, no 24-week confirmed disability progression (CDP), no new/newly enlarging T2 lesions, and no new gadolinium-enhancing lesions. As NEDA outcomes in studies may be confounded by initial disease activity, the objective of this analysis was to evaluate NEDA-3 in EVOLVE-MS-1 for newly enrolled patients and patients who were re-baselined after approximately 7 weeks. METHODS: Patients entered EVOLVE-MS-1 as either newly enrolled or having completed the 5-week phase 3 EVOLVE-MS-2 study of DRF and DMF. Magnetic Resonance Imaging (MRI) was performed at baseline before each study (approx. 7 weeks apart) and at weeks 48 and 96 in EVOLVE-MS-1. Therefore, patients entering from EVOLVE-MS-2 were re-baselined after approximately 7 weeks. NEDA-3 outcomes on DRF are reported for prior DRF, prior DMF, and de novo patient groups. RESULTS: Of 1057 patients in EVOLVE-MS-1, 239 (22.6%) had rolled over from receiving DRF in EVOLVE-MS-2 ("prior DRF"), 225 (21.3%) had rolled over from receiving DMF in EVOLVE-MS-2 ("prior DMF"), and 593 (56.1%) were newly enrolled ("de novo"). At week 48, Kaplan-Meier estimates of NEDA-3 were 72.3% (prior DRF), 72.1% (prior DMF), and 62.1% (de novo); at week 96, estimates were 50.2% (prior DRF), 48.2% (prior DMF), and 36.5% (de novo). CONCLUSIONS: In EVOLVE-MS-1, after re-baselining at approximately 7 weeks, approximately half of DRF-treated patients achieved NEDA-3 at week 96, compared with 36.5% of patients who were not re-baselined. Re-baselining may be useful for assessing efficacy of DMTs by mitigating the influence of disease activity prior to the onset of efficacy. CLINICAL TRIAL REGISTRATIONS: NCT03093324 (EVOLVE-MS-2); NCT02634307 (EVOLVE-MS-1).

Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers.

Type II diabetes mellitus (T2DM) is a rising global health burden due to its rapidly increasing prevalence worldwide, and can result in serious complications. Therefore, it is of utmost importance to identify individuals at risk as early as possible to avoid long-term T2DM complications. In this study, we developed an interpretable machine learning model leveraging baseline levels of biomarkers of oxidative stress (OS), inflammation, and mitochondrial dysfunction (MD) for identifying individuals at risk of developing T2DM. In particular, Isolation Forest (iForest) was applied as an anomaly detection algorithm to address class imbalance. iForest was trained on the control group data to detect cases of high risk for T2DM development as outliers. Two iForest models were trained and evaluated through ten-fold cross-validation, the first on traditional biomarkers (BMI, blood glucose levels (BGL) and triglycerides) alone and the second including the additional aforementioned biomarkers. The second model outperformed the first across all evaluation metrics, particularly for F1 score and recall, which were increased from 0.61 ± 0.05 to 0.81 ± 0.05 and 0.57 ± 0.06 to 0.81 ± 0.08, respectively. The feature importance scores identified a novel combination of biomarkers, including interleukin-10 (IL-10), 8-isoprostane, humanin (HN), and oxidized glutathione (GSSG), which were revealed to be more influential than the traditional biomarkers in the outcome prediction. These results reveal a promising method for simultaneously predicting and understanding the risk of T2DM development and suggest possible pharmacological intervention to address inflammation and OS early in disease progression.

Efficiently Quantifying Egocentric Social Network Cannabis Use: Initial Psychometric Validation of the Brief Cannabis Social Density Assessment.

OBJECTIVE: Social environment is a key determinant of substance use, but cannabis-related social network analysis is not common, in part due to the assessment burden of comprehensive egocentric social network analysis. METHOD: The current pre-registered secondary analysis assessed the psychometric properties (i.e., convergent, criterion-related, incremental validity) of the Brief Cannabis Social Density Assessment (B-CaSDA) in a cross-sectional sample of adults who use cannabis (N = 310) using a survey-based design. The B-CaSDA assesses the quantity and frequency of cannabis use for the respondent's four closest (non-parent) relationships. RESULTS: Cannabis use severity was elevated for each additional person who used cannabis at all or daily in the individual's social network. B-CaSDA indices (i.e., frequency, quantity, total score) were positively correlated with cannabis consumption, cannabis use severity indicators, and established risk factors for harmful cannabis use. B-CaSDA indices also discriminated between those above and below a clinical cutoff on the Cannabis Use Disorder Identification Test - Revised (CUDIT-R). Finally, in omnibus models that included common risk factors for cannabis use severity, the B-CaSDA quantity index contributed additional variance when predicting CUDIT-R total score, and B-CaSDA frequency contributed additional variance in predicting the CUDIT-R quantity-frequency subscale. CONCLUSIONS: The results suggest that the B-CaSDA has the potential to expand social network research on cannabis use and misuse by increasing its assessment feasibility in diverse designs.

Physician and informal care use explained by the Pediatric Quality of Life Inventory (PedsQL) in children with suspected genetic disorders.

PURPOSE: To examine associations between Pediatric Quality of Life Inventory (PedsQL) 4.0 Generic Core Scales and PedsQL Infant Scales with formal health care resource utilization (HCRU) and informal caregiver burden. METHODS: We studied a pediatric cohort of 837 patients (median age: 8.4 years) with suspected genetic disorders enrolled January 2019 through July 2021 in the NYCKidSeq program for diagnostic sequencing. Using linked ~ nine-month longitudinal survey and physician claims data collected through May 2022, we modeled the association between baseline PedsQL scores and post-baseline HCRU (median follow-up: 21.1 months) and informal care. We also assessed the longitudinal change in PedsQL scores with physician services using linear mixed-effects models. RESULTS: Lower PedsQL total and physical health scores were independently associated with increases in 18-month physician services, encounters, and weekly informal care. Comparing low vs. median total scores, increases were 10.6 services (95% CI: 1.0-24.6), 3.3 encounters (95% CI: 0.5-6.8), and $668 (95% CI: $350-965), respectively. For the psychosocial domain, higher scores were associated with decreased informal care. Based on adjusted linear mixed-effects modeling, every additional ten physician services was associated with diminished improvement in longitudinal PedsQL total score trajectories by 1.1 point (95% confidence interval: 0.6-1.6) on average. Similar trends were observed in the physical and psychosocial domains. CONCLUSION: PedsQL scores were independently associated with higher utilization of physician services and informal care. Moreover, longitudinal trajectories of PedsQL scores became less favorable with increased physician services. Adding PedsQL survey instruments to conventional measures for improved risk stratification should be evaluated in further research.

The Pediatric Quality of Life Inventory (PedsQL) is widely used to measure health-related quality of life in pediatric patients; however, few studies have examined whether the PedsQL is indicative of longitudinal outcomes of morbidity and health care needs. This study captures associations between PedsQL scores with utilization of physician and informal care in children with suspected genetic disorders. We demonstrate that lower PedsQL total and physical health scores are independently associated with greater utilization of physician services and informal care. Moreover, longitudinal trajectories of PedsQL scores become less favorable with increased physician services. Results can inform future applications of PedsQL instruments.

Pathological narcissism's impact on psychodynamic group therapy for perfectionism.

Several decades of theory suggest that pathological narcissism (PN) may limit psychotherapy success, but empirical evidence for such theories is limited and mixed. In addition, it has been proposed that individuals with high levels of PN may benefit more from supportive compared to interpretive psychodynamic therapies, but no studies thus far have investigated this question empirically. As such, our study aimed to extend past research by investigating (a) whether higher levels of pretreatment PN predict poorer treatment outcome and (b) whether the type of psychodynamic therapy (supportive or interpretive therapy) moderates these findings, in a sample of patients undergoing group psychodynamic psychotherapy for perfectionism. The sample was drawn from the University of British Columbia Perfectionism Treatment Study II (Hewitt et al., 2023) and consisted of 80 treatment-seeking adults with elevated perfectionism. Contrary to expectations, multilevel and multiple regression analyses showed that pretreatment PN did not significantly predict posttreatment changes in symptom severity, life satisfaction, or work and social impairment. We also did not find that either grandiose or vulnerable narcissism predicted likelihood of patient dropout. Finally, treatment type did not moderate the relationship between pretreatment PN and treatment outcome, suggesting that, contrary to our hypotheses, PN does not impact treatment outcome regardless of the interpretive nature of the psychodynamic group therapy. These results, taken together with past findings, suggest that PN may not be associated with poorer psychotherapy outcomes in certain contexts, such as in the case of supportive or interpretive psychodynamic group psychotherapy for perfectionism. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

ProBac-seq, a bacterial single-cell RNA sequencing methodology using droplet microfluidics and large oligonucleotide probe sets.

Methods that measure the transcriptomic state of thousands of individual cells have transformed our understanding of cellular heterogeneity in eukaryotic cells since their introduction in the past decade. While simple and accessible protocols and commercial products are now available for the processing of mammalian cells, these existing technologies are incompatible with use in bacterial samples for several fundamental reasons including the absence of polyadenylation on bacterial messenger RNA, the instability of bacterial transcripts and the incompatibility of bacterial cell morphology with existing methodologies. Recently, we developed ProBac sequencing (ProBac-seq), a method that overcomes these technical difficulties and provides high-quality single-cell gene expression data from thousands of bacterial cells by using messenger RNA-specific probes. Here we provide details for designing large oligonucleotide probe sets for an organism of choice, amplifying probe sets to produce sufficient quantities for repeated experiments, adding unique molecular indexes and poly-A tails to produce finalized probes, in situ probe hybridization and single-cell encapsulation and library preparation. This protocol, from the probe amplification to the library preparation, requires ~7 d to complete. ProBac-seq offers several advantages over other methods by capturing only the desired target sequences and avoiding nondesired transcripts, such as highly abundant ribosomal RNA, thus enriching for signal that better informs on cellular state. The use of multiple probes per gene can detect meaningful single-cell signals from cells expressing transcripts to a lesser degree or those grown in minimal media and other environmentally relevant conditions in which cells are less active. ProBac-seq is also compatible with other organisms that can be profiled by in situ hybridization techniques.

Deep learning of left atrial structure and function provides link to atrial fibrillation risk.

Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk.

Familial aggregation of seizure outcomes in four familial epilepsy cohorts.

OBJECTIVE: To assess the possible effects of genetics on seizure outcome by estimating the familial aggregation of three outcome measures: seizure remission, history of ≥4 tonic-clonic seizures, and seizure control for individuals taking antiseizure medication. METHODS: We analyzed families containing multiple persons with epilepsy in four previously collected retrospective cohorts. Seizure remission was defined as being 5 and 10 years seizure-free at last observation. Total number of tonic-clonic seizures was dichotomized at <4 and ≥4 seizures. Seizure control in patients taking antiseizure medication was defined as no seizures for 1, 2, and 3 years. We used Bayesian generalized linear mixed-effects model (GLMM) to estimate the intraclass correlation coefficient (ICC) of the family-specific random effect, controlling for epilepsy type, age at epilepsy onset, and age at last data collection as fixed effects. We analyzed each cohort separately and performed meta-analysis using GLMMs. RESULTS: The combined cohorts included 3644 individuals with epilepsy from 1463 families. A history of ≥4 tonic-clonic seizures showed strong familial aggregation in three separate cohorts and meta-analysis (ICC .28, 95% confidence interval [CI] .21-.35, Bayes factor 8 × 1016). Meta-analyses did not reveal significant familial aggregation of seizure remission (ICC .08, 95% CI .01-.17, Bayes factor 1.46) or seizure control for individuals taking antiseizure medication (ICC .13, 95% CI 0-.35, Bayes factor 0.94), with heterogeneity among cohorts. SIGNIFICANCE: A history of ≥4 tonic-clonic seizures aggregated strongly in families, suggesting a genetic influence, whereas seizure remission and seizure control for individuals taking antiseizure medications did not aggregate consistently in families. Different seizure outcomes may have different underlying biology and risk factors. These findings should inform the future molecular genetic studies of seizure outcomes.

A longevity-specific bank of induced pluripotent stem cells from centenarians and their offspring.

Centenarians provide a unique lens through which to study longevity, healthy aging, and resiliency. Moreover, models of human aging and resilience to disease that allow for the testing of potential interventions are virtually non-existent. We obtained and characterized over 50 centenarian and offspring peripheral blood samples including those connected to functional independence data highlighting resistance to disability and cognitive impairment. Targeted methylation arrays were used in molecular aging clocks to compare and contrast differences between biological and chronological age in these specialized subjects. Isolated peripheral blood mononuclear cells (PBMCs) were then successfully reprogrammed into high-quality induced pluripotent stem cell (iPSC) lines which were functionally characterized for pluripotency, genomic stability, and the ability to undergo directed differentiation. The result of this work is a one-of-a-kind resource for studies of human longevity and resilience that can fuel the discovery and validation of novel therapeutics for aging-related disease.

Omental preadipocytes stimulate matrix remodeling and IGF signaling to support ovarian cancer metastasis.

Ovarian cancer can metastasize to the omentum, which is associated with a complex tumor microenvironment. Omental stromal cells facilitate ovarian cancer colonization by secreting cytokines and growth factors. Improved understanding of the tumor supportive functions of specific cell populations in the omentum could identify strategies to prevent and treat ovarian cancer metastasis. Here, we showed that omental preadipocytes enhance the tumor initiation capacity of ovarian cancer cells. Secreted factors from preadipocytes supported cancer cell viability during nutrient and isolation stress and enabled prolonged proliferation. Co-culturing with pre-adipocytes led to upregulation of genes involved in extracellular matrix (ECM) organization, cellular response to stress, and regulation of insulin-like growth factor (IGF) signaling in ovarian cancer cells. IGF-1 induced ECM genes and increased alternative NF-κB signaling by activating RelB. Inhibiting the IGF-1 receptor (IGF1R) initially increased tumor omental adhesion but decreased growth of established preadipocyte-induced subcutaneous tumors as well as established intraperitoneal tumors. Together, this study shows that omental preadipocytes support ovarian cancer progression, which has implications for targeting metastasis.

Cold temperatures drive the latitudinal range limits and inhibit overwintering survival of the redbanded stink bug (Hemiptera: Pentatomidae).

For non-native insects that are economically damaging, understanding the drivers of range expansions and contractions is important for forecasting pest pressure. The invasion of the redbanded stink bug, Piezodorus guildinii (Westwood) (Hemiptera: Pentatomidae), reached Louisiana, United States, in 2000, after which the northern range limits of this species have fluctuated annually. Low winter temperatures have been implicated as a major driver of this pattern, but the importance of cold temperatures-or other abiotic factors-for the persistence of this pest over large geographic scales are incompletely understood. We coupled occurrence data of P. guildinii with climatic data to investigate trends in P. guildinii presence in relation to winter temperatures and develop species distribution models, forecasting habitat suitability based on current and future climatic scenarios. Our results show that (i) some P. guildinii persisted in locations where ambient temperatures reached -12°C, (ii) overwintering temperatures drive P. guildinii range dynamics, and (iii) with intermediate projections of climatic warming, northward expansion by P. guildinii in North America is likely to be minimal. While the northern extent of P. guildinii's range may now be largely realized in North America, our results suggest that increased frequency of mild winters could reduce interannual fluctuations of P. guildinii and enable it to become a more consistent economic concern for soybean growers throughout the Midsouth region of the United States.