Distinct Functional Patterns Research Articles

Soil metagenomics reveals reduced tillage improves soil functional profiles of carbon, nitrogen, and phosphorus cycling in bulk and rhizosphere soils

Conservation tillage practices (CAT) are known to benefit soil health and soil ecosystem functions relative to conventional tillage (CVT); however, much uncertainty remains concerning microbial functional traits and their subsequent effects on soil nutrients under different tillage practices. We analyzed the functional profiles of the C, N, and P cycles in response to CAT of no-tillage (NT), reduced tillage (RT), and CVT of moldboard plowing (MP) in bulk and rhizosphere soils using shotgun sequencing. CAT induced distinct microbial functional patterns relative to CVT, and these differences were generally more evident in rhizosphere soils than in bulk soils. CAT promotes multiple metabolic pathways such as C and N decomposition, fermentation, CO oxidation, N fixation, nitrate reduction and inorganic-P and organic-P transformations in bulk and/or rhizosphere soils. Variations in these metabolic pathways were mainly driven by Bradyrhizobium, Mesorhizobium, Nitrososphaera, Phenylobacterium, Rhizobium which are affiliated with Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidota and Thaumarchaeota. Furthermore, 24 high-quality metagenome-assembled genomes (MAGs) were reconstructed, of which three novel MAGs (TL100, TL46, and TL57) harbored functional genes regulating all metabolic pathways. In particular, NT-enriched MAGs (such as Sphingomonas) promote fermentation, resulting in the reduction of soil total carbon (TC) relative to MP in bulk soils. RT retained the contents of soil TC and total nitrogen (TN) well and up-regulated the phoR gene carried by Streptomyces, which promoted the regulation of P-starvation concomitantly with the increase in the contents of total phosphorous (TP) and available phosphorous (AP) in bulk soil. Additionally, assimilatory nitrate reduction coupled with organic-P mineralization was facilitated by CAT in rhizosphere soil, leading to the mitigation of N loss and the activation of soil organic-P for crop uptake. Overall, our results revealed that CAT significantly accelerated multiple metabolic potentials, and RT could sustain soil nutrient contents better than NT.

Integrative Proteome and Metabolomics Analyses of Cryptococcus neoformans Responses to Melanin Substrates Niger seed and L-DOPA.

Melanin, as a pivotal antioxidant pigment, plays a critical role in the pathogenicity of Cryptococcus neoformans. However, the underlying signaling pathways responsible for melanin biosynthesis in C. neoformans are not yet fully elucidated. In this study, proteome and metabolomics analyses were conducted to investigate the response of C. neoformans to melanin substrate Niger seed or L-DOPA. The proteome analysis identified significant differential expression of proteins in cells treated with Niger seed compared to L-DOPA, with distinct functional enrichment patterns observed. Subcellular localization analysis showed unique protein distribution in cells treated with each substrate. Metabolomics analysis revealed distinct metabolic profiles in response to Niger seed or L-DOPA, with notable differences in metabolite regulation between the two treatments. KEGG classification highlighted specific metabolic pathways affected by each substrate. Overall, this study provides valuable insights into the complex regulatory mechanisms underlying C. neoformans response to melanin substrates.

Distinct microbiota assembly and functional patterns in disease-resistant and susceptible varieties of tobacco.

The plant microbiota is believed to be an accessory genome that extends plant functions, forming holobionts together with the host plant. Plant disease resistance, therefore, is inextricably linked with plant microbiota, which play important roles in plant growth and health. To explore the relationship between plant microbiota and disease resistance, we investigated the tobacco microbiome of two varieties with contrasting disease-resistance levels to bacterial wilt and black shank diseases. Comparative microbiome analysis indicated that the resistant variety assembled a distinct microbiota with higher network complexity and diversity. While Pseudomonas and Ensifer, which contain biocontrol and beneficial members, were enriched in the rhizosphere of the resistant variety, Ralstonia, a genus including the known causative pathogen, was enriched in the susceptible variety. Metagenome sequencing revealed that biocontrol functions, such as hydrogen cyanide synthase, pyochelin biosynthesis, and arthrofactin-type cyclic lipopeptide synthetase, were more abundant in the resistant variety. Further analysis indicated that contigs encoding the corresponding genes were mostly assigned to Pseudomonas. Among all the metagenome-assembled genomes, positive selection was suggested in the genome assigned to Pseudomonas only in the rhizosphere of the resistant variety. The search of biosynthetic gene clusters in the Pseudomonas genome revealed a non-ribosomal peptide synthetase, the compound of which was brabantamide A, with known antimicrobial activity. Collectively, our study suggests that the plant microbiota might be involved in microbe-mediated disease resistance. Particularly, our results highlight Pseudomonas in the rhizosphere of the disease-resistant variety as a promising biocontrol candidate. Our study may facilitate further screening of bacterial isolates and the targeted design of microbial communities.

Dysanapsis is differentially related to lung function trajectories with distinct structural and functional patterns in COPD and variable risk for adverse outcomes

Abnormal lung function trajectories are associated with increased risk of chronic obstructive pulmonary disease (COPD) and premature mortality; several risk factors for following these trajectories have been identified. Airway under-sizing dysanapsis (small airway lumens relative to lung size), is associated with an increased risk for COPD. The relationship between dysanapsis and lung function trajectories at risk for adverse outcomes of COPD is largely unexplored. We test the hypothesis that dysanapsis differentially affects distinct lung function trajectories associated with adverse outcomes of COPD. To identify lung function trajectories, we applied Bayesian trajectory analysis to longitudinal FEV1 and FVC Z-scores in the COPDGene Study, an ongoing longitudinal study that collected baseline data from 2007 to 2012. To ensure clinical relevance, we selected trajectories based on risk stratification for all-cause mortality and prospective exacerbations of COPD (ECOPD). Dysanapsis was measured in baseline COPDGene CT scans as the airway lumen-to-lung volume (a/l) ratio. We compared a/l ratios between trajectories and evaluated their association with trajectory assignment, controlling for previously identified risk factors. We also assigned COPDGene participants for whom only baseline data is available to their most likely trajectory and repeated our analysis to further evaluate the relationship between trajectory assignment and a/l ratio measures. We identified seven trajectories: supranormal, reference, and five trajectories at increased risk for mortality and exacerbations. Three at-risk trajectories are characterized by varying degrees of concomitant FEV1 and FVC impairments and exhibit airway predominant COPD patterns as assessed by quantitative CT imaging. These trajectories have lower a/l ratio values and increased risk for mortality and ECOPD compared to the reference trajectory. Two at-risk trajectories are characterized by disparate levels of FEV1 and FVC impairment and exhibit mixed airway and emphysema COPD patterns on quantitative CT imaging. These trajectories have markedly lower a/l ratio values compared to both the reference trajectory and airway-predominant trajectories and are at greater risk for mortality and ECOPD compared to the airway-predominant trajectories. These findings were observed among the participants with baseline-only data as well. The degree of dysanapsis appears to portend patterns of progression leading to COPD. Assignment of individuals-including those without spirometric obstruction-to distinct trajectories is possible in a clinical setting and may influence management strategies. Strategies that combine CT-assessed dysanapsis together with spirometric measures of lung function and smoke exposure assessment are likely to further improve trajectory assignment accuracy, thereby improving early detection of those most at risk for adverse outcomes. United States National Institute of Health, COPD Foundation, and Brigham and Women's Hospital.

Distinct functional patterns in child and adolescent bipolar and unipolar depression during emotional processing.

Accumulating evidence from functional magnetic resonance imaging studies supported brain dysfunction during emotional processing in bipolar disorder (BD) and major depressive disorder (MDD). However, child and adolescent BD and MDD could display different activation patterns, which have not been fully understood. This study aimed to investigate common and distinct activation patterns of pediatric BD (PBD) and MDD (p-MDD) during emotion processing using meta-analytic approaches. Literature search identified 25 studies, contrasting 252 PBD patients, and 253 healthy controls (HCs) as well as 311 p-MDD patients and 263 HCs. A total of nine meta-analyses were conducted pulling PBD and p-MDD experiments together and separately. The results revealed that PBD and p-MDD showed distinct patterns during negative processing. PBD patients exhibited activity changes in bilateral precuneus, left inferior parietal gyrus, left angular gyrus, and right posterior cingulate cortex while p-MDD patients showed functional disruptions in the left rectus, left triangular part of the inferior frontal gyrus, left orbital frontal cortex, left insula, and left putamen. In conclusion, the activity changes in PBD patients were mainly in regions correlated with emotion perception while the dysfunction among p-MDD patients was in the fronto-limbic circuit and reward-related regions in charge of emotion appraisal and regulation.

Understanding consumers in-store behavior: The dual role of episode-specific motive adjustment and motive selection

Understanding purchase motivations is vital but challenging due to their unobservable, concomitant, and dynamic nature. Recent research has proposed frameworks to examine their impact on choice by treating motivations as latent states. This study contributes to this line of research by introducing the notion of “episode-specific motive adjustment,” that accounts for variations in consumers' willingness to pursue specific motives during a shopping trip. Utilizing this concept, the study uncovers valuable insights into how different types of purchase motivations influence consumer product interactions and choices. Analyzing a comprehensive dataset from multiple Indian cities, the research contributes to a theoretical understanding of and practical applications for businesses seeking to comprehend and influence consumer behavior. Theoretically, we show that consumers display diverse patterns of in-store product engagement behavior as they adjust the intensity of instrumental and experiential motives across purchase episodes. Furthermore, we illustrate that the relationship between willingness to pursue a motive (motive intensity) and the likelihood of making a choice follows distinct functional patterns. We highlight the significance of considering individual-level heterogeneity and dynamic behavioral patterns to enhance consumer experiences and purchase decisions. Practically, this research identifies the key drivers that influence motive intensity in stores, providing managers with insights to optimize store layouts and effectively influence consumer purchase motives that align with their business objectives. Emphasis is placed on context-specific strategies, as the impact of these drivers varies with purchase context.

Multimodal fusion of multiple rest fMRI networks and MRI gray matter via parallel multilink joint ICA reveals highly significant function/structure coupling in Alzheimer's disease.

In this article, we focus on estimating the joint relationship between structural magnetic resonance imaging (sMRI) gray matter (GM), and multiple functional MRI (fMRI) intrinsic connectivity networks (ICNs). To achieve this, we propose a multilink joint independent component analysis (ml-jICA) method using the same core algorithm as jICA. To relax the jICA assumption, we propose another extension called parallel multilink jICA (pml-jICA) that allows for a more balanced weight distribution over ml-jICA/jICA. We assume a shared mixing matrix for both the sMRI and fMRI modalities, while allowing for different mixing matrices linking the sMRI data to the different ICNs. We introduce the model and then apply this approach to study the differences in resting fMRI and sMRI data from patients with Alzheimer's disease (AD) versus controls. The results of the pml-jICA yield significant differences with large effect sizes that include regions in overlapping portions of default mode network, and also hippocampus and thalamus. Importantly, we identify two joint components with partially overlapping regions which show opposite effects for AD versus controls, but were able to be separated due to being linked to distinct functional and structural patterns. This highlights the unique strength of our approach and multimodal fusion approaches generally in revealing potentially biomarkers of brain disorders that would likely be missed by a unimodal approach. These results represent the first work linking multiple fMRI ICNs to GM components within a multimodal data fusion model and challenges the typical view that brain structure is more sensitive to AD than fMRI.

Deep Learning in the Identification of Electroencephalogram Sources Associated with Sexual Orientation

Introduction: It is unclear if sexual orientation is a biological trait that has neurofunctional footprints. With deep learning, the power to classify biological datasets without an a priori selection of features has increased by magnitudes. The aim of this study was to correctly classify resting-state electroencephalogram (EEG) data from males with different sexual orientation using deep learning and to explore techniques to identify the learned distinguishing features. Methods: Three cohorts (homosexual men, heterosexual men, and a mixed sex cohort), one pretrained network on sex classification, and one newly trained network for sexual orientation classification were used to classify sex. Further, Grad-CAM methodology and source localization were used to identify the spatiotemporal patterns that were used for differentiation by the networks. Results: Using a pretrained network for classification of males and females, no differences existed between classification of homosexual and heterosexual males. The newly trained network was able, however, to correctly classify the cohorts with a total accuracy of 83%. The retrograde activation using Grad-CAM technology yielded distinctive functional EEG patterns in the Brodmann area 40 and 1 when combined with Fourier analysis and a source localization. Discussion: This study shows that electrophysiological trait markers of male sexual orientation can be identified using deep learning. These patterns are different from the differentiating signatures of males and females in a resting-state EEG.

Convergent and distinct neural structural and functional patterns of mild cognitive impairment: a multimodal meta-analysis.

Mild cognitive impairment (MCI) is regarded as a transitional stage between normal aging and Alzheimer's disease. Numerous voxel-based morphometry (VBM) and resting-state fMRI (rs-fMRI) studies have provided strong evidence of abnormalities in the structure and intrinsic function of brain regions in MCI. Studies have recently begun to explore their association but have not employed systematic information in this pursuit. Herein, a multimodal meta-analysis was performed, which included 43 VBM datasets (1,247 patients and 1,352 controls) of gray matter volume (GMV) and 42 rs-fMRI datasets (1,468 patients and 1,605 controls) that combined 3 metrics: amplitude of low-frequency fluctuation, the fractional amplitude of low-frequency fluctuation, and regional homogeneity. Compared to controls, patients with MCI displayed convergent reduced regional GMV and altered intrinsic activity, mainly in the default mode network and salience network. Decreased GMV alone in ventral medial prefrontal cortex and altered intrinsic function alone in bilateral dorsal anterior cingulate/paracingulate gyri, right lingual gyrus, and cerebellum were identified, respectively. This meta-analysis investigated complex patterns of convergent and distinct brain alterations impacting different neural networks in MCI patients, which contributes to a further understanding of the pathophysiology of MCI.

Clustering and prediction of long-term functional recovery patterns in first-time stroke patients.

The purpose of this study was to cluster long-term multifaceted functional recovery patterns and to establish prediction models for functional outcome in first-time stroke patients using unsupervised machine learning. This study is an interim analysis of the dataset from the Korean Stroke Cohort for Functioning and Rehabilitation (KOSCO), a long-term, prospective, multicenter cohort study of first-time stroke patients. The KOSCO screened 10,636 first-time stroke patients admitted to nine representative hospitals in Korea during a three-year recruitment period, and 7,858 patients agreed to enroll. Early clinical and demographic features of stroke patients and six multifaceted functional assessment scores measured from 7 days to 24 months after stroke onset were used as input variables. K-means clustering analysis was performed, and prediction models were generated and validated using machine learning. A total of 5,534 stroke patients (4,388 ischemic and 1,146 hemorrhagic; mean age 63·31 ± 12·86; 3,253 [58.78%] male) completed functional assessments 24 months after stroke onset. Through K-means clustering, ischemic stroke (IS) patients were clustered into five groups and hemorrhagic stroke (HS) patients into four groups. Each cluster had distinct clinical characteristics and functional recovery patterns. The final prediction models for IS and HS patients achieved relatively high prediction accuracies of 0.926 and 0.887, respectively. The longitudinal, multi-dimensional, functional assessment data of first-time stroke patients were successfully clustered, and the prediction models showed relatively good accuracies. Early identification and prediction of long-term functional outcomes will help clinicians develop customized treatment strategies.

Two subtypes of cutaneous melanoma with distinct mutational signatures and clinico-genomic characteristics.

Background: To decipher mutational signatures and their associations with biological implications in cutaneous melanomas (CMs), including those with a low ultraviolet (UV) signature. Materials and Methods: We applied non-negative matrix factorization (NMF) and unsupervised clustering to the 96-class mutational context of The Cancer Genome Atlas (TCGA) cohort (N = 466) as well as other publicly available datasets (N = 527). To explore the feasibility of mutational signature-based classification using panel sequencing data, independent panel sequencing data were analyzed. Results: NMF decomposition of the TCGA cohort and other publicly available datasets consistently found two mutational signatures: UV (SBS7a/7b dominant) and non-UV (SBS1/5 dominant) signatures. Based on mutational signatures, TCGA CMs were classified into two clusters: UV-high and UV-low. CMs belonging to the UV-low cluster showed significantly worse overall survival and landmark survival at 1-year than those in the UV-high cluster; low or high UV signature remained the most significant prognostic factor in multivariate analysis. The UV-low cluster showed distinct genomic and functional characteristic patterns: low mutation counts, increased proportion of triple wild-type and KIT mutations, high burden of copy number alteration, expression of genes related to keratinocyte differentiation, and low activation of tumor immunity. We verified that UV-high and UV-low clusters can be distinguished by panel sequencing. Conclusion: Our study revealed two mutational signatures of CMs that divide CMs into two clusters with distinct clinico-genomic characteristics. Our results will be helpful for the clinical application of mutational signature-based classification of CMs.

Genetic overlap between mood instability and alcohol-related phenotypes suggests shared biological underpinnings.

Alcohol use disorder (AUD) is a pervasive and devastating mental illness with high comorbidity rates with other mental disorders. Understanding the genetic architecture of this comorbidity could be improved by focusing on intermediate traits that show positive genetic correlation with the disorders. Thus, we aimed to characterize the shared vs. unique polygenicity of AUD, alcohol consumption (AC) and mood instability (MOOD)-beyond genetic correlation, and boost discovery for jointly-associated loci. Summary statistics for MOOD (a binary measure of the tendency to report frequent mood swings), AC (number of standard drinks over a typical consumption week) and AUD GWASs (Ns > 200,000) were analyzed to characterize the cross-phenotype associations between MOOD and AC, MOOD and AUD and AC and AUD. To do so, we used a newly established pipeline that combines (i) the bivariate causal mixture model (MiXeR) to quantify polygenic overlap and (ii) the conjunctional false discovery rate (conjFDR) to discover specific jointly associated genomic loci, which were mapped to genes and biological functions. MOOD was highly polygenic (10.4k single nucleotide polymorphisms, SNPs, SD = 2k) compared to AC (4.9k SNPs, SD = 0.6k) and AUD (4.3k SNPs, SD = 2k). The polygenic overlap of MOOD and AC was twice that of MOOD and AUD (98% vs. 49%), with opposite genetic correlation (-0.2 vs. 0.23), as confirmed in independent samples. MOOD&AUD associated SNPs were significantly enriched for brain genes, conversely to MOOD&AC. Among 38 jointly associated loci, fifteen were novel for MOOD, AC and AUD. MOOD, AC and AUD were also strongly associated at the phenotypic level. Overall, using multilevel polygenic quantification, joint loci discovery and functional annotation methods, we evidenced that the polygenic overlap between MOOD and AC/AUD implicated partlyshared biological underpinnings, yet, clearly distinct functional patterns between MOOD&AC and MOOD&AUD, suggesting new mechanisms for the comorbidity of AUD with mood disorders.

Normalization effect of levodopa on hierarchical brain function in Parkinson's disease.

Hierarchical brain organization, in which the rich club and diverse club situate in core position, is critical for global information integration in the human brain network. Parkinson’s disease (PD), a common movement disorder, has been conceptualized as a network disorder. Levodopa is an effective treatment for PD. Whether there is a functional divergence in the hierarchical brain system under PD pathology, and how this divergence is regulated by immediate levodopa therapy, remains unknown. We constructed a functional network in 61 PD patients and 89 normal controls and applied graph theoretical analyses to examine the neural mechanism of levodopa short response from the perspective of brain hierarchical configuration. The results revealed the following: (a) PD patients exhibited disrupted function within rich-club organization, while the diverse club preserved function, indicating a differentiated brain topological organization in PD. (b) Along the rich-club derivate hierarchical system, PD patients showed impaired network properties within rich-club and feeder subnetworks, and decreased nodal degree centrality in rich-club and feeder nodes, along with increased nodal degree in peripheral nodes, suggesting distinct functional patterns in different types of nodes. And (c) levodopa could normalize the abnormal network architecture of the rich-club system. This study provides evidence for levodopa effects on the hierarchical brain system with divergent functions.

EEG Microstate-Specific Functional Connectivity and Stroke-Related Alterations in Brain Dynamics.

The brain, as a complex dynamically distributed information processing system, involves the coordination of large-scale brain networks such as neural synchronization and fast brain state transitions, even at rest. However, the neural mechanisms underlying brain states and the impact of dysfunction following brain injury on brain dynamics remain poorly understood. To this end, we proposed a microstate-based method to explore the functional connectivity pattern associated with each microstate class. We capitalized on microstate features from eyes-closed resting-state EEG data to investigate whether microstate dynamics differ between subacute stroke patients (N = 31) and healthy populations (N = 23) and further examined the correlations between microstate features and behaviors. An important finding in this study was that each microstate class was associated with a distinct functional connectivity pattern, and it was highly consistent across different groups (including an independent dataset). Although the connectivity patterns were diminished in stroke patients, the skeleton of the patterns was retained to some extent. Nevertheless, stroke patients showed significant differences in most parameters of microstates A, B, and C compared to healthy controls. Notably, microstate C exhibited an opposite pattern of differences to microstates A and B. On the other hand, there were no significant differences in all microstate parameters for patients with left-sided vs. right-sided stroke, as well as patients before vs. after lower limb training. Moreover, support vector machine (SVM) models were developed using only microstate features and achieved moderate discrimination between patients and controls. Furthermore, significant negative correlations were observed between the microstate-wise functional connectivity and lower limb motor scores. Overall, these results suggest that the changes in microstate dynamics for stroke patients appear to be state-selective, compensatory, and related to brain dysfunction after stroke and subsequent functional reconfiguration. These findings offer new insights into understanding the neural mechanisms of microstates, uncovering stroke-related alterations in brain dynamics, and exploring new treatments for stroke patients.

Ecological and genomic responses of soil microbiomes to high-severity wildfire: linking community assembly to functional potential

Increasing wildfire severity, which is common throughout the western United States, can have deleterious effects on plant regeneration and large impacts on carbon (C) and nitrogen (N) cycling rates. Soil microbes are pivotal in facilitating these elemental cycles, so understanding the impact of increasing fire severity on soil microbial communities is critical. Here, we assess the long-term impact of high-severity fires on the soil microbiome. We find that high-severity wildfires result in a multi-decadal (>25 y) recovery of the soil microbiome mediated by concomitant differences in aboveground vegetation, soil chemistry, and microbial assembly processes. Our results depict a distinct taxonomic and functional successional pattern of increasing selection in post-fire soil microbial communities. Changes in microbiome composition corresponded with changes in microbial functional potential, specifically altered C metabolism and enhanced N cycling potential, which related to rates of potential decomposition and inorganic N availability, respectively. Based on metagenome-assembled genomes, we show that bacterial genomes enriched in our earliest site (4 y since fire) harbor distinct traits such as a robust stress response and a high potential to degrade pyrogenic, polyaromatic C that allow them to thrive in post-fire environments. Taken together, these results provide a biological basis for previously reported process rate measurements and explain the temporal dynamics of post-fire biogeochemistry, which ultimately constrains ecosystem recovery.

Identification of Key Proteins from the Alternative Lengthening of Telomeres-Associated Promyelocytic Leukemia Nuclear Bodies Pathway.

Simple SummaryThe alternative lengthening of telomeres is a telomere maintenance mechanism used by some cancer types to elongate their telomeres without the aid of telomerase. This mechanism contributes to the proliferation and immortality of cancer cells. One of the hallmarks of this mechanism is the interaction with promyelocytic leukemia nuclear bodies, which are suspected to be the key places where telomere extension occurs. Despite the discovery of some mechanisms, elements, key genes, and proteins from the pathway, the alternative lengthening of telomeres mechanism is still poorly understood, and it is highly associated with a poor prognosis. In this study, we combined multiomics approaches with genomic, transcriptomic, and proteomic analyses of 71 genes/proteins related to promyelocytic leukemia nuclear bodies in more than 10,000 cancer samples from The Cancer Genome Atlas Consortium. As a result, 13 key proteins were proposed as candidates for future experimental studies that will validate these proteins as therapeutic markers, which will improve the understanding and treatment of these type of cancers. Alternative lengthening of telomeres-associated promyelocytic leukemia nuclear bodies (APBs) are a hallmark of telomere maintenance. In the last few years, APBs have been described as the main place where telomeric extension occurs in ALT-positive cancer cell lines. A different set of proteins have been associated with APBs function, however, the molecular mechanisms behind their assembly, colocalization, and clustering of telomeres, among others, remain unclear. To improve the understanding of APBs in the ALT pathway, we integrated multiomics analyses to evaluate genomic, transcriptomic and proteomic alterations, and functional interactions of 71 APBs-related genes/proteins in 32 Pan-Cancer Atlas studies from The Cancer Genome Atlas Consortium (TCGA). As a result, we identified 13 key proteins which showed distinctive mutations, interactions, and functional enrichment patterns across all the cancer types and proposed this set of proteins as candidates for future ex vivo and in vivo analyses that will validate these proteins to improve the understanding of the ALT pathway, fill the current research gap about APBs function and their role in ALT, and be considered as potential therapeutic targets for the diagnosis and treatment of ALT-positive cancers in the future.

Single-Cell Clonal Tracking in Allogeneic Hematopoietic Stem Cell Transplantation Reveals Time Dependent and Distinct Functional Patterns in Traceable Donor T Cell Clones

Allogeneic hematopoietic stem cell transplantation (alloHSCT) is the only curative treatment option for various malignant hematological diseases. The therapeutic effect of alloHSCT is a long-lasting graft-versus-leukemia (GvL) effect of the transferred graft. T cells are important mediators of GvL and the longitudinal tracking of T-cell clones from donor to recipient is of particular interest in the setting of alloHSCT as this might provide further insight into mechanisms leading to survival and expansion of particular clones. In a broader sense, we used the unique setting of alloHSCT to study survival and expansion of mature T-cell clones after transfer into an immune cell depleted and allogeneic patient.We used single-cell RNA sequencing (scRNAseq) to integrate immune subset delineation, clone identification and transcriptome information of about 35500 single T cells in peripheral blood of 14 paired donor-recipient samples in four alloHSCT pairs. Donor samples were collected before and after treatment with Granulocyte-Colony Stimulating Factor (GCSF), and recipient samples were collected on days +90 and +180 post-transplant.Looking at common diversity scores of pooled donor versus pooled recipient time points we observe an expected decrease of TCR diversity after transplantation (median inv. Simpson's 379 in donor vs. 20 in recipient samples, p=0.011, Figure 1A). On single cell level, we observe a substantial decrease of unique T-cell clones after transplantation compared to donor samples, which in return means that certain TCR clones markedly expand, contributing to a skewing of the TCR repertoire in the post-transplant course. The majority of these cells represent CD8 effector memory T cells.Our main interest was a better understanding of traceable and persisting T-cell clones. In a first step, we looked at the overall clonal overlap between time points of the different donor-recipient pairs, using only combined TCR alpha and beta chain information to define specific T-cell clones. We find the highest overlaps of T-cell clones between time points within individuals (e.g., Morisita score 0.91 between preGCSF and postGCSF of donor 16 and Morisita score 0.65 between days +90 and +180 of recipient 16, Figure 1B). Additionally, we demonstrate an inter-individual overlap between donors and their respective recipients in all pairs on single cell level (Figure 1C).Next, we compared the differential gene expression of traceable and non-traceable T cell clones and found that the traceable T cell clones exhibit a distinct transcriptional program, characterized by upregulation of genes related to T cell proliferation and chemotaxis as well as antigen presentation, while housekeeping functions such as translation are downregulated.In order to examine the dynamic changes of the T-cell transcriptome, we looked at the differential gene expression at the consecutive time points of pooled traceable clones in all pairs. This shows an induction of an activation pattern during the donor-recipient transfer and post-transplant phase involving genes related to the cell cycle and graft-versus-host disease (Figure 1D). Phenotype analysis via antibody-derived tags accordingly revealed an upregulation of activation markers in the recipients.To our knowledge, this is the first time that longitudinal inter-individual (donor-to-recipient) overlap of single-cell TCR alpha/beta clones is demonstrated in the setting of alloHSCT revealing time point-dependent and distinct functional patterns in traceable donor T cell clones. [Display omitted] DisclosuresPenack: Neovii: Honoraria; MSD: Honoraria; Incyte: Research Funding; Priothera: Consultancy; Therakos: Honoraria; Gilead: Honoraria; Novartis: Honoraria; Pfizer: Honoraria; Takeda: Research Funding; Astellas: Honoraria; Jazz: Honoraria; Omeros: Consultancy; Shionogi: Consultancy. Bullinger: Amgen: Honoraria; Jazz Pharmaceuticals: Consultancy, Honoraria, Research Funding; Hexal: Consultancy; Abbvie: Consultancy, Honoraria; Bristol-Myers Squibb: Consultancy, Honoraria; Astellas: Honoraria; Pfizer: Consultancy, Honoraria; Janssen: Consultancy, Honoraria; Bayer: Research Funding; Seattle Genetics: Honoraria; Novartis: Consultancy, Honoraria; Daiichi Sankyo: Consultancy, Honoraria; Menarini: Consultancy; Gilead: Consultancy; Celgene: Consultancy, Honoraria; Sanofi: Honoraria. Na: Bristol Myers Squibb: Research Funding; Shire/Takeda: Honoraria, Research Funding; Octapharma: Honoraria, Research Funding.

A functional approach for characterizing safety risk of signalized intersections at the movement level: An exploratory analysis

Safety evaluation of signalized intersections is often conducted by developing statistical and data-driven methods based on data aggregated at certain temporal and spatial levels (e.g., yearly, hourly, or per signal cycle; intersection or approach leg). However, such aggregations are subject to a major simplification that masks the underlying spatio-temporal safety risk patterns within the data aggregation levels. Consequently, high-resolution analysis such as safety risk within signal cycles and at traffic movement level cannot be performed. This study contributes to the literature by proposing a new functional data analysis (FDA) approach for a novel characterization of safety risk patterns of signalized intersections. Functional data smoothing methods that can mitigate overfitting and account for the nonnegative characteristics of safety risk are proposed to model the time series of safety risk within signal cycles at the traffic movement level. Functional analysis of variance method (FANOVA) that can compare the group level differences of functional curves is used to test differences of safety risk functions among different traffic movements. A typical signalized intersection with representative signal types and channelizations is selected as the study location and approximately 1-hour traffic video data recorded by an unmanned aerial vehicle are used to extract traffic conflicts. New movement-level safety risk patterns are characterized based on the safety risk functions that can reveal the temporal distribution of risk within signal cycles. Most of the tested traffic movements have significantly distinct functional risk patterns according to the FANOVA results while risk patterns for most of the traffic movements cannot be differentiated based on the data aggregated at the cycle and approach levels. The proposed functional approach has the potential to be used for facilitating proactive safety management, calibrating microsimulation models for safety evaluation, and optimizing signal timing while considering traffic safety at more disaggregated levels.

Interleukin-6 receptor alpha and CD27 discriminate intratumoral T helper 17 subpopulations with distinct functional properties in a mouse lung cancer model.

BackgroundT helper 17 (Th17) cells actively participate in the tumor immune response in lung cancer. However, the heterogeneity and plasticity of intratumoral Th17 cells in lung cancer remain elusive. In this study, Th17 subpopulations were characterized in a mouse lung cancer model.MethodsUrethane was administered to induce lung cancer in interleukin (IL)‐17‐EGFP transgenic mice. Flow cytometry was used to analyze the phenotypes, signaling status, and functions of Th17 subpopulations either in vivo or in vitro. The adoptive transfer assay and real‐time polymerase chain reaction were applied to analyze the plasticity of Th17 subpopulations.ResultsIL‐6RαhighCD27− Th17 and IL‐6RαlowCD27+ Th17 were identified in intratumoral Th17 cells. The two subpopulations expressed equivalent RORγt. However, the former expressed higher T‐bet but lower Foxp3, more IL‐17A and IFN‐γ but less IL‐10 than the latter. Furthermore, IL‐6RαhighCD27− Th17 moderately inhibited the proliferation of lung cancer cells while IL‐6RαlowCD27+ Th17 could not. IL‐6RαhighCD27− Th17 exhibited weaker Jun N‐terminal kinases (JNK) signaling but stronger signal transducer and activator of transcription 3 (Stat3) signaling than IL‐6RαlowCD27+ Th17. The adoptive transfer assay indicated that both subpopulations downregulated RORγt in recipients' spleens but maintained RORγt in recipients' lungs. Meanwhile, IL‐6RαhighCD27− Th17 expressed higher T‐bet and IFN‐γ than IL‐6RαlowCD27+ Th17 in the recipients. IL‐6RαlowCD27+ Th17 expressed Foxp3 and IL‐10 in recipients' spleens but not lungs.ConclusionsThis study reveals intratumoral Th17 subpopulations with distinct functional properties and signaling patterns, thus offering valuable insight into Th17 heterogeneity and plasticity in lung cancer.

Hyperemic hemodynamic characteristics of serial coronary lesions assessed by pullback pressure gradients

To characterize hemodynamics of serial coronary stenoses using fractional flow reserve (FFR) pullbacks and the pullback pressure gradients (PPG) index. The cross-talk between stenoses within the same coronary artery makes the prediction of the functional contribution of each lesion challenging. One-hundred seventeen patients undergoing coronary angiography for stable angina were prospectively recruited. Serial lesions were defined as two or more narrowings with visual diameter stenosis >50% on conventional angiography. Motorized FFR pullback tracings were obtained at 1 mm/s. Pullbacks were visually adjudicated as presenting two, one, and no focal pressure drops. The pattern of disease (i.e., focal or diffuse) was quantified using the PPG index. Twenty-five vessels presented serial lesions (mean PPG 0.48 ± 0.17). Two, one or no focal pressure drops were observed in 40% (n= 10; PPG 0.59 ± 0.17), 52% (n= 13; PPG 0.44 ± 0.12) and 8% of cases (n= 2; PPG 0.27 ± 0.01; p-value=0.01). Distal FFR was similar between vessels with two, one and no focal pressure drops in the pullback curve (p-value=0.27). The PPG index independently predicted the presence of two focal pressure drops in the pullback curve (p= 0.04). FFR pullbacks in serial coronary lesions exhibit three distinct functional patterns. High PPG was associated with pullback curves presenting two pressure drops. The PPG provides a quantitative assessment of the pattern of coronary artery disease in cases with serial lesions and might be useful to assess the appropriateness of percutaneous revascularization.