Biological Underpinnings Research Articles

Proteins involved in synapse organization are associated with longitudinal loss in gray matter network connectivity in prodromal AD

AbstractBackgroundLower gray matter connectivity measures are related to faster cognitive decline in prodromal Alzheimer’s Disease (AD). We investigated the biological underpinnings responsible for this loss of brain connections using cerebrospinal fluid (CSF) proteomics.MethodData from ADNI was used to select 82 individuals with MCI and abnormal CSF amyloid (mean age±SD 74±7) and 37 controls with normal amyloid levels (mean age±SD 75±5), for whom repeated MRI‐scans (median of 6 scans over 1.5 follow‐up years) and baseline CSF proteomic data (in total 306 proteins) were available. We used an automated pipeline (Tijms, Series et al. 2012) to construct gray matter networks from 3D‐T1 sequences and calculate connectivity density and gamma – measures previously found to be most strongly related to cognitive decline (Dicks, Vermunt et al. 2020). Linear mixed models were applied to test associations between baseline CSF protein levels and repeated connectivity metrics, controlling for intracranial volume, age and sex. Proteins that demonstrated significant associations (p<0.05) were identified and examined for their biological relevance according to Gene Ontology.ResultConnectivity density declined over time with ‐0.8% per year (pMCI = 0.001; figure 1), which did not differ from controls (pdif = 0.15). Gamma declined faster in MCI than in controls (bMCI = ‐0.016 vs. bCN = ‐0.007, pdif <0.001; figure 1). Higher levels of 99/108 proteins were associated with faster decline in connectivity density (a.o. tau and neuroprentaxins). These proteins were involved in regulation of synapse organization (GO:0050807; pFDR = 7.00E‐07; figure 2). Higher levels of 9/108 proteins associated with less decline and were involved in chemotaxis (GO:0048247; pFDR = 1.40E‐02). Similarly, 25 out 30 proteins (83%) associated with alterations in gamma over time, showed associations of higher levels with less gamma decline and were also involved in chemotaxis (GO:0050926; pFDR = 9.42E‐05; figure 3). There were no protein‐connectivity relationships in controls.ConclusionProteins involved in synapse organization were associated with faster decline in connectivity density over time in patients, while chemotaxis was associated with less decline. This suggests that synaptic degeneration may predict disease progression while immune responses may serve as a resilience mechanism in AD.

A study to determine the mechanisms underlying changes in synaptic vesicle glycoprotein 2A density in Alzheimer’s disease

AbstractBackgroundSynaptic vesicle glycoprotein 2A (SV2A) is a transmembrane synaptic vesicle protein that regulates vesicle exocytosis and neurotransmitter release. Positron emission tomography (PET) studies reveal that the levels of SV2A are decreased in the brains of patients with Alzheimer’s disease (AD) and other brain disorders. Whether changes in SV2A PET signal are caused by a loss of synapses or a loss of SV2A protein is unclear and an impediment to developing novel treatments for neurodegenerative diseases. The goal of this study is to determine the biological underpinnings of reduced SV2A signal in individuals with AD.MethodWe examined post‐mortem brain samples from the entorhinal cortex (EC) and cerebellum (CBM) in AD patients and age‐ and sex‐matched controls without neurological conditions (EC n = 19 AD, 15 controls; CBM n = 11 AD, 12 controls). We explored differences in synaptic density and synaptic ultrastructure using array tomography and electron microscopy, and analyzed data using linear mixed‐effects models (with post‐hoc Tukey comparisons) and Spearman correlations.ResultSV2A puncta density measured with array tomography in both brain regions strongly correlates with synaptophysin puncta density, the gold standard marker for synapse density in post‐mortem brain tissue (ñ = 0.87 EC, ñ = 0.72 CBM, p<0.001 both regions). We observed lower SV2A puncta density in AD cases compared to controls in EC (p = 0.001), slightly higher SV2A puncta density in AD in CBM (p = 0.02), and no difference in the proportion of synaptophysin puncta labelled with SV2A in AD vs control (p = 0.37). Electron microscopy revealed immunogold labelled SV2A protein localized specifically to presynaptic vesicles and no difference in the number of immuno‐gold labelled SV2A particles per synapse.ConclusionOur data indicate that the loss of SV2A PET signal reported in vivo in patients with AD is likely caused by a loss of synapses and not the loss of SV2A protein within remaining synapses. These findings indicate that changes in SV2A PET signal are due to synaptic loss and that strategies for reversing synaptic loss or associated neuropathophysiological effects may be important to the treatment of AD and other synaptopathies. This work is part of the SV2A PET Project, a program of the FNIH Biomarker Consortium.

Analysis of Posttraumatic Stress Disorder Gene Expression Profiles in a Prospective, Community-Based Cohort

abstract: Posttraumatic stress disorder (PTSD) is a common and debilitating psychiatric disorder that may occur in individuals exposed to traumatic events such as accidents, interpersonal violence, war, combat, or natural disasters. Additionally, PTSD has been implicated in the development of a variety of chronic conditions, including cardiovascular and metabolic diseases, suggesting the biological alterations associated with the disorder can manifest as chronic diseases in those suffering from PTSD. The biological underpinnings of the disorder are not well understood. Gene expression studies can illuminate the complex physiology of PTSD reflecting the embodiment of trauma, that is, the process in which traumatic experiences in our social environments could potentially manifest in our body by genomic mechanisms. To date, gene expression studies that examine the whole transcriptome are scarce and limited to single-time-point assessments. Here we applied a transcriptome-wide gene expression screen with RNA-seq to whole blood samples from predominantly African American community-dwelling participants to elucidate the gene expression signatures associated with the development of PTSD. The study participants ( N = 72) comprised a trauma-exposed subsample of participants enrolled in a longitudinal, prospective cohort study of adults living in Detroit, Michigan. PTSD was assessed in a structured telephone interview, and whole blood samples were taken both before and after trauma exposure. Among the 72 study participants, 10 had PTSD at baseline and 21 developed it during the study. We found 45 differentially expressed genes associated with PTSD development with an estimated log2-fold change > 1.5 at a nominal p -value of <0.05. Six genes ( PAX6 , TSPAN7 , PXDN , VWC2 , SULF1 , and NFATC4 ) were also ubiquitously expressed in all brain regions. Longitudinal sampling provides a promising mean to elucidate the pathophysiology underlying the embodiment of trauma.

Arsenic Exposure and Epigenetic Aging: The Association with Cardiovascular Disease and All-Cause Mortality in the Strong Heart Study.

Inorganic arsenic (As) may increase the risk of cardiovascular disease (CVD) and all-cause mortality through accelerated aging, which can be estimated using epigenetic-based measures. We evaluated three DNA methylation-based aging measures (PhenoAge, GrimAge, DunedinPACE) (epigenetic aging measures) as potential mediators of the previously reported association of As exposure with CVD incidence, CVD mortality, and all-cause mortality in the Strong Heart Study (SHS), an epidemiological cohort of American Indian adults. Blood DNA methylation and urinary As levels were measured in 2,323 SHS participants (41.5% men, mean age of 55 years old). PhenoAge and GrimAge values were calculated using a residual-based method. We tested the association of urinary As with epigenetic aging measures using linear regression, the association of epigenetic aging measures with the three health outcomes using additive hazards models, and the mediation of As-related CVD incidence, CVD mortality, and all-cause mortality by epigenetic aging measures using the product of coefficients method. SHS participants with higher vs. lower urinary As levels had similar PhenoAge age, older GrimAge age, and faster DunedinPACE. An interquartile range increase in urinary As was associated with higher of PhenoAge age acceleration [ (0.17, 0.80) years], GrimAge age acceleration [0.80 (0.60, 1.00) years], and DunedinPACE [0.011 (0.005, 0.018)], after adjusting for age, sex, center location, genetic components, smoking status, and body mass index. Of the 347 incident CVD events per 100,000 person-years associated with a doubling in As exposure, 21.3% (9.1, 57.1) and 22.6% (9.5, 56.9), were attributable to differences in GrimAge and DunedinPACE, respectively. Arsenic exposure was associated with older GrimAge and faster DunedinPACE measures of biological age. Furthermore, accelerated biological aging measured from DNA methylation accounted for a relevant fraction of As-associated risk for CVD, CVD mortality, and all-cause mortality in the SHS, supporting the role of As in accelerated aging. Research of the biological underpinnings can contribute to a better understanding of the role of aging in arsenic-related disease. https://doi.org/10.1289/EHP11981.

Unveiling the Mind’s Mystery: Exploring the Neurological Basis of Consciousness and its Impact on Human Health

Viewed as one of the biggest mysteries in science, consciousness was once overlooked as a research topic and is now an expanding field in neuroscience. Currently, there is no set definition for consciousness, and its biological underpinnings are not fully understood. Disorders of consciousness, where appropriate levels of consciousness are not achieved for a given period, can have a significant impact on quality of life. To recognize the implications of consciousness, it is important to understand the neurological basis of consciousness disorders. Today, research is limited to patients that suffer from long-term states of unconsciousness. However, scientists have been able to form a framework for the biological circuits involved in consciousness. It has been determined that consciousness is regulated through the integration of neural circuits between several parts of the brain. Many theories associated with the neural circuits that allow the human body to sustain consciousness exist: the Global Neuronal Workspace Theory, Information Integration Theory, and Temporospatial Theory of Consciousness. External factors can also lead to disorders of consciousness. While researchers have developed a clearer picture of the biological underpinnings of consciousness, treatments and recovery from disorders of consciousness remains challenging. As such, further work is needed to improve patient outcomes in disorders of consciousness. Here, we will define the areas of the brain related to consciousness, discuss the biological systems involved in conscious systems, and discuss disorders of consciousness and their current treatments. Finally, we will discuss new approaches to treatments and how they can improve patient outcomes.

Biological underpinnings of radiomic magnetic resonance imaging phenotypes for risk stratification in IDH wild-type glioblastoma

BackgroundTo develop and validate a conventional MRI-based radiomic model for predicting prognosis in patients with IDH wild-type glioblastoma (GBM) and reveal the biological underpinning of the radiomic phenotypes.MethodsA total of 801 adult patients (training set, N = 471; internal validation set, N = 239; external validation set, N = 91) diagnosed with IDH wild-type GBM were included. A 20-feature radiomic risk score (Radscore) was built for overall survival (OS) prediction by univariate prognostic analysis and least absolute shrinkage and selection operator (LASSO) Cox regression in the training set. GSEA and WGCNA were applied to identify the intersectional pathways underlying the prognostic radiomic features in a radiogenomic analysis set with paired MRI and RNA-seq data (N = 132). The biological meaning of the conventional MRI sequences was revealed using a Mantel test.ResultsRadscore was demonstrated to be an independent prognostic factor (P < 0.001). Incorporating the Radscore into a clinical model resulted in a radiomic-clinical nomogram predicting survival better than either the Radscore model or the clinical model alone, with better calibration and classification accuracy (a total net reclassification improvement of 0.403, P < 0.001). Three pathway categories (proliferation, DNA damage response, and immune response) were significantly correlated with the prognostic radiomic phenotypes.ConclusionOur findings indicated that the prognostic radiomic phenotypes derived from conventional MRI are driven by distinct pathways involved in proliferation, DNA damage response, and immunity of IDH wild-type GBM.

Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.

Depression is one of the most common mental disorders that affects >300 million people worldwide. There is a shortage of providers trained in the provision of mental health care, and the nursing workforce is essential in filling this gap. The diagnosis of depression relies heavily on self-reported symptoms and clinical interviews, which are subject to implicit biases. The omics methods, including genomics, transcriptomics, epigenomics, and microbiomics, are novel methods for identifying the biological underpinnings of depression. Machine learning is used to analyze genomic data that includes large, heterogeneous, and multidimensional data sets. This scoping review aims to review the existing literature on machine learning methods for omics data analysis to identify individuals with depression, with the goal of providing insight into alternative objective and driven insights into the diagnostic process for depression. This scoping review was reported following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Searches were conducted in 3 databases to identify relevant publications. A total of 3 independent researchers performed screening, and discrepancies were resolved by consensus. Critical appraisal was performed using the Joanna Briggs Institute Critical Appraisal Checklist for Analytical Cross-Sectional Studies. The screening process identified 15 relevant papers. The omics methods included genomics, transcriptomics, epigenomics, multiomics, and microbiomics, and machine learning methods included random forest, support vector machine, k-nearest neighbor, and artificial neural network. The findings of this scoping review indicate that the omics methods had similar performance in identifying omics variants associated with depression. All machine learning methods performed well based on their performance metrics. When variants in omics data are associated with an increased risk of depression, the important next step is for clinicians, especially nurses, to assess individuals for symptoms of depression and provide a diagnosis and any necessary treatment.

Genome-wide association and functional genomic analyses for various hoof health traits in North American Holstein cattle

Hoof diseases are a major welfare and economic issue in the global dairy cattle production industry, which can be minimized through improved management and breeding practices. Optimal genetic improvement of hoof health could benefit from a deep understanding of the genetic background and biological underpinning of indicators of hoof health. Therefore, the primary objectives of this study were to perform genome-wide association studies, using imputed high-density genetic markers data from North American Holstein cattle, for 8 hoof-related traits: digital dermatitis, sole ulcer, sole hemorrhage, white line lesion, heel horn erosion, interdigital dermatitis, interdigital hyperplasia, and toe ulcer, and a hoof health index. De-regressed estimated breeding values from 25,580 Holstein animals were used as pseudo-phenotypes for the association analyses. The genomic quality control, genotype phasing, and genotype imputation were performed using the PLINK (version 1.9), Eagle (version 2.4.1), and Minimac4 software, respectively. The functional genomic analyses were performed using the GALLO R package and the DAVID platform. We identified 22, 34, 14, 22, 28, 33, 24, 43, and 15 significant markers for digital dermatitis, heel horn erosion, interdigital dermatitis, interdigital hyperplasia, sole hemorrhage, sole ulcer, toe ulcer, white line lesion disease, and the hoof health index, respectively. The significant markers were located across all autosomes, except BTA10, BTA12, BTA20, BTA26, BTA27, and BTA28. Moreover, the genomic regions identified overlap with various previously reported quantitative trait loci for exterior, health, meat and carcass, milk, production, and reproduction traits. The enrichment analyses identified 44 significant gene ontology terms. These enriched genomic regions harbor various candidate genes previously associated with bone development, metabolism, and infectious and immunological diseases. These findings indicate that hoof health traits are highly polygenic and influenced by a wide range of biological processes.

Large-Scale Post-Transplant TCR Deep Sequencing Reveals a Major T Cell Diversity Bottleneck with Post-Transplant Cyclophosphamide with Implications for Both Efficacy and Toxicity: Results of the BMT CTN 1801 Study

Background: BMT CTN 1703 was a Phase 3 trial comparing Tac/MTX (n = 217) to post-transplant cyclophosphamide (PT-Cy)/Tac/MMF (n = 214) GVHD prophylaxis after reduced intensity conditioning unrelated donor hematopoietic cell transplant (HCT). PT-Cy significantly improved GVHD-free Relapse-Free Survival (Bolanos-Meade et al NEJM 2023). However overall survival was not significantly different and the # of Grade 2 infections was higher with PT-Cy. To investigate its biologic underpinnings 1703 was linked to a mechanistic study, BMT CTN 1801, which co-enrolled 324 pts (159 CNI/MTX 165 PT-Cy). The resulting dataset enabled an analysis of post-HCT T cell reconstitution at an unprecedented level of detail. Methods: Samples were collected from the HCT infusion and on D+7, 14, 28, 63, 98, 180, 270, 1- and 2-yr. We extracted DNA (2,225 samples) and performed Adaptive Biotech β-strand T Cell Receptor (TCR) Immunosequencing (TCR-Seq). We profiled 44,077 (median) TCRs/sample resulting in 215,155,719 T cells analyzed. We evaluated multiple TCR diversity measures longitudinally and before/after cGVHD and infections. Each measure evaluates a specific aspect of TCR diversity, including Simpsons Clonality (focuses on the most expanded clones) Diversity Slope (mid-range clones) Richness (both mid-range and less-expanded clones) and Singletons (the TCR-Seq equivalent of naïve T cells). This analysis was robust to the full range (1000-50,000) of down-sampled TCRs sequenced. Results: We identified major differences in TCR reconstitution in Tac/MTX vs PT-Cy that begin early and persist through 2 yr post-HCT. These resulted in a significantly less diverse TCR repertoire with PT-Cy with substantial implications for its efficacy and toxicity. The most salient differences are: (1) PT-Cy caused significant in-vivo T cell depletion early post-HCT (% T cells/total WBC at D+28: 8.2% (Tac/MTX) vs 2.2% (PT-Cy) p=2.2x10 -23). (2) The remaining donor T cells in PT-Cy undergo substantial clonal expansion ( Fig 1) resulting in significantly constrained TCR diversity measured by Clonality, Slope, Richness, Singletons (not shown) and Singletons/Richness ( Fig 2) which began early (D+14) and persisted for 2 yr. (3) Linked T cell depletion and clonal expansion resulted in a TCR diversity bottleneck after PT-Cy ( Fig 1). Due to the bottleneck, while the fraction of T cells originating from the transplant infusion at =/&amp;gt; 1 yr was similar between arms (18% vs 24%, p=0.7) there were significantly fewer Singleton TCRs in PT-Cy, suggesting a reduction in naïve T cells ( Fig 2), an outcome that has critical consequences for both cGVHD and infection. Thus while cGVHD onset with Tac/MTX was linked to a significant drop in TCR diversity due to singleton clonal expansion (median log-2 fold change in singletons with Tac/MTX = -0.25 p = 0.01) this drop was not observed with PT-Cy (log-2 fold change = -0.05, p = 0.1). Along with the significantly decreased # singletons at all time-points in PT-Cy ( Fig 2) this suggests that while cGVHD after Tac/MTX is linked to substantial naïve T cell expansion, the same is not true with PT-Cy, identifying a novel mechanism for cGVHD control with PT-Cy. The substantially fewer singletons in PT-Cy vs Tac/MTX may also increase the risk of Grade 2+ infections. T cell responses to new infections rely on antigen recognition/effector maturation of naïve (singleton TCR) T cells. After infection, the TCR repertoire became less diverse for both Tac/MTX and PT-Cy (log-2-fold singleton change -0.17 and -1.2 p = 0.0006 and 0.01) indicating that singletons in both cohorts were capable of expanding to pathogens. However the markedly decreased # singletons in PT-Cy at all timepoints was associated with significantly fewer singletons prior to infection (mean 3979 at 5K read-depth for Tac/MTX vs 2820 for PT-Cy p&amp;lt;0.001). This singleton deficit would place PT-Cy patients at higher risk of missing the critical mass of TCRs required to respond effectively to infections, consistent with their increased rate of Gr 2 infections. Conclusions: We performed longitudinal TCR-Seq on &amp;gt;200 million T cells from 324 HCT patients, representing the largest post-HCT TCR-Seq analysis in the history of the field, and enabling novel insights into TCR dynamics. These data identify a previously unappreciated global TCR diversity bottleneck with PT-Cy that simultaneously protects patients from cGVHD and places them at higher risk of infectious complications.

Real-World Performance of 2022 Revised World Health Organization (WHO) Criteria for Mixed Phenotype Acute Leukemia (MPAL) B/Myeloid Defined Only By Isolated MPO Expression (isoMPO)

Mixed phenotype acute leukemia (MPAL) is biologically and immunophenotypically heterogeneous, and optimal diagnostic criteria remain unclear. The two current pathologic classifications [2022 World Health Organization (WHO) 5 th Ed. and International Consensus Classification (ICC)] group MPAL by cell lineages and, when present, cytogenetic drivers. In both, myeloperoxidase (MPO) is a key determinant of myeloid lineage. However, the diagnosis of MPAL has been inconsistently applied to cases of acute leukemia expressing MPO alone to define myeloid lineage. To address this, the 2022 WHO revised prior cell lineage definitions to emphasize antigen intensity (≥50%) relative to a positive control population, attempting to standardize lineage assignment across flow cytometer settings and pathologist interpreters. Distinction between pediatric B-lymphoblastic leukemia (B-ALL) and MPAL B/Myeloid with isolated MPO is crucial for therapeutic decision-making. For example, on Children's Oncology Group (COG) regimens, patients with MPAL B/Myeloid receive an anthracycline during induction and intensified chemotherapy post-induction, whereas standard-risk B-ALL patients do not. We hypothesized that the 2022 WHO criteria applied to MPO expression in children with acute leukemia would identify leukemic subtypes (B-ALL vs. MPAL B/Myeloid with isolated MPO) with differences in clinical characteristics and/or outcome. We applied 2022 WHO criteria to flow cytometry data from a multicenter pediatric cohort of leukemias originally classified as MPAL B/Myeloid or B-ALL (Oberley AJCP 2017; Raikar Blood 2018; Oberley Leukemia 2020). Maximal antigen intensity on the leukemic population was measured in 0.5 log increments and normalized to maximal intensity of a control (neutrophil) population. MPO expression was scored as none, low (&amp;lt;50% intensity of mature neutrophils, the typical level in B-ALL with isolated MPO expression) or high (≥50% intensity of neutrophils, meeting 2022 WHO criteria for myeloid lineage assignment). Since the current study focused on MPAL B/Myeloid cases with isolated MPO expression, cases of MPAL T/Myeloid and of MPAL B/Myeloid with myeloid lineage assignment based on monocytic markers were excluded. Clinicopathologic data were compared with MPO expression scores by univariate and multivariate analyses. Primary endpoint was categorical difference in presenting features. Secondary endpoints were end of induction minimal residual disease (EOI MRD) ≥0.01%, event-free survival (EFS) and overall survival (OS). All analyses were two-sided; p&amp;lt;0.05 was the threshold for statistical significance. Among all patients at diagnosis (n=176), median age was 6.14 years (range 0.3-21), 68% were &amp;lt;10 years old, 59% were male, 63% were Hispanic, 76% had initial white blood cell (WBC) count &amp;lt;50K/µL, and 81% were CNS1. MPO was absent, low (&amp;lt;50%) or high (≥50%) in 48%, 29% and 23% of the cohort, respectively. Prevalence of favorable cytogenetics ( ETV6:: RUNX1 or double trisomy) was inversely proportional to MPO intensity [no MPO (50%), low MPO (27%), high MPO (17%); p&amp;lt;0.001). MPO expression (low or high) was more prevalent in patients &amp;gt;10 years of age (p&amp;lt;0.001). There was no difference in MPO by presenting WBC count (p=0.40). Among patients treated with ALL induction with known EOI MRD results (n=166), MPO intensity was not significantly associated with EOI MRD positivity, EFS or OS (multivariate p&amp;gt;0.40 for all three endpoints after accounting for presenting features and initial therapy). Our study demonstrates that 2022 WHO criteria for interpreting MPO intensity can be consistently applied to flow cytometry data from a variety of laboratories. Based on our analysis, only 44% of previous MPAL B/Myeloid cases with isolated MPO were now classified as MPAL. However, the clinical significance of this distinction remains unclear. After accounting for differences in presenting features, MPO expression was not additionally predictive of MRD positivity, EFS or OS. Further investigation is required to support the proposed threshold of 50% MPO intensity of a neutrophil control population as clinically relevant to assign myeloid lineage. Prospective standardized application of 2022 WHO cell lineage assignment criteria across centers will facilitate detailed understanding of the biological underpinnings and optimal therapeutic strategies for this heterogeneous disease.

Methylation Biology of a Blood-Based MDS Risk Stratification Test

Background: Myelodysplastic syndromes (MDS) are a heterogeneous group of myeloid malignancies associated with a myriad of deleterious outcomes and a 5-year relative survival of 37%. MDS risk stratification is key for optimal treatment decisions. We previously demonstrated that methylation-based markers may help stratify MDS risk and achieved comparable performance relative to IPSS-R (the Revised International Prognostic Scoring System) by assessing the risk prediction ability of a classifier trained with features from either Bone Marrow (BM) whole-genome bisulfite sequencing (WGBS) or serum targeted methylation (TM) sequencing. Here, we elucidate the biological underpinnings of the methylation-based classifier and explore the survival stratification performance of our previously reported classifier. Methods: Utilizing datasets from BM WGBS and matched Serum TM sequencing of 127 patients with MDS (N=104) and secondary AML (N=23), we delineated the features used by the classifier by identifying differentially methylated regions (DMRs) that distinguished patients with overall survival of &amp;gt;3 years from those with &amp;lt;3 years. Our approach also included a functional enrichment and driver gene analysis associated with these DMRs, as well as cluster analysis across patients. We performed further evaluations of the survival outcomes of our BM WGBS and matched Serum TM prognostic classifier using concordance indices and log-rank tests. Results: Our analysis revealed 7,742 DMRs in BM WGBS and 14,093 DMRs in the Serum TM that significantly separated the long and short-term survival groups. Unsupervised hierarchical clustering of differential methylation patterns in both BM WGBS and Serum TM highlighted three distinctive subgroups of samples: short-term survivors, long-term survivors, and a group with intermediate survival outcomes. Notably, the short-term survivor group exhibited extensive hypermethylation across a majority of DMRs, which were linked to key biological pathways including calcium signaling, cAMP, MAPK, Rap1, and PI3K-Akt. Enrichment analysis of the BM WGBS DMRs between individuals with &amp;lt;3 and &amp;gt;3 years of survival underscored the differential hypermethylation of promoters, CpG islands, and CpG shores in association with worse survival outcomes. Our analysis of BM WGBS DMR driver genes via Hypergeometric Optimization of Motif EnRichment (HOMER) revealed potential contributors to differential survival outcomes, including known genes associated with MDS/AML, such as EWS/FLI-1, ERG, Pu.1, and RUNX1. Both pathway and driver gene analysis recapitulated previously known drivers of MDS to AML progression. The methylation classifier achieved comparable risk stratification performance for both BM WGBS comparing with IPSS-R (C-index: 0.69 [95% CI: 0.61-0.77] for methylation vs 0.71 [95% CI: 0.64-0.77] for IPSS-R) and Serum TM (C-index: 0.68 [95% CI: 0.60-0.75] for methylation vs. 0.70 [95% CI: 0.63-0.78] for IPSS-R). At a specificity threshold of 0.8, both BM WGBS and Serum TM prognostic classifiers were able to stratify between predicted short and predicted long survivors (BM WGBS HR: 2.83, p&amp;lt;0.0001; Serum TM HR: 3.01, p=0.0008). Conclusions: Our findings generated biological insights into the methylation-based risk stratification of MDS, shedding light on the specific genomic features and biological pathways that underlie these methylation patterns. We also demonstrated that methylation-based MDS risk stratification performs comparably with IPSS-R. These insights not only expand our understanding of the complexity of MDS, but also reinforce the potential of methylation-based sequencing as a viable, possibly less-invasive, alternative for MDS risk stratification compared to traditional IPSS-R methods.

Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study

BackgroundSeveral studies have indicated that magnetic resonance imaging radiomics can predict survival in patients with breast cancer, but the potential biological underpinning remains indistinct. Herein, we aim to develop an interpretable deep-learning-based network for classifying recurrence risk and revealing the potential biological mechanisms.MethodsIn this multicenter study, 1113 nonmetastatic invasive breast cancer patients were included, and were divided into the training cohort (n = 698), the validation cohort (n = 171), and the testing cohort (n = 244). The Radiomic DeepSurv Net (RDeepNet) model was constructed using the Cox proportional hazards deep neural network DeepSurv for predicting individual recurrence risk. RNA-sequencing was performed to explore the association between radiomics and tumor microenvironment. Correlation and variance analyses were conducted to examine changes of radiomics among patients with different therapeutic responses and after neoadjuvant chemotherapy. The association and quantitative relation of radiomics and epigenetic molecular characteristics were further analyzed to reveal the mechanisms of radiomics.ResultsThe RDeepNet model showed a significant association with recurrence-free survival (RFS) (HR 0.03, 95% CI 0.02–0.06, P < 0.001) and achieved AUCs of 0.98, 0.94, and 0.92 for 1-, 2-, and 3-year RFS, respectively. In the validation and testing cohorts, the RDeepNet model could also clarify patients into high- and low-risk groups, and demonstrated AUCs of 0.91 and 0.94 for 3-year RFS, respectively. Radiomic features displayed differential expression between the two risk groups. Furthermore, the generalizability of RDeepNet model was confirmed across different molecular subtypes and patient populations with different therapy regimens (All P < 0.001). The study also identified variations in radiomic features among patients with diverse therapeutic responses and after neoadjuvant chemotherapy. Importantly, a significant correlation between radiomics and long non-coding RNAs (lncRNAs) was discovered. A key lncRNA was found to be noninvasively quantified by a deep learning-based radiomics prediction model with AUCs of 0.79 in the training cohort and 0.77 in the testing cohort.ConclusionsThis study demonstrates that machine learning radiomics of MRI can effectively predict RFS after surgery in patients with breast cancer, and highlights the feasibility of non-invasive quantification of lncRNAs using radiomics, which indicates the potential of radiomics in guiding treatment decisions.

Assessment of brainstem function and haemodynamics by MRI: challenges and clinical prospects.

MRI offers techniques for non-invasively measuring a range of aspects of brain tissue function. Blood oxygenation level dependent (BOLD) functional magnetic resonance imaging (fMRI) is widely used to assess neural activity, based on the brain's haemodynamic response, while arterial spin labelling (ASL) MRI is a non-invasive method of quantitatively mapping cerebral perfusion. Both techniques can be applied to measure cerebrovascular reactivity (CVR), an important marker of the health of the cerebrovascular system. BOLD, ASL and CVR have been applied to study a variety of disease processes and are already used in certain clinical circumstances. The brainstem is a critical component of the central nervous system and is implicated in a variety of disease processes. However, its function is difficult to study using MRI because of its small size and susceptibility to physiological noise. In this article, we review the physical and biological underpinnings of BOLD and ASL and their application to measure CVR, discuss the challenges associated with applying them to the brainstem and the opportunities for brainstem MRI in the research and clinical settings. With further optimisation, functional MRI techniques could feasibly be used to assess brainstem haemodynamics and neural activity in the clinical setting.

Management of Central and Peripheral Nervous System Tumors in Patients with Neurofibromatosis.

Neurofibromatosis type I (NF1), neurofibromatosis type 2 (NF2), and schwannomatosis represent a diverse group of genetic tumor predisposition syndromes with a shared feature of tumors affecting the peripheral nerve sheaths. PURPOSE OF REVIEW: Many advancements have been made in understanding the biologic underpinnings of these conditions, and in 2016 the first drug was approved by the FDA to treat pediatric symptomatic unresectable plexiform neurofibromas. RECENT FINDINGS: Mek inhibitors have provided a much-needed therapeutic avenue for NF1 patients with unresectable plexiform neurofibromas (PN), both for reduction of tumor bulk and for improvement in symptoms. Selumetinib is the first FDA approved drug for PN, but is only approved for children. Some research suggests that alternative Mek inhibitors and other mixed tyrosine kinase inhibitors may have better efficacy in adults. Vascular endothelial growth factor (VEGF) inhibitor bevacizumab can prolong hearing and delay the need for surgery in NF2 patients with bilateral vestibular schwannomas. This article provides an update regarding considerations and approaches when treating the tumors associated with the neurofibromatoses (NF), including risk and prognosis metrics, clinical trial results, surgical techniques, and radiation therapy recommendations.

XPO1-Mediated mRNA Export of Genome Maintenance Regulators Drives Chemotherapy Resistance in Aggressive Lymphoma.

Diffuse large B-cell lymphoma (DLBCL) is the most common lymphoid malignancy and displays vast genetic and transcriptomic heterogeneity. Current treatment guidelines recommend first-line chemoimmunotherapy consisting of an anthracycline backbone, which produces cure rates of approximately 65%. However, the remaining patients will face relapsed or refractory disease, which, even in the era of chimeric antigen receptor T cells, is difficult to treat. In this issue of Cancer Research, Marullo and colleagues investigate the biological underpinnings of the tumor-suppressive activity of the newly approved XPO1 inhibitor selinexor in the treatment of lymphoma. In a translational effort covering genomic and biochemical approaches, combined with in vivo validation experiments and a phase I clinical trial, they demonstrate that upon DNA damage, XPO1 selectively exports transcripts encoding proteins involved in genome maintenance via the RNA-binding proteins THOC4 and eIF4E. Pharmacologic interception of this export process enhances chemosensitivity in various lymphoma models, and combined selinexor plus chemoimmunotherapy displays a favorable toxicity profile and early evidence of efficacy in patients. See related article by Marullo et al., p. 101.

Unveiling the phenotypic landscape of stalk lodging resistance in diverse maize hybrids

ContextStalk lodging causes up to 43 % of yield losses in maize (Zea mays L.) worldwide, significantly worsening food and feed shortages. Stalk lodging resistance is a complex trait specified by several structural, material, and geometric phenotypes. However, the identity, relative contribution, and genetic tractability of these intermediate phenotypes remain unknown. ObjectiveThe study is designed to identify and evaluate plant-, organ-, and tissue-level intermediate phenotypes associated with stalk lodging resistance following standardized phenotyping protocols and to understand the variation and genetic tractability of these intermediate phenotypes. MethodsWe examined 16 diverse maize hybrids in two environments to identify and evaluate intermediate phenotypes associated with stalk flexural stiffness, a reliable indicator of stalk lodging resistance, at physiological maturity. Engineering-informed and machine learning models were employed to understand relationships among intermediate phenotypes and stalk flexural stiffness. ResultsStalk flexural stiffness showed significant genetic variation and high heritability (0.64) in the evaluated hybrids. Significant genetic variation and comparable heritability for the cross-sectional moment of inertia and Young's modulus indicated that geometric and material properties are under tight genetic control and play a combinatorial role in determining stalk lodging resistance. Among the twelve internode-level traits measured on the bottom and the ear internode, most traits exhibited significant genetic variation among hybrids, moderate to high heritability, and considerable effect of genotype × environment interaction. The marginal statistical model based on structural engineering beam theory revealed that 74–80 % of the phenotypic variation for flexural stiffness was explained by accounting for the major diameter, minor diameter, and rind thickness of the stalks. The machine learning model explained a relatively modest proportion (58–62 %) of the variation for flexural stiffness. ConclusionsCharacterization of stalk and internode properties using standard phenotyping methods revealed tremendous variation for intermediate phenotypes underlying stalk lodging resistance. The intermediate phenotypes showed moderate to high heritability, indicating their genetic tractability for improving stalk lodging resistance. Stalk geometric and material properties showed complementarity in determining stalk flexural stiffness. Engineering-informed models outperformed machine learning approaches in explaining variation for flexural stiffness. ImplicationsIdentification of genetically tractable intermediate phenotypes will boost efforts toward genetic improvement of stalk lodging resistance in maize. Discovering the genetic architecture of the intermediate traits will enhance our understanding of the biological underpinning of stalk lodging resistance.

Modelling the overlap and divergence of autistic and schizotypal traits on hippocampal subfield volumes and regional cerebral blood flow

Psychiatric disorders show high co-morbidity, including co-morbid expressions of subclinical psychopathology across multiple disease spectra. Given the limitations of classical case-control designs in elucidating this overlap, new approaches are needed to identify biological underpinnings of spectra and their interaction. We assessed autistic-like traits (using the Autism Quotient, AQ) and schizotypy - as models of subclinical expressions of disease phenotypes and examined their association with volumes and regional cerebral blood flow (rCBF) of anterior, mid- and posterior hippocampus segments from structural MRI scans in 318 and arterial spin labelling (ASL) in 346 nonclinical subjects, which overlapped with the structural imaging sample (N = 298). We demonstrate significant interactive effects of positive schizotypy and AQ social skills as well as of positive schizotypy and AQ imagination on hippocampal subfield volume variation. Moreover, we show that AQ attention switching modulated hippocampal head rCBF, while positive schizotypy by AQ attention to detail interactions modulated hippocampal tail rCBF. In addition, we show significant correlation of hippocampal volume and rCBF in both region-of-interest and voxel-wise analyses, which were robust after removal of variance related to schizotypy and autistic traits. These findings provide empirical evidence for both the modulation of hippocampal subfield structure and function through subclinical traits, and in particular how only the interaction of phenotype facets leads to significant reductions or variations in these parameters. This makes a case for considering the synergistic impact of different (subclinical) disease spectra on transdiagnostic biological parameters in psychiatry.

Models for the retention of duplicate genes and their biological underpinnings.

Gene content in genomes changes through several different processes, with gene duplication being an important contributor to such changes. Gene duplication occurs over a range of scales from individual genes to whole genomes, and the dynamics of this process can be context dependent. Still, there are rules by which genes are retained or lost from genomes after duplication, and probabilistic modeling has enabled characterization of these rules, including their context-dependence. Here, we describe the biology and corresponding mathematical models that are used to understand duplicate gene retention and its contribution to the set of biochemical functions encoded in a genome.

Sex modifies effects of imaging and CSF biomarkers on cognitive and functional outcomes: a study of Alzheimer's disease

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by memory and functional impairments. Two of 3 patients with AD are biologically female; therefore, the biological underpinnings of this diagnosis disparity may inform interventions slowing the AD progression. To bridge this gap, we conducted analyses of 1078 male and female participants from the Alzheimer’s Disease Neuroimaging Initiative to examine associations between levels of cerebral spinal fluid (CSF)/neuroimaging biomarkers and cognitive/functional outcomes. The Chow test was used to quantify sex differences by determining if biological sex affects relationships between the studied biomarkers and outcomes. Multiple magnetic resonance imaging (whole brain, entorhinal cortex, middle temporal gyrus, fusiform gyrus, hippocampus), position emission tomography (AV45), and CSF (P-TAU, TAU) biomarkers were differentially associated with cognitive and functional outcomes. Post-hoc bootstrapped and association analyses confirmed these differential effects and emphasized the necessity of using separate, sex-stratified models. The studied imaging/CSF biomarkers may account for some of the sex-based variation in AD pathophysiology. The identified sex-varying relationships between CSF/imaging biomarkers and cognitive/functional outcomes warrant future biological investigation in independent cohorts.

Metabolic fingerprinting on retinal pigment epithelium thickness for individualized risk stratification of type 2 diabetes mellitus

The retina is an important target organ of diabetes mellitus, with increasing evidence from patients and animal models suggesting that retinal pigment epithelium (RPE) may serve as an early marker for diabetes-related damages. However, their longitudinal relationship and the biological underpinnings remain less well understood. Here, we demonstrate that reduced in vivo measurements of RPE thickness (RPET) represents a significant risk factor for future type 2 diabetes mellitus (T2DM) and its microvascular phenotypes. After performing systematic analyses of circulating plasma metabolites using two complementary approaches, we identify a wide range of RPET metabolic fingerprints that are independently associated with reduced RPET. These fingerprints hold their potential to improve predictability and clinical utility for stratifying future T2DM and related microvascular phenotypes beyond traditional clinical indicators, providing insights into the promising role of retinas as a window to systemic health.