Multiple Phenotypes Research Articles

A flexible empirical Bayes approach to multivariate multiple regression, and its improved accuracy in predicting multi-tissue gene expression from genotypes.

Predicting phenotypes from genotypes is a fundamental task in quantitative genetics. With technological advances, it is now possible to measure multiple phenotypes in large samples. Multiple phenotypes can share their genetic component; therefore, modeling these phenotypes jointly may improve prediction accuracy by leveraging effects that are shared across phenotypes. However, effects can be shared across phenotypes in a variety of ways, so computationally efficient statistical methods are needed that can accurately and flexibly capture patterns of effect sharing. Here, we describe new Bayesian multivariate, multiple regression methods that, by using flexible priors, are able to model and adapt to different patterns of effect sharing and specificity across phenotypes. Simulation results show that these new methods are fast and improve prediction accuracy compared with existing methods in a wide range of settings where effects are shared. Further, in settings where effects are not shared, our methods still perform competitively with state-of-the-art methods. In real data analyses of expression data in the Genotype Tissue Expression (GTEx) project, our methods improve prediction performance on average for all tissues, with the greatest gains in tissues where effects are strongly shared, and in the tissues with smaller sample sizes. While we use gene expression prediction to illustrate our methods, the methods are generally applicable to any multi-phenotype applications, including prediction of polygenic scores and breeding values. Thus, our methods have the potential to provide improvements across fields and organisms.

Phenotypic Profiling of Human Stem Cell-Derived Midbrain Dopaminergic Neurons.

Parkinson's disease (PD) is linked to a range of cell biological processes that cause midbrain dopaminergic (mDA) neuron loss. Many current in vitro PD cellular models lack complexity and do not take multiple phenotypes into account. Phenotypic profiling in human induced pluripotent stem cell (iPSC)-derived mDA neurons can address these shortcomings by simultaneously measuring a range of neuronal phenotypes in a PD-relevant cell type in parallel. Here, we describe a protocol to obtain and analyze phenotypic profiles from commercially available human mDA neurons. A neuron-specific fluorescent staining panel is used to visualize the nuclear, α-synuclein, Tyrosine hydroxylase (TH), and Microtubule-associated protein 2 (MAP2) related phenotypes. The described phenotypic profiling protocol is scalable as it uses 384-well plates, automatic liquid handling and high-throughput microscopy. The utility of the protocol is exemplified using healthy donor mDA neurons and mDA neurons carrying the PD-linked G2019S mutation in the Leucine-rich repeat kinase 2 (LRRK2) gene. Both cell lines were treated with the LRRK2 kinase inhibitor PFE-360 and phenotypic changes were measured. Additionally, we demonstrate how multidimensional phenotypic profiles can be analyzed using clustering or machine learning-driven supervised classification methods. The described protocol will particularly interest researchers working on neuronal disease modeling or studying chemical compound effects in human neurons.

Identifying a distinct fibrosis subset of NAFLD via molecular profiling and the involvement of profibrotic macrophages

BackgroundThere are emerging studies suggesting that non-alcoholic fatty liver disease (NAFLD) is a heterogeneous disease with multiple etiologies and molecular phenotypes. Fibrosis is the key process in NAFLD progression. In this study, we aimed to explore molecular phenotypes of NAFLD with a particular focus on the fibrosis phenotype and also aimed to explore the changes of macrophage subsets in the fibrosis subset of NAFLD.MethodsTo assess the transcriptomic alterations of key factors in NAFLD and fibrosis progression, we included 14 different transcriptomic datasets of liver tissues. In addition, two single-cell RNA sequencing (scRNA-seq) datasets were included to construct transcriptomic signatures that could represent specific cells. To explore the molecular subsets of fibrosis in NAFLD based on the transcriptomic features, we used a high-quality RNA-sequencing (RNA-seq) dataset of liver tissues from patients with NAFLD. Non-negative matrix factorization (NMF) was used to analyze the molecular subsets of NAFLD based on the gene set variation analysis (GSVA) enrichment scores of key molecule features in liver tissues.ResultsThe key transcriptomic signatures on NAFLD including non-alcoholic steatohepatitis (NASH) signature, fibrosis signature, non-alcoholic fatty liver (NAFL) signature, liver aging signature and TGF-β signature were constructed by liver transcriptome datasets. We analyzed two liver scRNA-seq datasets and constructed cell type-specific transcriptomic signatures based on the genes that were highly expressed in each cell subset. We analyzed the molecular subsets of NAFLD by NMF and categorized four main subsets of NAFLD. Cluster 4 subset is mainly characterized by liver fibrosis. Patients with Cluster 4 subset have more advanced liver fibrosis than patients with other subsets, or may have a high risk of liver fibrosis progression. Furthermore, we identified two key monocyte-macrophage subsets which were both significantly correlated with the progression of liver fibrosis in NAFLD patients.ConclusionOur study revealed the molecular subtypes of NAFLD by integrating key information from transcriptomic expression profiling and liver microenvironment, and identified a novel and distinct fibrosis subset of NAFLD. The fibrosis subset is significantly correlated with the profibrotic macrophages and M2 macrophage subset. These two liver macrophage subsets may be important players in the progression of liver fibrosis of NAFLD patients.

A novel ARPKD mouse model with near-complete deletion of the Polycystic Kidney and Hepatic Disease 1 (Pkhd1) genomic locus presents with multiple phenotypes but not renal cysts

A novel ARPKD mouse model with near-complete deletion of the Polycystic Kidney and Hepatic Disease 1 (Pkhd1) genomic locus presents with multiple phenotypes but not renal cysts

A rat model of metabolic syndrome-related heart failure with preserved ejection fraction phenotype: pathological alterations and possible molecular mechanisms.

Heart failure with preserved ejection fraction (HFpEF) represents a syndrome involving multiple pathophysiologic disorders and clinical phenotypes. This complexity makes it challenging to develop a comprehensive preclinical model, which presents an obstacle to elucidating disease mechanisms and developing new drugs. Metabolic syndrome (MetS) is a major phenotype of HFpEF. Thus, we produced a rat model of the MetS-related HFpEF phenotype and explored the molecular mechanisms underpinning the observed pathological changes. A rat model of the MetS-related HFpEF phenotype was created by feeding spontaneously hypertensive rats a high-fat-salt-sugar diet and administering streptozotocin solution intraperitoneally. Subsequently, pathological changes in the rat heart and their possible molecular mechanisms were explored. The HFpEF rats demonstrated primary features of MetS, such as hypertension, hyperglycemia, hyperlipidemia, insulin resistance, and cardiac anomalies, such as left ventricular (LV) remodeling and diastolic impairment, and left atrial dilation. Additionally, inflammation, myocardial hypertrophy, and fibrosis were observed in LV myocardial tissue, which may be associated with diverse cellular and molecular signaling cascades. First, the inflammatory response might be related to the overexpression of inflammatory regulators (growth differentiation factor 15 (GDF-15), intercellular adhesion molecule-1 (ICAM-1), and vascular endothelial cell adhesion molecule-1 (VCAM-1)). Secondly, phosphorylated glycogen synthase kinase 3β (GSK-3β) may stimulate cardiac hypertrophy, which was regulated by activated -RAC-alpha serine/threonine-protein kinase (AKT). Finally, the transforming growth factor-β1 (TGF-β1)/Smads pathway might regulate collagen production and fibroblast activation, promoting myocardial fibrosis. The HFpEF rat replicates the pathology and clinical presentation of human HFpEF with MetS and may be a reliable preclinical model that helps elucidate HFpEF pathogenesis and develop effective treatment strategies.

Afectarea hepatică în mucoviscidoză

Among the systemic impairment in cystic fibrosis there is also hepatobiliary tract involvement, with some genetic determinants of the disease manifesting as cystic fibrosis-associated liver disease (CFLD). The pathogenesis, natural history and cyrrhogenic evolution of CFLD are discussed in relation to the multiple phenotypes of cystic fibrosis. The CFLD therapy is nonspecific, the disease progression to cirrhosis is unpredictable, and the CFTR modulators offer a still underinvestigated therapeutic alternative.

Deep Learning for Microfluidic-Assisted Caenorhabditis elegans Multi-Parameter Identification Using YOLOv7.

The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union (mAP@0.5) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an mAP@0.5 of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms.

Identifying subgroups of patients with type 2 diabetes based on real-world traditional chinese medicine electronic medical records.

Introduction: Type 2 diabetes (T2D) is a multifactorial complex chronic disease with a high prevalence worldwide, and Type 2 diabetes patients with different comorbidities often present multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of the clinical Type 2 diabetes population to help identify more accurate disease subtypes for personalized treatment. Methods: Here, utilizing the traditional Chinese medicine (TCM) clinical electronic medical records (EMRs) of 2137 Type 2 diabetes inpatients, we followed a heterogeneous medical record network (HEMnet) framework to construct heterogeneous medical record networks by integrating the clinical features from the electronic medical records, molecular interaction networks and domain knowledge. Results: Of the 2137 Type 2 diabetes patients, 1347 were male (63.03%), and 790 were female (36.97%). Using the HEMnet method, we obtained eight non-overlapping patient subgroups. For example, in H3, Poria, Astragali Radix, Glycyrrhizae Radix et Rhizoma, Cinnamomi Ramulus, and Liriopes Radix were identified as significant botanical drugs. Cardiovascular diseases (CVDs) were found to be significant comorbidities. Furthermore, enrichment analysis showed that there were six overlapping pathways and eight overlapping Gene Ontology terms among the herbs, comorbidities, and Type 2 diabetes in H3. Discussion: Our results demonstrate that identification of the Type 2 diabetes subgroup based on the HEMnet method can provide important guidance for the clinical use of herbal prescriptions and that this method can be used for other complex diseases.

Small-molecule Ro-08-2750 interacts with many RNA-binding proteins and elicits MUSASHI2-independent phenotypes.

RNA-binding proteins (RBPs) are key regulators of gene expression. Small molecules targeting these RBP-RNA interactions are a rapidly emerging class of therapeutics for treating a variety of diseases. Ro-08-2750 (Ro) is a small molecule identified as a competitive inhibitor of Musashi (MSI)-RNA interactions. Here, we show that multiple Ro-dependent cellular phenotypes, specifically adrenocortical steroid production and cell viability, are Musashi-2 (MSI2)-independent. Using an unbiased proteome-wide approach, we discovered Ro broadly interacts with RBPs, many containing RRM domains. To confirm this finding, we leveraged the large-scale ENCODE data to identify a subset of RBPs whose depletion phenocopies Ro inhibition, indicating Ro is a promiscuous inhibitor of multiple RBPs. Consistent with broad disruption of ribonucleoprotein complexes, Ro treatment leads to stress granule formation. This strategy represents a generalizable framework for validating the specificity and identifying targets of RBP inhibitors in a cellular context.

Association of digital measures and self-reported fatigue: a remote observational study in healthy participants and participants with chronic inflammatory rheumatic disease.

Fatigue is a subjective, complex and multi-faceted phenomenon, commonly experienced as tiredness. However, pathological fatigue is a major debilitating symptom associated with overwhelming feelings of physical and mental exhaustion. It is a well-recognized manifestation in chronic inflammatory rheumatic diseases, such as Sjögren's Syndrome and Systemic Lupus Erythematosus and an important predictor of patient's health-related quality of life (HRQoL). Patient reported outcome questions are the key instruments to assess fatigue. To date, there is no consensus about reliable quantitative assessments of fatigue. Observational data for a period of one month were collected from 296 participants in the United States. Data comprised continuous multimodal digital data from Fitbit, including heart rate, physical activity and sleep features, and app-based daily and weekly questions covering various HRQoL factors including pain, mood, general physical activity and fatigue. Descriptive statistics and hierarchical clustering of digital data were used to describe behavioural phenotypes. Gradient boosting classifiers were trained to classify participant-reported weekly fatigue and daily tiredness from multi-sensor and other participant-reported data, and extract a set of key predictive features. Cluster analysis of Fitbit parameters highlighted multiple digital phenotypes, including sleep-affected, fatigued and healthy phenotypes. Features from participant-reported data and Fitbit data both contributed as key predictive features of weekly physical and mental fatigue and daily tiredness. Participant answers to pain and depressed mood-related daily questions contributed the most as top features for predicting physical and mental fatigue, respectively. To classify daily tiredness, participant answers to questions on pain, mood and ability to perform daily activities contributed the most. Features related to daily resting heart rate and step counts and bouts were overall the most important Fitbit features for the classification models. These results demonstrate that multimodal digital data can be used to quantitatively and more frequently augment pathological and non-pathological participant-reported fatigue.

Joint analysis of GWAS and multi-omics QTL summary statistics reveals a large fraction of GWAS signals shared with molecular phenotypes

Molecular quantitative trait loci (xQTLs) are often harnessed to prioritize genes or functional elements underpinning variant-trait associations identified from genome-wide association studies (GWASs). Here, we introduce OPERA, a method that jointly analyzes GWAS and multi-omics xQTL summary statistics to enhance the identification of molecular phenotypes associated with complex traits through shared causal variants. Applying OPERA to summary-level GWAS data for 50 complex traits (n= 20,833-766,345) and xQTL data from seven omics layers (n= 100-31,684) reveals that 50% of the GWAS signals are shared with at least one molecular phenotype. GWAS signals shared with multiple molecular phenotypes, such as those at the MSMB locus for prostate cancer, are particularly informative for understanding the genetic regulatory mechanisms underlying complex traits. Future studies with more molecular phenotypes, measured considering spatiotemporal effects in larger samples, are required to obtain a more saturated map linking molecular intermediates to GWAS signals.

Chromosome translocation affects multiple phenotypes, causes genome-wide dysregulation of gene expression, and remodels metabolome in hexaploid wheat.

Chromosomal rearrangements (CRs) may occur in newly formed polyploids due to compromised meiotic fidelity. Moreover, CRs can be more readily tolerated in polyploids allowing their longer-term retention and hence potential spreading/fixation within a lineage. The direct functional consequences of CRs in plant polyploids remain unexplored. Here, we identified a heterozygous individual from a synthetic allohexaploid wheat in which the terminal parts of the long-arms of chromosomes 2D (approximately 193 Mb) and 4A (approximately 167 Mb) were reciprocally translocated. Five homogeneous translocation lines including both unbalanced and balanced types were developed by selfing fertilization of the founder mutant (RT [2DL; 4AL]-ter/1, reciprocal translocation). We investigated impacts of these translocations on phenotype, genome-wide gene expression and metabolome. We find that, compared with sibling wild-type, CRs in the form of both unbalanced and balanced translocations induced substantial changes of gene expression primarily via trans-regulation in the nascent allopolyploid wheat. The CRs also manifested clear phenotypic and metabolic consequences. In particular, the genetically balanced, stable reciprocal translocations lines showed immediate enhanced reproductive fitness relative to wild type. Our results underscore the profound impact of CRs on gene expression in nascent allopolyploids with wide-ranging phenotypic and metabolic consequences, suggesting CRs are an important source of genetic variation that can be exploited for crop breeding.

Single-cell microfluidics enabled dynamic evaluation of drug combinations on antibiotic resistance bacteria

The rapid spread of antibiotic resistance has become a significant threat to global health, yet the development of new antibiotics is outpaced by emerging new resistance. To treat multidrug-resistant bacteria and prolong the lifetime of existing antibiotics, a productive strategy is to use combinations of antibiotics and/or adjuvants. However, evaluating drug combinations is primarily based on end-point checkerboard measurements, which provide limited information to study the mechanism of action and the discrepancies in the clinical outcomes. Here, single-cell microfluidics is used for rapid evaluation of the efficacy and mode of action of antibiotic combinations within 3 h. Focusing on multidrug-resistant Acinetobacter baumannii, the combination between berberine hydrochloride (BBH, as an adjuvant) and carbapenems (meropenem, MEM) or β-lactam antibiotic is evaluated. Real-time tracking of individual cells to programmable delivered antibiotics reveals multiple phenotypes (i.e., susceptible, resistant, and persistent cells) with fidelity. Our study discovers that BBH facilitates the accumulation of antibiotics within cells, indicating synergistic effects (FICI = 0.5). For example, the combination of 256 mg/L BBH and 16 mg/L MEM has a similar killing effect (i.e., the inhibition rates >90%) as the MIC of MEM (64 mg/L). Importantly, the synergistic effect of a combination can diminish if the bacteria are pre-stressed with any single drug. Such information is vital for understanding the underlying mechanisms of combinational treatments. Overall, our platform provides a promising approach to evaluate the dynamic and heterogenous response of a bacterial population to antibiotics, which will facilitate new drug discovery and reduce emerging antibiotic resistance.

Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes

BackgroundWe aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct...

Patient Engagement in a Multimodal Digital Phenotyping Study of Opioid Use Disorder.

Multiple digital data sources can capture moment-to-moment information to advance a robust understanding of opioid use disorder (OUD) behavior, ultimately creating a digital phenotype for each patient. This information can lead to individualized interventions to improve treatment for OUD. The aim is to examine patient engagement with multiple digital phenotyping methods among patients receiving buprenorphine medication for OUD. The study enrolled 65 patients receiving buprenorphine for OUD between June 2020 and January 2021 from 4 addiction medicine programs in an integrated health care delivery system in Northern California. Ecological momentary assessment (EMA), sensor data, and social media data were collected by smartphone, smartwatch, and social media platforms over a 12-week period. Primary engagement outcomes were meeting measures of minimum phone carry (≥8 hours per day) and watch wear (≥18 hours per day) criteria, EMA response rates, social media consent rate, and data sparsity. Descriptive analyses, bivariate, and trend tests were performed. The participants' average age was 37 years, 47% of them were female, and 71% of them were White. On average, participants met phone carrying criteria on 94% of study days, met watch wearing criteria on 74% of days, and wore the watch to sleep on 77% of days. The mean EMA response rate was 70%, declining from 83% to 56% from week 1 to week 12. Among participants with social media accounts, 88% of them consented to providing data; of them, 55% of Facebook, 54% of Instagram, and 57% of Twitter participants provided data. The amount of social media data available varied widely across participants. No differences by age, sex, race, or ethnicity were observed for any outcomes. To our knowledge, this is the first study to capture these 3 digital data sources in this clinical population. Our findings demonstrate that patients receiving buprenorphine treatment for OUD had generally high engagement with multiple digital phenotyping data sources, but this was more limited for the social media data. RR2-10.3389/fpsyt.2022.871916.

LTK and ALK promote neuronal polarity and cortical migration by inhibiting IGF1R activity

The establishment of axon‐dendrite polarity is fundamental for radial migration of neurons, cortical patterning, and formation of neuronal circuits. Here, we show that the receptor tyrosine kinases, Ltk and Alk, are required for proper neuronal polarization. In isolated primary mouse embryonic neurons, the loss of Ltk and/or Alk causes a multiple axon phenotype. In mouse embryos and newborn pups, the absence of Ltk and Alk delays neuronal migration and subsequent cortical patterning. In adult cortices, neurons with aberrant neuronal projections are evident and axon tracts in the corpus callosum are disrupted. Mechanistically, we show that the loss of Alk and Ltk increases the cell‐surface expression and activity of the insulin‐like growth factor 1 receptor (Igf‐1r), which activates downstream PI3 kinase signaling to drive the excess axon phenotype. Our data reveal Ltk and Alk as new regulators of neuronal polarity and migration whose disruption results in behavioral abnormalities.

Consideration of the thoracic phenotype of cerebro-costo-mandibular syndrome.

Cerebro-costo-mandibular syndrome (CCMS) is a congenital condition with skeletal and orofacial abnormalities that often results in respiratory distress in neonates. The three main phenotypes in the thorax are posterior rib gaps, abnormal costovertebral articulation and absent ribs. Although the condition can be lethal, accurate diagnosis, and subsequent management help improve the survival rate. Mutations in the causative gene SNRPB have been identified, however, the mechanism whereby the skeletal phenotypes affect respiratory function is not well-studied due to the multiple skeletal phenotypes, lack of anatomy-based studies into the condition and rarity of CCMS cases. This review aims to clarify the extent to which the three main skeletal phenotypes in the thorax contribute to respiratory distress in neonates with CCMS. Despite the posterior rib gaps being unique to this condition and visually striking on radiographic images, anatomical consideration, and meta-analyses suggested that they might not be the significant factor in causing respiratory distress in neonates. Rather, the increase in chest wall compliance due to the rib gaps and the decrease in compliance at the costovertebral complex was considered to result in an equilibrium, minimizing the impact of these abnormalities. The absence of floating ribs is likely insignificant as seen in the general population; however, a further absence of ribs or vestigial rib formation is associated with respiratory distress and increased lethality. Based on these, we propose to evaluate the number of absent or vestigial ribs as a priority indicator to develop a personalized treatment plan based on the phenotypes exhibited.

Abstract A071: SOX2 induced plasticity and microenvironment lead to intratumoral and tissue-specific phenotypic heterogeneity in metastatic castration-resistant prostate cancer (mCRPC)

Abstract Currently, the development of efficacious agents against metastatic castration resistance prostate cancer is hampered by the lack of preclinical models that accurately reflect disease heterogeneity and treatment response. To overcome this barrier, we have developed a preclinical metastasis model using LuCap136 (PDM136), an androgen receptor (AR) positive patient-derived xenograft (PDX) that forms metastases in bone and soft tissues 3 weeks after intracardiac injection. With a combined PTEN −/−, TP53 −/mut, RB1 −/− genotype, PDM136 represents a subgroup of difficult-to-treat mPCRC. PDM136 is organotropic for bone. Mice usually reach the end point after 6-7 weeks due to paraplegia and/or cachexia. Metastatic cells maintain the expression of nuclear AR and epithelial marker cytokeratin 8. Surgical castration significantly inhibits metastatic progression and prolonged the survival rate, however, tumor will eventually progress to castration resistance. To our knowledge, PDM136 is the first AR-positive prostate cancer metastasis model that recapitulates the disease progress of mCRPC. To define the molecular mechanisms of castration resistance in PDM136, we performed RNA expression profiling on bone and soft tissue metastasis collected from intact and castrated mice. Differential expression analysis revealed over 500 transcripts were significantly altered after castration, with SOX2 and ASCL1 among the top up-regulated genes. Gene set enrichment analysis showed decreased AR signaling and increased neuroepithelium transformation in the castrated group. To validate the RNA-seq data, we performed IHC on proteins whose genes were identified by RNAseq including SOX2, ASCL1, as well as androgen receptor (AR), glucocorticoid receptor (GR) and neuroendocrine marker SYP. Surprisingly, we found that the expression of the five markers is organ-specific upon castration. Unsupervised clustering of the IHC stained markers from the bone of castrated mice revealed that AR and GR were undetectable in majority of foci whereas substantial numbers of foci in soft tissue metastasis expressed AR and GR at moderate intensity. Furthermore, we found that AR and GR expression were mutually exclusive in many foci from soft tissue metastasis. SOX2 and ASCL1 were mainly expressed in AR negative tumors, while SYP was expressed in a subpopulation of SOX2+ positive foci. Notably, liver metastasis had the most SYP expressing foci among all organs suggesting liver microenvironment promotes neuroendocrine differentiation. In summary, our IHC results revealed inter-tumor and organ-specific cellular heterogeneity induced by castration. PDM136 is a platform to understand the mechanism of responsible for mCRPC, but also a valuable tool for anti-mCRPC drug discovery. The co-existence of multiple phenotypes indicated that combination therapies targeting multiple pathways are essential to harness this disease. Citation Format: Anson Ku, Juan Yin, Adam G. Sowalsky, Kathleen Kelly. SOX2 induced plasticity and microenvironment lead to intratumoral and tissue-specific phenotypic heterogeneity in metastatic castration-resistant prostate cancer (mCRPC) [abstract]. In: Proceedings of the AACR Special Conference: Advances in Prostate Cancer Research; 2023 Mar 15-18; Denver, Colorado. Philadelphia (PA): AACR; Cancer Res 2023;83(11 Suppl):Abstract nr A071.

Increased Expression of Kif11/Kinesin‐5 mRNA Offsets Cognitive Deficits In An Alzheimer’s Mouse Model, Blocks Aβ‐Mediated Decreases in Long‐Term Potentiation and Spine Density, and is Associated With Better Cognitive Performance in Alzheimer’s Patients

AbstractBackgroundInteractions between the Aβ with intra‐ and extra‐cellular molecules disrupt neuronal structures and functions, leading to cognitive dysfunction in Alzheimer’s disease (AD). We previously have shown that Aβ competitively inhibits the microtubule motor protein KIF11 (Kinesin‐5/Eg5), which regulates microtubule dynamics, cellular trafficking, neuronal migration, and neuronal structures important for learning and memory. In vitro cell culture and ex vivo electrophysiology experiments later revealed that the inhibition of KIF11 by Aβ underlies multiple phenotypes associated with AD, including decreases in LTP, breakdown of the microtubule network, and deficits in neurotransmitter receptor localization and function. Here we aimed to determine whether increased Kif11 expression overrides phenotypes associated with AD.MethodsKif11 overexpressing mice were crossed with the 5xFAD mouse model of AD to generate 5xFAD mice that overexposes Kif11. Learning and memory of these mice was measured through the performance of 6‐8 month old mice in the radial‐arm water maze and LTP was measured by the fEPSP response to high‐frequency stimulation of the CA1 region in hippocampal slices of our transgenic mice. Primary rat neurons were transiently transfected to express human APP harboring the Swedish mutation (APPswe) with or without Kif11 overexpression to determine whether increased Kif11 expression affects Aβ‐mediated decreases in dendritic spine, We then evaluated cognitive function in amyloid positive individuals in the ROS/MAP study with their KIF11 mRNA expression in 4 brain regions, the DLPFC (N = 939), PCC (N = 527), and the CN (N = 715).ResultsKIF11 overexpression overrides Aβ toxicity and rescues Aβ‐mediated decreases in LTP in hippocampal slices and dendritic spine density in cultured neurons. Furthermore, we found that spatial and working memory deficits were prevented in a 5xFAD Alzheimer’s mouse model that overexpresses Kif11. Analyses of RNAseq data from human participants in the Religious Order Study and Memory and Aging (ROS/MAP) revealed that higher KIF11 expression in AD patients correlated with better cognitive performance.ConclusionKIF11 is essential for maintaining synaptic structures and functions critical for learning and memory in AD, and preventing or overriding the inhibitory interaction of Aβ with Kif11 could be a promising therapeutic target for multiple AD phenotypes.

Neuronal, glial and microglial maps of epigenetic and transcriptomics dysregulations in Alzheimer’s disease

AbstractBackgroundRegions of open chromatin house regulatory elements required to mediate cell‐type and tissue‐specific gene expression. Human brain studies have shown that dysregulation of these regulatory mechanisms is associated with Alzheimer’s Disease (AD). Here, we present the largest cell type and brain region‐specific differential chromatin accessibility analysis and fine‐mapping analysis in AD.MethodUsing both fresh and frozen postmortem tissue from over 200 cases with AD and controls, we performed ATAC‐seq to profile chromatin accessibility in three distinct populations of cells (neurons, glia, and microglia), isolated by FACS from three brain regions. We characterized epigenetics changes associated with multiple AD phenotype ratings. We further studied the correlation structures in chromatin accessibility to define high‐resolution maps of cis‐regulatory domains. Lastly, we examined population‐level variation of gene expression and chromatin accessibility to pinpoint genetically driven regulation of transcription.ResultWe observed widespread differences in chromatin accessibility associated with AD. The differential cis‐regulatory domains were highly concordant with gene expression perturbations suggesting coordinated changes of the 3D genome regulation of the transcriptome in AD. For microglia cells, the fine‐mapping analysis identified putative regulatory mechanisms for 21 AD risk loci, of which 18 were refined to a single gene, including 3 novel genes (KCNN4, FIBP and LRRC25). Transcription factor regulatory networks captured AD risk variation and identified SPI1, IRF1, and PURA as the key regulators of microglia expression changes in AD. The same analysis in neurons pinpointed upstream transcription factor 2 (USF2), which regulates the genes participating in lysosome function, which we validated by overexpression and knockdown experiments. Lastly, we determined enhancer‐promoter interactions, by generating additional omics in brain tissue (Hi‐C, H3K27ac ChIP‐seq) and jointly analyzing with ATAC‐seq based on the “activity‐by‐contact” approach. To prove the relevance of these putative enhancers, we performed validations in neuronal progenitor cells.ConclusionOverall, our human neuronal, glia, and microglia multi‐scale omics datasets uncovered disease‐associated perturbations impacting chromatin accessibility, transcription factor regulatory networks, and the 3D genome, and implicated transcriptional dysregulation in AD. Furthermore, we were able to fine‐map multiple AD loci, identifying not only the relevant genes but, in some cases, proposing the regulatory mechanisms contributing to disease.