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Brain development in the early stages of life has been suggested to be one of the factors that may influence an individual's risk of Alzheimer disease (AD) later in life. Four microcephaly genes, which regulate brain development in utero and have been suggested to play a role in the evolution of the human brain, were selected as candidate genes that may modulate the risk of AD. We examined the association between single nucleotide polymorphisms tagging common sequence variations in these genes and risk of AD in two case-control samples. We found that the G allele of rs2442607 in microcephalin 1 was associated with an increased risk of AD (under an additive genetic model, P=0.01; odds ratio=3.41; confidence interval, 1.77-6.57). However, this association was not replicated using another case-control sample research participants from the Alzheimer Disease Neuroimaging Initiative. We conclude that the common variations we measured in the 4 microcephaly genes do not affect the risk of AD or that their effect size is small. [ABSTRACT FROM AUTHOR]

Background: A water extract of the Ayurvedic plant Centella asiatica (L.) Urban, family Apiaceae (CAW), improves cognitive function in mouse models of aging and Alzheimer's disease and affects dendritic arborization, mitochondrial activity, and oxidative stress in mouse primary neurons. Triterpenes (TT) and caffeoylquinic acids (CQA) are constituents associated with these bioactivities of CAW, although little is known about how interactions between these compounds contribute to the plant's therapeutic benefit. Methods: Mouse primary cortical neurons were treated with CAW or equivalent concentrations of four TT combined, eight CQA combined, or these twelve compounds combined (TTCQA). Treatment effects on the cell transcriptome (18,491 genes) and metabolome (192 metabolites) relative to vehicle control were evaluated using RNAseq and metabolomic analyses, respectively. Results: Extensive differentially expressed genes (DEGs) were seen with all treatments, as well as evidence of interactions between compounds. Notably, many DEGs seen with TT treatment were not observed in the TTCQA condition, possibly suggesting CQA reduced the effects of TT. Moreover, additional gene activity seen with CAW as compared to TTCQA indicates the presence of additional compounds in CAW that further modulate TTCQA interactions. Weighted Gene Correlation Network Analysis (WGCNA) identified 4 gene co-expression modules altered by treatments that were associated with extracellular matrix organization, fatty acid metabolism, cellular response to stress and stimuli, and immune function. Compound interaction patterns were seen at the eigengene level in these modules. Interestingly, in metabolomics analysis, the TTCQA treatment saw the highest number of changes in individual metabolites (20), followed by CQA (15), then TT (8), and finally CAW (3). WGCNA analysis found two metabolomics modules with significant eigenmetabolite differences for TT and CQA and possible compound interactions at this level. Conclusions: Four gene expression modules and two metabolite modules were altered by the four treatment types applied. This methodology demonstrated the existence of both negative and positive interactions between TT, CQA, and additional compounds found in CAW on the transcriptome and metabolome of mouse primary cortical neurons.

Background: A water extract of the Ayurvedic plant Centella asiatica (CAW) improves cognitive function in mouse models of aging and Alzheimer's disease, and affects dendritic arborization, mitochondrial activity and oxidative stress in mouse primary neurons. Triterpenes (TT) and caffeoylquinic acids (CQA) are constituents associated with these bioactivities of CAW although little is known about how interactions between these compounds contribute to the plant's therapeutic benefit., Methods: Mouse primary cortical neurons were treated with CAW, or equivalent concentrations of four TT combined, eight CQA combined, or these twelve compounds combined (TTCQA). Treatment effects on the cell transcriptome (18,491 genes) and metabolome (192 metabolites) relative to vehicle control were evaluated using RNAseq and metabolomic analyses respectively., Results: Extensive differentially expressed genes (DEGs) were seen with all treatments, as well as evidence of interactions between compounds. Notably many DEGs seen with TT treatment were not observed in the TTCQA condition, possibly suggesting CQA reduced the effects of TT. Moreover, additional gene activity seen with CAW as compared to TTCQA indicate the presence of additional compounds in CAW that further modulate TTCQA interactions. Weighted Gene Correlation Network Analysis (WGCNA) identified 4 gene co-expression modules altered by treatments that were associated with extracellular matrix organization, fatty acid metabolism, cellular response to stress and stimuli, and immune function. Compound interaction patterns were seen at the eigengene level in these modules. Interestingly, in metabolomics analysis, the TTCQA treatment saw the highest number of changes in individual metabolites (20), followed by CQA (15), then TT (8) and finally CAW (3). WGCNA analysis found two metabolomics modules with significant eigenmetabolite differences for TT and CQA, and possible compound interactions at this level., Conclusions: Four gene expression modules and two metabolite modules were altered by the four types of treatments applied. This methodology demonstrated the existence of both negative and positive interactions between TT, CQA and additional compounds found in CAW on the transcriptome and metabolome of mouse primary cortical neurons., Competing Interests: Conflicts of Interest: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

As the world comes to terms with the realities surrounding COVID-19, media sources have likened quarantine experiences to that of incarceration. Individuals who have experienced incarceration and individuals who have experienced the incarceration of loved ones (LO), have already experienced periods of time apart. We are exploring the experiences of individuals who have some experience with incarceration; whether they were personally incarcerated, or they experienced the incarceration of a LO. Utilizing snowball sampling, a mixed methods survey was circulated on social media. Survey items included demographic information, questions about incarceration, issues related to COVID-19 quarantines, and the UCLA Loneliness Scale. Results follow similar patterns to previous studies. However, this unique population argues that COVID-19 quarantines are not the same as periods of incarceration. Similarly, future research and community agencies need to examine the unique needs of those who have experienced the incarceration of a loved one., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Abstract: Identifying and targeting microenvironment-driven pathways that are active across acute myeloid leukemia (AML) genetic subtypes should allow the development of more broadly effective therapies. The proinflammatory cytokine interleukin-1β (IL-1β) is abundant in the AML microenvironment and promotes leukemic growth. Through RNA-sequencing analysis, we identify that IL-1β-upregulated ASF1B (antisilencing function-1B), a histone chaperone, in AML progenitors compared with healthy progenitors. ASF1B, along with its paralogous protein ASF1A, recruits H3-H4 histones onto the replication fork during S-phase, a process regulated by Tousled-like kinase 1 and 2 (TLKs). Although ASF1s and TLKs are known to be overexpressed in multiple solid tumors and associated with poor prognosis, their functional roles in hematopoiesis and inflammation-driven leukemia remain unexplored. In this study, we identify that ASF1s and TLKs are overexpressed in multiple genetic subtypes of AML. We demonstrate that depletion of ASF1s significantly reduces leukemic cell growth in both in vitro and in vivo models using human cells. Using a murine model, we show that overexpression of ASF1B accelerates leukemia progression. Moreover, Asf1b or Tlk2 deletion delayed leukemia progression, whereas these proteins are dispensable for normal hematopoiesis. Through proteomics and phosphoproteomics analyses, we uncover that the TLK-ASF1 pathway promotes leukemogenesis by affecting the cell cycle and DNA damage pathways. Collectively, our findings identify the TLK1-ASF1 pathway as a novel mediator of inflammatory signaling and a promising therapeutic target for AML treatment across diverse genetic subtypes. Selective inhibition of this pathway offers potential opportunities to intervene effectively, address intratumoral heterogeneity, and ultimately improve clinical outcomes in AML.

High-quality data is crucial for accurate machine learning and actionable analytics, however, mislabeled or noisy data is a common problem in many domains. Distinguishing low- from high-quality data can be challenging, often requiring expert knowledge and considerable manual intervention. Data Valuation algorithms are a class of methods that seek to quantify the value of each sample in a dataset based on its contribution or importance to a given predictive task. These data values have shown an impressive ability to identify mislabeled observations, and filtering low-value data can boost machine learning performance. In this work, we present a simple alternative to existing methods, termed Data Valuation with Gradient Similarity (DVGS). This approach can be easily applied to any gradient descent learning algorithm, scales well to large datasets, and performs comparably or better than baseline valuation methods for tasks such as corrupted label discovery and noise quantification. We evaluate the DVGS method on tabular, image and RNA expression datasets to show the effectiveness of the method across domains. Our approach has the ability to rapidly and accurately identify low-quality data, which can reduce the need for expert knowledge and manual intervention in data cleaning tasks.

Immuno-oncology involves the study of approaches which harness the patient's immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ML applications in the clinic. We highlight four clinical/biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: The bone tumor, osteosarcoma, remains challenging to treat in children and young adults, especially when patients present with metastatic disease. Developing new therapies based on genomic data from sequencing projects has proven difficult given the lack of recurrent genetic lesions across tumors. MYC overexpression has been associated with poor outcomes in osteosarcoma. However, other genomic markers of disease severity are lacking., Materials and Methods: We utilized whole genome sequencing of 106 tumors and matched normal controls in order to define genomic characteristics that correlate with overall survival. Single nucleotide variants were overlaid onto annotated molecular pathways in order to define aberrant pathway signatures specific to aggressive osteosarcoma. Additionally, we calculated differential gene expression in a subsample of 71 tumors. Differentially expressed genes were then queried for known MYC-responsive genes., Results: Molecular pathways specific to nuclear pore complex disassembly (NPCD) show significant correlation with poor overall survival in osteosarcoma when mutations were present. Genes involved in immune response and immune regulation are enriched in the differential expression analysis of samples with and without NPCD pathway aberrations. Furthermore, neither MYC nor MYC-responsive genes show differential expression between NPCD-aberrant and non-aberrant groups. The NPCD pathway mutations are dominated by regulatory region variants rather than protein-altering mutations, suggesting that dysregulation of genetic regulatory networks may be the underlying mechanism for their relation to osteosarcoma phenotype., Discussion: Overall survival is significantly worse in patients whose tumors show aberrations in the NPCD pathway. Moreover, this difference in survival is not driven by MYC-overexpression, suggesting a novel mechanism for some aggressive osteosarcomas. These findings add light to the evolving understanding of the drivers of osteosarcoma and may aid in the search for new treatments based on patient-specific genetic data., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Jacobs, Davis and McWeeney.)

Computational modeling of perturbation biology identifies relationships between molecular elements and cellular response, and an accurate understanding of these systems will support the full realization of precision medicine. Traditional deep learning, while often accurate in predicting response, is unlikely to capture the true sequence of involved molecular interactions. Our work is motivated by two assumptions: 1) Methods that encourage mechanistic prediction logic are likely to be more trustworthy, and 2) problem-specific algorithms are likely to outperform generic algorithms. We present an alternative to Graph Neural Networks (GNNs) termed Graph Structured Neural Networks (GSNN), which uses cell signaling knowledge, encoded as a graph data structure, to add inductive biases to deep learning. We apply our method to perturbation biology using the LINCS L1000 dataset and literature-curated molecular interactions. We demonstrate that GSNNs outperform baseline algorithms in several prediction tasks, including 1) perturbed expression, 2) cell viability of drug combinations, and 3) disease-specific drug prioritization. We also present a method called GSNNExplainer to explain GSNN predictions in a biologically interpretable form. This work has broad application in basic biological research and pre-clincal drug repurposing. Further refinement of these methods may produce trustworthy models of drug response suitable for use as clinical decision aids., Availability and Implementation: Our implementation of the GSNN method is available at https://github.com/nathanieljevans/GSNN. All data used in this work is publicly available., Competing Interests: Conflict of Interest: G.B.M. SAB/Consultant: Amphista, Astex, AstraZeneca, BlueDot, Chrysallis Biotechnology, Ellipses Pharma, GSK, ImmunoMET, Infinity, Ionis, Leapfrog Bio, Lilly, Medacorp, Nanostring, Nuvectis, PDX Pharmaceuticals, Qureator, Roche, Signalchem Lifesciences, Tarveda, Turbine, Zentalis Pharmaceuticals; Stock/Options/Financial: Bluedot, Catena Pharmaceuticals, ImmunoMet, Nuvectis, SignalChem, Tarveda, Turbine; Licensed Technology: HRD assay to Myriad Genetics, DSP patents with Nanostring; Sponsored research: AstraZeneca

While immune checkpoint inhibitors show success in treating a subset of patients with certain late-stage cancers, these treatments fail in many other patients as a result of mechanisms that have yet to be fully characterized. The process of CD8 T cell exhaustion, by which T cells become dysfunctional in response to prolonged antigen exposure, has been implicated in immunotherapy resistance. Single-cell RNA sequencing (scRNA-seq) produces an abundance of data to analyze this process; however, due to the complexity of the process, contributions of other cell types to a process within a single cell type cannot be simply inferred. We constructed an analysis framework to first rank human skin tumor samples by degree of exhaustion in tumor-infiltrating CD8 T cells and then identify immune cell type-specific gene-regulatory network patterns significantly associated with T cell exhaustion. Using this framework, we further analyzed scRNA-seq data from human tumor and chronic viral infection samples to compare the T cell exhaustion process between these two contexts. In doing so, we identified transcription factor activity in the macrophages of both tissue types associated with this process. Our framework can be applied beyond the tumor immune microenvironment to any system involving cell-cell communication, facilitating insights into key biological processes that underpin the effective treatment of cancer and other complicated diseases.

Human cytomegalovirus (HCMV) encodes multiple putative G protein-coupled receptors (GPCRs). US28 functions as a viral chemokine receptor and is expressed during both latent and lytic phases of virus infection. US28 actively promotes cellular migration, transformation, and plays a major role in mediating viral latency and reactivation; however, knowledge about the interaction partners involved in these processes is still incomplete. Herein, we utilized a proximity-dependent biotinylating enzyme (TurboID) to characterize the US28 interactome when expressed in isolation, and during both latent (CD34+ hematopoietic progenitor cells) and lytic (fibroblasts) HCMV infection. Our analyses indicate that the US28 signalosome converges with RhoA and EGFR signal transduction pathways, sharing multiple mediators that are major actors in processes such as cellular proliferation and differentiation. Integral members of the US28 signaling complex were validated in functional assays by immunoblot and small-molecule inhibitors. Importantly, we identified RhoGEFs as key US28 signaling intermediaries. In vitro latency and reactivation assays utilizing primary CD34+ hematopoietic progenitor cells (HPCs) treated with the small-molecule inhibitors Rhosin or Y16 indicated that US28 -RhoGEF interactions are required for efficient viral reactivation. These findings were recapitulated in vivo using a humanized mouse model where inhibition of RhoGEFs resulted in a failure of the virus to reactivate. Together, our data identifies multiple new proteins in the US28 interactome that play major roles in viral latency and reactivation, highlights the utility of proximity-sensor labeling to characterize protein interactomes, and provides insight into targets for the development of novel anti-HCMV therapeutics., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Medica et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Functional heterogeneity of healthy human tissues complicates interpretation of molecular studies, impeding precision therapeutic target identification and treatment. Considering this, we generated a graph neural network with Reactome-based architecture and trained it using 9,115 samples from Genotype-Tissue Expression (GTEx). Our graph neural network (GNN) achieves adjusted Rand index (ARI) = 0.7909, while a Resnet18 control model achieves ARI = 0.7781, on 370 held-out healthy human tissue samples from The Cancer Genome Atlas (TCGA), despite the Resnet18 using over 600 times the parameters. Our GNN also succeeds in separating 83 healthy skin samples from 95 lesional psoriasis samples, revealing that upregulation of 26S- and NUB1-mediated degradation of NEDD8, UBD, and their conjugates is central to the largest perturbed reaction network component in psoriasis. We show that our results are not discoverable using traditional differential expression and hypergeometric pathway enrichment analyses yet are supported by separate human multi-omics and small-molecule mouse studies, suggesting future molecular disease studies may benefit from similar GNN analytical approaches., Competing Interests: The authors declare no competing interests., (© 2023 The Author(s).)