Microglia Function in the Central Nervous System During Health and Neurodegeneration.

Microglia are resident cells of the brain that regulate brain development, maintenance of neuronal networks, and injury repair. Microglia serve as brain macrophages but are distinct from other tissue macrophages owing to their unique homeostatic phenotype and tight regulation by the central nervous system (CNS) microenvironment. They are responsible for the elimination of microbes, dead cells, redundant synapses, protein aggregates, and other particulate and soluble antigens that may endanger the CNS. Furthermore, as the primary source of proinflammatory cytokines, microglia are pivotal mediators of neuroinflammation and can induce or modulate a broad spectrum of cellular responses. Alterations in microglia functionality are implicated in brain development and aging, as well as in neurodegeneration. Recent observations about microglia ontogeny combined with extensive gene expression profiling and novel tools to study microglia biology have allowed us to characterize the spectrum of microglial phenotypes during development, homeostasis, and disease. In this article, we review recent advances in our understanding of the biology of microglia, their contribution to homeostasis, and their involvement in neurodegeneration. Moreover, we highlight the complexity of targeting microglia for therapeutic intervention in neurodegenerative diseases.

A genomic compendium of cultivated human gut fungi characterizes the gut mycobiome and its relevance to common diseases.

The gut fungal community represents an essential element of human health, yet its functional and metabolic potential remains insufficiently elucidated, largely due to the limited availability of reference genomes. To address this gap, we presented the cultivated gut fungi (CGF) catalog, encompassing 760 fungal genomes derived from the feces of healthy individuals. This catalog comprises 206 species spanning 48 families, including 69 species previously unidentified. We explored the functional and metabolic attributes of the CGF species and utilized this catalog to construct a phylogenetic representation of the gut mycobiome by analyzing over 11,000 fecal metagenomes from Chinese and non-Chinese populations. Moreover, we identified significant common disease-related variations in gut mycobiome composition and corroborated the associations between fungal signatures and inflammatory bowel disease (IBD) through animal experimentation. These resources and findings substantially enrich our understanding of the biological diversity and disease relevance of the human gut mycobiome.

Transgenerational inheritance of acquired epigenetic signatures at CpG islands in mice.

Transgenerational epigenetic inheritance in mammals remains a debated subject. Here, we demonstrate that DNA methylation of promoter-associated CpG islands (CGIs) can be transmitted from parents to their offspring in mice. We generated DNA methylation-edited mouse embryonic stem cells (ESCs), in which CGIs of two metabolism-related genes, the Ankyrin repeat domain 26 and the low-density lipoprotein receptor, were specifically methylated and silenced. DNA methylation-edited mice generated by microinjection of the methylated ESCs exhibited abnormal metabolic phenotypes. Acquired methylation of the targeted CGI and the phenotypic traits were maintained and transmitted across multiple generations. The heritable CGI methylation was subjected to reprogramming in parental PGCs and subsequently reestablished in the next generation at post-implantation stages. These observations provide a concrete step toward demonstrating transgenerational epigenetic inheritance in mammals, which may have implications in our understanding of evolutionary biology as well as the etiology, diagnosis, and prevention of non-genetically inherited human diseases.

Top 10 Challenges in Cancer Immunotherapy.

Cancer immunotherapy is a validated and critically important approach for treating patients with cancer. Given the vast research and clinical investigation efforts dedicated to advancing both endogenous and synthetic immunotherapy approaches, there is a need to focus on crucial questions and define roadblocks to the basic understanding and clinical progress. Here, we define ten key challenges facing cancer immunotherapy, which range from lack of confidence in translating pre-clinical findings to identifying optimal combinations of immune-based therapies for any given patient. Addressing these challenges will require the combined efforts of basic researchers and clinicians, and the focusing of resources to accelerate understanding of the complex interactions between cancer and the immune system and the development of improved treatment options for patients with cancer.

Are We Meeting the Promise of Endotypes and Precision Medicine in Asthma?

While the term asthma has long been known to describe heterogeneous groupings of patients, only recently have data evolved which enable a molecular understanding of the clinical differences. The evolution of transcriptomics (and other 'omics platforms) and improved statistical analyses in combination with large clinical cohorts opened the door for molecular characterization of pathobiologic processes associated with a range of asthma patients. When linked with data from animal models and clinical trials of targeted biologic therapies, emerging distinctions arose between patients with and without elevations in type 2 immune and inflammatory pathways, leading to the confirmation of a broad categorization of type 2-Hi asthma. Differences in the ratios, sources, and location of type 2 cytokines and their relation to additional immune pathway activation appear to distinguish several different (sub)molecular phenotypes, and perhaps endotypes of type 2-Hi asthma, which respond differently to broad and targeted anti-inflammatory therapies. Asthma in the absence of type 2 inflammation is much less well defined, without clear biomarkers, but is generally linked with poor responses to corticosteroids. Integration of "big data" from large cohorts, over time, using machine learning approaches, combined with validation and iterative learning in animal (and human) model systems is needed to identify the biomarkers and tightly defined molecular phenotypes/endotypes required to fulfill the promise of precision medicine.

A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders.

Brain imaging and genomics are critical tools enabling characterization of the genetic basis of brain disorders. However, imaging large cohorts is expensive and may be unavailable for legacy datasets used for genome-wide association studies (GWASs). Using an integrated feature selection/aggregation model, we developed an image-mediated association study (IMAS), which utilizes borrowed imaging/genomics data to conduct association mapping in legacy GWAS cohorts. By leveraging the UK Biobank image-derived phenotypes (IDPs), the IMAS discovered genetic bases underlying four neuropsychiatric disorders and verified them by analyzing annotations, pathways, and expression quantitative trait loci (eQTLs). A cerebellar-mediated mechanism was identified to be common to the four disorders. Simulations show that, if the goal is identifying genetic risk, our IMAS is more powerful than a hypothetical protocol in which the imaging results were available in the GWAS dataset. This implies the feasibility of reanalyzing legacy GWAS datasets without conducting additional imaging, yielding cost savings for integrated analysis of genetics and imaging.

A syndromic neurodevelopmental disorder caused by rare variants in PPFIA3.

PPFIA3 encodes the protein-tyrosine phosphatase, receptor-type, F-polypeptide-interacting-protein-alpha-3 (PPFIA3), which is a member of the LAR-protein-tyrosine phosphatase-interacting-protein (liprin) family involved in synapse formation and function, synaptic vesicle transport, and presynaptic active zone assembly. The protein structure and function are evolutionarily well conserved, but human diseases related to PPFIA3 dysfunction are not yet reported in OMIM. Here, we report 20 individuals with rare PPFIA3 variants (19 heterozygous and 1 compound heterozygous) presenting with developmental delay, intellectual disability, hypotonia, dysmorphisms, microcephaly or macrocephaly, autistic features, and epilepsy with reduced penetrance. Seventeen unique PPFIA3 variants were detected in 18 families. To determine the pathogenicity of PPFIA3 variants in vivo, we generated transgenic fruit flies producing either human wild-type (WT) PPFIA3 or five missense variants using GAL4-UAS targeted gene expression systems. In the fly overexpression assays, we found that the PPFIA3 variants in the region encoding the N-terminal coiled-coil domain exhibited stronger phenotypes compared to those affecting the C-terminal region. In the loss-of-function fly assay, we show that the homozygous loss of fly Liprin-α leads to embryonic lethality. This lethality is partially rescued by the expression of human PPFIA3 WT, suggesting human PPFIA3 function is partially conserved in the fly. However, two of the tested variants failed to rescue the lethality at the larval stage and one variant failed to rescue lethality at the adult stage. Altogether, the human and fruit fly data reveal that the rare PPFIA3 variants are dominant-negative loss-of-function alleles that perturb multiple developmental processes and synapse formation.

Assessing the utility of large language models for phenotype-driven gene prioritization in the diagnosis of rare genetic disease.

Phenotype-driven gene prioritization is fundamental to diagnosing rare genetic disorders. While traditional approaches rely on curated knowledge graphs with phenotype-gene relations, recent advancements in large language models (LLMs) promise a streamlined text-to-gene solution. In this study, we evaluated five LLMs, including two generative pre-trained transformers (GPT) series and three Llama2 series, assessing their performance across task completeness, gene prediction accuracy, and adherence to required output structures. We conducted experiments, exploring various combinations of models, prompts, phenotypic input types, and task difficulty levels. Our findings revealed that the best-performed LLM, GPT-4, achieved an average accuracy of 17.0% in identifying diagnosed genes within the top 50 predictions, which still falls behind traditional tools. However, accuracy increased with the model size. Consistent results were observed over time, as shown in the dataset curated after 2023. Advanced techniques such as retrieval-augmented generation (RAG) and few-shot learning did not improve the accuracy. Sophisticated prompts were more likely to enhance task completeness, especially in smaller models. Conversely, complicated prompts tended to decrease output structure compliance rate. LLMs also achieved better-than-random prediction accuracy with free-text input, though performance was slightly lower than with standardized concept input. Bias analysis showed that highly cited genes, such as BRCA1, TP53, and PTEN, are more likely to be predicted. Our study provides valuable insights into integrating LLMs with genomic analysis, contributing to the ongoing discussion on their utilization in clinical workflows.

Assessment and the regulation of adaptive phenotypic plasticity.

Organisms can react to environmental variation by altering their phenotype, and such phenotypic plasticity is often adaptive. This plasticity contributes to the diversity of phenotypes across the tree of life. Generally, the production of these phenotypes must be preceded by assessment, where the individual acquires information about its environment and phenotype relative to that environment, and then determines if and how to respond with an alternative phenotype. The role of assessment in adaptive plasticity is, therefore, crucial. In this Review, we (1) highlight the need for explicitly considering the role of assessment in plasticity; (2) present two different models for how assessment and the facultative production of phenotypes are related; and (3) describe an overarching framework for how assessment evolves. In doing so, we articulate avenues of future work and suggest that explicitly considering the role of assessment in the evolution of plasticity is key to explaining how and when plasticity occurs. Moreover, we emphasize the need to understand the role of assessment in adaptive versus maladaptive plasticity, which is an issue that will become increasingly important in a rapidly changing world.

Dissecting the high-resolution genetic architecture of complex phenotypes by accurately estimating gene-based conditional heritability.

Despite extensive research on global heritability estimation for complex traits, few methods accurately dissect local heritability. A precise local heritability estimate is crucial for high-resolution mapping in genetics. Here, we report the effective heritability estimator (EHE) that can use p values from genome-wide association studies (GWASs) for local heritability estimation by directly converting marginal heritability estimates of SNPs to a non-redundant heritability estimate of a gene or a small genomic region. EHE provides higher accuracy and precision for local heritability estimation among seven compared methods. Importantly, EHE can be applied to estimate the conditional heritability of nearby genes, where redundant heritability among the genes can also be removed further. The conditional estimation can be guided by tissue-specific expression profiles (or other functional scores) to prioritize and quantify more functionally important genes of complex phenotypes. Applying EHE to 42 complex phenotypes from the UK Biobank, we revealed the existence of two types of distinct genetic architectures for various complex phenotypes and found that highly pleiotropic genes are not enriched for more heritability compared to other candidate susceptibility genes. EHE provides an accurate and robust way to dissect the genetic architecture of complex phenotypes.

Deep conservation and co-option of programmed cell death facilitates evolution of alternative phenotypes at multiple biological levels.

Alternative phenotypes, such as polyphenisms and sexual dimorphisms, are widespread in nature and appear at all levels of biological organization, from genes and cells to morphology and behavior. Yet, our understanding of the mechanisms through which alternative phenotypes develop and how they evolve remains understudied. In this review, we explore the association between alternative phenotypes and programmed cell death, a mechanism responsible for the elimination of superfluous cells during development. We discuss the ancient origins and deep conservation of programmed cell death (its function, forms and underlying core regulatory gene networks), and propose that it was co-opted repeatedly to generate alternative phenotypes at the level of cells, tissues, organs, external morphology, and even individuals. We review several examples from across the tree of life to explore the conditions under which programmed cell death is likely to facilitate the evolution of alternative phenotypes.

Biological Phenotyping in Sepsis and Acute Respiratory Distress Syndrome.

Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data ("omics"), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schemas are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schemas applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools.

The emerging significance of splicing in vertebrate development.

Splicing is a crucial regulatory node of gene expression that has been leveraged to expand the proteome from a limited number of genes. Indeed, the vast increase in intron number that accompanied vertebrate emergence might have aided the evolution of developmental and organismal complexity. Here, we review how animal models for core spliceosome components have provided insights into the role of splicing in vertebrate development, with a specific focus on neuronal, neural crest and skeletal development. To this end, we also discuss relevant spliceosomopathies, which are developmental disorders linked to mutations in spliceosome subunits. Finally, we discuss potential mechanisms that could underlie the tissue-specific phenotypes often observed upon spliceosome inhibition and identify gaps in our knowledge that, we hope, will inspire further research.

multiMiAT: an optimal microbiome-based association test for multicategory phenotypes.

Microbes can affect the metabolism and immunity of human body incessantly, and the dysbiosis of human microbiome drives not only the occurrence but also the progression of disease (i.e. multiple statuses of disease). Recently, microbiome-based association tests have been widely developed to detect the association between the microbiome and host phenotype. However, the existing methods have not achieved satisfactory performance in testing the association between the microbiome and ordinal/nominal multicategory phenotypes (e.g. disease severity and tumor subtype). In this paper, we propose an optimal microbiome-based association test for multicategory phenotypes, namely, multiMiAT. Specifically, under the multinomial logit model framework, we first introduce a microbiome regression-based kernel association test for multicategory phenotypes (multiMiRKAT). As a data-driven optimal test, multiMiAT then integrates multiMiRKAT, score test and MiRKAT-MC to maintain excellent performance in diverse association patterns. Massive simulation experiments prove the success of our method. Furthermore, multiMiAT is also applied to real microbiome data experiments to detect the association between the gut microbiome and clinical statuses of colorectal cancer as well as for diverse statuses of Clostridium difficile infections.

Large-scale next generation sequencing based analysis of SLCO1B1 pharmacogenetics variants in the Saudi population.

BACKGROUND: SLCO1B1 plays an important role in mediating hepatic clearance of many different drugs including statins, angiotensin-converting enzyme inhibitors, chemotherapeutic agents and antibiotics. Several variants in SLCO1B1 have been shown to have a clinically significant impact, in relation to efficacy of these medications. This study provides a comprehensive overview of SLCO1B1 variation in Saudi individuals, one of the largest Arab populations in the Middle East. METHODS: The dataset of 11,889 (9,961 exomes and 1,928 pharmacogenetic gene panel) Saudi nationals, was used to determine the presence and frequencies of SLCO1B1 variants, as described by the Clinical Pharmacogenetic Implementation Consortium (CPIC). RESULTS: We identified 141 previously described SNPs, of which rs2306283 (50%) and rs4149056 (28%), were the most common. In addition, we observed six alleles [*15 (24.7%) followed by *20 (8.04%), *14 (5.86%), *5 (3.84%), *31 (0.21%) and *9 (0.03%)] predicted to be clinically actionable. Allele diplotype to phenotype conversion revealed 41 OATP1B1 diplotypes. We estimated the burden of rare, and novel predicted deleterious variants, resulting from 17 such alterations. CONCLUSIONS: The data we present, from one of the largest Arab cohorts studied to date, provides the most comprehensive overview of SLCO1B1 variants, and the subsequent OATP1B1 activity of this ethnic group, which thus far remains relatively underrepresented in available international genomic databases. We believe that the presented data provides a basis for further clinical investigations and the application of personalized statin drug therapy guidance in Arabs.

Oncofetal morphogenesis similar to embryonic gut formation by a subpopulation of DLD-1 human colon cancer cells.

Cancer stem cells (CSC) are thought to be responsible for cancer phenotypes and cellular heterogeneity. Here we demonstrate that the human colon cancer cell line DLD1 contains two types of CSC-like cells that undergo distinct morphogenesis in the reconstituted basement membrane gel Matrigel. In our method with cancer cell spheroids, the parent cell line (DLD1-P) developed grape-like budding structures, whereas the other (DLD1-Wm) and its single-cell clones dynamically developed worm-like ones. Gene expression analysis suggested that the former mimicked intestinal crypt-villus morphogenesis, while the latter mimicked embryonic hindgut development. The organoids of DLD1-Wm cells rapidly extended in two opposite directions by expressing dipolar proteolytic activity. The invasive morphogenesis required the expression of MMP-2 and CD133 genes and ROCK activity. These cells also exhibited gastrula-like morphogenesis even in two-dimensional cultures without Matrigel. Moreover, the two DLD1 cell lines showed clear differences in cellular growth, tumor growth and susceptibility to paclitaxel. This study also provides a simple organoid culture method for human cancer cell lines. HT-29 and other cancer cell lines underwent characteristic morphogenesis in direct contact with normal fibroblasts. Such organoid cultures would be useful for investigating the nature of CSCs and for screening anti-cancer drugs. Our results lead to the hypothesis that CSC-like cells with both invasive activity and a fetal phenotype, i. e. oncofetal CSCs, are generated in some types of colon cancers.

Associations between brain imaging-derived phenotypes and cognitive functions.

We aimed to evaluate the potential causal relationship between brain imaging-derived phenotypes and cognitive functions via Mendelian randomization analyses. Genetic instruments for 470 brain imaging-derived phenotypes were selected from a genome-wide association study based on the UK Biobank (n = 33,224). Statistics for cognitive functions were obtained from the genome-wide association study based on the UK Biobank. We used the inverse variance weighted Mendelian randomization method to investigate the associations between brain imaging-derived phenotypes and cognitive functions, and reverse Mendelian randomization analyses were performed for significant brain imaging-derived phenotypes to examine the reverse causation for the identified associations. We identified three brain imaging-derived phenotypes to be associated with verbal-numerical reasoning, including cortical surface area of the left fusiform gyrus (beta, 0.18 [95% confidence interval, 0.11 to 0.25], P = 4.74 × 10-7), cortical surface area of the right superior temporal gyrus (beta, 0.25 [95% confidence interval, 0.15 to 0.35], P = 6.30 × 10-7), and orientation dispersion in the left superior longitudinal fasciculus (beta, 0.14 [95% confidence interval, 0.09 to 0.20], P = 8.37 × 10-7). The reverse Mendelian randomization analysis indicated that verbal-numerical reasoning had no effect on these three brain imaging-derived phenotypes. This Mendelian randomization study identified cortical surface area of the left fusiform gyrus, cortical surface area of the right superior temporal gyrus, and orientation dispersion in the left superior longitudinal fasciculus as predictors of verbal-numerical reasoning.

Genetic analyses identify brain imaging-derived phenotypes associated with the risk of intracerebral hemorrhage.

Previous observational studies have reported associations between brain imaging-derived phenotypes (IDPs) and intracerebral hemorrhage (ICH), but the causality between them remains uncertain. We aimed to investigate the potential causal relationship between IDPs and ICH by a two-sample Mendelian randomization (MR) study. We selected genetic instruments for 363 IDPs from a genome-wide association study (GWASs) based on the UK Biobank (n = 33,224). Summary-level data on ICH was derived from a European-descent GWAS with 1,545 cases and 1,481 controls. Inverse variance weighted MR method was applied in the main analysis to investigate the associations between IDPs and ICH. Reverse MR analyses were performed for significant IDPs to examine the reverse causation for the identified associations. Among the 363 IDPs, isotropic or free water volume fraction (ISOVF) in the anterior limb of the left internal capsule was identified to be associated with the risk of ICH (OR per 1-SD increase, 4.62 [95% CI, 2.18-9.81], P = 6.63 × 10-5). In addition, the reverse MR analysis indicated that ICH had no effect on ISOVF in the anterior limb of the left internal capsule (beta, 0.010 [95% CI, -0.010-0.030], P = 0.33). MR-Egger regression analysis showed no directional pleiotropy for the association between ISOVF and ICH, and sensitivity analyses with different MR models further confirmed these findings. ISOVF in the anterior limb of the left internal capsule might be a potential causal mediator of ICH, which may provide predictive guidance for the prevention of ICH. Further studies are warranted to replicate our findings and clarify the underlying mechanisms.

Computer gaming alters resting-state brain networks, enhancing cognitive and fluid intelligence in players: evidence from brain imaging-derived phenotypes-wide Mendelian randomization.

The debate on whether computer gaming enhances players' cognitive function is an ongoing and contentious issue. Aiming to delve into the potential impacts of computer gaming on the players' cognitive function, we embarked on a brain imaging-derived phenotypes (IDPs)-wide Mendelian randomization (MR) study, utilizing publicly available data from a European population. Our findings indicate that computer gaming has a positive impact on fluid intelligence (odds ratio [OR] = 6.264, P = 4.361 × 10-10, 95% confidence interval [CI] 3.520-11.147) and cognitive function (OR = 3.322, P = 0.002, 95% CI 1.563-7.062). Out of the 3062 brain IDPs analyzed, only one phenotype, IDP NET100 0378, was significantly influenced by computer gaming (OR = 4.697, P = 1.10 × 10-5, 95% CI 2.357-9.361). Further MR analysis suggested that alterations in the IDP NET100 0378 caused by computer gaming may be a potential factor affecting fluid intelligence (OR = 1.076, P = 0.041, 95% CI 1.003-1.153). Our MR study lends support to the notion that computer gaming can facilitate the development of players' fluid intelligence by enhancing the connectivity between the motor cortex in the resting-state brain and key regions such as the left dorsolateral prefrontal cortex and the language center.

Host and Microbe Blood Metagenomics Reveals Key Pathways Characterizing Critical Illness Phenotypes.

Rationale: Two molecular phenotypes of sepsis and acute respiratory distress syndrome, termed hyperinflammatory and hypoinflammatory, have been consistently identified by latent class analysis in numerous cohorts, with widely divergent clinical outcomes and differential responses to some treatments; however, the key biological differences between these phenotypes remain poorly understood.Objectives: We used host and microbe metagenomic sequencing data from blood to deepen our understanding of biological differences between latent class analysis-derived phenotypes and to assess concordance between the latent class analysis-derived phenotypes and phenotypes reported by other investigative groups (e.g., Sepsis Response Signature [SRS1-2], molecular diagnosis and risk stratification of sepsis [MARS1-4], reactive and uninflamed).Methods: We analyzed data from 113 patients with hypoinflammatory sepsis and 76 patients with hyperinflammatory sepsis enrolled in a two-hospital prospective cohort study. Molecular phenotypes had been previously assigned using latent class analysis.Measurements and Main Results: The hyperinflammatory and hypoinflammatory phenotypes of sepsis had distinct gene expression signatures, with 5,755 genes (31%) differentially expressed. The hyperinflammatory phenotype was associated with elevated expression of innate immune response genes, whereas the hypoinflammatory phenotype was associated with elevated expression of adaptive immune response genes and, notably, T cell response genes. Plasma metagenomic analysis identified differences in prevalence of bacteremia, bacterial DNA abundance, and composition between the phenotypes, with an increased presence and abundance of Enterobacteriaceae in the hyperinflammatory phenotype. Significant overlap was observed between these phenotypes and previously identified transcriptional subtypes of acute respiratory distress syndrome (reactive and uninflamed) and sepsis (SRS1-2). Analysis of data from the VANISH trial indicated that corticosteroids might have a detrimental effect in patients with the hypoinflammatory phenotype.Conclusions: The hyperinflammatory and hypoinflammatory phenotypes have distinct transcriptional and metagenomic features that could be leveraged for precision treatment strategies.