Biology subjects

Zhan, L.

Publications and source records attributed to Zhan, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Telmisartan alters the transcriptomes of cancer associated fibroblasts and fibrocytes in a solid tumour model

Cancer associated fibroblasts (CAFs) are the main contributors to fibrosis in solid tumours. Fibrosis can lead to increased tumoural stiffness, immune cell exclusion, and poor response to cancer therapies. However, therapeutically depleting CAFs has been shown to increase tumour progression and metastasis in some pre-clinical tumour models. Targeted approaches to inhibit CAF activity and decrease fibrosis without abrogating CAFs altogether may improve the effect of existing cancer therapies. We have previously shown that the blood pressure medication telmisartan decreases tumoural collagen, resulting in improved vascular perfusion, decreased hypoxia, and improved radiation treatment efficacy. To understand how telmisartan influences CAF populations in solid tumours, we conducted single cell RNA sequencing analyses of host cells isolated from tumours in mice treated with telmisartan. Telmisartan modified the transcriptomes of CAF populations, promoting the expression of chemokine signaling while decreasing the expression of extracellular matrix components in myCAFs. Telmisartan decreased expression of collagen transcripts in myCAFs and fibrocytes, indicating that telmisartan does not remove myCAFs but changes the myCAF transcriptome to become less fibrillar. Our work provides further support for using telmisartan to manipulate CAF populations in the solid tumour microenvironment to decrease collagen deposition and render tumours more responsive to current cancer therapies.

cancer biology↗

bioRxiv2026

Cancer Cell Line Heterogeneity Imposes a Primary Bottleneck for Virtual Perturbation Screening at Scale

UniPert-G2CP (Li et al., Cell, 2026) bridges genetic and chemical screens from molecular representation to phenotype modeling across five cancer cell lines [1]. Here we extend this architecture to 162 cell lines, 32,039 compounds, and genome-wide output (12,328 genes), and find that the resulting prediction platform reveals a striking performance divide: normal and primary cell lines achieve substantially higher prediction fidelity (mean per-cell-line Pearson Correlation Coefficient (PCC) = 0.480, median 0.502) than cancer lines (0.296, median 0.324; Mann-Whitney p = 3.93e-11), identifying cancer cell line heterogeneity as a primary bottleneck for virtual perturbation screening at scale. To understand the sources of this divide, we analyzed per-cell-line performance, genome-wide directional accuracy, mechanism-clustering (SMD), and compound-protein interaction (CPI) enrichment. Directional accuracy on top-5% effect-size genes reaches 73.8% (genome-wide 60.8%), with pathway-dependent recovery: of four literature-supported perturbation-gene pairs queried across three cell lines, two were recapitulated (dexamethasone-TSC22D3/NFKBIA/FKBP5; bortezomib-BAG3/DNAJB1/HSPA1A), one was absent (CD36 depletion-PPARG/CEBPA in ASC), and one was partially recapitulated (metformin-SLC7A5 in HEPG2). Mechanism-clustering SMD of the learned embedding reached 1.636 (vs. original 1.85; 88.5% retention at 32x cell-line coverage), exceeding the ECFP4 fingerprint baseline (1.613), while self-consistency Mantel rho=0.852 confirmed the model retains compound mechanism structure internally. Overall held-out performance: genetic perturbation PCC=0.442 (978-gene subset); novel drug PCC=0.3047 (genome-wide). Analysis of CPI enrichment reveals that training-data overlap inflates apparent performance: 54.9% of Touchstone evaluation pairs overlap with our ChEMBL-derived training CPI pairs, reducing effective EF from 139 to 109 at top 0.5% yet remaining far above random (1.0). These findings establish the first large-scale characterization of cell-type-dependent generalization in perturbation-to-phenotype prediction. The observed performance stratification between normal and cancer lines generates testable hypotheses for why virtual cell models degrade on heterogeneous cancer contexts, and provides a diagnostic framework for identifying where and why such models fail--informing future architecture improvements targeting the CPI vocabulary gap, protein encoder design, and cell-type-aware training strategies.

bioinformatics↗

bioRxiv2026

Lactate-fueled hydride transfer metabolon drives breast cancer metastasis

Metabolic plasticity of cancer cells plays a major role in metastasis. Use of alternative carbon fuels (e.g., lactate) boosts metabolic plasticity, but the underlying mechanisms remain obscure. We show that lactate dehydrogenase B (LDHB) cooperates with the hydride ion transfer complex (HTC) comprised of pyruvate carboxylase (PC), malic enzyme (ME1) and malate dehydrogenase (MDH1) to form a metabolon that confers metabolic flexibility through lactate assimilation under physiological conditions. HTC/LDHB metabolon assembles in the aggressive breast cancer subtypes and reprograms nicotinamide adenine dinucleotide (NAD) metabolism to promote migration, invasion and escape from anoikis - thereby driving metastasis. Altogether, this work identifies a lactate-fueled metabolon that propels metastatic dissemination of breast cancer.

Cancer Biology↗

bioRxiv2026

SnoRNA Expression and RNA 2'-O-Methylation in Drosophila melanogaster S2 Cells

Small nucleolar RNAs (snoRNAs) are a class of non-coding RNAs that play critical roles in guiding 2-O-methylation (Nm) and pseudouridylation modifications of RNAs. In Drosophila melanogaster, snoRNAs undergo dynamic changes in expression during development. In this study, we identified 239 snoRNAs that are robustly expressed in Drosophila S2 cells, representing 87% of all annotated Drosophila snoRNAs. Given that box C/D snoRNAs guide site-specific 2-O-methylation (Nm) of RNA, we next characterized the Nm landscape of S2 cells using RibOxi-seq2, a high-throughput approach capable of detecting Nm modifications with single-nucleotide resolution. RibOxi-seq2 revealed 17 Nm sites in 18S rRNA with a 94% concordance to previously reported RiboMeth-Seq data. In 28S rRNA, 30 Nm sites were identified, corresponding to an 71.4% overlap with established references. Additionally, we detected both a known Nm site (Gm74) and a novel site (Um66) in 5.8S rRNA, further validating the sensitivity and specificity of the approach. RibOxi-seq2 further identified Nm sites in small nuclear RNAs (snRNAs), expanding the annotation of modified non-coding RNAs. Additionally, the method revealed Nm modifications within internal regions of mRNAs. In total, we detected Nm modifications in 2,057 unique mRNAs, underscoring the widespread presence of this epitranscriptomic modification in coding transcripts. Strikingly, although we could not identify any snoRNAs predicted to guide the mRNA 2-O-methylation modifications by canonical mechanisms, we identified strong consensus sequences surrounding many of these mRNA sites. Together, our findings not only expand the known landscape of Nm-modified RNAs but also highlight the robustness of RibOxi-seq2 for transcriptome-wide RNA modification profiling. Collectively, this study presents a comprehensive atlas of snoRNA expression and 2-O-methylation sites in Drosophila S2 cells, offering valuable insights into the epitranscriptomic landscape orchestrated by snoRNAs.

genomics↗

bioRxiv2026

The Microglia Forebrain Assembloid Model Recapitulates Human Brain Development and Neuroimmune Biology.

Microglia are innate immune cells of the CNS whose dysfunction contributes to inflammation and metabolic changes across neurodegenerative and CNS disorders. Across all stages of life, microglia are essential for immune surveillance, neural homeostasis, and synaptic pruning; however, their role in neurodevelopment is less understood. Microglia invade the brain during early neurogenesis, prior to neuronal/glial differentiation, but their potential role at this stage remains undescribed. To model neuroimmune interactions during human cortical development, we created an "assembloid" of human ESC-derived forebrain organoids combined with developmentally matched microglia during cortex formation. Functional contributions of microglia were compared to control organoids using histology and metabolomics.

neuroscience↗

bioRxiv2026

Soluble DLK1 secreted by telomere-shortening-induced senescent microglia impairs oligodendrocyte functions and alters neuronal activity

Aging is the major risk factor for neurodegenerative disease, yet the mechanisms linking physiological aging to brain dysfunction remain unclear. Because telomere erosion is a hallmark of aging, we examined its impact on glial and neuronal physiology. Telomere shortened mice showed lipofuscinosis, hypomyelination, microglial atrophy, and cognitive deficits. Single nuclei RNA-seq revealed accelerated glial aging, elevated microglial senescence pathways, and impaired oligodendrocyte functions. Inducing senescence in human iPSC derived microglia with shortened telomeres identified soluble DLK1 as a novel senescence associated ligand. sDLK1 was increased in the cerebrospinal fluid of telomere shortened and naturally aged mice, and this increase was eliminated by microglial depletion. AAV delivery of sDLK1 in vivo caused hypomyelination and blocked oligodendrocyte lineage progression, demonstrating the detrimental nature of excessive sDLK1. In human iPSC systems, sDLK1 impaired oligodendrocyte maturation and altered calcium signaling in excitatory neurons. These findings identify microglial senescence as a core consequence of telomere shortening and reveal sDLK1 as a microglia-derived senescence ligand that drives oligodendrocyte and neuronal dysfunction in aging.

neuroscience↗

bioRxiv2025

Distinct patterns of de novo coding variants contribute to Tourette Syndrome etiology

Tourette syndrome (TS) is a highly heritable childhood-onset neuropsychiatric disorder characterized by persistent motor and vocal tics. While both common and rare variants contribute to TS susceptibility, the role of rare de novo mutations (DNMs) remains incompletely characterized. Here, we report findings from the largest TS whole-exome sequencing study to date, analyzing 1,466 TS trios alongside 6,714 autism spectrum disorder (ASD) trios and 5,880 unaffected sibling controls from the Simons Simplex Collection (SSC) and SPARK cohorts. Leveraging a trio-based design across these cohorts enabled calibrated assessment of DNM burden while controlling for background mutation rates. We observed a significant exome-wide enrichment of protein-truncating DNMs in TS probands, particularly within genes intolerant to loss-of-function variation (pLI [≥] 0.9), with little contribution from damaging missense variants. Notably, TS probands did not exhibit enrichment in previously implicated ASD or developmental delay (DD) genes, but elsewhere in the genome, suggesting a distinct rare variant architecture. Using a Bayesian statistical framework that integrates both de novo and rare inherited coding variants, we identified three candidate TS risk genes with FDR [≤] 0.05: PPP5C, EXOC1, and GXYLT1. Literature shows that they have prior links to neurodevelopmental and psychiatric disorders. These findings reveal a rare variant burden in TS that is genetically distinguishable from ASD, underscore the importance of loss-of-function mutations in TS risk, and nominate novel candidate genes for future functional investigation.

neuroscience↗

bioRxiv2025

Mapping the Causal Roles of Non-Primary Motor Areas in Human Reach Planning and Execution

Non-primary motor areas, including the dorsal premotor cortex (PMd), ventral premotor cortex (PMv), and posterior parietal cortex (PPC), are involved in movement planning, but their precise contributions remain unclear. We used functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), and kinematic assessments to explore how these areas influence reaching performance in neurologically intact adults. Participants performed a reaching task using the KINARM exoskeleton robot. Brief TMS pulse trains were initiated before movement onset to perturb cortical activity at subthreshold and suprathreshold intensities targeting bilateral PMd, PMv, and PPC. Subthreshold stimulation of contralateral PMd and PMv reduced reach efficiency and smoothness, while suprathreshold stimulation of contralateral PPC increased deviation error and reduced smoothness. Among ipsilateral targets, PMd showed consistent TMS-induced effects, and was the only target where resting-state connectivity predicted behavioral response. Stronger interhemispheric connectivity in the primary motor cortex and weaker interhemispheric PPC connectivity were associated with greater reductions in straightness and smoothness during subthreshold ipsilateral PMd stimulation. These findings show that perturbation of premotor and parietal areas during early movement execution alters reach trajectories, and differences in network organization explain variability in behavioral response. Identifying contributions of cortical areas and connectivity patterns may help personalize interventions after stroke. This study was registered at ClinicalTrials.gov under ID NCT04286516.

bioengineering↗

bioRxiv2025

MarkerMatch: A Proximity-Based Probe-Matching Algorithm for Joint Analysis of Copy-Number Variants from Different Genotyping Arrays

MotivationCopy-number variants (CNVs) are a form of genetic structural variation with increasing importance in complex human disorders. Both DNA sequencing and microarray data can be used to call CNVs, which can be used in association tests, such as association between CNV number and disease status. Unlike genotypes, CNV detection in microarrays requires the use of observed intensity signals at each probe, which limits the imputability for analyses that span multiple array types. Thus far, a consensus set of probes (the intersection encompassing the probes that occur in common on all arrays) has been used to circumvent the problem of differing array-specific sensitivities. This has, however, led to excessive reduction in overall sensitivity of CNV calls as arrays can have an undesirably low overlap of probe sets. To overcome this limitation, we developed MarkerMatch, a proximity-based algorithm that matches probes across different genotyping microarrays to maximize the number of probes considered in the CNV calling algorithm, thereby increasing the resolution and sensitivity while preserving precision. ResultsBy analyzing CNV calls from 4,906 individuals genotyped across three different arrays (Global Screening Array, Omni2.5 array, and Omni Express Exome array), we show that the MarkerMatch approach improves sensitivity by increasing the density of probes available for CNV calling while maintaining precision or improving it relative to the current practice (e.g., use of consensus probes only). We further demonstrate that MarkerMatch exceeds the output from current practice in terms of F1 score, Fowlkes-Mallows index, and Jaccard index. We also optimize MarkerMatch parameters, DMAX and Method, and find an optimal DMAX setting at 10kb, with no clear optimal candidate based on Method, indicating that parameters for this metric should be determined on a use case basis.

bioinformatics↗

bioRxiv2025

Selenoprotein P Deficiency Drives Hepatocellular Carcinoma Progression via Induction of Neutrophil Senescence and Immunosuppressive Microenvironment

The tumor immune microenvironment (TIME) is critically shaped by immunosuppressive neutrophils, yet the molecular mechanisms underlying the functional reprogramming of these neutrophils remain poorly understood. Here, through integrative analysis of single-cell RNA sequencing (scRNA-seq) we showed the presence, in hepatocellular carcinoma (HCC) mice, of a novel subpopulation of senescent-like neutrophils that exhibit hepatic depletion of Sepp1 in concert with the accumulation of the cellular senescence marker Cdkn1a, the immunosuppressive proteins S100a8 and 9, and the pro-angiogenic factor Vegfa. Furthermore, we demonstrated that the hepatic depletion of Sepp1, in combination with this unique subset of senescent neutrophils, drives immunosuppressive activity and further accelerates tumor growth. Mechanistically, the loss of tumor cell-derived Sepp1 impaired selenium uptake in tumor-infiltrating neutrophils (TINs) by disrupting Lrp8 receptor-mediated transport, thereby suppressing intracellular selenium metabolism. This metabolic perturbation led to reduced hydrogen selenide (H2Se) production and accumulation of S-adenosylmethionine (SAM), resulting in increased levels of trimethylation of lysine 4 on histone H3 protein (H3K4me3) modification in neutrophils and collectively establishing a pro-senescence chromatin landscape in tumor-associated neutrophils. Furthermore, we demonstrated that selenium supplementation not only restores Sepp1 expression but also is essential to reshaping anti-tumor immunity in HCC mice, including reversal of the senescent-like phenotype and providing synergistic effects with anti-Programmed cell death 1 (anti-PD-1) therapy. Collectively, our findings identify SEPP1 as a master regulator acting against neutrophil senescence and the potent immunosuppressive effects of senescence in HCC. We discuss the therapeutic potential of strategies to modulate senescent-like neutrophils and underscore the potential utility of selenium supplementation as an adjuvant to enhance immunotherapy efficacy in liver cancer.

cancer biology↗

bioRxiv2025

Deepening imaging-based spatial proteomics at high spatial resolution through controlled tissue resizing and in-situ bottom-up mass spectrometry

Understanding the spatial organization of proteins in tissues is essential for elucidating biological function, but current proteomic imaging methods face limitations in resolution, sensitivity, and multiplexing capability. Here, we present IMPACT (Imaging Mass Spectrometry for Proteome Analysis on Compressed Tissue), a hydrogel-based workflow that integrates controlled vertical tissue compression following expansion to enhance protein detection while improving spatial resolution in mass spectrometry imaging (MSI). Through a 6-fold lateral expansion and 100-fold thickness reduction, analytes were concentrated at the surface to improve detection and enable analysis of thick specimen. Coupled with optimized in-gel digestion using hybrid enzymes, IMPACT increased protein identifications 3-fold compared to conventional MSI while maintaining spatial fidelity. We demonstrate IMPACTs utility in mapping low-abundance proteins and resolving fine anatomical structures. Orthogonal validation with immunofluorescence and integration with spatial transcriptomics further reveal protein-RNA distribution discrepancies, highlighting the need for direct proteomic imaging. IMPACT bridges critical gaps in spatial proteomics by enabling high-resolution, high-sensitivity, and multiplexed untargeted protein imaging, opening new avenues for tissue biology and biomarker discovery.

biophysics↗

bioRxiv2025

EasyMultiProfiler: An Efficient Multi-Omics Data Integration and Analysis Workflow for Microbiome Research

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/649266v1_ufig1.gif" ALT="Figure 1"> View larger version (60K): org.highwire.dtl.DTLVardef@14dd0b1org.highwire.dtl.DTLVardef@1c04a79org.highwire.dtl.DTLVardef@159894dorg.highwire.dtl.DTLVardef@1454a01_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG HighlightsO_LIEasyMultiProfiler provides unified integration of microbial multi-omics data C_LIO_LIEasyMultiProfiler enables natural language-style analytical workflows C_LIO_LIEasyMultiProfiler solves key challenges in standardization and reproducibility C_LIO_LIEasyMultiProfiler extracts meaningful biological insights efficiently C_LI Host-microbiome interactions are crucial in maintaining physiological homeostasis and influencing disease progression. While traditional microbiome research, focused on microbial diversity and abundance, provided essential frameworks, the integration of multi-omics approaches established a far more comprehensive, systems-level understanding of microbiome functionality. However, significant methodological challenges in multi-omics data integration remain, including inconsistent sample coverage, heterogeneous data formats, and complex downstream analytical workflows. These challenges impact the reproducibility and reliability of results and underscore the critical requirement for developing standardized, systematic workflows. Thus, we developed the EasyMultiProfiler (EMP) workflow, a streamlined and efficient analytical framework for multi-omics data analysis. EMP provides a comprehensive infrastructure based on the SummarizedExperiment and MultiAssayExperiment classes, creating a unified framework for storing and analyzing omics data. The frameworks architecture comprises five interconnected functional modules: data extraction, preparation, support, analysis, and visualization. These modules are smoothly integrated into natural language-style analytical workflows, offering users an efficient and standardized solution for multi-omics analysis. EMP addresses critical challenges in multi-omics data analysis, including data integration, workflow standardization, and result reproducibility. The platforms modular architecture and intuitive interface provide researchers and clinicians with a robust, flexible workflow to systematically extract biologically relevant insights from complex multi-omics datasets.

bioinformatics↗

bioRxiv2024

Instantaneous Frequency: A New Functional Biomarker for Dynamic Brain Causal Networks

This study introduces instantaneous frequency (IF) analysis as a novel method for characterizing dynamic brain causal networks from fMRI blood-oxygen-level-dependent (BOLD) signals. Effective connectivity, estimated using dynamic causal modeling (DCM), is analyzed to derive IF sequences, with the average IF across brain regions serving as a potential biomarker for global network oscillatory behavior. Analysis of data from the Alzheimers Disease Neuroimaging Initiative (ADNI), Open Access Series of Imaging Studies (OASIS), and Human Connectome Project (HCP) demonstrates the methods efficacy in distinguishing between clinical and demographic groups, such as cognitive decline stages, sex differences, and sleep quality levels. Statistical analyses reveal significant group differences in IF metrics, highlighting its potential as a sensitive indicator for early diagnosis and monitoring of neurodegenerative and cognitive conditions. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/628965v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@10da58org.highwire.dtl.DTLVardef@6eee6borg.highwire.dtl.DTLVardef@e6e756org.highwire.dtl.DTLVardef@15dff2a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIThe study introduces instantaneous frequency (IF) as a novel biomarker derived from dynamic brain effective connectivity, capturing temporal fluctuations in brain networks. C_LIO_LIThe proposed IF biomarker effectively differentiates between various clinical stages, such as Mild Cognitive Impairment (MCI) and Alzheimers Disease (AD), and demographic factors, including sex and sleep quality. C_LIO_LIThe robustness and clinical relevance of the IF biomarker are validated using three independent datasets: ADNI, OASIS, and HCP, demonstrating its potential in cognitive and neurological research. C_LI

neuroscience↗

bioRxiv2024

Reduction in ETFDH expression optimizes cancer cell bioenergetics

Mitochondrial electron transport flavoprotein (ETF) insufficiency causes metabolic diseases known as a multiple acyl-CoA dehydrogenase deficiency (MADD). In contrast to muscle, ETFDH is a non-essential gene in acute lymphoblastic leukemia NALM-6 cells, and its expression is reduced across human cancers. ETF insufficiency caused by decreased ETFDH expression limits flexibility of OXPHOS fuel utilization but paradoxically increases cancer cell bioenergetics and accelerates neoplastic growth by activation of the mTORC1/BCL-6/4E-BP1 axis. Collectively, these findings reveal that while ETF insufficiency is rare and has detrimental effects in non-malignant tissues, it is common in neoplasia, where ETFDH downregulation leads to bioenergetic and signaling reprogramming that accelerate neoplastic growth.

cancer biology↗

bioRxiv2024

A blended genome and exome sequencing method captures genetic variation in an unbiased, high-quality, and cost-effective manner

We deployed the Blended Genome Exome (BGE), a DNA library blending approach that generates low pass whole genome (1-4x mean depth) and deep whole exome (30-40x mean depth) data in a single sequencing run. This technology is cost-effective, empowers most genomic discoveries possible with deep whole genome sequencing, and provides an unbiased method to capture the diversity of common SNP variation across the globe. To evaluate this new technology at scale, we applied BGE to sequence >53,000 samples from the Populations Underrepresented in Mental Illness Associations Studies (PUMAS) Project, which included participants across African, African American, and Latin American populations. We evaluated the accuracy of BGE imputed genotypes against raw genotype calls from the Illumina Global Screening Array. All PUMAS cohorts had R2 concordance [≥]95% among SNPs with MAF[≥]1%, and never fell below [≥]90% R2 for SNPs with MAF<1%. Furthermore, concordance rates among local ancestries within two recently admixed cohorts were consistent among SNPs with MAF[≥]1%, with only minor deviations in SNPs with MAF<1%. We also benchmarked the discovery capacity of BGE to access protein-coding copy number variants (CNVs) against deep whole genome data, finding that deletions and duplications spanning at least 3 exons had a positive predicted value of [~]90%. Our results demonstrate BGE scalability and efficacy in capturing SNPs, indels, and CNVs in the human genome at 28% of the cost of deep whole-genome sequencing. BGE is poised to enhance access to genomic testing and empower genomic discoveries, particularly in underrepresented populations.

genomics↗

bioRxiv2023

Liver Protects Cytoarchitecture, Neuron Viability and Electrocortical Activity in Post-Cardiac Arrest Brain Injury

BACKGROUNDBrain injury is the major reason for patient deaths in victims who survive after cardiac arrest. Clinical studies have shown that the presence of hypoxic hepatitis and pre-cardiac arrest liver disease is associated with increased mortality and inferior neurological recovery. However, how the liver might impact the pathogenesis of post-cardiac arrest is still unknown. METHODSAn in vivo global cerebral ischemia model was established to assess how simultaneous liver ischemia affected the recovery of brain ischemic injury. In addition, an ex vivo brain normothermic machine perfusion (NMP) model was established to evaluate how addition of a functioning liver might impact the circulation, cytoarchitecture, neuron viability and electrocortical activity of the reperfused brain post-cardiac arrest. RESULTSIn the in vivo model, we observed a larger infarct area in the frontal lobe, elevated tissue injury scores in the CA1 region, as well as increased intravascular immune cell adhesion in the reperfused brains with hepatic ischemia, compared to those without simultaneous hepatic ischemia. The results of the ex vivo model demonstrated that the addition of a functioning liver to the brain NMP circuit significantly reduced post-cardiac arrest brain injury, increased neuronal viability and improved electrocortical activity. Furthermore, we observed significant alterations in gene expressions and metabolites in the presence or absence of hepatic ischemia. CONCLUSIONSOur research highlights the crucial role of the liver in the pathogenesis of post-cardiac arrest brain injury. These findings shed lights on a cardio-pulmonary-hepatic brain resuscitation strategy for patients with cardiac arrest.

neuroscience↗

bioRxiv2023

Disrupted excitation-inhibition balance in cognitively normal individuals at risk of Alzheimers disease

BackgroundSex differences impact Alzheimers disease (AD) neuropathology, but cell-to-network level dysfunctions in the prodromal phase are unclear. Alterations in hippocampal excitation-inhibition balance (EIB) have recently been linked to early AD pathology. ObjectiveExamine how AD risk factors (age, APOE-{varepsilon}4, amyloid-{beta}) relate to hippocampal EIB in cognitively normal males and females using connectome-level measures. MethodsIndividuals from the OASIS-3 cohort (age 42-95) were studied (N = 437), with a subset aged 65+ undergoing neuropsychological testing (N = 231). ResultsIn absence of AD risk factors (APOE-{varepsilon}4/A{beta}+), whole-brain EIB decreases with age more significantly in males than females (p = 0.021, {beta} = -0.007). Regression modeling including APOE-{varepsilon}4 allele carriers (A{beta}-) yielded a significant positive AGE-by-APOE interaction in the right hippocampus for females only (p = 0.013, {beta} = 0.014), persisting with inclusion of A{beta}+ individuals (p = 0.012, {beta} = 0.014). Partial correlation analyses of neuropsychological testing showed significant associations with EIB in females: positive correlations between right hippocampal EIB with categorical fluency and whole-brain EIB with the trail-making test (p < 0.05). ConclusionSex differences in EIB emerge during normal aging and progresses differently with AD risk. Results suggest APOE-{varepsilon}4 disrupts hippocampal balance more than amyloid in females. Increased excitation correlates positively with neuropsychological performance in the female group, suggesting a duality in terms of potential beneficial effects prior to cognitive impairment. This underscores the translational relevance of APOE-{varepsilon}4 related hyperexcitation in females, potentially informing therapeutic targets or early interventions to mitigate AD progression in this vulnerable population.

neuroscience↗

bioRxiv2023

Widespread PRC barrel proteins play critical roles in archaeal cell division

Cell division is fundamental to all cellular life. Most of the archaea employ one of two alternative division machineries, one centered around the prokaryotic tubulin homolog FtsZ and the other around the endosomal sorting complex required for transport (ESCRT). However, neither of these mechanisms has been thoroughly characterized in archaea. Here, we show that three of the four PRC (Photosynthetic Reaction Center) barrel domain proteins of Haloferax volcanii (renamed Cell division proteins B1/2/3 (CdpB1/2/3)), play important roles in division. CdpB1 interacts directly with the FtsZ membrane anchor SepF and is essential for division, whereas deletion of cdpB2 and cdpB3 causes a major and a minor division defect, respectively. Orthologs of CdpB proteins are also involved in cell division in other haloarchaea. Phylogenetic analysis shows that PRC barrel proteins are widely distributed among archaea, including the highly conserved CdvA protein of the crenarchaeal ESCRT-based division system. Thus, diverse PRC barrel proteins appear to be central to cell division in most if not all archaea. Further study of these proteins is expected to elucidate the division mechanisms in archaea and their evolution.

microbiology↗