In vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth.

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here, we noninvasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the gamma-aminobutyric acid (GABA) agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in the association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 y old) and Asian (7.2 to 7.9 y old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth.

Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging.

Brain imaging research enjoys increasing adoption of supervised machine learning for single-participant disease classification. Yet, the success of these algorithms likely depends on population diversity, including demographic differences and other factors that may be outside of primary scientific interest. Here, we capitalize on propensity scores as a composite confound index to quantify diversity due to major sources of population variation. We delineate the impact of population heterogeneity on the predictive accuracy and pattern stability in 2 separate clinical cohorts: the Autism Brain Imaging Data Exchange (ABIDE, n = 297) and the Healthy Brain Network (HBN, n = 551). Across various analysis scenarios, our results uncover the extent to which cross-validated prediction performances are interlocked with diversity. The instability of extracted brain patterns attributable to diversity is located preferentially in regions part of the default mode network. Collectively, our findings highlight the limitations of prevailing deconfounding practices in mitigating the full consequences of population diversity.

Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study.

There is significant interest in using neuroimaging data to predict behavior. The predictive models are often interpreted by the computation of feature importance, which quantifies the predictive relevance of an imaging feature. Tian and Zalesky (2021) suggest that feature importance estimates exhibit low split-half reliability, as well as a trade-off between prediction accuracy and feature importance reliability across parcellation resolutions. However, it is unclear whether the trade-off between prediction accuracy and feature importance reliability is universal. Here, we demonstrate that, with a sufficient sample size, feature importance (operationalized as Haufe-transformed weights) can achieve fair to excellent split-half reliability. With a sample size of 2600 participants, Haufe-transformed weights achieve average intra-class correlation coefficients of 0.75, 0.57 and 0.53 for cognitive, personality and mental health measures respectively. Haufe-transformed weights are much more reliable than original regression weights and univariate FC-behavior correlations. Original regression weights are not reliable even with 2600 participants. Intriguingly, feature importance reliability is strongly positively correlated with prediction accuracy across phenotypes. Within a particular behavioral domain, there is no clear relationship between prediction performance and feature importance reliability across regression models. Furthermore, we show mathematically that feature importance reliability is necessary, but not sufficient, for low feature importance error. In the case of linear models, lower feature importance error is mathematically related to lower prediction error. Therefore, higher feature importance reliability might yield lower feature importance error and higher prediction accuracy. Finally, we discuss how our theoretical results relate with the reliability of imaging features and behavioral measures. Overall, the current study provides empirical and theoretical insights into the relationship between prediction accuracy and feature importance reliability.

Sinomenine Hydrochloride Can Ameliorate Benign Prostatic Hyperplasia by Lowering the 5α-Reductase 2 Level and Regulating the Balance between the Proliferation and Apoptosis of Cells.

Benign prostatic hyperplasia (BPH) is a chronic disease that affects the quality of life of older males. Sinomenine hydrochloride (SIN) is the major bioactive alkaloid isolated from the roots of the traditional Chinese medicinal plant Sinomenium acutum Rehderett Wilson. We wondered if the SIN administration exerted a regulatory effect on BPH and its potential mechanism of action. Mice with testosterone propionate-induced BPH subjected to bilateral orchiectomy were employed for in vivo experiments. A human BPH cell line (BPH-1) was employed for in vitro experiments. SIN administration inhibited the proliferation of BPH-1 cells (p < 0.05) by regulating the expression of androgen-related proteins (steroid 5-alpha reductase 2 (SRD5A2), androgen receptors, prostate-specific antigen), apoptosis-related proteins (B-cell lymphoma 2 (Bcl-2), Bcl-2-associated X protein (Bax)) and proliferation-related proteins (proliferating cell nuclear antigen (PCNA), mammalian target of rapamycin, inducible nitric oxide synthase) in vitro. SIN administration decreased the prostate-gland weight coefficient (p < 0.05) and improved the histological status of mice suffering from BPH. The regulatory effects of SIN administration on SRD5A2, an apoptosis-related protein (Bcl-2), and proliferation-related proteins (PCNA, matrix metalloproteinase-2) were consistent with in vitro data. SIN exerted a therapeutic effect against BPH probably related to lowering the SRD5A2 level and regulating the balance between the proliferation and apoptosis of cells. Our results provide an important theoretical basis for the development of plant medicines for BPH therapy.

Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI.

A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.

FAEE exerts a protective effect against osteoporosis by regulating the MAPK signalling pathway.

CONTEXT: Ferulic acid ethyl ester (FAEE) is abundant in Ligusticum chuanxiong Hort. (Apiaceae) and grains, and possesses diverse biological activities; but the effects of FAEE on osteoporosis has not been reported. OBJECTIVE: This study investigated whether FAEE can attenuate osteoclastogenesis and relieve ovariectomy-induced osteoporosis via attenuating mitogen-activated protein kinase (MAPK). MATERIALS AND METHODS: We stimulated RAW 264.7 cells with receptor activator of NF-κB ligand (RANKL) followed by FAEE. The roles of FAEE in osteoclast production and osteogenic resorption of mature osteoclasts were evaluated by tartrate resistant acid phosphatase (TRAP) staining, expression of osteoclast-specific genes, proteins and MAPK. Ovariectomized (OVX) female Sprague-Dawley rats were administered FAEE (20 mg/kg/day) for 12 weeks to explore its potential in vivo, and then histology was undertaken in combination with cytokines analyses. RESULTS: FAEE suppressed RANKL-induced osteoclast formation (96 ± 0.88 vs. 15 ± 1.68) by suppressing the expression of osteoclast-specific genes, proteins and MAPK signalling pathway related proteins (p-ERK/ERK, p-JNK/JNK and p-P38/P38) in vitro. In addition, OVX rats exposed to FAEE maintained their normal calcium (Ca) (2.72 ± 0.02 vs. 2.63 ± 0.03, p < 0.05) balance, increased oestradiol levels (498.3 ± 9.43 vs. 398.7 ± 22.06, p < 0.05), simultaneously reduced levels of bone mineral density (BMD) (0.159 ± 0.0016 vs. 0.153 ± 0.0025, p < 0.05) and bone mineral content (BMC) (0.8 ± 0.0158 vs. 0.68 ± 0.0291, p < 0.01). DISCUSSION AND CONCLUSIONS: These findings suggested that FAEE could be used to ameliorate osteoporosis by the MAPK signalling pathway, suggesting that FAEE could be a potential therapeutic candidate for osteoporosis.

Estimating the Global Prevalence, Disease Progression, and Clinical Outcome of Hepatitis Delta Virus Infection.

BACKGROUND: Hepatitis delta virus (HDV) coinfects with hepatitis B virus (HBV) causing the most severe form of viral hepatitis. However, its exact global disease burden remains largely obscure. We aim to establish the global epidemiology, infection mode-stratified disease progression, and clinical outcome of HDV infection. METHODS: We conducted a meta-analysis with a random-effects model and performed data synthesis. RESULTS: The pooled prevalence of HDV is 0.80% (95% confidence interval [CI], 0.63-1.00) among the general population and 13.02% (95% CI, 11.96-14.11) among HBV carriers, corresponding to 48-60 million infections globally. Among HBV patients with fulminant hepatitis, cirrhosis, or hepatocellular carcinoma, HDV prevalence is 26.75% (95% CI, 19.84-34.29), 25.77% (95% CI, 20.62-31.27), and 19.80% (95% CI, 10.97-30.45), respectively. The odds ratio (OR) of HDV infection among HBV patients with chronic liver disease compared with asymptomatic controls is 4.55 (95% CI, 3.65-5.67). Hepatitis delta virus-coinfected patients are more likely to develop cirrhosis than HBV-monoinfected patients with OR of 3.84 (95% CI, 1.79-8.24). Overall, HDV infection progresses to cirrhosis within 5 years and to hepatocellular carcinoma within 10 years, on average. CONCLUSIONS: Findings suggest that HDV poses a heavy global burden with rapid progression to severe liver diseases, urging effective strategies for screening, prevention, and treatment.

Mitochondrial electron transport chain complex III sustains hepatitis E virus replication and represents an antiviral target.

Hepatitis E virus (HEV) infection has emerged as a global health problem. However, no approved medication is available, and the infection biology remains largely elusive. Electron transport chain (ETC), a key component of the mitochondria, is the main site that produces ATP and reactive oxygen species (ROS). By profiling the role of the different complexes of the mitochondrial ETC, we found that pharmacological inhibition of complex III, a well-defined drug target for the treatment of malaria and Pneumocystis pneumonia, potently restricts HEV replication. This effect demonstrated in our HEV models is equivalent to the anti-HEV potency of ribavirin, a widely used off-label treatment for patients with chronic HEV. Mechanistically, we found that this effect is independent of ATP production, ROS level, and pyridine depletion. By using pharmacological inhibitors and genetic approaches, we found that mitochondrial permeability transition pore (MPTP), a newly identified component of ETC, provides basal defense against HEV infection. HEV interferes with the opening of the MPTP. Furthermore, inhibition of the MPTP attenuated the anti-HEV effect of complex III inhibitors, suggesting that the MPTP mediates the antiviral effects of these inhibitors. These findings reveal new insights on HEV-host interactions and provide viable anti-HEV targets for therapeutic development.-Qu, C., Zhang, S., Wang, W., Li, M., Wang, Y., van der Heijde-Mulder, M., Shokrollahi, E., Hakim, M. S., Raat, N. J. H., Peppelenbosch, M. P., Pan, Q. Mitochondrial electron transport chain complex III sustains hepatitis E virus replication and represents an antiviral target.

Mitochondria in the biology, pathogenesis, and treatment of hepatitis virus infections.

Hepatitis virus infections affect a large proportion of the global population. The host responds rapidly to viral infection by orchestrating a variety of cellular machineries, in particular, the mitochondrial compartment. Mitochondria actively regulate viral infections through modulation of the cellular innate immunity and reprogramming of metabolism. In turn, hepatitis viruses are able to modulate the morphodynamics and functions of mitochondria, but the mode of actions are distinct with respect to different types of hepatitis viruses. The resulting mutual interactions between viruses and mitochondria partially explain the clinical presentation of viral hepatitis, influence the response to antiviral treatment, and offer rational avenues for novel therapy. In this review, we aim to consider in depth the multifaceted interactions of mitochondria with hepatitis virus infections and emphasize the implications for understanding pathogenesis and advancing therapeutic development.

Recombinant identification, molecular classification and proposed reference genomes for hepatitis delta virus.

Hepatitis delta virus (HDV), as a defective sub-virus that co-infects with hepatitis B virus, imposes an emerging global health burden. However, genetic characteristics and molecular classification of HDV remain under investigated. In this study, we have systematically retrieved and analysed a large set of HDV full-length genome sequences and identified novel recombinants. Based on phylogenetic and genetic analyses, we have established an updated classification system for HDV when recombinants were excluded. Furthermore, we have mapped the global distribution of different genotypes and subtypes. Finally, we have compiled a complete set of reference genomes for each subtype and proposed criteria for future identification of novel genotypes and subtypes. Of note, the global distribution map indicates that currently available HDV genetic data remain limited, and thus our proposed classification will likely evolve as future epidemiological data will accumulate. These results will facilitate the future research on the diagnosis, screening, epidemiology, evolution, prevention and clinical management of HDV infection.

Macroscale intrinsic dynamics are associated with microcircuit function in focal and generalized epilepsies.

Epilepsies are a group of neurological disorders characterized by abnormal spontaneous brain activity, involving multiscale changes in brain functional organizations. However, it is not clear to what extent the epilepsy-related perturbations of spontaneous brain activity affect macroscale intrinsic dynamics and microcircuit organizations, that supports their pathological relevance. We collect a sample of patients with temporal lobe epilepsy (TLE) and genetic generalized epilepsy with tonic-clonic seizure (GTCS), as well as healthy controls. We extract massive temporal features of fMRI BOLD time-series to characterize macroscale intrinsic dynamics, and simulate microcircuit neuronal dynamics used a large-scale biological model. Here we show whether macroscale intrinsic dynamics and microcircuit dysfunction are differed in epilepsies, and how these changes are linked. Differences in macroscale gradient of time-series features are prominent in the primary network and default mode network in TLE and GTCS. Biophysical simulations indicate reduced recurrent connection within somatomotor microcircuits in both subtypes, and even more reduced in GTCS. We further demonstrate strong spatial correlations between differences in the gradient of macroscale intrinsic dynamics and microcircuit dysfunction in epilepsies. These results emphasize the impact of abnormal neuronal activity on primary network and high-order networks, suggesting a systematic abnormality of brain hierarchical organization.

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences.

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization outcomes. We propose two DNN-based harmonization approaches that explicitly account for covariate distribution differences across datasets: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three different continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Therefore, future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat.

MRI economics: Balancing sample size and scan duration in brain wide association studies.

A pervasive dilemma in neuroimaging is whether to prioritize sample size or scan duration given fixed resources. Here, we systematically investigate this trade-off in the context of brain-wide association studies (BWAS) using resting-state functional magnetic resonance imaging (fMRI). We find that total scan duration (sample size × scan duration per participant) robustly explains individual-level phenotypic prediction accuracy via a logarithmic model, suggesting that sample size and scan duration are broadly interchangeable. The returns of scan duration eventually diminish relative to sample size, which we explain with principled theoretical derivations. When accounting for fixed costs associated with each participant (e.g., recruitment, non-imaging measures), we find that prediction accuracy in small-scale BWAS might benefit from much longer scan durations (>50 min) than typically assumed. Most existing large-scale studies might also have benefited from smaller sample sizes with longer scan durations. Both logarithmic and theoretical models of the relationships among sample size, scan duration and prediction accuracy explain well-predicted phenotypes better than poorly-predicted phenotypes. The logarithmic and theoretical models are also undermined by individual differences in brain states. These results replicate across phenotypic domains (e.g., cognition and mental health) from two large-scale datasets with different algorithms and metrics. Overall, our study emphasizes the importance of scan time, which is ignored in standard power calculations. Standard power calculations inevitably maximize sample size at the expense of scan duration. The resulting prediction accuracies are likely lower than would be produced with alternate designs, thus impeding scientific discovery. Our empirically informed reference is available for future study design: WEB_APPLICATION_LINK.

In-vivo whole-cortex marker of excitation-inhibition ratio indexes cortical maturation and cognitive ability in youth.

A balanced excitation-inhibition ratio (E/I ratio) is critical for healthy brain function. Normative development of cortex-wide E/I ratio remains unknown. Here we non-invasively estimate a putative marker of whole-cortex E/I ratio by fitting a large-scale biophysically-plausible circuit model to resting-state functional MRI (fMRI) data. We first confirm that our model generates realistic brain dynamics in the Human Connectome Project. Next, we show that the estimated E/I ratio marker is sensitive to the GABA-agonist benzodiazepine alprazolam during fMRI. Alprazolam-induced E/I changes are spatially consistent with positron emission tomography measurement of benzodiazepine receptor density. We then investigate the relationship between the E/I ratio marker and neurodevelopment. We find that the E/I ratio marker declines heterogeneously across the cerebral cortex during youth, with the greatest reduction occurring in sensorimotor systems relative to association systems. Importantly, among children with the same chronological age, a lower E/I ratio marker (especially in association cortex) is linked to better cognitive performance. This result is replicated across North American (8.2 to 23.0 years old) and Asian (7.2 to 7.9 years old) cohorts, suggesting that a more mature E/I ratio indexes improved cognition during normative development. Overall, our findings open the door to studying how disrupted E/I trajectories may lead to cognitive dysfunction in psychopathology that emerges during youth.

Anti-hyperuricemia effect of hesperetin is mediated by inhibiting the activity of xanthine oxidase and promoting excretion of uric acid.

Hesperetin is a natural flavonoid with many biological activities. In view of hyperuricemia treatment, the effects of hesperetin in vivo and in vitro, and the underlying mechanisms, were explored. Hyperuricemia models induced by yeast extract (YE) or potassium oxonate (PO) in mice were created, as were models based on hypoxanthine and xanthine oxidase (XOD) in L-O2 cells and sodium urate in HEK293T cells. Serum level of uric acid (UA), creatinine (CRE), and urea nitrogen (BUN) were reduced significantly after hesperetin treatment in vivo. Hesperetin provided hepatoprotective effects and inhibited xanthine oxidase activity markedly, altered the level of malondialdehyde (MDA), glutathione peroxidase (GSH-PX) and catalase (CAT), downregulated the XOD protein expression, toll-like receptor (TLR)4, nucleotide binding oligomerization domain-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome, interleukin-18 (IL-18), upregulated forkhead box O3a (FOXO3a), manganese superoxide dismutase (MnSOD) in a uric acid-synthesis model in mice. Protein expression of organic anion transporter 1 (OAT1), OAT3, organic cationic transporter 1 (OCT1), and OCT2 was upregulated by hesperetin intervention in a uric acid excretion model in mice. Our results proposal that hesperetin exerts a uric acid-lowering effect through inhibiting xanthine oxidase activity and protein expression, intervening in the TLR4-NLRP3 inflammasome signaling pathway, and up-regulating expression of FOXO3a, MnSOD, OAT1, OAT3, OCT1, and OCT2 proteins. Thus, hesperetin could be a promising therapeutic agent against hyperuricemia.

Neferine ameliorates nonalcoholic steatohepatitis through regulating AMPK pathway.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD), peculiarly nonalcoholic steatohepatitis (NASH), has become the main cause of liver transplantation and liver-related death. However, the US Food and Drug Administration has not approved a specific medication for treating NASH. Neferine (NEF), a natural bisbenzylisoquinoline alkaloid separated from the traditional Chinese medicine Nelumbinis plumula, has a variety of pharmacological properties, especially on metabolic diseases. Nevertheless, the anti-NASH effect and mechanisms of NEF remain unclear. PURPOSE: This study aimed to investigate the amelioration of NEF on NASH and the potential mechanisms. STUDY DESIGN: HepG2 cells, hepatic stellate cells (HSCs) and high-fat diet (HFD)+carbon tetrachloride (CCl4) induced C57BL/6 mice were used to observe the effect of NEF against NASH and investigate the engaged mechanism. METHODS: HSCs and HepG2 cells stimulated by oleic acid (OA) were treated with NEF. C57BL/6 mice were fed with HFD+CCl4 to induce NASH mouse model and treated with or without NEF (5 mg/kg or 10 mg/kg, once daily, i.p) for 4 weeks. RESULTS: NEF significantly attenuated the accumulation of lipid droplets, intracellular triglyceride (TG) levels and hepatocytes apoptosis in OA-exposed HepG2 cells. NEF not only enhanced the AMPK and ACC phosphorylation in OA-stimulated HepG2 cells, but also reduced inflammatory response and fibrosis in lipopolysaccharide (LPS)-stimulated HepG2 and in LX-2, respectively. In HFD+CCl4-induced NASH mice, pathological staining confirmed NEF treatment mitigated hepatic lipid deposition, inflammatory cell infiltration as well as hepatic fibrosis. Furthermore, the liver weight, serum and hepatic TG and total cholesterol (TC) and aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were decreased compared with the model group. HFD+CCl4 also induced the upregulation of specific proteins and genes associated to inflammation (ILs, TNF-α, NLRP3, ASC, CCL2 and CXCL10) and hepatic fibrosis (collagens, α-SMA, TGF-ß and TIPM1), which were also suppressed by NEF treatment. CONCLUSION: Our results demonstrated that NEF played a protective role in hepatic steatosis via the regulation of AMPK pathways, which may serve as an attractive candidate for a potential novel strategy on prevention and treatment of NASH.

Multilayer meta-matching: translating phenotypic prediction models from multiple datasets to small data.

Resting-state functional connectivity (RSFC) is widely used to predict phenotypic traits in individuals. Large sample sizes can significantly improve prediction accuracies. However, for studies of certain clinical populations or focused neuroscience inquiries, small-scale datasets often remain a necessity. We have previously proposed a "meta-matching" approach to translate prediction models from large datasets to predict new phenotypes in small datasets. We demonstrated large improvement of meta-matching over classical kernel ridge regression (KRR) when translating models from a single source dataset (UK Biobank) to the Human Connectome Project Young Adults (HCP-YA) dataset. In the current study, we propose two meta-matching variants ("meta-matching with dataset stacking" and "multilayer meta-matching") to translate models from multiple source datasets across disparate sample sizes to predict new phenotypes in small target datasets. We evaluate both approaches by translating models trained from five source datasets (with sample sizes ranging from 862 participants to 36,834 participants) to predict phenotypes in the HCP-YA and HCP-Aging datasets. We find that multilayer meta-matching modestly outperforms meta-matching with dataset stacking. Both meta-matching variants perform better than the original "meta-matching with stacking" approach trained only on the UK Biobank. All meta-matching variants outperform classical KRR and transfer learning by a large margin. In fact, KRR is better than classical transfer learning when less than 50 participants are available for finetuning, suggesting the difficulty of classical transfer learning in the very small sample regime. The multilayer meta-matching model is publicly available at GITHUB_LINK.

Deciphering Tumour Microenvironment of Liver Cancer through Deconvolution of Bulk RNA-Seq Data with Single-Cell Atlas.

Liver cancers give rise to a heavy burden on healthcare worldwide. Understanding the tumour microenvironment (TME) underpins the development of precision therapy. Single-cell RNA sequencing (scRNA-seq) technology has generated high-quality cell atlases of the TME, but its wider application faces enormous costs for various clinical circumstances. Fortunately, a variety of deconvolution algorithms can instead repurpose bulk RNA-seq data, alleviating the need for generating scRNA-seq datasets. In this study, we reviewed major public omics databases for relevance in this study and utilised eight RNA-seqs and one microarray dataset from clinical studies. To decipher the TME of liver cancer, we estimated the fractions of liver cell components by deconvoluting the samples with Cibersortx using three reference scRNA-seq atlases. We also confirmed that Cibersortx can accurately deconvolute cell types/subtypes of interest. Compared with non-tumorous liver, liver cancers showed multiple decreased cell types forming normal liver microarchitecture, as well as elevated cell types involved in fibrogenesis, abnormal angiogenesis, and disturbed immune responses. Survival analysis shows that the fractions of five cell types/subtypes significantly correlated with patient outcomes, indicating potential therapeutic targets. Therefore, deconvolution of bulk RNA-seq data with scRNA-seq atlas references can be a useful tool to help understand the TME.

Sensory-motor cortices shape functional connectivity dynamics in the human brain.

Large-scale biophysical circuit models provide mechanistic insights into the micro-scale and macro-scale properties of brain organization that shape complex patterns of spontaneous brain activity. We developed a spatially heterogeneous large-scale dynamical circuit model that allowed for variation in local synaptic properties across the human cortex. Here we show that parameterizing local circuit properties with both anatomical and functional gradients generates more realistic static and dynamic resting-state functional connectivity (FC). Furthermore, empirical and simulated FC dynamics demonstrates remarkably similar sharp transitions in FC patterns, suggesting the existence of multiple attractors. Time-varying regional fMRI amplitude may track multi-stability in FC dynamics. Causal manipulation of the large-scale circuit model suggests that sensory-motor regions are a driver of FC dynamics. Finally, the spatial distribution of sensory-motor drivers matches the principal gradient of gene expression that encompasses certain interneuron classes, suggesting that heterogeneity in excitation-inhibition balance might shape multi-stability in FC dynamics.

Mitochondrial Fusion Via OPA1 and MFN1 Supports Liver Tumor Cell Metabolism and Growth.

Metabolic reprogramming universally occurs in cancer. Mitochondria act as the hubs of bioenergetics and metabolism. The morphodynamics of mitochondria, comprised of fusion and fission processes, are closely associated with mitochondrial functions and are often dysregulated in cancer. In this study, we aim to investigate the mitochondrial morphodynamics and its functional consequences in human liver cancer. We observed excessive activation of mitochondrial fusion in tumor tissues from hepatocellular carcinoma (HCC) patients and in vitro cultured tumor organoids from cholangiocarcinoma (CCA). The knockdown of the fusion regulator genes, OPA1 (Optic atrophy 1) or MFN1 (Mitofusin 1), inhibited the fusion process in HCC cell lines and CCA tumor organoids. This resulted in inhibition of cell growth in vitro and tumor formation in vivo, after tumor cell engraftment in mice. This inhibitory effect is associated with the induction of cell apoptosis, but not related to cell cycle arrest. Genome-wide transcriptomic profiling revealed that the inhibition of fusion predominately affected cellular metabolic pathways. This was further confirmed by the blocking of mitochondrial fusion which attenuated oxygen consumption and cellular ATP production of tumor cells. In conclusion, increased mitochondrial fusion in liver cancer alters metabolism and fuels tumor cell growth.