Biology subjects

He, T.

Publications and source records attributed to He, T..

67 records · Page 4Linked to original sources

bioRxiv2020

Half of the world's tree biodiversity is unprotected and is increasingly threatened by human activities

Although trees are key to ecosystem functioning, many forests and tree species across the globe face strong threats. Preserving areas of high biodiversity is a core priority for conservation; however, different dimensions of biodiversity and varied conservation targets make it difficult to respond effectively to this challenge. Here, we (i) identify priority areas for global tree conservation using comprehensive coverage of tree diversity based on taxonomy, phylogeny, and functional traits; and (ii) compare these findings to existing protected areas and global biodiversity conservation frameworks. We find that ca. 51% of the top-priority areas for tree biodiversity are located in current protected areas. The remaining half top-priority areas are subject to moderate to high human pressures, indicating conservation actions are needed to mitigate these human impacts. Our findings emphasize the effectiveness of using tree conservation priority areas for future global conservation planning.

ecology↗

bioRxiv2020

Convergent molecular, cellular, and neural signatures of major depressive disorder

Major depressive disorder emerges from the complex interactions of biological systems that span across genes and molecules through cells, circuits, networks, and behavior. Establishing how neurobiological processes coalesce to contribute to the onset and maintenance of depression requires a multi-scale approach, encompassing measures of brain structure and function as well as genetic and cell-specific genomic data. Here, we examined anatomical (cortical thickness) and functional (functional variability, global brain connectivity) correlates of depression and negative affect across three population-imaging datasets: UK Biobank, Genome Superstruct Project, and ENIGMA (combined N[≥]23,723). Integrative analyses incorporated measures of cortical gene expression, post-mortem patient transcriptional data, depression GWAS, and single-cell transcription. Neuroimaging correlates of depression and negative affect were consistent across the three independent datasets. Linking ex-vivo gene downregulation with in-vivo neuroimaging, we found that genomic correlates of depression-linked neuroimaging phenotypes tracked gene downregulation in post-mortem cortical tissue samples of patients with depression. Integrated analysis of single-cell and Allen Human Brain Atlas expression data implicated somatostatin interneurons and astrocytes as consistent cell associates of depression, through both in-vivo imaging and ex-vivo cortical gene dysregulation. Providing converging evidence for these observations, GWAS derived polygenic risk for depression was enriched for genes expressed in interneurons, but not glia. Underscoring the translational potential of multi-scale approaches, the genomic correlates of depression-linked brain function and structure were enriched for known and novel disorder relevant molecular pathways. These findings bridge across levels to connect specific genes, cell classes, and biological pathways to in-vivo imaging correlates of depression. Key FindingsO_LIMajor depressive disorder and negative affect are associated with replicable profiles of cortical anatomy and function across independent population-level neuroimaging datasets (combined N[≥]23,723). C_LIO_LISomatostatin interneurons are consistent spatial transcriptional associates of in-vivo depression-linked imaging phenotypes. C_LIO_LIIntegrative single-cell gene expression analysis associate somatostatin interneurons and astrocytes with both in-vivo depression-linked imaging and ex-vivo gene downregulation in independent MDD cortical tissue samples. C_LIO_LITranscriptional correlates of in-vivo depression imaging phenotypes selectively capture gene downregulation in post-mortem tissue samples from patients with depression, but not other psychiatric disorders. C_LIO_LIIndicating that some cell classes are preferentially sensitive to inherited disease liability, genome-wide risk for depression is enriched among interneurons, but not glia. C_LIO_LIGene associates of depression-linked anatomy and function identify specific neurotransmitter systems, molecular signaling pathways, and receptors, suggesting possible targets for pharmaceutical intervention. C_LI

neuroscience↗

bioRxiv2019

Semaphorin 3a reduces the side effects of radiation on BMSCs by reducing ROS

1.Background/AimsRadiotherapy does not only kill tumor cells but also impairs the function of adjacent tissues, especially bone metabolism by damaging bone marrow stromal stem cells (BMSCs). This study aimed to investigate the effect of semaphorin 3a (Sema3a) on BMSCs exposed to 2 Gy radiation. MaterialsBMSCs were divided into four groups, namely, group A (0 Gy), group B (2 Gy), group C (0 Gy + Sema3a), and group D (2 Gy + Sema3a). A Cell Counting Kit-8 kit, Alizarin-Red and Oil-Red-O staining, alkaline phosphatase activity kit, and dichlorodihydro-fluoresce in diacetate were used to test cell proliferation, cell cycle, osteogenic ability, adipogenic ability, and the level of reactive oxygen species (ROS), respectively, in each group. Real-time PCR was performed to test the expression of osteogenic (osteocalcin and Runt-related transcription factor 2), adipogenic (peroxisome proliferator-activated receptor gamma), interleukin (IL)-6, and tumor necrosis factor (TNF)- genes. ResultsBMSC proliferation, osteogenic differentiation, and the number of cells undergoing division (S+G2 phase of the cell cycle) were found to be lower in group B than in group A. and the cellular levels of ROS, adipogenic differentiation, and expression of inflammatory factors (IL-6 and TNF-) were higher in group B than in group A. Furthermore, osteogenic differentiation ability was higher in group D than in group B, and adipogenic differentiation ability, cellular levels of ROS, and gene expression of TNF- and IL-6 were lower in group D than in group B. ConclusionThis study demonstrated that 2 Gy radiation could decrease the osteogenic differentiation ability of BMSCs and increase their adipogenic differentiation ability by increasing the production of ROS. However, Sema3a could reduce these side effects by decreasing the levels of ROS.

cell biology↗

bioRxiv2019

Melanoma plasticity is controlled by a TRIM28-JUNB mediated switch

The introduction of immune checkpoint blockade has revolutionized the treatment of metastatic melanoma1. However, 40-60% of patients with metastatic melanoma do not respond to immune checkpoint blockade, and a significant fraction of patients acquire resistance to treatment2,3. This resilience and aggressiveness of melanoma tumors is partly due to their ability to switch between invasive and proliferative states4,5. The transition between phenotypic states indicates that phenotype switching occurs through reversible epigenetic mechanisms rather than by acquisition of mutations6,7. Identifying the epigenetic mechanisms that underlie phenotype switching of melanoma cells could potentially lead to new therapeutic strategies. Here we report that the bromodomain protein TRIM28 (KAP1/TIF1{beta}) regulates a JUNB dependent phenotypic switch in melanoma cells. Knockdown of TRIM28 in melanoma cells reduced the expression of pro-invasive YAP1 signature genes, and led to reduced invasiveness and lung colonization. In contrast, TRIM28 knockdown increased the expression of KRAS signature genes and promoted tumor growth. TRIM28 interacted with the transcriptional elongation factors CDK9 and HEXIM1, and negatively regulated the transcriptional elongation of JUNB by RNA polymerase II. Rescue experiments demonstrated that the effects of TRIM28 knockdown were directly mediated by JUNB. Mechanistically, JUNB played a pivotal role in phenotype switching by inhibiting the invasiveness of melanoma cells and increasing the growth of melanoma tumors. Our results contribute to the understanding of melanoma plasticity, and suggest that cancer drugs inhibiting the transcriptional elongation of RNA polymerase II should be carefully evaluated in melanoma to exclude the risk for increased metastasis.

cancer biology↗

bioRxiv2019

Predicting Alzheimer’s disease progression using deep recurrent neural networks

Early identification of individuals at risk of developing Alzheimers disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimers Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al. 2018) from the Alzheimers Disease Neuroimaging Initiative (ADNI). We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future. Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a "preprocessing" issue, by imputing the missing data using the previous timepoint ("forward filling") or linear interpolation ("linear filling). The third strategy utilized the minimalRNN model itself to fill in the missing data both during training and testing ("model filling"). Our analyses suggest that the minimalRNN with "model filling" compared favorably with baseline algorithms, including support vector machine/regression, linear state space (LSS) model, and long short-term memory (LSTM) model. Importantly, although the training procedure utilized longitudinal data, we found that the trained minimalRNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data. An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 63 entries as of June 3rd, 2020.

neuroscience↗

bioRxiv2019

Visual working memory representations in visual and parietal cortex do not remap after eye movements

It has been suggested that our visual system does not only process stimuli that are directly available to our eyes, but also has a role in maintaining information in VWM over a period of seconds. It remains unclear however what happens to VWM representations in the visual system when we make saccades. Here, we tested the hypothesis that VWM representations are remapped within the visual system after making saccades. We directly compared the content of VWM for saccade and no-saccade conditions using MVPA of delay-related activity measured with fMRI. We found that when participants did not make a saccade, VWM representations were robustly present in contralateral early visual cortex. When making a saccade, VWM representations degraded in contralateral V1-V3 after the saccade shifted the location of the remembered grating to the opposite visual field. However, contrary to our hypothesis we found no evidence for the representations of the remembered grating at the saccadic target location in the opposite visual field, suggesting that there is no evidence for remapping of VWM in early visual cortex. Interestingly, IPS showed persistent VWM representations in both the saccade and no-saccade condition. Together, our results indicate that VWM representations in early visual cortex are not remapped across eye movements, potentially limiting the role of early visual cortex in VWM storage. HighlightsO_LIVisual working memory (VWM) representations do not remap after making saccades C_LIO_LIEye movement degrade VWM representations in early visual cortex, limiting the role of early visual cortex in VWM storage C_LIO_LIParietal cortex shows persistent VWM representations across saccades C_LI

neuroscience↗

bioRxiv2019

Empathic contagious pain and consolation in laboratory rodents: species and sex differences

Laboratory rodents are gregarious in nature and have a feeling of empathy when witnessing a familiar conspecific in pain. The rodent observers express two levels of empathic responses: observational contagious pain (OCP) and consolation. Here we examined the sex and species difference of OCP and consolation in male and female mice and rats. We observed no species difference in both OCP and consolation, but significant species difference in general social (allo-mouth and/or allo-tail sniffing) and non-social (self-grooming) behaviors. For sex difference, male mouse observers showed more allolicking and allogrooming behaviors toward a familiar conspecific in pain during and longer time increase in pain sensitivity after the PDSI than female mouse observers. However, no sex difference was observed in rats. Our results highlighted an evolutionary view of empathy that social animals including rodents also have the ability to feel, recognize, understand and share the others distressing states.

animal behavior and cognition↗

bioRxiv2018

Do Deep Neural Networks Outperform Kernel Regression for Functional Connectivity Prediction of Behavior?

There is significant interest in the development and application of deep neural networks (DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their classical counterparts in a variety of neuroimaging applications, yet there are few direct comparisons of relative utility. Here, we compared the performance of three DNN architectures and a classical machine learning algorithm (kernel regression) in predicting individual phenotypes from whole-brain resting-state functional connectivity (RSFC) patterns. One of the DNNs was a generic fully-connected feedforward neural network, while the other two DNNs were recently published approaches specifically designed to exploit the structure of connectome data. By using a combined sample of almost 10,000 participants from the Human Connectome Project (HCP) and UK Biobank, we showed that the three DNNs and kernel regression achieved similar performance across a wide range of behavioral and demographic measures. Furthermore, the generic feedforward neural network exhibited similar performance to the two state-of-the-art connectome-specific DNNs. When predicting fluid intelligence in the UK Biobank, performance of all algorithms dramatically improved when sample size increased from 100 to 1000 subjects. Improvement was smaller, but still significant, when sample size increased from 1000 to 5000 subjects. Importantly, kernel regression was competitive across all sample sizes. Overall, our study suggests that kernel regression is as effective as DNNs for RSFC-based behavioral prediction, while incurring significantly lower computational costs. Therefore, kernel regression might serve as a useful baseline algorithm for future studies.

neuroscience↗

bioRxiv2018

A Simple Approximation To The Bias Of Gene-Environment Interactions In Case/Control Studies With Silent Disease

One of the most important research areas in case-control Genome-Wide Association Studies is to determine how the effect of a genotype varies across the environment or to measure the gene-environment interaction (GxE). We consider the scenario when some of the \"healthy\" controls actually have the disease and when the frequency of these latent cases varies by the environmental variable of interest. In this scenario, performing logistic regression of clinically defined case status on the genetic variant, environmental variable, and their interaction will result in biased estimates of GxE interaction. Here, we derive a general theoretical approximation to the bias in the estimates of the GxE interaction and show, through extensive simulation, that this approximation is accurate in finite samples. Moreover, we apply this approximation to evaluate the bias in the effect estimates of the genetic variants related to mitochondrial proteins a large-scale Prostate Cancer study.

genetics↗

bioRxiv2018

Accurate Nonlinear Mapping between MNI Volumetric and FreeSurfer Surface Coordinate Systems

The results of most neuroimaging studies are reported in volumetric (e.g., MNI152) or surface (e.g., fsaverage) coordinate systems. Accurate mappings between volumetric and surface coordinate systems can facilitate many applications, such as projecting fMRI group analyses from MNI152/Colin27 to fsaverage for visualization, or projecting resting-state fMRI parcellations from fsaverage to MNI152/Colin27 for volumetric analysis of new data. However, there has been surprisingly little research on this topic. Here, we evaluated three approaches for mapping data between MNI152/Colin27 and fsaverage coordinate systems by simulating the above applications: projection of group-average data from MNI152/Colin27 to fsaverage and projection of fsaverage parcellations to MNI152/Colin27. Two of the approaches are currently widely used. A third approach (registration fusion) was previously proposed, but not widely adopted. Two implementations of the registration fusion (RF) approach were considered, with one implementation utilizing the Advanced Normalization Tools (ANTs). We found that RF-ANTs performed the best for mapping between fsaverage and MNI152/Colin27, even for new subjects registered to MNI152/Colin27 using a different software tool (FSL FNIRT). This suggests that RF-ANTs would be useful even for researchers not using ANTs. Finally, it is worth emphasizing that the most optimal approach for mapping data to a coordinate system (e.g., fsaverage) is to register individual subjects directly to the coordinate system, rather than via another coordinate system. Only in scenarios where the optimal approach is not possible (e.g., mapping previously published results from MNI152 to fsaverage), should the approaches evaluated in this manuscript be considered. In these scenarios, we recommend RF-ANTs (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/registration/Wu2017_RegistrationFusion).

neuroscience↗

bioRxiv2018

An optimal kernel-based method for gene set association analysis

Single-variant based genome-wide association studies have successfully detected many genetic variants that are associated with many complex traits. However, their power is limited due to weak marginal signals and ignoring potential complex interactions among genetic variants. Set-based strategy was proposed to provide a remedy where multiple genetic variants in a given set (e.g., gene or pathway) are jointly evaluated, so that the systematic effect of the set is considered. Among many, the kernel-based testing (KBT) framework is one of the most popular and powerful methods in set-based association studies. Given a set of candidate kernels, method has been proposed to choose the one with the smallest p-value. Such a method, however, can yield inflated type I error, especially when the number of variants in a set is large. Alternatively one can get p-values by permutations which, however, could be very time consuming. In this work, we proposed an efficient testing procedure that can not only control type I error rate but also generate power close to the one obtained under the optimal kernel. Our method is built upon the KBT framework and is based on asymptotic results under a high-dimensional setting. Hence it can efficiently deal with the case where the number of variants in a set is much larger than the sample size. Both simulation and real data analysis demonstrate the advantages of the method compared with its counterparts.

genetics↗

bioRxiv2018

Predictive remapping of visual features beyond saccadic targets

Visual stability is thought to be mediated by predictive remapping of the relevant object information from its current, pre-saccadic locations to its future, post-saccadic location on the retina. However, it is heavily debated whether and what feature information is predictively remapped during the pre-saccadic interval. Using an orientation adaptation paradigm, we investigated whether predictive remapping occurs for stimulus features and whether adaptation itself is remapped. We found strong evidence for predictive remapping of a stimulus presented shortly before saccade onset, but no remapping of adaptation. Furthermore, we establish that predictive remapping also occurs for stimuli that are not saccade targets, pointing toward a forward remapping process operating across the whole visual field. Together, our findings suggest that predictive feature remapping of object information plays an important role in mediating visual stability.

neuroscience↗

bioRxiv2017

Contribution of transition and stabilization processes to speciation is a function of the ancestral trait state and selective environment in Hakea

Currently the origin and trajectories of novel traits are emphasised in evolutionary studies, the role of stabilization is neglected, and interpretations are often post hoc rather than as hypothesised responses to stated agents of selection. Here we evaluated the impact of changing environmental conditions on trait evolution and stabilization and their relative contribution to diversification in a prominent Australian genus, Hakea (Proteaceae). We assembled a time-based phylogeny for Hakea, reconstructed its ancestral traits for six attributes and determined their evolutionary trajectories in response to the advent or increasing presence of fire, seasonality, aridity, nectar-feeding birds and (in)vertebrate herbivores/granivores. The ancestral Hakea arose 18 million years ago (Ma) and was broad-leaved, non-spinescent, insect-pollinated, had medium-sized, serotinous fruits and resprouted after fire. Of the 190 diversification events that yielded the 82 extant species analysed, 8-50% involved evolution, stabilization or re-evolution (reversal) of individual novel traits. Needle leaves appeared 14 Ma and increased through the Neogene/Quaternary coinciding with intensifying seasonality and aridity. Spinescence arose 12 Ma consistent with the advent of vertebrate herbivores. Bird-pollination appeared 14 Ma in response to advent of the Meliphagidae in the early Miocene. Small and large woody fruits evolved from 12 Ma as alternative defenses against granivory. Fire-caused death evolved 14 Ma, accounting for 50% of subsequent events, as fire became less stochastic. Loss of serotiny began in the late Miocene as non-fireprone habitats became available but only contributed 8% of events. Innovation and subsequent stabilization of functional traits promoted the overall species diversification rate in Hakea by 15 times such that only three species now retain the ancestral phenotype. Our approach holds great promise for understanding the processes responsible for speciation of organisms when the ancestral condition can be identified and the likely selective agents are understood.

ecology↗