Biology subjects

Cao, R.

Publications and source records attributed to Cao, R..

At least 73 records · Page 4Linked to original sources

bioRxiv2021

Face identity selectivity in the deep neural network and human brain

A central challenge in face perception research is to understand how neurons encode various face identities. However, this challenge has not been met largely due to the lack of simultaneous access to the activity of the entire face processing neural network as well as the lack of a comprehensive multifaceted model that is able to characterize a large number of facial features. In this study, we address this challenge by conducting in silico experiments using a deep neural network (DNN) capable of face recognition with a diverse array of stimuli. We identified a subset of DNN neurons selective to face identities, and these identity-selective neurons demonstrated generalized discriminability to novel faces not involved in the training and in many different styles. Visualization of the network explained the response of the DNN neurons and manipulation of the network confirmed the importance of identity-selective neurons in face recognition. Importantly, using our human single-neuron recordings, we directly compared the response of artificial neurons with 490 real human neurons to the same stimuli and found that artificial neurons did share a similar representation of facial features as human neurons. We also observed a novel region-based feature coding mechanism in DNN neurons as in human neurons, which may explain how the DNN performs face recognition. Together, by directly linking between artificial and human neurons, our results shed light on how human neurons encode face identities.

neuroscience↗

bioRxiv2021

Evolutionary insights into a non-coding deletion of SARS-CoV-2 B.1.1.7

Three prevalent SARS-CoV-2 Variants of Concern (VOCs) were emerged and caused epidemic waves. It is essential to uncover the key genetic changes that cause the high transmissibility of VOCs. However, different viral mutations are generally tightly linked so traditional population genetic methods may not reliably detect beneficial mutation. In this study, we proposed a new pandemic-scale phylogenomic approach to detect mutations crucial to transmissibility. We analyzed 3,646,973 high-quality SARS-CoV-2 genomic sequences and the epidemiology metadata. Based on the sequential occurrence order of mutations and the instantaneously accelerated furcation rate, the analysis revealed that two non-coding mutations at the position of 28271 (g.a28271-/t) might be crucial for the high transmissibility of Alpha, Delta and Omicron VOCs. Both two mutations cause an A-to-T change at the core Kozak site of the N gene. The analysis also revealed that the non-coding mutations (g.a28271-/t) alone are unlikely to cause high viral transmissibility, indicating epistasis or multilocus interaction in viral transmissibility. A convergent evolutionary analysis revealed that g.a28271-/t, S:P681H/R and N:R203K/M occur independently in the three-VOC lineages, suggesting a potential interaction among these mutations. Therefore, this study unveils that non-synonymous and non-coding mutations could affect the transmissibility synergistically.

evolutionary biology↗

bioRxiv2021

Maintenance of Bound or Independent Features in Visual Working Memory is Task-dependent

Over the last decade, seemingly conflicting results were obtained regarding the question of whether features of an object are stored separately, or bound together, in visual working memory. Many of these studies are based on an implicit assumption about a default, or fixed, mode of working memory storage. In contrast, here we asked whether the anticipated memory probes used in a given experiment might determine the format in which information is maintained in working memory, consistent with its task-oriented function. In order to test this flexible maintenance hypothesis, we recorded EEG while subjects performed a delayed-match-to-sample task with different requirements and load. In Experiment 1, by contrasting conditions with and without the requirement of maintaining bound features, we found significant differences in EEG signals recorded in central-parietal channels while controlling for differences in visual stimulation. Such difference was confirmed by multivariate pattern analysis (MVPA). Moreover, behavior results, topographical distributions and cross-contrast MVPA decoding jointly suggested that this effect is not the same as task load effect. These results were confirmed in Experiment 2 with an independent group of subjects with the same paradigm, providing reliable evidence that the format of object maintenance in visual working memory could be flexibly shaped by the current task.

neuroscience↗

bioRxiv2021

Unbalanced dietary patterns contribute to the pathogenesis of precocious puberty by affecting gut microbiota and host metabolites

Precocious puberty (PP) mostly stems from endocrine disorders. However, its triggering factors, especially for the early onset of partial PP, and the associated pathogenic mechanisms remain ambiguous. In this study, a systematic analysis in the form of a questionnaire of lifestyles, gut microbiome, and serum metabolome data was carried out to examine the pathogenesis of PP in a cohort comprised of 200 girls, with or without PP. The analysis revealed substantial alterations in gut microbiota, serum metabolites, as well as lifestyle patterns in the PP group, which were characterized by an elevated abundance of {beta}-glucuronidase-producing and butyrate-producing bacteria, and excessive lipid concentration with decreased levels of organic nitrogen compounds in the serum of the participants. These differential microbes and metabolites tend to be reliable non-invasive diagnostic biomarkers aiding the early diagnosis of PP and exhibit a strong discriminative power (AUC = 0.93 and AUC = 0.97, respectively). Furthermore, the microbial biomarkers were confirmed in an independent validation cohort (n = 83, AUC = 0.85). Moreover, structural equation modeling revealed that unhealthy dietary habits were the primary contributors for the alteration of gut microbiota and serum metabolites, triggering the imbalance in the host hormones that leads to premature physical development. Our study determines a causal relationship among the gut microbiota, host metabolites, diet, and clinical characteristics of preadolescent girls who experienced early onset of PP, and formulates non-invasive diagnostic tools demonstrating excellent performance for the early detection of PP.

microbiology↗

bioRxiv2021

ZoomQA: Residue-Level Single-Model QA Support Vector Machine Utilizing Sequential and 3D Structural Features

MotivationThe Estimation of Model Accuracy problem is a cornerstone problem in the field of Bioinformatics. When predictions are made for proteins of which we do not know the native structure, we run into an issue to tell how good a tertiary structure prediction is, especially the protein binding regions, which are useful for drug discovery. Currently, most methods only evaluate the overall quality of a protein decoy, and few can work on residue level and protein complex. Here we introduce ZoomQA, a novel, single-model method for assessing the accuracy of a tertiary protein structure / complex prediction at residue level. ZoomQA differs from others by considering the change in chemical and physical features of a fragment structure (a portion of a protein within a radius r of the target amino acid) as the radius of contact increases. Fourteen physical and chemical properties of amino acids are used to build a comprehensive representation of every residue within a protein and grades their placement within the protein as a whole. Moreover, ZoomQA can evaluate the quality of protein complex, which is unique. ResultsWe benchmark ZoomQA on CASP14, it outperforms other state of the art local QA methods and rivals state of the art QA methods in global prediction metrics. Our experiment shows the efficacy of these new features, and shows our method is able to match the performance of other state-of-the-art methods without the use of homology searching against database or PSSM matrix. Availabilityhttp://zoomQA.renzhitech.com Contactcaora@plu.edu

bioinformatics↗

bioRxiv2021

SynthQA - Hierarchical Machine Learning-based Protein Quality Assessment

MotivationIt has been a challenge for biologists to determine 3D shapes of proteins from a linear chain of amino acids and understand how proteins carry out lifes tasks. Experimental techniques, such as X-ray crystallography or Nuclear Magnetic Resonance, are time-consuming. This highlights the importance of computational methods for protein structure predictions. In the field of protein structure prediction, ranking the predicted protein decoys and selecting the one closest to the native structure is known as protein model quality assessment (QA), or accuracy estimation problem. Traditional QA methods dont consider different types of features from the protein decoy, lack various features for training machine learning models, and dont consider the relationship between features. In this research, we used multi-scale features from energy score to topology of the protein structure, and proposed a hierarchical architecture for training machine learning models to tackle the QA problem. ResultsWe introduce a new single-model QA method that incorporates multi-scale features from protein structures, utilizes the hierarchical architecture of training machine learning models, and predicts the quality of any protein decoy. Based on our experiment, the new hierarchical architecture is more accurate compared to traditional machine learning-based methods. It also considers the relationship between features and generates additional features so machine learning models can be trained more accurately. We trained our new tool, SynthQA, on the CASP dataset (CASP10 to CASP12), and validated our method on 33 targets from the latest CASP 14 dataset. The result shows that our method is comparable to other state-of-the-art single-model QA methods, and consistently outperforms each of the 14 used features. Availabilityhttps://github.com/Cao-Labs/SynthQA.git Contactcaora@plu.edu

bioinformatics↗

bioRxiv2021

A neuronal social trait space for first impressions in the human amygdala and hippocampus

People instantaneously evaluate faces with significant agreement on evaluations of social traits. However, the neural basis for such rapid spontaneous face evaluation remains largely unknown. Here, we recorded from 490 neurons in the amygdala and hippocampus in 5 neurosurgical patients and show that amygdala and hippocampal neurons encode a social trait space. We further investigated the temporal evolution and modulation on the social trait representation, and we employed encoding and decoding models to reveal the critical social traits for the trait space. We also recorded from another 259 neurons and replicated our findings using different social traits. Lastly, the neuronal social trait space may have a behavioral consequence likely involved in the abnormal processing of social information in autism. Together, our results suggest that there exists a neuronal population code for a comprehensive social trait space in the human amygdala and hippocampus that underlie spontaneous first impressions.

neuroscience↗

bioRxiv2020

An interactive viral genome evolution network analysis system enabling rapid large-scale molecular tracing of SARS-CoV-2

Comprehensive analyses of viral genomes can provide a global picture on SARS-CoV-2 transmission and help to predict the oncoming trends of pandemic. This molecular tracing is mainly conducted through extensive phylogenetic network analyses. However, the rapid accumulation of SARS-CoV-2 genomes presents an unprecedented data size and complexity that has exceeded the capacity of existing methods in constructing evolution network through virus genotyping. Here we report a Viral genome Evolution Network Analysis System (VENAS), which uses Hamming distances adjusted by the minor allele frequency to construct viral genome evolution network. The resulting network was topologically clustered and divided using community detection algorithm, and potential evolution paths were further inferred with a network disassortativity trimming algorithm. We also employed parallel computing technology to achieve rapid processing and interactive visualization of >10,000 viral genomes, enabling accurate detection and subtyping of the viral mutations through different stages of Covid-19 pandemic. In particular, several core viral mutations can be independently identified and linked to early transmission events in Covid-19 pandemic. As a general platform for comprehensive viral genome analysis, VENAS serves as a useful computational tool in the current and future pandemics.

evolutionary biology↗

bioRxiv2020

Encoding of facial features by single neurons in the human amygdala and hippocampus

The human amygdala and hippocampus play a key role in face processing. However, it has been unknown how the neurons in the human amygdala and hippocampus encode facial feature information and directs eye movements to salient facial features such as the eyes and mouth. In this study, we identified a population of neurons that differentiated fixations on the eyes vs. mouth. The response of these feature-selective neurons was not dependent on fixation order, and eye-preferring and mouth-preferring neurons were not of different neuronal types. We found another population of neurons that differentiated saccades to the eyes vs. mouth. Population decoding confirmed our results and further revealed the temporal dynamics of face feature coding. Interestingly, we found that the amygdala and hippocampus played a different role in encoding face features. Lastly, we revealed two functional roles of feature-selective neurons that they encoded the salient region for face recognition and they encoded perceived social trait judgment. Together, we revealed and characterized a new class of neurons that encoded facial features. These neurons may play an important role in social perception and recognition of faces.

neuroscience↗

bioRxiv2020

Structural and biochemical characterization of bifunctional XynA

Xylan and cellulose are the two major constituents in numerous types of lignocellulosic biomass, representing a promising resource for biofuels and other biobased industries. The efficient degradation of lignocellulose requires the synergistic actions of cellulase and xylanase. Thus, bifunctional enzyme incorporated xylanase/cellulase activity has attracted considerable attention since it has great cost savings potential. Recently, a novel GH10 family enzyme XynA identified from Bacillus sp. is found to degrade both cellulose and xylan. To understand its molecular catalytic mechanism, here we first solve the crystal structure of XynA at 2.3 [A]. XynA is characterized with a classic (/{beta})8 TIM-barrel fold (GH10 domain) flanked by the flexible N-terminal domain and C-terminal domain. Circular dichroism, protein thermal shift and enzyme activity assays reveal that conserved residues Glu182 and Glu280 are both important for catalytic activities of XynA, which is verified by the crystal structure of XynA with E182A/E280A double mutant. Molecular docking studies of XynA with xylohexaose and cellohexaose as well as site-directed mutagenesis and enzyme activity assay demonstrat that Gln250 and His252 are indispensible to cellulase and bifunctional activity, separately. These results elucidate the structural and biochemical features of XynA, providing clues for further modification of XynA for industrial application.

molecular biology↗

bioRxiv2020

Feature-based encoding of face identity by single neurons in the human medial temporal lobe

Neurons in the human amygdala and hippocampus that are selective for the identity of specific people are classically thought to encode a persons identity invariant to visual features (e.g., skin tone, eye shape). However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semantic representation of an individual person. Here, we show that some amygdala and hippocampal neurons are selective to multiple different unrelated face identities based on shared visual features. The encoded identities form clusters in the representation of a deep neural network trained to recognize faces. Contrary to prevailing views, these neurons thus represent an individuals face with a visual feature-based code rather than one based on association with known concepts. Feature neurons encoded faces regardless of their identity, race, gender, familiarity, or pixel-level visual features; and the region of feature space to which feature neurons are tuned predicted their response to new face stimuli. Our results reveal a new class of neurons that bridge the perception-driven representation of facial features in the higher visual cortex with mnemonic semantic representations in the MTL, which may form the basis for declarative memory.

neuroscience↗

bioRxiv2020

Single extracellular vesicles protein profiling classifies renal fibrosis stages in mice model

Renal fibrosis is a common consequence of various chronic kidney diseases (CKD), leading to the loss of renal function and even end-stage renal failure. Extracellular vesicles (EVs) were shown to be involved in development of CKD and renal fibrosis. In this study, we induced renal fibrosis in mice model through unilateral ureteral obstruction (UUO) and extracted EVs from the kidney with induced fibrosis. Proximity barcoding assay (PBA) was performed to detect the expression of 112 proteins at individual EVs level for renal fibrosis of Grade I to IV and sham control group as well. The single EVs are classified into subpopulations according to the surface proteomic characteristics. We discovered several EV subpopulations, with presence of ITGAM, ITGA6, CD73, CD13 and ALDH1, increase significantly with development of renal fibrosis. These findings indicate that besides protein expression, proteomic fingerprints of single EVs can be potential biomarkers for surveillance of CKD and renal fibrosis.

molecular biology↗

bioRxiv2020

MicroRNA-1 promotes the development of and prolongs engorgement time in Hyalomma anatolicum anatolicum (Acari: Ixodidae) ticks

MicroRNAs act as mRNA posttranscriptional regulators, playing important roles in cell differentiation, transcriptional regulation, growth and development. In this study, microRNA expression profiles of Hyalomma anatolicum anatolicum ticks at different developmental stages were detected by high-throughput sequencing and functionally assessed. In total, 2,585,169, 1,252,678, 1,558,217 and 1,155,283 unique reads were obtained from eggs, larvae, nymphs and adults, respectively, with 42, 46, 45 and 41 conserved microRNAs in these stages, respectively. Using eggs as a control, 48, 43 and 39 microRNAs were upregulated and 3, 10 and 9 downregulated in larvae, nymphs and adults, respectively. microRNA-1 (miR-1) was expressed in high abundance throughout Ha. anatolicum development, with an average of nearly one million transcripts, and it is highly conserved among tick species. Quantitative real-time PCR (qPCR) showed that miR-1 expression gradually increased with tick development, reaching the highest level at engorgement. Differential tissue expression was detected, with significantly higher levels in the salivary glands and epidermis than in the midgut. Inhibition assays showed no significant change in body weight or spawning time or amount between experimental and control groups, but there was a significant difference (p<0.01) in engorgement time. With miR-1 inhibition, ticks displayed obvious deformities during later development. To more fully explain the microRNA mechanism of action, the miR-1 family was analyzed regarding target gene; members acting on Hsp60 include miR-5, miR-994, miR-969, and miR-1011, which jointly play a role. Therefore, microRNAs are critical for normal tick development, and the primary structure of the mature sequence of miR-1 is highly conserved. Nonetheless, different developmental stages and tissues show different expression patterns, with a certain role in prolonging feeding. miR-1, together with other family members, regulates mRNA function and may be used as a molecular marker for species origin and evolution analysis and internal reference gene selection.

microbiology↗

bioRxiv2020

Outcomes of the 2019 EMDataResource model challenge: validation of cryo-EM models at near-atomic resolution

This paper describes outcomes of the 2019 Cryo-EM Map-based Model Metrics Challenge sponsored by EMDataResource (www.emdataresource.org). The goals of this challenge were (1) to assess the quality of models that can be produced using current modeling software, (2) to check the reproducibility of modeling results from different software developers and users, and (3) compare the performance of current metrics used for evaluation of models. The focus was on near-atomic resolution maps with an innovative twist: three of four target maps formed a resolution series (1.8 to 3.1 [A]) from the same specimen and imaging experiment. Tools developed in previous challenges were expanded for managing, visualizing and analyzing the 63 submitted coordinate models, and several novel metrics were introduced. The results permit specific recommendations to be made about validating near-atomic cryo-EM structures both in the context of individual laboratory experiments and holdings of structure data archives such as the Protein Data Bank. Our findings demonstrate the relatively high accuracy and reproducibility of cryo-EM models derived from these benchmark maps by 13 participating teams, representing both widely used and novel modeling approaches. We also evaluate the pros and cons of the commonly used metrics to assess model quality and recommend the adoption of multiple scoring parameters to provide full and objective annotation and assessment of the model, reflective of the observed density in the cryo-EM map.

biochemistry↗

bioRxiv2020

Instability with a purpose: how the visual brain makes decisions in a volatile world

In ambiguous or conflicting sensory situations, perception is often multistable in that it changes abruptly at irregular intervals, shifting perpetually between distinct alternatives. Intriguingly, the interval statistics of these alternations exhibits quasi-universal characteristics, suggesting a general mechanism. Here we show that the stereotypical features of multistable perception, exemplified by binocular rivalry, are reproduced in detail by a hierarchical dynamics operating out of equilibrium. Its constitutive elements are discretely stochastic and idealize the metastability of cortical networks. Independent elements accumulate visual evidence at one level, while groups of coupled elements compete for dominance at another level. As soon as one group dominates perception, feedback inhibition suppresses supporting evidence. This mechanism is corroborated compellingly by unexpected serial dependencies of perceptual alternations. Moreover, it satisfies normative constraints of continuous decision-making. We conclude that multistable perception reflects decision-making in a volatile world: integrating evidence over space and time, choosing categorically between hypotheses, while concurrently evaluating alternatives.

neuroscience↗

bioRxiv2020

Proinflammatory S100A9 Regulate Differentiation and Aggregation of Neural Stem Cells

Inflammation is the primary pathological feature of neurodegenerative diseases such as Alzheimers (AD) and Parkinsons disease. Proinflammatory molecules (e.g. S100A9) play important roles during progression of the diseases by regulating behavior and fate of multiple cell types in the nervous system (1). Our earlier studies reveal that S100A9 is toxic to neurons, and its interaction with A{beta} peptides leads to the formation of large non-toxic amyloidogenic aggregates, suggesting a protective role of A{beta} amyloids (2). We herein, demonstrate that S100A9 interacts with neural stem cells (NSCs) and causes NSC differentiation. In the brain of transgenic AD mouse models, we found large quantities of proinflammatory S100A9, which colocalizes with the differentiated NSCs. NSC sphere formation, which is a representative character of NSC stemness, is also substantially inhibited by S100A9. These results suggest that S100A9 is a representative marker for the inflammatory conditions in AD, and it promotes NSC differentiation. Intriguingly, in contrast to the death of both stem and differentiated NSCs caused by high S100A9 doses, S100A9 at a moderate concentration is toxic only to the early differentiated NSCs (i.e. progenitor cells and immature neurons), but not the stem cells. We therefore postulate that at the early stage of AD, expression of S100A9 leads to NSC differentiation, which remedies the neuron damages. The application drugs, which help maintain NSC stemness (e.g. PDGF), may help overcome the acute inflammatory conditions and improve the efficacy of NSC transplantation therapy.

cell biology↗

bioRxiv2020

Potential microenvironment of SARS-CoV-2 infection in airway epithelial cells revealed by Human Protein Atlas database analysis

The outbreak of COVID-19 has caused serious epidemic events in China and other countries. With the rapid spread of COVID-19, it is urgent to explore the pathogenesis of this novel coronavirus. However, the foundational research of COVID-19 is very weak. Although angiotensin converting enzyme 2 (ACE2) is the reported receptor of SARS-CoV-2, information about SARS-CoV-2 invading airway epithelial cells is very limited. Based on the analysis of the Human Protein Atlas database, we compared the virus-related receptors of epithelial-derived cells from different organs and found potential key molecules in the local microenvironment for SARS-CoV-2 entering airway epithelial cells. In addition, we found that these proteins were associated with virus reactive proteins in host airway epithelial cells, which may promote the activation of the immune system and the release of inflammatory factors. Our findings provide a new research direction for understanding the potential microenvironment required by SARS-CoV-2 infection in airway epithelial, which may assist in the discovery of potential drug targets against SARS-CoV-2 infection.

cell biology↗

bioRxiv2020

The pathognomonic FOXL2 C134W mutation alters DNA binding specificity

The somatic missense point mutation c.402C>G (p.C134W) in the FOXL2 transcription factor is pathognomonic for adult-type granulosa cell tumours (AGCT) and a diagnostic marker for this tumour type. However, the molecular consequences of this mutation and its contribution to the mechanisms of AGCT pathogenesis remain unclear. To explore the mechanisms driving FOXL2C134W pathogenicity we engineered V5-FOXL2WT and V5-FOXL2C134W inducible isogenic cell lines and performed ChIP-seq and transcriptome profiling. We found that FOXL2C134W associates with the majority of the FOXL2 WT DNA elements as well as a large collection of unique elements genome-wide. We confirmed an altered DNA binding specificity for FOXL2C134W in vitro and identified unique targets of FOXL2C134W including SLC35F2 whose expression increased sensitivity to YM155 in our model. Statement of SignificanceMechanistic understanding of FOXL2C134W induced regulatory state alterations drives discovery of a rationally designed therapeutic strategy.

cancer biology↗