Chlorogenic Acid Attenuates Hepatic Steatosis by Suppressing ZFP30.

Nonalcoholic fatty liver disease (NAFLD) has become a major global health problem with no approved pharmacological treatment for this disease. Thus, it is urgent to develop effective therapeutic targets for clinical intervention. Here, we show for the first time that ZFP30, a member of the KRAB-ZFP family, is significantly increased in NAFLD models. ZFP30 silencing ameliorates free fatty acid (FFA)-induced lipid accumulation; in contrast, the ZFP30 overexpression exacerbates the triglyceride accumulation and steatosis in hepatocytes. Further investigation revealed that the effects of ZFP30 on hepatic lipid accumulation were mainly attributed to the PPARα downregulation in the NAFLD model. Mechanistically, ZFP30 directly binded to the promoter of PPARα and recruited KAP1 to suppress its transcription. Moreover, chlorogenic acid (CGA) reversed the upregulation of ZFP30 in NAFLD, promoting the PPARα expression, resulting in enhanced fatty acid oxidation and alleviated hepatic steatosis. Collectively, our study indicates ZFP30 as a potential target for NAFLD treatment.

Research on the MEG of Depression Patients Based on Multivariate Transfer Entropy.

The pathogenesis of depression is complex, and the current means of medical diagnosis is single. Patients with severe depression may even have great physical pain and suicidal tendencies. Magnetoencephalography (MEG) has the characteristics of ultrahigh spatiotemporal resolution and safety. It is a good medical means for the diagnosis of depression. In this paper, multivariate transfer entropy algorithm is used to study MEG of depression. In this paper, the subjects are divided into the same brain region and the multichannel combination between different brain regions, and the multivariate transfer entropy of patients with depression and healthy controls under different EEG signal frequency bands is calculated. Finally, the significant difference between the two groups of experimental samples is verified by the results of independent sample t-test. The experimental results show that for the same combination of brain channels, the multivariate transfer entropy in the depression group is generally lower than that in the healthy control group, and the difference is the best in γ frequency band and the largest in the frontal region.

Act Tough and Soft: Video Monitoring, Hongbao Gifts, and the Job Satisfaction of Domestic Workers.

There is a rapidly growing demand for domestic services among urban families in China. However, domestic work remains a low-status occupation with a high turnover rate. Focusing on the job satisfaction of domestic workers is useful to interpret this phenomenon. We investigate how the job satisfaction of domestic workers in China is affected by to two distinct labor control strategies used by their employers: the installation of video-monitoring devices in employers' homes (a "tough" control strategy), and the Chinese custom of giving monetary gifts, or "hongbao" (a "soft" control strategy). By analyzing data from surveys of domestic workers in four cities in China (N = 699), we find that video monitoring in employers' homes negatively impacts domestic workers' job satisfaction, and that hongbao gifts from employers significantly promote domestic workers' job satisfaction. The analysis of the causal mechanism based on a structural equation model suggests that video monitoring can increase the discrimination that domestic workers perceive, which in turn reduces their job satisfaction. In particular, we find that domestic workers' perception of discrimination completely mediates the effect of video monitoring on their job satisfaction. However, we also find that hongbao gifts significantly reduce domestic workers' perceptions of discrimination, and thus promote their job satisfaction; that is, the relationship between hongbao gifts and job satisfaction is partially mediated by discrimination. Our study provides a more comprehensive understanding of Chinese employers' labor control strategies and their effects on the job satisfaction of domestic workers.

Who Got Infected with COVID-19? A Study of College Students in Wuhan (China).

College students represent a large group of people who frequently travel across regions, which increased their risk of infection and exacerbated the risk of COVID-19 spread throughout China. This study uses survey data from the end of April 2020 to analyze the status of COVID-19-infected cases, the group differences, and influencing factors in college students in Wuhan. The sample size was made up 4355 participants, including 70 COVID-19-infected students. We found that during the COVID-19 outbreak in early 2020, college students in Wuhan were primarily infected during off-campus events after winter break or infected in their hometowns after leaving Wuhan; the percentage of college students with severe cases was relatively low, and most had mild cases; however, a large proportion of asymptomatic cases may exist; there were significant group differences in gender, age and place of residence; and the risk of infection was closely related to the campus environment, in which the population density and number of faculty and students on campus had a significant impact. The results indicated that the infection of students did not occur at random, thus strengthening student health education and campus management can help curb the spread of COVID-19 among students.

Trauma exposure and the PTSD symptoms of college teachers during the peak of the COVID-19 outbreak.

This study aimed to explore influencing factors for the psychological impact of coronavirus disease 2019 (COVID-19) on Wuhan college teachers, posttraumatic stress symptoms in particular, so as to inform evidence-based strategy development to ameliorate such adverse impacts. An online survey was conducted from 26 to 29 April 2020, and 1650 teachers (47.54% male; M = 40.28 years, SD = 8.3 years) enrolled in Wuhan universities and colleges participated. The results showed that the overall incidence of posttraumatic stress disorder (PTSD) among college teachers was as high as 24.55%, but the average level of PTSD score was low (M = 1.06, SD = 0.72). Logistic regression analysis showed that for those with confirmed COVID-19, the ratio was much higher, up to 2.814 (95% confidence interval [CI]: [1.542, 5.136], p < 0.001); that is, compared with those without symptoms, the ratio of PTSD increased by 181%. For those who had family members or relatives who died of COVID-19, the ratio was 5.592 (95% CI: [2.271, 13.766], p < 0.001), 459% higher than those who had no one who died. But the living places during the pandemic had no significant effect on PTSD. The findings suggest that mental health services reducing PTSD should be provided. Teachers who confirmed COVID-19 or lost loved ones to COVID-19 should be given particular care.

COVID-19 Stress and Mental Health of Students in Locked-Down Colleges.

The impact of the COVID-19 pandemic on the mental health of students in locked-down colleges remains obscure. This study aimed to explore influencing factors for the psychological impact of COVID-19 on Wuhan college students, post-traumatic stress symptoms in particular, so as to inform evidence-based strategy development to ameliorate such adverse impacts. An online survey was conducted from 26 to 29 April 2020, and 4355 students enrolled in Wuhan universities and colleges participated. Post-Traumatic Stress Disorder via the Impact of Event-Scale-Revised was assessed. COVID-19 disproportionately affected older male Master's and doctoral students living in Wuhan. The overall prevalence of PTSD was 16.3%. The three-level socio-interpersonal model of PTSD was empirically validated, and college students faced individual level risks such as infection with COVID-19, close relationship level risks such as family support (infection suspicion of family members, the loss of loved ones, and the family income decrease) and online course difficulties (little interaction, disturbing learning environment, and difficulty in adaption), and distant level risks such as excessive collection of personal information, estrangement of family relatives, and harassment and insult from strangers. The findings suggest the severity of the psychological impact of COVID-19. Mental health services reducing PTSD should be provided. Students who have lost loved ones and suffered family financial loss should be given particular care.

Oncogenic Gene-Expression Programs in Leiomyosarcoma and Characterization of Conventional, Inflammatory, and Uterogenic Subtypes.

Leiomyosarcoma (LMS) is a mesenchymal neoplasm with complex copy-number alterations and characteristic loss of tumor suppressor genes without known recurrent activating mutations. Clinical management of advanced LMS relies on chemotherapy and complementary palliative approaches, and research efforts to date have had limited success identifying clinically actionable biomarkers or targeted therapeutic vulnerabilities. To explore the biological underpinning of LMS, we evaluated gene-expression patterns of this disease in comparison with diverse sarcomas, nonmesenchymal neoplasms, and normal myogenic tissues. We identified a recurrent gene-expression program in LMS, with evidence of oncogenic evolution of an underlying smooth-muscle lineage-derived program characterized by activation of E2F1 and downstream effectors. Recurrently amplified or highly expressed genes in LMS were identified, including IGF1R and genes involved in retinoid signaling pathways. Though the majority of expressed transcripts were conserved across LMS samples, three separate subtypes were identified that were enriched for muscle-associated transcripts (conventional LMS), immune markers (inflammatory LMS), or a uterine-like gene-expression program (uterogenic LMS). Each of these subtypes expresses a unique subset of genes that may be useful in the management of LMS: IGF1R was enriched in conventional LMS, worse disease-specific survival was observed in inflammatory LMS, and prolactin was elaborated by uterogenic LMS. These results extend our understanding of LMS biology and identify several strategies and challenges for further translational investigation. IMPLICATIONS: LMS has a recurrent oncogenic transcriptional program and consists of molecular subtypes with biological and possible clinical implications.

Prediction of Epidemic Spread of the 2019 Novel Coronavirus Driven by Spring Festival Transportation in China: A Population-Based Study.

After the 2019 novel coronavirus (2019-nCoV) outbreak, we estimated the distribution and scale of more than 5 million migrants residing in Wuhan after they returned to their hometown communities in Hubei Province or other provinces at the end of 2019 by using the data from the 2013-2018 China Migrants Dynamic Survey (CMDS). We found that the distribution of Wuhan's migrants is centred in Hubei Province (approximately 75%) at a provincial level, gradually decreasing in the surrounding provinces in layers, with obvious spatial characteristics of circle layers and echelons. The scale of Wuhan's migrants, whose origins in Hubei Province give rise to a gradient reduction from east to west within the province, and account for 66% of Wuhan's total migrants, are from the surrounding prefectural-level cities of Wuhan. The distribution comprises 94 districts and counties in Hubei Province, and the cumulative percentage of the top 30 districts and counties exceeds 80%. Wuhan's migrants have a large proportion of middle-aged and high-risk individuals. Their social characteristics include nuclear family migration (84%), migration with families of 3-4 members (71%), a rural household registration (85%), and working or doing business (84%) as the main reason for migration. Using a quasi-experimental analysis framework, we found that the size of Wuhan's migrants was highly correlated with the daily number of confirmed cases. Furthermore, we compared the epidemic situation in different regions and found that the number of confirmed cases in some provinces and cities in Hubei Province may be underestimated, while the epidemic situation in some regions has increased rapidly. The results are conducive to monitoring the epidemic prevention and control in various regions.

An inter-species protein-protein interaction network across vast evolutionary distance.

In cellular systems, biophysical interactions between macromolecules underlie a complex web of functional interactions. How biophysical and functional networks are coordinated, whether all biophysical interactions correspond to functional interactions, and how such biophysical-versus-functional network coordination is shaped by evolutionary forces are all largely unanswered questions. Here, we investigate these questions using an "inter-interactome" approach. We systematically probed the yeast and human proteomes for interactions between proteins from these two species and functionally characterized the resulting inter-interactome network. After a billion years of evolutionary divergence, the yeast and human proteomes are still capable of forming a biophysical network with properties that resemble those of intra-species networks. Although substantially reduced relative to intra-species networks, the levels of functional overlap in the yeast-human inter-interactome network uncover significant remnants of co-functionality widely preserved in the two proteomes beyond human-yeast homologs. Our data support evolutionary selection against biophysical interactions between proteins with little or no co-functionality. Such non-functional interactions, however, represent a reservoir from which nascent functional interactions may arise.

Widespread macromolecular interaction perturbations in human genetic disorders.

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

A proteome-scale map of the human interactome network.

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.

Protein interaction network of alternatively spliced isoforms from brain links genetic risk factors for autism.

Increased risk for autism spectrum disorders (ASD) is attributed to hundreds of genetic loci. The convergence of ASD variants have been investigated using various approaches, including protein interactions extracted from the published literature. However, these datasets are frequently incomplete, carry biases and are limited to interactions of a single splicing isoform, which may not be expressed in the disease-relevant tissue. Here we introduce a new interactome mapping approach by experimentally identifying interactions between brain-expressed alternatively spliced variants of ASD risk factors. The Autism Spliceform Interaction Network reveals that almost half of the detected interactions and about 30% of the newly identified interacting partners represent contribution from splicing variants, emphasizing the importance of isoform networks. Isoform interactions greatly contribute to establishing direct physical connections between proteins from the de novo autism CNVs. Our findings demonstrate the critical role of spliceform networks for translating genetic knowledge into a better understanding of human diseases.

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.

Genome-wide functional annotation and structural verification of metabolic ORFeome of Chlamydomonas reinhardtii.

BACKGROUND: Recent advances in the field of metabolic engineering have been expedited by the availability of genome sequences and metabolic modelling approaches. The complete sequencing of the C. reinhardtii genome has made this unicellular alga a good candidate for metabolic engineering studies; however, the annotation of the relevant genes has not been validated and the much-needed metabolic ORFeome is currently unavailable. We describe our efforts on the functional annotation of the ORF models released by the Joint Genome Institute (JGI), prediction of their subcellular localizations, and experimental verification of their structural annotation at the genome scale. RESULTS: We assigned enzymatic functions to the translated JGI ORF models of C. reinhardtii by reciprocal BLAST searches of the putative proteome against the UniProt and AraCyc enzyme databases. The best match for each translated ORF was identified and the EC numbers were transferred onto the ORF models. Enzymatic functional assignment was extended to the paralogs of the ORFs by clustering ORFs using BLASTCLUST. In total, we assigned 911 enzymatic functions, including 886 EC numbers, to 1,427 transcripts. We further annotated the enzymatic ORFs by prediction of their subcellular localization. The majority of the ORFs are predicted to be compartmentalized in the cytosol and chloroplast. We verified the structure of the metabolism-related ORF models by reverse transcription-PCR of the functionally annotated ORFs. Following amplification and cloning, we carried out 454FLX and Sanger sequencing of the ORFs. Based on alignment of the 454FLX reads to the ORF predicted sequences, we obtained more than 90% coverage for more than 80% of the ORFs. In total, 1,087 ORF models were verified by 454 and Sanger sequencing methods. We obtained expression evidence for 98% of the metabolic ORFs in the algal cells grown under constant light in the presence of acetate. CONCLUSIONS: We functionally annotated approximately 1,400 JGI predicted metabolic ORFs that can facilitate the reconstruction and refinement of a genome-scale metabolic network. The unveiling of the metabolic potential of this organism, along with structural verification of the relevant ORFs, facilitates the selection of metabolic engineering targets with applications in bioenergy and biopharmaceuticals. The ORF clones are a resource for downstream studies.

A public genome-scale lentiviral expression library of human ORFs.

Functional characterization of the human genome requires tools for systematically modulating gene expression in both loss-of-function and gain-of-function experiments. We describe the production of a sequence-confirmed, clonal collection of over 16,100 human open-reading frames (ORFs) encoded in a versatile Gateway vector system. Using this ORFeome resource, we created a genome-scale expression collection in a lentiviral vector, thereby enabling both targeted experiments and high-throughput screens in diverse cell types.

Next-generation sequencing to generate interactome datasets.

Next-generation sequencing has not been applied to protein-protein interactome network mapping so far because the association between the members of each interacting pair would not be maintained in en masse sequencing. We describe a massively parallel interactome-mapping pipeline, Stitch-seq, that combines PCR stitching with next-generation sequencing and used it to generate a new human interactome dataset. Stitch-seq is applicable to various interaction assays and should help expand interactome network mapping.

Edgetic perturbation models of human inherited disorders.

Cellular functions are mediated through complex systems of macromolecules and metabolites linked through biochemical and physical interactions, represented in interactome models as 'nodes' and 'edges', respectively. Better understanding of genotype-to-phenotype relationships in human disease will require modeling of how disease-causing mutations affect systems or interactome properties. Here we investigate how perturbations of interactome networks may differ between complete loss of gene products ('node removal') and interaction-specific or edge-specific ('edgetic') alterations. Global computational analyses of approximately 50,000 known causative mutations in human Mendelian disorders revealed clear separations of mutations probably corresponding to those of node removal versus edgetic perturbations. Experimental characterization of mutant alleles in various disorders identified diverse edgetic interaction profiles of mutant proteins, which correlated with distinct structural properties of disease proteins and disease mechanisms. Edgetic perturbations seem to confer distinct functional consequences from node removal because a large fraction of cases in which a single gene is linked to multiple disorders can be modeled by distinguishing edgetic network perturbations. Edgetic network perturbation models might improve both the understanding of dissemination of disease alleles in human populations and the development of molecular therapeutic strategies.

Large-scale RACE approach for proactive experimental definition of C. elegans ORFeome.

Although a highly accurate sequence of the Caenorhabditis elegans genome has been available for 10 years, the exact transcript structures of many of its protein-coding genes remain unsettled. Approximately two-thirds of the ORFeome has been verified reactively by amplifying and cloning computationally predicted transcript models; still a full third of the ORFeome remains experimentally unverified. To fully identify the protein-coding potential of the worm genome including transcripts that may not satisfy existing heuristics for gene prediction, we developed a computational and experimental platform adapting rapid amplification of cDNA ends (RACE) for large-scale structural transcript annotation. We interrogated 2000 unverified protein-coding genes using this platform. We obtained RACE data for approximately two-thirds of the examined transcripts and reconstructed ORF and transcript models for close to 1000 of these. We defined untranslated regions, identified new exons, and redefined previously annotated exons. Our results show that as much as 20% of the C. elegans genome may be incorrectly annotated. Many annotation errors could be corrected proactively with our large-scale RACE platform.

Metabolic network analysis integrated with transcript verification for sequenced genomes.

With sequencing of thousands of organisms completed or in progress, there is a growing need to integrate gene prediction with metabolic network analysis. Using Chlamydomonas reinhardtii as a model, we describe a systems-level methodology bridging metabolic network reconstruction with experimental verification of enzyme encoding open reading frames. Our quantitative and predictive metabolic model and its associated cloned open reading frames provide useful resources for metabolic engineering.