Dialog between artificial intelligence&natural intelligence

Recently,Quantitative Biology(QB)held a discussion on"Al(artificial intelligence)for Life Science"among editorial board members and interested scholars in anticipation of rapid development of this growing area after AlphaGo and ChatGPT mania.Many young people tend to get confused between facts and fictions;heated debates are unavoidable even among their mentors.When deep learning as represented by convolutional neural networks and LSTM(long shortterm memory)was made available for bioinformatics students,many of them rushed into this research field and tried to adopt these methods in all their projects without knowing the history that these tools were becoming successful consistently with Moore's Law(relating to rapid computer technology advances)

LncRNA Platr22 promotes super-enhancer activity and stem cell pluripotency

Super-enhancers(SEs)comprise large clusters of enhancers,which are co-occupied by multiple lineage-specific and master tran-scription factors,and play pivotal roles in regulating gene expression and cell fate determination.However,it is still largely un-known whether and how SEs are regulated by the noncoding portion of the genome.Here,through genome-wide analysis,wefound that tpng noncoding RNA(IncRNA)genes preferentially lie next to SEs.In mouse embryonic stem cells(mESCs),depletionof$E-associated IlncRNA transcripts dysregulated the activity of their nearby SEs.Specifically,we revealed a critical regulatoryrole of the IncRNA gene Platr22 in modulating the activity of a nearby SE and the expression of the nearby pluripotency regulatorZFP281.Through these regulatory events,Platr22 contributes to pluripotency maintenance and proper differentiation of mESCs.Mechanistically,Platr22 transcripts coat chromatin near the SE region and interact with DDX5 and hnRNP-L.DDX5 further recruitsp300 and other factors related to active transcription.We propose that these factors assemble into a transcription hub,thus pro-moting an open and active epigenetic chromatin state.0ur study highlights an unanticipated role for a class of lncRNAs in epige-netically controlling the activity and vulnerability to perturbation of nearby SEs for cell fate determination.

Regulation by competition:a hidden layer of gene regulatory network

Background:Molecular competition brings about trade-offs of shared limited resources among the cellular components,and thus introduces a hidden layer of regulatory mechanism by connecting components even without direct physical interactions.Several molecular competition scenarios have been observed recently,but there is still a lack of systematic quantitative understanding to reveal the essence of molecular competition.Methods:Here,by abstracting the analogous competition mechanism behind diverse molecular systems,we built a unified coarse-grained competition motif model to systematically integrate experimental evidences in these processes and analyzed general properties shared behind them from steady-state behavior to dynamic responses.Results:We could predict in what molecular environments competition would reveal threshold behavior or display a negative linear dependence.We quantified how competition can shape regulator-target dose-response curve,modulate dynamic response speed,control target expression noise,and introduce correlated fluctuations between targets.Conclusions:This work uncovered the complexity and generality of molecular competition effect as a hidden layer of gene regulatory network,and therefore provided a unified insight and a theoretical framework to understand and employ competition in both natural and synthetic systems.

A personal journey on cracking the genomic codes

In February 2001,the IHGSC(International Human Genome Sequencing Consortium)[1]and Celera Genomics[2]each reported draft sequences providing a first overall view of the human genome.On the occasion of the 20th anniversary celebration,I would like to provide some of my personal account on how the HGP(Human Genome Project)has impacted my academic research interests and to offer some candid advice for the new comers.1.

Avian influenza viruses suppress innate immunity by inducingtrans-transcriptional readthrough via SSU72

Innate immunity plays critical antiviral roles. The highly virulent avian influenza viruses (AIVs) H5N1, H7N9, and H5N6 can betterescape host innate immune responses than the less virulent seasonal H1N1 virus. Here, we report a mechanism by whichtranscriptional readthrough (TRT)-mediated suppression of innate immunity occurs post AIV infection. By using cell lines, mouselungs, and patient PBMCs, we showed that genes on the complementary strand (“trans” genes) influenced by TRT were involved inthe disruption of host antiviral responses during AIV infection. The trans-TRT enhanced viral lethality, and TRT abolishmentincreased cell viability and STAT1/2 expression. The viral NS1 protein directly bound to SSU72, and degradation of SSU72 inducedTRT. SSU72 overexpression reduced TRT and alleviated mouse lung injury. Our results suggest that AIVs infection induce TRT byreducing SSU72 expression, thereby impairing host immune responses, a molecular mechanism acting through the NS1-SSU72-trans-TRT-STAT1/2 axis. Thus, restoration of SSU72 expression might be a potential strategy for preventing AIV pandemics.

HCSGD：An integrated database of human cellular senescence genes

Cellular senescence is an irreversible cell cycle arrest program in response to various exogenous and endogenous stimuli like telomere dysfunction and DNA damage.It has been widely accepted as an antitumor program and is also found closely related to embryo development,tissue repair,organismal aging and age-related degenerative diseases.In the past decades,numerous efforts have been made to uncover the gene regulatory mechanisms of cellular senescence.There is a strong demand to integrate these data from various resources into one open platform.To facilitate researchers on cellular senescence,we have developed Human Cellular Senescence Gene Database（HCSGD） by integrating multiple online published data sources into a comprehensive senescence gene annotation platform（http：//bioinfo.au.tsinghua.edu.cn/member/xwwang/HCSGD）.Potential Human Cellular Senescence Genes（HCSGS）were collected by combining information from published literatures,gene expression profiling data and Protein-Protein Interaction networks.Additionally,genes are annotated with gene ontology annotation and microRNA/drug/compound target information.HCSGD provides a valuable resource to visualize cellular senescence gene networks,browse annotated functional information,and retrieve senescenceassociated genes with a user-friendly web interface.