Building digital life systems for future biology and medicine

The rapid development of biological technology (BT) and information technology (IT) especially of genomics and artificial intelligence (AI) is bringing great potential for revolutionizing future medicine. We propose the concept and framework of Digital Life Systems or dLife as a new paradigm to unleash this potential. It includes the multi-scale and multi-granule measure and representation of life in the digital space, the mathematical and/or computational modeling of the biology behind physiological and pathological processes, and ultimately cyber twins of healthy or diseased human body in the virtual space that can be used to simulate complex biological processes and deduce effects of medical treatments. We advocate that dLife is the route toward future AI precision medicine and should be the new paradigm for future biological and medical research.

Single-cell genomic profile-based analysis of tissue differentiation in colorectal cancer

Colorectal cancer(CRC) progression is associated with cancer cell dedifferentiation and stemness acquisition. Several methods have been developed to identify stemness signatures in CRCs. However, studies that directly measured the degree of dedifferentiation in CRC tissues are limited. It is unclear how the differentiation states change during CRC progression. To address this, we develop a method to analyze the tissue differentiation spectrum in colorectal cancer using normal gastrointestinal singlecell transcriptome data. Applying this method on 281 tumor samples from The Cancer Genome Atlas Colon Adenocarcinoma dataset, we identified three major CRC subtypes with distinct tissue differentiation pattern. We observed that differentiation states are closely correlated with anti-tumor immune response and patient outcomes in CRC. Highly dedifferentiated CRC samples escaped the immune surveillance and exhibited poor outcomes;mildly dedifferentiated CRC samples showed resistance to anti-tumor immune responses and had a worse survival rate;well-differentiated CRC samples showed sustained anti-tumor immune responses and had a good prognosis. Overall, the spectrum of tissue differentiation observed in CRCs can be used for future clinical risk stratification and subtype-based therapy selection.

Featured articles dedicated to the 20th anniversary of the human genome

The year 2021 is the 20th anniversary of the publication of the draft human genome[1,2].The sequencing of the human genome has brought life sciences into a new era,the era to understand the information systems of life in a quantitative manner.Such understanding lights up the bright future of individualized precision medicine,and enables the rational design of synthetic biological systems that can benefit mankind in broad aspects from industry,agriculture to environment and health.Using the human genome sequence as a basic reference has becoming a routine practice in current biological and medical studies.It is so common that people almost take the existence of the reference genome as granted.But the launching and completion of the Human Genome Project(HGP)was far from a routine practice.It was revolutionary in the history of science in the scientific vision,technological advancement,as well as in the joint efforts of multiple disciplines to accomplish the big scientific goal,in starting the culture and building the infrastructure of data sharing,and in the effective international multi-center collaboration.

Characterizing RNA Pseudouridylation by Convolutional Neural Networks

Pseudouridine(Ψ)is the most prevalent post-transcriptional RNA modification and is widespread in small cellular RNAs and m RNAs.However,the functions,mechanisms,and precise distribution ofΨs(especially in m RNAs)still remain largely unclear.The landscape ofΨs across the transcriptome has not yet been fully delineated.Here,we present a highly effective model based on a convolutional neural network(CNN),called Pseudo Uridy Lation Site Estimator(PULSE),to analyze large-scale profiling data ofΨsites and characterize the contextual sequence features of pseudouridylation.PULSE,consisting of two alternatively-stacked convolution and pooling layers followed by a fully-connected neural network,can automatically learn the hidden patterns of pseudouridylation from the local sequence information.Extensive validation tests demonstrated that PULSE can outperform other state-of-the-art prediction methods and achieve high prediction accuracy,thus enabling us to further characterize the transcriptome-wide landscape ofΨsites.We further showed that the prediction results derived from PULSE can provide novel insights into understanding the functional roles of pseudouridylation,such as the regulations of RNA secondary structure,codon usage,translation,and RNA stability,and the connection to single nucleotide variants.The source code and final model for PULSE are available at https://github.com/mlcb-thu/PULSE.