De novo protein identification in mammalian sperm using in situ cryoelectron tomography and AlphaFold2 docking.

To understand the molecular mechanisms of cellular pathways, contemporary workflows typically require multiple techniques to identify proteins, track their localization, and determine their structures in vitro. Here, we combined cellular cryoelectron tomography (cryo-ET) and AlphaFold2 modeling to address these questions and understand how mammalian sperm are built in situ. Our cellular cryo-ET and subtomogram averaging provided 6.0-Å reconstructions of axonemal microtubule structures. The well-resolved tertiary structures allowed us to unbiasedly match sperm-specific densities with 21,615 AlphaFold2-predicted protein models of the mouse proteome. We identified Tektin 5, CCDC105, and SPACA9 as novel microtubule-associated proteins. These proteins form an extensive interaction network crosslinking the lumen of axonemal doublet microtubules, suggesting their roles in modulating the mechanical properties of the filaments. Indeed, Tekt5 -/- sperm possess more deformed flagella with 180° bends. Together, our studies presented a cellular visual proteomics workflow and shed light on the in vivo functions of Tektin 5.

A basophil-neuronal axis promotes itch.

Itch is an evolutionarily conserved sensation that facilitates expulsion of pathogens and noxious stimuli from the skin. However, in organ failure, cancer, and chronic inflammatory disorders such as atopic dermatitis (AD), itch becomes chronic, intractable, and debilitating. In addition to chronic itch, patients often experience intense acute itch exacerbations. Recent discoveries have unearthed the neuroimmune circuitry of itch, leading to the development of anti-itch treatments. However, mechanisms underlying acute itch exacerbations remain overlooked. Herein, we identify that a large proportion of patients with AD harbor allergen-specific immunoglobulin E (IgE) and exhibit a propensity for acute itch flares. In mice, while allergen-provoked acute itch is mediated by the mast cell-histamine axis in steady state, AD-associated inflammation renders this pathway dispensable. Instead, a previously unrecognized basophil-leukotriene (LT) axis emerges as critical for acute itch flares. By probing fundamental itch mechanisms, our study highlights a basophil-neuronal circuit that may underlie a variety of neuroimmune processes.

Structural Insights into Mdn1, an Essential AAA Protein Required for Ribosome Biogenesis.

Mdn1 is an essential AAA (ATPase associated with various activities) protein that removes assembly factors from distinct precursors of the ribosomal 60S subunit. However, Mdn1's large size (∼5,000 amino acid [aa]) and its limited homology to other well-studied proteins have restricted our understanding of its remodeling function. Here, we present structures for S. pombe Mdn1 in the presence of AMPPNP at up to ∼4 Å or ATP plus Rbin-1, a chemical inhibitor, at ∼8 Å resolution. These data reveal that Mdn1's MIDAS domain is tethered to its ring-shaped AAA domain through an ∼20 nm long structured linker and a flexible ∼500 aa Asp/Glu-rich motif. We find that the MIDAS domain, which also binds other ribosome-assembly factors, docks onto the AAA ring in a nucleotide state-specific manner. Together, our findings reveal how conformational changes in the AAA ring can be directly transmitted to the MIDAS domain and thereby drive the targeted release of assembly factors from ribosomal 60S-subunit precursors.

Potent, Reversible, and Specific Chemical Inhibitors of Eukaryotic Ribosome Biogenesis.

All cellular proteins are synthesized by ribosomes, whose biogenesis in eukaryotes is a complex multi-step process completed within minutes. Several chemical inhibitors of ribosome function are available and used as tools or drugs. By contrast, we lack potent validated chemical probes to analyze the dynamics of eukaryotic ribosome assembly. Here, we combine chemical and genetic approaches to discover ribozinoindoles (or Rbins), potent and reversible triazinoindole-based inhibitors of eukaryotic ribosome biogenesis. Analyses of Rbin sensitivity and resistance conferring mutations in fission yeast, along with biochemical assays with recombinant proteins, provide evidence that Rbins' physiological target is Midasin, an essential ∼540-kDa AAA+ (ATPases associated with diverse cellular activities) protein. Using Rbins to acutely inhibit or activate Midasin function, in parallel experiments with inhibitor-sensitive or inhibitor-resistant cells, we uncover Midasin's role in assembling Nsa1 particles, nucleolar precursors of the 60S subunit. Together, our findings demonstrate that Rbins are powerful probes for eukaryotic ribosome assembly.

FACS-based genome-wide CRISPR screens define key regulators of DNA damage signaling pathways.

DNA damage-activated signaling pathways are critical for coordinating multiple cellular processes, which must be tightly regulated to maintain genome stability. To provide a comprehensive and unbiased perspective of DNA damage response (DDR) signaling pathways, we performed 30 fluorescence-activated cell sorting (FACS)-based genome-wide CRISPR screens in human cell lines with antibodies recognizing distinct endogenous DNA damage signaling proteins to identify critical regulators involved in DDR. We discovered that proteasome-mediated processing is an early and prerequisite event for cells to trigger camptothecin- and etoposide-induced DDR signaling. Furthermore, we identified PRMT1 and PRMT5 as modulators that regulate ATM protein level. Moreover, we discovered that GNB1L is a key regulator of DDR signaling via its role as a co-chaperone specifically regulating PIKK proteins. Collectively, these screens offer a rich resource for further investigation of DDR, which may provide insight into strategies of targeting these DDR pathways to improve therapeutic outcomes.

Visualization of oxygen vacancies and self-doped ligand holes in La3Ni2O7-δ.

The recent discovery of superconductivity in La3Ni2O7-δ under high pressure with a transition temperature around 80 K (ref. 1) has sparked extensive experimental2-6 and theoretical efforts7-12. Several key questions regarding the pairing mechanism remain to be answered, such as the most relevant atomic orbitals and the role of atomic deficiencies. Here we develop a new, energy-filtered, multislice electron ptychography technique, assisted by electron energy-loss spectroscopy, to address these critical issues. Oxygen vacancies are directly visualized and are found to primarily occupy the inner apical sites, which have been proposed to be crucial to superconductivity13,14. We precisely determine the nanoscale stoichiometry and its correlation to the oxygen K-edge spectra, which reveals a significant inhomogeneity in the oxygen content and electronic structure within the sample. The spectroscopic results also reveal that stoichiometric La3Ni2O7 has strong charge-transfer characteristics, with holes that are self-doped from Ni sites into O sites. The ligand holes mainly reside on the inner apical O and the planar O, whereas the density on the outer apical O is negligible. As the concentration of O vacancies increases, ligand holes on both sites are simultaneously annihilated. These observations will assist in further development and understanding of superconducting nickelate materials. Our imaging technique for quantifying atomic deficiencies can also be widely applied in materials science and condensed-matter physics.

Revealing protein-protein interactions at the transcriptome scale by sequencing.

We describe PROPER-seq (protein-protein interaction sequencing) to map protein-protein interactions (PPIs) en masse. PROPER-seq first converts transcriptomes of input cells into RNA-barcoded protein libraries, in which all interacting protein pairs are captured through nucleotide barcode ligation, recorded as chimeric DNA sequences, and decoded at once by sequencing and mapping. We applied PROPER-seq to human embryonic kidney cells, T lymphocytes, and endothelial cells and identified 210,518 human PPIs (collected in the PROPER v.1.0 database). Among these, 1,365 and 2,480 PPIs are supported by published co-immunoprecipitation (coIP) and affinity purification-mass spectrometry (AP-MS) data, 17,638 PPIs are predicted by the prePPI algorithm without previous experimental validation, and 100 PPIs overlap human synthetic lethal gene pairs. In addition, four previously uncharacterized interaction partners with poly(ADP-ribose) polymerase 1 (PARP1) (a critical protein in DNA repair) known as XPO1, MATR3, IPO5, and LEO1 are validated in vivo. PROPER-seq presents a time-effective technology to map PPIs at the transcriptome scale, and PROPER v.1.0 provides a rich resource for studying PPIs.

Depressed 660-km discontinuity caused by akimotoite-bridgmanite transition.

The 660-kilometre seismic discontinuity is the boundary between the Earth's lower mantle and transition zone and is commonly interpreted as being due to the dissociation of ringwoodite to bridgmanite plus ferropericlase (post-spinel transition)1-3. A distinct feature of the 660-kilometre discontinuity is its depression to 750 kilometres beneath subduction zones4-10. However, in situ X-ray diffraction studies using multi-anvil techniques have demonstrated negative but gentle Clapeyron slopes (that is,  the ratio between pressure and temperature changes) of the post-spinel transition that do not allow a significant depression11-13. On the other hand, conventional high-pressure experiments face difficulties in accurate phase identification due to inevitable pressure changes during heating and the persistent presence of metastable phases1,3. Here we determine the post-spinel and akimotoite-bridgmanite transition boundaries by multi-anvil experiments using in situ X-ray diffraction, with the boundaries strictly based on the definition of phase equilibrium. The post-spinel boundary has almost no temperature dependence, whereas the akimotoite-bridgmanite transition has a very steep negative boundary slope at temperatures lower than ambient mantle geotherms. The large depressions of the 660-kilometre discontinuity in cold subduction zones are thus interpreted as the akimotoite-bridgmanite transition. The steep negative boundary of the akimotoite-bridgmanite transition will cause slab stagnation (a stalling of the slab's descent) due to significant upward buoyancy14,15.

Single-cell RNA sequencing unveils unique transcriptomic signatures of endothelial cells and role of ENO1 in response to disturbed flow.

Flow patterns exert significant effects on vascular endothelial cells (ECs) to lead to the focal nature of atherosclerosis. Using a step flow chamber to investigate the effects of disturbed shear (DS) and pulsatile shear (PS) on ECs in the same flow channel, we conducted single-cell RNA sequencing analyses to explore the distinct transcriptomic profiles regulated by DS vs. PS. Integrated analysis identified eight cell clusters and demonstrated that DS induces EC transition from atheroprotective to proatherogenic phenotypes. Using an automated cell type annotation algorithm (SingleR), we showed that DS promoted endothelial-to-mesenchymal transition (EndMT) by inducing the transcriptional phenotypes for inflammation, hypoxia responses, transforming growth factor-beta (TGF-ß) signaling, glycolysis, and fatty acid synthesis. Enolase 1 (ENO1), a key gene in glycolysis, was one of the top-ranked genes in the DS-induced EndMT cluster. Pseudotime trajectory analysis revealed that the kinetic expression of ENO1 was significantly associated with EndMT and that ENO1 silencing repressed the DS- and TGF-ß-induced EC inflammation and EndMT. Consistent with these findings, ENO1 was highly expressed in ECs at the inner curvature of the mouse aortic arch (which is exposed to DS) and atherosclerotic lesions, suggesting its proatherogenic role in vivo. In summary, we present a comprehensive single-cell atlas of ECs in response to different flow patterns within the same flow channel. Among the DS-regulated genes, ENO1 plays an important role in DS-induced EC inflammation and EndMT. These results provide insights into how hemodynamic forces regulate vascular endothelium in health and disease.

In vivo CRISPR screens identify Mga as an immunotherapy target in triple-negative breast cancer.

Immune evasion is not only critical for tumor initiation and progression, but also determines the efficacy of immunotherapies. Through iterative in vivo CRISPR screens with seven syngeneic tumor models, we identified core and context-dependent immune evasion pathways across cancer types. This valuable high-confidence dataset is available for the further understanding of tumor intrinsic immunomodulators, which may lead to the discovery of effective anticancer therapeutic targets. With a focus on triple-negative breast cancer (TNBC), we found that Mga knock-out significantly enhances antitumor immunity and inhibits tumor growth. Transcriptomics and single-cell RNA sequencing analyses revealed that Mga influences various immune-related pathways in the tumor microenvironment. Our findings suggest that Mga may play a role in modulating the tumor immune landscape, though the precise mechanisms require further investigation. Interestingly, we observed that low MGA expression in breast cancer patients correlates with a favorable prognosis, particularly in those with active interferon-γ signaling. These observations provide insights into tumor immune escape mechanisms and suggest that further exploration of MGA's function could potentially lead to effective therapeutic strategies in TNBC.

Current computational tools for protein lysine acylation site prediction.

As a main subtype of post-translational modification (PTM), protein lysine acylations (PLAs) play crucial roles in regulating diverse functions of proteins. With recent advancements in proteomics technology, the identification of PTM is becoming a data-rich field. A large amount of experimentally verified data is urgently required to be translated into valuable biological insights. With computational approaches, PLA can be accurately detected across the whole proteome, even for organisms with small-scale datasets. Herein, a comprehensive summary of 166 in silico PLA prediction methods is presented, including a single type of PLA site and multiple types of PLA sites. This recapitulation covers important aspects that are critical for the development of a robust predictor, including data collection and preparation, sample selection, feature representation, classification algorithm design, model evaluation, and method availability. Notably, we discuss the application of protein language models and transfer learning to solve the small-sample learning issue. We also highlight the prediction methods developed for functionally relevant PLA sites and species/substrate/cell-type-specific PLA sites. In conclusion, this systematic review could potentially facilitate the development of novel PLA predictors and offer useful insights to researchers from various disciplines.

FERREG: ferroptosis-based regulation of disease occurrence, progression and therapeutic response.

Ferroptosis is a non-apoptotic, iron-dependent regulatory form of cell death characterized by the accumulation of intracellular reactive oxygen species. In recent years, a large and growing body of literature has investigated ferroptosis. Since ferroptosis is associated with various physiological activities and regulated by a variety of cellular metabolism and mitochondrial activity, ferroptosis has been closely related to the occurrence and development of many diseases, including cancer, aging, neurodegenerative diseases, ischemia-reperfusion injury and other pathological cell death. The regulation of ferroptosis mainly focuses on three pathways: system Xc-/GPX4 axis, lipid peroxidation and iron metabolism. The genes involved in these processes were divided into driver, suppressor and marker. Importantly, small molecules or drugs that mediate the expression of these genes are often good treatments in the clinic. Herein, a newly developed database, named 'FERREG', is documented to (i) providing the data of ferroptosis-related regulation of diseases occurrence, progression and drug response; (ii) explicitly describing the molecular mechanisms underlying each regulation; and (iii) fully referencing the collected data by cross-linking them to available databases. Collectively, FERREG contains 51 targets, 718 regulators, 445 ferroptosis-related drugs and 158 ferroptosis-related disease responses. FERREG can be accessed at https://idrblab.org/ferreg/.

Comprehensive proteomic analysis reveals distinct features and a diagnostic biomarker panel for early pregnancy loss in histological subtypes.

Early pregnancy loss (EPL) is a common event in human reproduction and is classified into histological subtypes such as hydropic abortion (HA) and hydatidiform moles (HMs), including complete hydatidiform moles (CHMs) and partial hydatidiform moles (PHMs). However, accurate diagnosis and improved patient management remain challenging due to high rates of misdiagnosis and diverse prognostic risks. Therefore, diagnostic biomarkers for EPL are urgently needed. Our study aimed to identify biomarkers for EPL through comprehensive proteomic analysis. Ten CHMs, six PHMs, ten HAs and ten normal control (NC) products of conception (POC) were used to obtain a proteomic portrait. Parallel reaction monitoring (PRM)-targeted proteomic and regression analyses were used to verify and select the diagnostic signatures. Finally, 14 proteins were selected and a panel of diagnostic classifiers (DLK1, SPTB/COL21A1, and SAR1A) was built to represent the CHM, PHM, and NC groups (auROC=0.900, 0.804/0.885, and 0.991, respectively). This high diagnostic power was further validated in another independent cohort (n = 148) by immunohistochemistry (IHC) (n = 120) and western blot (WB) analyses (n = 28). The protein SPTB was selected for further biological behaviour experiments in vitro. Our data suggest that SPTB maintains trophoblast cell proliferation, angiogenesis, cell motility and the cytoskeleton network. This study provides a comprehensive proteomic portrait and identifies potential diagnostic biomarkers. These findings enhance our understanding of EPL pathogenesis and offer novel targets for diagnosis and therapeutic interventions.

EndoQuad: a comprehensive genome-wide experimentally validated endogenous G-quadruplex database.

G-quadruplexes (G4s) are non-canonical four-stranded structures and are emerging as novel genetic regulatory elements. However, a comprehensive genomic annotation of endogenous G4s (eG4s) and systematic characterization of their regulatory network are still lacking, posing major challenges for eG4 research. Here, we present EndoQuad (https://EndoQuad.chenzxlab.cn/) to address these pressing issues by integrating high-throughput experimental data. First, based on high-quality genome-wide eG4s mapping datasets (human: 1181; mouse: 24; chicken: 2) generated by G4 ChIP-seq/CUT&Tag, we generate a reference set of genome-wide eG4s. Our multi-omics analyses show that most eG4s are identified in one or a few cell types. The eG4s with higher occurrences across samples are more structurally stable, evolutionarily conserved, enriched in promoter regions, mark highly expressed genes and associate with complex regulatory programs, demonstrating higher confidence level for further experiments. Finally, we integrate millions of functional genomic variants and prioritize eG4s with regulatory functions in disease and cancer contexts. These efforts have culminated in the comprehensive and interactive database of experimentally validated DNA eG4s. As such, EndoQuad enables users to easily access, download and repurpose these data for their own research. EndoQuad will become a one-stop resource for eG4 research and lay the foundation for future functional studies.

VARIDT 3.0: the phenotypic and regulatory variability of drug transporter.

The phenotypic and regulatory variability of drug transporter (DT) are vital for the understanding of drug responses, drug-drug interactions, multidrug resistances, and so on. The ADME property of a drug is collectively determined by multiple types of variability, such as: microbiota influence (MBI), transcriptional regulation (TSR), epigenetics regulation (EGR), exogenous modulation (EGM) and post-translational modification (PTM). However, no database has yet been available to comprehensively describe these valuable variabilities of DTs. In this study, a major update of VARIDT was therefore conducted, which gave 2072 MBIs, 10 610 TSRs, 46 748 EGRs, 12 209 EGMs and 10 255 PTMs. These variability data were closely related to the transportation of 585 approved and 301 clinical trial drugs for treating 572 diseases. Moreover, the majority of the DTs in this database were found with multiple variabilities, which allowed a collective consideration in determining the ADME properties of a drug. All in all, VARIDT 3.0 is expected to be a popular data repository that could become an essential complement to existing pharmaceutical databases, and is freely accessible without any login requirement at: https://idrblab.org/varidt/.

ADCdb: the database of antibody-drug conjugates.

Antibody-drug conjugates (ADCs) are a class of innovative biopharmaceutical drugs, which, via their antibody (mAb) component, deliver and release their potent warhead (a.k.a. payload) at the disease site, thereby simultaneously improving the efficacy of delivered therapy and reducing its off-target toxicity. To design ADCs of promising efficacy, it is crucial to have the critical data of pharma-information and biological activities for each ADC. However, no such database has been constructed yet. In this study, a database named ADCdb focusing on providing ADC information (especially its pharma-information and biological activities) from multiple perspectives was thus developed. Particularly, a total of 6572 ADCs (359 approved by FDA or in clinical trial pipeline, 501 in preclinical test, 819 with in-vivo testing data, 1868 with cell line/target testing data, 3025 without in-vivo/cell line/target testing data) together with their explicit pharma-information was collected and provided. Moreover, a total of 9171 literature-reported activities were discovered, which were identified from diverse clinical trial pipelines, model organisms, patient/cell-derived xenograft models, etc. Due to the significance of ADCs and their relevant data, this new database was expected to attract broad interests from diverse research fields of current biopharmaceutical drug discovery. The ADCdb is now publicly accessible at: https://idrblab.org/adcdb/.

Interactomes of SARS-CoV-2 and human coronaviruses reveal host factors potentially affecting pathogenesis.

Host-virus protein-protein interactions play key roles in the life cycle of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We conducted a comprehensive interactome study between the virus and host cells using tandem affinity purification and proximity-labeling strategies and identified 437 human proteins as the high-confidence interacting proteins. Further characterization of these interactions and comparison to other large-scale study of cellular responses to SARS-CoV-2 infection elucidated how distinct SARS-CoV-2 viral proteins participate in its life cycle. With these data mining, we discovered potential drug targets for the treatment of COVID-19. The interactomes of two key SARS-CoV-2-encoded viral proteins, NSP1 and N, were compared with the interactomes of their counterparts in other human coronaviruses. These comparisons not only revealed common host pathways these viruses manipulate for their survival, but also showed divergent protein-protein interactions that may explain differences in disease pathology. This comprehensive interactome of SARS-CoV-2 provides valuable resources for the understanding and treating of this disease.

NS1-mediated enhancement of MVC transcription and replication promoted by KAT5/H4K12ac.

Histone modifications function in both cellular and viral gene expression. However, the roles of acetyltransferases and histone acetylation in parvoviral infection remain poorly understood. In the current study, we found the histone deacetylase (HDAC) inhibitor, trichostatin A (TSA), promoted the replication and transcription of parvovirus minute virus of canines (MVC). Notably, the expression of host acetyltransferases KAT5, GTF3C4, and KAT2A was increased in MVC infection, as well as H4 acetylation (H4K12ac). KAT5 is not only responsible for H4K12ac but also crucial for viral replication and transcription. The viral nonstructural protein NS1 interacted with KAT5 and enhanced its expression. Further study showed that Y44 in KAT5, which may be tyrosine-phosphorylated, is indispensable for NS1-mediated enhancement of KAT5 and efficient MVC replication. The data demonstrated that NS1 interacted with KAT5, which resulted in an enhanced H4K12ac level to promote viral replication and transcription, implying the epigenetic addition of H4K12ac in viral chromatin-like structure by KAT5 is vital for MVC replication.IMPORTANCEParvoviral genomes are chromatinized with host histones. Therefore, histone acetylation and related acetyltransferases are required for the virus to modify histones and open densely packed chromatin structures. This study illustrated that histone acetylation status is important for MVC replication and transcription and revealed a novel mechanism that the viral nonstructural protein NS1 hijacks the host acetyltransferase KAT5 to enhance histone acetylation of H4K12ac, which relies on a potential tyrosine phosphorylation site, Y44 in KAT5. Other parvoviruses share a similar genome organization and coding potential and may adapt a similar strategy for efficient viral replication and transcription.

ResNetKhib: a novel cell type-specific tool for predicting lysine 2-hydroxyisobutylation sites via transfer learning.

Lysine 2-hydroxyisobutylation (Khib), which was first reported in 2014, has been shown to play vital roles in a myriad of biological processes including gene transcription, regulation of chromatin functions, purine metabolism, pentose phosphate pathway and glycolysis/gluconeogenesis. Identification of Khib sites in protein substrates represents an initial but crucial step in elucidating the molecular mechanisms underlying protein 2-hydroxyisobutylation. Experimental identification of Khib sites mainly depends on the combination of liquid chromatography and mass spectrometry. However, experimental approaches for identifying Khib sites are often time-consuming and expensive compared with computational approaches. Previous studies have shown that Khib sites may have distinct characteristics for different cell types of the same species. Several tools have been developed to identify Khib sites, which exhibit high diversity in their algorithms, encoding schemes and feature selection techniques. However, to date, there are no tools designed for predicting cell type-specific Khib sites. Therefore, it is highly desirable to develop an effective predictor for cell type-specific Khib site prediction. Inspired by the residual connection of ResNet, we develop a deep learning-based approach, termed ResNetKhib, which leverages both the one-dimensional convolution and transfer learning to enable and improve the prediction of cell type-specific 2-hydroxyisobutylation sites. ResNetKhib is capable of predicting Khib sites for four human cell types, mouse liver cell and three rice cell types. Its performance is benchmarked against the commonly used random forest (RF) predictor on both 10-fold cross-validation and independent tests. The results show that ResNetKhib achieves the area under the receiver operating characteristic curve values ranging from 0.807 to 0.901, depending on the cell type and species, which performs better than RF-based predictors and other currently available Khib site prediction tools. We also implement an online web server of the proposed ResNetKhib algorithm together with all the curated datasets and trained model for the wider research community to use, which is publicly accessible at https://resnetkhib.erc.monash.edu/.

Integrating massive RNA-seq data to elucidate transcriptome dynamics in Drosophila melanogaster.

The volume of ribonucleic acid (RNA)-seq data has increased exponentially, providing numerous new insights into various biological processes. However, due to significant practical challenges, such as data heterogeneity, it is still difficult to ensure the quality of these data when integrated. Although some quality control methods have been developed, sample consistency is rarely considered and these methods are susceptible to artificial factors. Here, we developed MassiveQC, an unsupervised machine learning-based approach, to automatically download and filter large-scale high-throughput data. In addition to the read quality used in other tools, MassiveQC also uses the alignment and expression quality as model features. Meanwhile, it is user-friendly since the cutoff is generated from self-reporting and is applicable to multimodal data. To explore its value, we applied MassiveQC to Drosophila RNA-seq data and generated a comprehensive transcriptome atlas across 28 tissues from embryogenesis to adulthood. We systematically characterized fly gene expression dynamics and found that genes with high expression dynamics were likely to be evolutionarily young and expressed at late developmental stages, exhibiting high nonsynonymous substitution rates and low phenotypic severity, and they were involved in simple regulatory programs. We also discovered that human and Drosophila had strong positive correlations in gene expression in orthologous organs, revealing the great potential of the Drosophila system for studying human development and disease.