NRG1 Regulates Proliferation, Migration and Differentiation of Human Limbal Epithelial Stem Cells.

Limbal epithelial stem/progenitor cells (LESCs) proliferate, migrate and differentiate into mature corneal epithelium cells (CECs) that cover the ocular surface. LESCs play a crucial role in the maintenance and regeneration of the corneal epithelium, and their dysfunction can lead to various corneal diseases. Neuregulin 1 (NRG1) is a member of the epidermal growth factor family that regulates the growth and differentiation of epithelial tissues. Here, we depicted the dynamic transcriptomic profiles during human CEC differentiation, identifying six gene co-expression modules that were specific to different differentiation stages. We found that the expression of NRG1 was high in human LESCs and decreased dramatically upon differentiation. Knockdown of NRG1 significantly inhibited LESC proliferation and upregulated the expression of the terminal differentiation marker genes KRT3, KRT12 and CLU. In addition, the scratch wound closure assay showed that knockdown of NRG1 attenuated wound closure of LESCs over 24 h. Together, we dissected the transcriptional regulatory dynamics during CEC differentiation and identified NRG1 as a key regulator that promoted LESC proliferation and migration and maintained the undifferentiated state.

A Microfluidic Platform for Screening Gene Expression Dynamics across Yeast Strain Libraries.

The relative ease of genetic manipulation in S. cerevisiae is one of its greatest strengths as a model eukaryotic organism. Researchers have leveraged this quality of the budding yeast to study the effects of a variety of genetic perturbations, such as deletion or overexpression, in a high-throughput manner. This has been accomplished by producing a number of strain libraries that can contain hundreds or even thousands of distinct yeast strains with unique genetic alterations. While these strategies have led to enormous increases in our understanding of the functions and roles that genes play within cells, the techniques used to screen genetically modified libraries of yeast strains typically rely on plate or sequencing-based assays that make it difficult to analyze gene expression changes over time. Microfluidic devices, combined with fluorescence microscopy, can allow gene expression dynamics of different strains to be captured in a continuous culture environment; however, these approaches often have significantly lower throughput compared to traditional techniques. To address these limitations, we have developed a microfluidic platform that uses an array pinning robot to allow for up to 48 different yeast strains to be transferred onto a single device. Here, we detail a validated methodology for constructing and setting up this microfluidic device, starting with the photolithography steps for constructing the wafer, then the soft lithography steps for making polydimethylsiloxane (PDMS) microfluidic devices, and finally the robotic arraying of strains onto the device for experiments. We have applied this device for dynamic screens of a protein aggregation library; however, this methodology has the potential to enable complex and dynamic screens of yeast libraries for a wide range of applications. Key features • Major steps of this protocol require access to specialized equipment (i.e., microfabrication tools typically found in a cleanroom facility and an array pinning robot). • Construction of microfluidic devices with multiple different feature heights using photolithography and soft lithography with PDMS. • Robotic spotting of up to 48 different yeast strains onto microfluidic devices.

Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells.

Macrophages must respond appropriately to pathogens and other pro-inflammatory stimuli in order to perform their roles in fighting infection. One way in which inflammatory stimuli can vary is in their dynamics-that is, the amplitude and duration of stimulus experienced by the cell. In this study, we performed long-term live cell imaging in a microfluidic device to investigate how the pro-inflammatory genes IRF1, CXCL10, and CXCL9 respond to dynamic interferon-gamma (IFNγ) stimulation. We found that IRF1 responds to low concentration or short duration IFNγ stimulation, whereas CXCL10 and CXCL9 require longer or higherconcentration stimulation to be expressed. We also investigated the heterogeneity in the expression of each gene and found that CXCL10 and CXCL9 have substantial cell-to-cell variability. In particular, the expression of CXCL10 appears to be largely stochastic with a subpopulation of nonresponding cells across all the stimulation conditions tested. We developed both deterministic and stochastic models for the expression of each gene. Our modeling analysis revealed that the heterogeneity in CXCL10 can be attributed to a slow chromatin-opening step that is on a similar timescale to that of adaptation of the upstream signal. In this way, CXCL10 expression in individual cells can remain stochastic in response to each pulse of repeated stimulation, which we also validated by experiments. Together, we conclude that pro-inflammatory genes in the same signaling pathway can respond to dynamic IFNγ stimulus with very different response features and that upstream signal adaptation can contribute to shaping heterogeneous gene expression.

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single-cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

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.

A dynamic in vitro developing testis model reflects structures and functions of testicular development in vivo.

To better define appropriate applications of our 3-dimensional testicular co-culture as a model for reproductive toxicology, we evaluated the ability of the model to capture structural and functional elements that can be targeted by reproductive toxicants. Testicular co-cultures were prepared from postnatal day 5 male rats and cultured with a Matrigel overlay. Following a 2-day acclimation period, we characterized functional pathway dynamics by evaluating morphology, protein expression, testosterone concentrations, and global gene expression at a range of timepoints from experimental days 0-21. Western blotting confirmed expression of Sertoli cell, Leydig cell, and spermatogonial cell-specific protein markers. Testosterone detected in cell culture media indicates active testosterone production. Quantitative pathway analysis identified Gene Ontology biological processes enriched among genes significantly changing over the course of 21 days. Processes enriched among genes significantly increasing through time include general developmental processes (morphogenesis, tissue remodeling, etc.), steroid regulation, Sertoli cell development, immune response, and stress and apoptosis. Processes enriched among genes significantly decreasing over time include several related to male reproductive development (seminiferous tubule development, male gonad development, Leydig cell differentiation, Sertoli cell differentiation), all of which appear to peak in expression between days 1 and 5 before decreasing at later timepoints. This analysis provides a temporal roadmap for specific biological process of interest for reproductive toxicology in the model and anchors the model to sensitive phases of in vivo development, helping to define the relevance of the model for in vivo processes.

iHypoxia: An Integrative Database of Protein Expression Dynamics in Response to Hypoxia in Animals.

Mammals have evolved mechanisms to sense hypoxia and induce hypoxic responses. Recently, high-throughput techniques have greatly promoted global studies of protein expression changes during hypoxia and the identification of candidate genes associated with hypoxia-adaptive evolution, which have contributed to the understanding of the complex regulatory networks of hypoxia. In this study, we developed an integrated resource for the expression dynamics of proteins in response to hypoxia (iHypoxia), and this database contains 2589 expression events of 1944 proteins identified by low-throughput experiments (LTEs) and 422,553 quantitative expression events of 33,559 proteins identified by high-throughput experiments from five mammals that exhibit a response to hypoxia. Various experimental details, such as the hypoxic experimental conditions, expression patterns, and sample types, were carefully collected and integrated. Furthermore, 8788 candidate genes from diverse species inhabiting low-oxygen environments were also integrated. In addition, we conducted an orthologous search and computationally identified 394,141 proteins that may respond to hypoxia among 48 animals. An enrichment analysis of human proteins identified from LTEs shows that these proteins are enriched in certain drug targets and cancer genes. Annotation of known posttranslational modification (PTM) sites in the proteins identified by LTEs reveals that these proteins undergo extensive PTMs, particularly phosphorylation, ubiquitination, and acetylation. iHypoxia provides a convenient and user-friendly method for users to obtain hypoxia-related information of interest. We anticipate that iHypoxia, which is freely accessible at https://ihypoxia.omicsbio.info, will advance the understanding of hypoxia and serve as a valuable data resource.

Deciphering Haplotypic Variation and Gene Expression Dynamics Associated with Nutritional and Cooking Quality in Rice.

Nutritional quality improvement of rice is the key to ensure global food security. Consequently, enormous efforts have been made to develop genomics and transcriptomics resources for rice. The available omics resources along with the molecular understanding of trait development can be utilized for efficient exploration of genetic resources for breeding programs. In the present study, 80 genes known to regulate the nutritional and cooking quality of rice were extensively studied to understand the haplotypic variability and gene expression dynamics. The haplotypic variability of selected genes were defined using whole-genome re-sequencing data of ~4700 diverse genotypes. The analytical workflow identified 133 deleterious single-nucleotide polymorphisms, which are predicted to affect the gene function. Furthermore, 788 haplotype groups were defined for 80 genes, and the distribution and evolution of these haplotype groups in rice were described. The nucleotide diversity for the selected genes was significantly reduced in cultivated rice as compared with that in wild rice. The utility of the approach was successfully demonstrated by revealing the haplotypic association of chalk5 gene with the varying degree of grain chalkiness. The gene expression atlas was developed for these genes by analyzing RNA-Seq transcriptome profiling data from 102 independent sequence libraries. Subsequently, weighted gene co-expression meta-analyses of 11,726 publicly available RNAseq libraries identified 19 genes as the hub of interactions. The comprehensive analyses of genetic polymorphisms, allelic distribution, and gene expression profiling of key quality traits will help in exploring the most desired haplotype for grain quality improvement. Similarly, the information provided here will be helpful to understand the molecular mechanism involved in the development of nutritional and cooking quality traits in rice.

Dynamical Systems Model of RNA Velocity Improves Inference of Single-cell Trajectory, Pseudo-time and Gene Regulation.

Recent development in inferring RNA velocity from single-cell RNA-seq opens up exciting new vista into developmental lineage and cellular dynamics. However, the estimated velocity only gives a snapshot of how the transcriptome instantaneously changes in individual cells, and it does not provide quantitative predictions and insights about the whole system. In this work, we develop RNA-ODE, a principled computational framework that extends RNA velocity to quantify systems level dynamics and improve single-cell data analysis. We model the gene expression dynamics by an ordinary differential equation (ODE) based formalism. Given a snapshot of gene expression at one time, RNA-ODE is able to predict and extrapolate the expression trajectory of each cell by solving the dynamic equations. Systematic experiments on simulations and on new data from developing brain demonstrate that RNA-ODE substantially improves many aspects of standard single-cell analysis. By leveraging temporal dynamics, RNA-ODE more accurately estimates cell state lineage and pseudo-time compared to previous state-of-the-art methods. It also infers gene regulatory networks and identifies influential genes whose expression changes can decide cell fate. We expect RNA-ODE to be a Swiss army knife that aids many facets of single-cell RNA-seq analysis.

Dynamic expression analysis of Vitelloin-related genes in Zeugodacus cucurbitae (Coquillett) driven by short-term high-temperature stress.

Zeugodacus cucurbitae (Coquillett) is an important pest of fruit and vegetable crops in tropical and subtropical regions. Previous studies have shown that short-term high-temperature stress has a significant effect on the oviposition behavior of three successive generations (F1 -F3 ) of Z. cucurbitae (Coquillett). For the clarification of the molecular response of the oviposition behavior of Z. cucurbitae (Coquillett) to short-term high-temperature stress, three Vitellogenin (Vg) genes, namely, Vg-1, Vg-2, and Vg-3, and one Vitellogenin receptor (VgR) gene were selected; 25°C was used as the control treatment; and 33°C, 37°C, 41°C, and 45°C were set as the high-temperature treatments. Newly emerged adults of the F1 generation were treated for 1 h, and the expression dynamics of the target genes were analyzed 7 days after the emergence of three successive generations of adults. Results showed that the expression of the Vg gene in the 33°C and 37°C groups was upregulated compared with that in the control group, and the difference among the 41°C, 45°C, and control groups was small. VgR gene expression level gradually increased in each treatment group with the increase in the number of days and peaked on Days 6 and 7. Compared with the control group, the expression of VgR gene in the F1 generation was downregulated in the high-temperature treatment group over 7 days. On Day 7, the expression level of the VgR gene in the F2 and F3 generations in the 37°C and 45°C groups was significantly higher than that in the F2 and F3 generations in the control group. In conclusion, Vg and VgR are transformed and utilized differently after short-term high-temperature treatment.

H2O2 concentration-dependent kinetics of gene expression: linking the intensity of oxidative stress and mycobacterial physiological adaptation.

Defence against oxidative stress is crucial for Mycobacterium tuberculosis to survive and replicate within macrophages. Mycobacteria have evolved multilayer antioxidant systems, including scavenging enzymes, iron homeostasis, repair pathways, and metabolic adaptation, for coping with oxidative stress. How these systems are coordinated to enable the physiological adaptation to different intensities of oxidative stress, however, remains unclear. To address this, we investigated the expression kinetics of the well-characterized antioxidant genes at bacteriostatic H2O2 concentrations ranging from 1 mM to 10 mM employing Mycolicibacterium smegmatis as a model. Our results showed that most of the selected genes were expressed in a H2O2 concentration-dependent manner, whereas a subset exhibited sustained induction or repression without dose-effect, reflecting H2O2 concentration-dependent physiological adaptations. Through analyzing the dynamics of the coordinated gene expression, we demonstrated that the expressions of the H2O2 scavenging enzymes, DNA damage response, and Fe-S cluster repair function were strikingly correlated to the intensity of oxidative stress. The sustained induction of mbtB, irtA, and dnaE2 indicated that mycobacteria might deploy increased iron acquisition and error-prone lesion bypass function as fundamental strategies to counteract oxidative damages, which are distinct from the defence tactics of Escherichia coli characterized by shrinking the iron pool and delaying the DNA repair. Moreover, the distinct gene expression kinetics among the tricarboxylic acid cycle, glyoxylate shunt, and methylcitrate cycle suggested that mycobacteria could dynamically redirect its metabolic fluxes according to the intensity of oxidative stress. This work defines the H2O2 concentration-dependent gene expression kinetics and provides unique insights into mycobacterial antioxidant defence strategies.

Multi-layer CRISPRa/i circuits for dynamic genetic programs in cell-free and bacterial systems.

CRISPR-Cas transcriptional circuits hold great promise as platforms for engineering metabolic networks and information processing circuits. Historically, prokaryotic CRISPR control systems have been limited to CRISPRi. Creating approaches to integrate CRISPRa for transcriptional activation with existing CRISPRi-based systems would greatly expand CRISPR circuit design space. Here, we develop design principles for engineering prokaryotic CRISPRa/i genetic circuits with network topologies specified by guide RNAs. We demonstrate that multi-layer CRISPRa/i cascades and feedforward loops can operate through the regulated expression of guide RNAs in cell-free expression systems and E. coli. We show that CRISPRa/i circuits can program complex functions by designing type 1 incoherent feedforward loops acting as fold-change detectors and tunable pulse-generators. By investigating how component characteristics relate to network properties such as depth, width, and speed, this work establishes a framework for building scalable CRISPRa/i circuits as regulatory programs in cell-free expression systems and bacterial hosts. A record of this paper's transparent peer review process is included in the supplemental information.

Expression dynamics of pregnane X receptor-controlled genes in 3D primary human hepatocyte spheroids.

The pregnane X receptor (PXR) is a ligand-activated nuclear receptor controlling hepatocyte expression of numerous genes. Although expression changes in xenobiotic-metabolizing, lipogenic, gluconeogenic and bile acid synthetic genes have been described after PXR activation, the temporal dynamics of their expression is largely unknown. Recently, 3D spheroids of primary human hepatocytes (PHHs) have been characterized as the most phenotypically relevant hepatocyte model. We used 3D PHHs to assess time-dependent expression profiles of 12 prototypic PXR-controlled genes in the time course of 168 h of rifampicin treatment (1 or 10 µM). We observed a similar bell-shaped time-induction pattern for xenobiotic-handling genes (CYP3A4, CYP2C9, CYP2B6, and MDR1). However, we observed either biphasic profiles for genes involved in endogenous metabolism (FASN, GLUT2, G6PC, PCK1, and CYP7A1), a decrease for SHP or oscillation for PDK4 and PXR. The rifampicin concentration determined the expression profiles for some genes. Moreover, we calculated half-lives of CYP3A4 and CYP2C9 mRNA under induced or basal conditions and we used a mathematical model to describe PXR-mediated regulation of CYP3A4 expression employing 3D PHHs. The study shows the importance of long-term time-expression profiling of PXR target genes in phenotypically stable 3D PHHs and provides insight into PXR function in liver beyond our knowledge from conventional 2D in vitro models.

Systematic temporal analysis of peripheral blood transcriptomes using TrendCatcher identifies early and persistent neutrophil activation as a hallmark of severe COVID-19.

Studying temporal gene expression shifts during disease progression provides important insights into the biological mechanisms that distinguish adaptive and maladaptive responses. Existing tools for the analysis of time course transcriptomic data are not designed to optimally identify distinct temporal patterns when analyzing dynamic differentially expressed genes (DDEGs). Moreover, there is a lack of methods to assess and visualize the temporal progression of biological pathways mapped from time course transcriptomic datasets. In this study, we developed an open-source R package TrendCatcher (https://github.com/jaleesr/TrendCatcher), which applies the smoothing spline ANOVA model and break point searching strategy to identify and visualize distinct dynamic transcriptional gene signatures and biological processes from longitudinal datasets. We used TrendCatcher to perform a systematic temporal analysis of COVID-19 peripheral blood transcriptomes, including bulk RNA-seq and scRNA-seq time course data. TrendCatcher uncovered the early and persistent activation of neutrophils and coagulation pathways as well as impaired type I interferon (IFN-I) signaling in circulating cells as a hallmark of patients who progressed to severe COVID-19, whereas no such patterns were identified in individuals receiving SARS-CoV-2 vaccinations or patients with mild COVID-19. These results underscore the importance of systematic temporal analysis to identify early biomarkers and possible pathogenic therapeutic targets.

Brief homogeneous TCR signals instruct common iNKT progenitors whose effector diversification is characterized by subsequent cytokine signaling.

Innate-like T cell populations expressing conserved TCRs play critical roles in immunity through diverse developmentally acquired effector functions. Focusing on the prototypical lineage of invariant natural killer T (iNKT) cells, we sought to dissect the mechanisms and timing of fate decisions and functional effector differentiation. Utilizing induced expression of the semi-invariant NKT cell TCR on double positive thymocytes, an initially highly synchronous wave of iNKT cell development was triggered by brief homogeneous TCR signaling. After reaching a uniform progenitor state characterized by IL-4 production potential and proliferation, effector subsets emerged simultaneously, but then diverged toward different fates. While NKT17 specification was quickly completed, NKT1 cells slowly differentiated and expanded. NKT2 cells resembled maturing progenitors, which gradually diminished in numbers. Thus, iNKT subset diversification occurs in dividing progenitor cells without acute TCR input but utilizes multiple active cytokine signaling pathways. These data imply a two-step model of iNKT effector differentiation.

An Automated Tabletop Continuous Culturing System with Multicolor Fluorescence Monitoring for Microbial Gene Expression and Long-Term Population Dynamics.

Real-time monitoring of gene expression dynamics and population levels in a multispecies microbial community could enable the study of the role of changing gene expression patterns on eco-evolutionary outcomes. Here we report the design and validation of a unique experimental platform with an in situ fluorescence measurement system that has high dynamic range and temporal resolution and is capable of monitoring multiple fluorophores for long-term gene expression and population dynamics experiments. We demonstrate the capability of our system to capture gene expression dynamics in response to external perturbations in two synthetic genetic systems: a simple inducible genetic circuit and a bistable toggle switch. Finally, in exploring the population dynamics of a two species microbial community, we show that our system can capture the switch between competitive exclusion and long-term coexistence in response to different nutrient conditions.

Expression dynamics of dehydration tolerance in the tropical plant Marchantia inflexa.

Tolerance to prolonged water deficit occurs along a continuum in plants, with dehydration tolerance (DhT) and desiccation tolerance (DT) representing some of the most extreme adaptations to water scarcity. Although DhT and DT presumably vary among individuals of a single species, this variability remains largely unstudied. Here, we characterized expression dynamics throughout a dehydration-rehydration time-course in six diverse genotypes of the dioecious liverwort Marchantia inflexa. We identified classical signatures of stress response in M. inflexa, including major changes in transcripts related to metabolism, expression of LEA and ELIP genes, and evidence of cell wall remodeling. However, we detected very little temporal synchronization of these responses across different genotypes of M. inflexa, which may be related to genotypic variation among samples, constitutive expression of dehydration-associated transcripts, the sequestration of mRNAs in ribonucleoprotein partials prior to drying, or the lower tolerance of M. inflexa relative to most bryophytes studied to date. Our characterization of intraspecific variation in expression dynamics suggests that differences in the timing of transcriptional adjustments contribute to variation among genotypes, and that developmental differences impact the relative tolerance of meristematic and differentiated tissues. This work highlights the complexity and variability of water stress tolerance, and underscores the need for comparative studies that seek to characterize variation in DT and DhT.

Investigation of Gene Sequence Divergence, Expression Dynamics, and Endocrine Regulation of the Vitellogenin Gene Family in the Whiteleg Shrimp Litopenaeus vannamei.

In this report, we studied the vitellogenin gene family in the whiteleg shrimp Litopenaeus vannamei by transcriptomics, bioinformatics, and molecular biology methods. At least three moderately homologous vitellogenin (Vg) genes (i.e. LvVg1, LvVg2, and LvVg3) were identified in the genome. The deduced LvVg proteins consisted of a vitellogenin_N domain, a DUF1943 domain, and a VWD domain typical of most vitellogenins from oviparous animals. LvVg1 was the most abundant Vg expressed in the hepatopancreas and ovary of maturing females. Furthermore, multiple isoforms of LvVg1 were evolved presumably due to the need for rapid Vg production during the rapid phase of vitellogenesis. LvVg transcripts were detected in different larval stages, juveniles, and subadults. During the non-reproductive cycle, LvVg expression in the hepatopancreas peaked at the intermolt stages. During the female vitellogenesis cycle, a two-phase expression pattern of LvVg1 gene was observed in the hepatopancreas and ovary. Moreover, the eyestalk optic nerve, brain, and thoracic ganglion consisted of factors that differentially regulated the expression of the three Vg genes. In addition to their reproduction-related roles, Vg may also be involved in growth and molt-related processes. Phylogenetic analysis revealed the early expansion and separation of these Vg genes, and it is most likely correlated with the expansion of Vg's function. In conclusion, the evolution of multiple LvVg1 isoforms and the acquisition of different Vg genes (i.e. LvVg2 and LvVg3) may occur universally in most decapods. Full information on the total number of Vg genes and precise knowledge on the expression pattern and endocrine regulation of each Vg during all life cycle stages are crucial for us to understand the roles of this emerging gene family in the control of shrimp reproduction and other non-reproductive processes.

Exploring the role of RALYL in Alzheimer's disease reserve by network-based approaches.

BACKGROUND: Alzheimer's disease (AD) reserve theory is based on specific individual characteristics that are associated with a higher resilience against neurodegeneration and its symptoms. A given degree of AD pathology may contribute to varying cognitive decline levels in different individuals. Although this phenomenon is attributed to reserve, the biological mechanisms that underpin it remain elusive, which restricts translational medicine research and treatment strategy development. METHODS: Network-based approaches were integrated to identify AD reserve related genes. Then, AD brain transcriptomics data were clustered into co-expression modules, and a Bayesian network was developed using these modules plus AD reserve related phenotypes. The directed acyclic graph suggested that the module was strongly associated with AD reserve. The hub gene of the module of interest was filtered using the topological method. Validation was performed in the multi-AD brain transcriptomic dataset. RESULTS: We revealed that the RALYL (RALY RNA Binding Protein-like) is the hub gene of the module which was highly associated with AD reserve related phenotypes. Pseudo-time projections of RALYL revealed the changes in relative expression drivers in the AD and control subjects over pseudo-time had distinct transcriptional states. Notably, the expression of RALYL decreased with the gradual progression of AD, and this corresponded to MMSE decline. Subjects with AD reserve exhibited significantly higher RALYL expression than those without AD reserve. CONCLUSION: The present study suggests that RALYL may be associated with AD reserve, and it provides novel insights into the mechanisms of AD reserve and highlights the potential role of RALYL in this process.

The expression of the acarbose biosynthesis gene cluster in Actinoplanes sp. SE50/110 is dependent on the growth phase.

BACKGROUND: Actinoplanes sp. SE50/110 is the natural producer of the diabetes mellitus drug acarbose, which is highly produced during the growth phase and ceases during the stationary phase. In previous works, the growth-dependency of acarbose formation was assumed to be caused by a decreasing transcription of the acarbose biosynthesis genes during transition and stationary growth phase. RESULTS: In this study, transcriptomic data using RNA-seq and state-of-the-art proteomic data from seven time points of controlled bioreactor cultivations were used to analyze expression dynamics during growth of Actinoplanes sp. SE50/110. A hierarchical cluster analysis revealed co-regulated genes, which display similar transcription dynamics over the cultivation time. Aside from an expected metabolic switch from primary to secondary metabolism during transition phase, we observed a continuously decreasing transcript abundance of all acarbose biosynthetic genes from the early growth phase until stationary phase, with the strongest decrease for the monocistronically transcribed genes acbA, acbB, acbD and acbE. Our data confirm a similar trend for acb gene transcription and acarbose formation rate. Surprisingly, the proteome dynamics does not follow the respective transcription for all acb genes. This suggests different protein stabilities or post-transcriptional regulation of the Acb proteins, which in turn could indicate bottlenecks in the acarbose biosynthesis. Furthermore, several genes are co-expressed with the acb gene cluster over the course of the cultivation, including eleven transcriptional regulators (e.g. ACSP50_0424), two sigma factors (ACSP50_0644, ACSP50_6006) and further genes, which have not previously been in focus of acarbose research in Actinoplanes sp. SE50/110. CONCLUSION: In conclusion, we have demonstrated, that a genome wide transcriptome and proteome analysis in a high temporal resolution is well suited to study the acarbose biosynthesis and the transcriptional and post-transcriptional regulation thereof.