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Primary Sjögren's syndrome (pSS) is a chronic, systemic autoimmune disease mostly affecting the exocrine glands. This debilitating condition is complex and specific treatments remain unavailable. There is a need for the development of novel diagnostic models for early screening. Four gene profiling datasets were downloaded from the Gene Expression Omnibus database. The 'limma' software package was used to identify differentially expressed genes (DEGs). A random forest-supervised classification algorithm was used to screen disease-specific genes, and three machine learning algorithms, including artificial neural networks (ANN), random forest (RF), and support vector machines (SVM), were used to build a pSS diagnostic model. The performance of the model was measured using its area under the receiver operating characteristic curve. Immune cell infiltration was investigated using the CIBERSORT algorithm. A total of 96 DEGs were identified. By utilizing a RF classifier, a set of 14 signature genes that are pivotal in transcription regulation and disease progression in pSS were identified. Through the utilization of training and testing datasets, diagnostic models for pSS were successfully designed using ANN, RF, and SVM, resulting in AUCs of 0.972, 1.00, and 0.9742, respectively. The validation set yielded AUCs of 0.766, 0.8321, and 0.8223. It was the RF model that produced the best prediction performance out of the three models tested. As a result, an early predictive model for pSS was successfully developed with high diagnostic performance, providing a valuable resource for the screening and early diagnosis of pSS. [ABSTRACT FROM AUTHOR]

Cancer chemotherapy combines multiple drugs, but predicting the effects of drug combinations on cancer cell proliferation remains challenging, even for simple in vitro systems. We hypothesized that by combining knowledge of single drug dose responses and cell state transition network dynamics, we could predict how a population of cancer cells will respond to drug combinations. We tested this hypothesis here using three targeted inhibitors of different cell cycle states in two different cell lines in vitro. We formulated a Markov model to capture temporal cell state transitions between different cell cycle phases, with single drug data constraining how drug doses affect transition rates. This model was able to predict the landscape of all three different pairwise drug combinations across all dose ranges for both cell lines with no additional data. While further application to different cell lines, more drugs, additional cell state networks, and more complex co-culture or in vivo systems remain, this work demonstrates how currently available or attainable information could be sufficient for prediction of drug combination response for single cell lines in vitro. Author summary: Cancer chemotherapy combines multiple drugs, but determining which drugs would be efficacious for particular patients remains extremely challenging. Experimental solutions to this problem are not yet possible due to the large space of possible combinations of hundreds of anti-cancer drugs. Computational models may help, but it is not yet clear how such models should be built and what elements they need to capture to predict drug combination response. In this work, we explored the idea that if we knew something about the proportions of different types of cancer cells in a population, how fast transitions happen between the different types, and how individual drugs affect those transitions, that we might be able to build a computational model that predicts drug combination responses based only on feasible single drug response experiments. We tested this idea using simple cell culture systems with two different cancer cell lines and three different anti-cancer drugs, and found surprisingly good agreement between computational model predictions and experimentally measured drug combination responses. While further application to different cell lines, more drugs, and more complex experimental systems remain, this work demonstrates how currently available or attainable information could be sufficient for prediction of drug combination responses. [ABSTRACT FROM AUTHOR]

Background: A series of previous investigations have revealed that p-Smad3 plays a facilitative role in the differentiation and maturation of osteoblasts, while also regulating the expression of certain intercellular communication factors. However, the effects of p-Smad3 in osteoblasts before and after maturation on the proliferation, migration, differentiation, apoptosis and other cellular behaviors of osteoclasts have not been reported., Methods: MC3T3-E1 cells were cultured in osteogenic induction medium for varying durations, After that, the corresponding conditioned medium was collected and the osteoclast lineage cells were treated. To elucidate the regulatory role of p-Smad3 within osteoblasts, we applied the activator TGF-β1 and inhibitor SIS3 to immature and mature osteoblasts and collected corresponding conditioned media for osteoclast intervention., Results: We observed an elevation of p-Smad3 and Smad3 during the early stage of osteoblast differentiation, followed by a decline in the later stage. we discovered that as osteoblasts mature, their conditioned media inhibit osteoclasts differentiation and the osteoclast-coupled osteogenic effect. However, it promotes apoptosis in osteoclasts and the angiogenesis coupled with osteoclasts. p-Smad3 in immature osteoblasts, through paracrine effects, promotes the migration, differentiation, and osteoclast-coupled osteogenic effects of osteoclast lineage cells. For mature osteoblasts, p-Smad3 facilitates osteoclast apoptosis and the angiogenesis coupled with osteoclasts., Conclusions: As pre-osteoblasts undergo maturation, p-Smad3 mediated a paracrine effect that transitions osteoclast cellular behaviors from inducing differentiation and stimulating bone formation to promoting apoptosis and coupling angiogenesis., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Lipids are the key component of all membranes composed of a variety of molecules that transduce intracellular signaling and provide energy to the cells in the absence of nutrients. Alteration in lipid metabolism is a major factor for cancer heterogeneity and a newly identified cancer hallmark. Reprogramming of lipid metabolism affects the diverse cancer phenotypes, especially epithelial-mesenchymal transition (EMT). EMT activation is considered to be an essential step for tumor metastasis, which exhibits a crucial role in the biological processes including development, wound healing, and stem cell maintenance, and has been widely reported to contribute pathologically to cancer progression. Altered lipid metabolism triggers EMT and activates multiple EMT-associated oncogenic pathways. Although the role of lipid metabolism-induced EMT in tumorigenesis is an attractive field of research, there are still significant gaps in understanding the underlying mechanisms and the precise contributions of this interplay. Further study is needed to clarify the specific molecular mechanisms driving the crosstalk between lipid metabolism and EMT, as well as to determine the potential therapeutic implications. The increased dependency of tumor cells on lipid metabolism represents a novel therapeutic target, and targeting altered lipid metabolism holds promise as a strategy to suppress EMT and ultimately inhibit metastasis., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The rise of single-cell data highlights the need for a nondeterministic view of gene expression, while offering new opportunities regarding gene regulatory network inference. We recently introduced two strategies that specifically exploit time-course data, where single-cell profiling is performed after a stimulus: HARISSA, a mechanistic network model with a highly efficient simulation procedure, and CARDAMOM, a scalable inference method seen as model calibration. Here, we combine the two approaches and show that the same model driven by transcriptional bursting can be used simultaneously as an inference tool, to reconstruct biologically relevant networks, and as a simulation tool, to generate realistic transcriptional profiles emerging from gene interactions. We verify that CARDAMOM quantitatively reconstructs causal links when the data is simulated from HARISSA, and demonstrate its performance on experimental data collected on in vitro differentiating mouse embryonic stem cells. Overall, this integrated strategy largely overcomes the limitations of disconnected inference and simulation. Author summary: Gene regulatory network (GRN) inference is an old problem, to which single-cell data has recently offered new challenges and breakthrough potential. Many GRN inference methods based on single-cell transcriptomic data have been developed over the last few years, while GRN simulation tools have also been proposed for generating synthetic datasets with realistic features. However, except for benchmarking purposes, these two fields remain largely disconnected. In this work, building on a combination of two methods we recently described, we show that a particular GRN model can be used simultaneously as an inference tool, to reconstruct a biologically relevant network from time-course single-cell gene expression data, and as a simulation tool, to generate realistic transcriptional profiles in a non-trivial way through gene interactions. This integrated strategy demonstrates the benefits of using the same executable model for both simulation and inference. [ABSTRACT FROM AUTHOR]

Cell fate decision processes are regulated by networks which contain different molecules and interactions. Different network topologies may exhibit synergistic or antagonistic effects on cellular functions. Here, we analyze six most common small networks with regulatory logic AND or OR, trying to clarify the relationship between network topologies and synergism (or antagonism) related to cell fate decisions. We systematically examine the contribution of both network topologies and regulatory logic to the cell fate synergism by bifurcation and combinatorial perturbation analysis. Initially, under a single set of parameters, the synergism of three types of networks with AND and OR logic is compared. Furthermore, to consider whether these results depend on the choices of parameter values, statistics on the synergism of five hundred parameter sets is performed. It is shown that the results are not sensitive to parameter variations, indicating that the synergy or antagonism mainly depends on the network topologies rather than the choices of parameter values. The results indicate that the topology with "Dual Inhibition" shows good synergism, while the topology with "Dual Promotion" or "Hybrid" shows antagonism. The results presented here may help us to design synergistic networks based on network structure and regulation combinations, which has promising implications for cell fate decisions and drug combinations. [ABSTRACT FROM AUTHOR]

circRNAs play an important role in the progression of osteoarthritis (OA). Therefore, we aimed to reveal the mechanism of action of circRNA-ZCCHC14 in OA. OA animal and cell models were constructed, and clinical samples were collected. The expression of circRNA-ZCCHC14 and miR-181a was detected by RT‒qPCR. The chondrogenic differentiation ability of peripheral blood-derived mesenchymal stem cells (PBMSCs) was detected by Alcian blue staining. The expression of chondrogenic differentiation-related proteins was detected by Western blotting. Double fluorescein experiments verified the targeting relationship of miR-181a with circRNA-ZCCHC14 and GREM1. Upregulation of circRNA-ZCCHC14 was observed in blood, in BMP-2- and TGF-β3-treated PBMSCs from OA patients and in animal models. Knockdown of circRNA-ZCCHC14 promoted the chondrogenic differentiation ability of PBMSCs. circRNA-ZCCHC14 was found to bind to miR-181a and negatively regulate miR-181a expression. Inhibition of miR-181a reversed the promoting effect of circRNA-ZCCHC14 knockdown on the chondrogenic differentiation ability of PBMSCs. GREM1 was identified as a target of miR-181a. Overexpression and knockdown of GREM1 regulated the expression of BMP2, which in turn affected the chondrogenic differentiation ability of PBMSCs, indicating that GREM1 and BMP2 have antagonistic effects and that they jointly regulate the chondrogenic differentiation of PBMSCs. circRNA-ZCCHC14 may promote the chondrogenic differentiation ability of PBMSCs by regulating miR-181a and inhibiting the expression of GREM1. [ABSTRACT FROM AUTHOR]

The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public. Measurement(s) Protein expression Technology Type(s) Mass Spectrometry Factor Type(s) genetically distinct human cell lines Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location United States [ABSTRACT FROM AUTHOR]

Platelets, generated from precursor megakaryocytes (MKs), are central mediators of hemostasis and thrombosis. The process of thrombopoiesis is extremely complex, regulated by multiple factors, and related to many cellular events including apoptosis. However, the role of apoptosis in thrombopoiesis has been controversial for many years. Some researchers believe that apoptosis is an ally of thrombopoiesis and platelets production is apoptosis-dependent, while others have suggested that apoptosis is dispensable for thrombopoiesis, and is even inhibited during this process. In this review, we will focus on this conflict, discuss the relationship between megakaryocytopoiesis, thrombopoiesis and apoptosis. In addition, we also consider why such a vast number of studies draw opposite conclusions of the role of apoptosis in thrombopoiesis, and try to figure out the truth behind the mystery. This review provides more comprehensive insights into the relationship between megakaryocytopoiesis, thrombopoiesis, and apoptosis and finds some clues for the possible pathological mechanisms of platelet disorders caused by abnormal apoptosis. [ABSTRACT FROM AUTHOR]

Hereditary thrombocytopenia is a heterogeneous group of congenital disorders with a wide range of symptoms depending on the severity of platelet dysfunction or thrombocytopenia. Because of its clinical phenotypes and the bone marrow morphology associated with this condition, hereditary thrombocytopenia can be misdiagnosed as primary immune thrombocytopenia and myelodysplastic syndrome. Therefore, genetic evidence is necessary for the accurate diagnosis of hereditary thrombocytopenia. Refractory cytopenia of childhood is a subgroup of myelodysplastic syndrome that was added to the World Health Organization classification in 2008. To investigate the germline and somatic variants associated with refractory cytopenia of childhood, we performed targeted multigene sequencing in three patients with refractory cytopenia of childhood. Of the three patients, one progressed from megakaryocytic hypoplasia with thrombocytopenia, and targeted multigene sequencing revealed THPO variants in this patient and his sister. We propose that the monoallelic deletion of THPO is a potential candidate for germline predisposition to myeloid malignancy. [ABSTRACT FROM AUTHOR]

There are hundreds of central nervous system (CNS) diseases, but there are few diseases for which the etiology or pathogenesis is understood as well as those of other organ-specific diseases. Cells in the CNS are selectively protected from external and internal insults by the blood–brain barrier. Thus, the neuroimmune system, including microglia and immune proteins, might control external or internal insults that the adaptive immune system cannot control or mitigate. The pathologic findings differ by disease and show a state of inflammation that reflects the relationship between etiological or inflammation-inducing substances and corresponding immune reactions. Current immunological concepts about infectious diseases and infection-associated immune-mediated diseases, including those in the CNS, can only partly explain the pathophysiology of disease because they are based on the idea that host cell injury is caused by pathogens. Because every disease involves etiological or triggering substances for disease-onset, the protein-homeostasis-system (PHS) hypothesis proposes that the immune systems in the host control those substances according to the size and biochemical properties of the substances. In this article, I propose a common immunopathogenesis of CNS diseases, including prion diseases, Alzheimer's disease, and genetic diseases, through the PHS hypothesis. [ABSTRACT FROM AUTHOR]

In solid organs, cells of the same "type" can vary in their molecular phenotype. The basis of this state variation is being revealed by characterizing cell features including the expression pattern of mRNAs and the internal distribution of proteins. Here, the variability of metabolic state between cells is probed by enzyme activity profiling. We study individual cells of types that can be identified during the post‐mitotic phase of oogenesis in Xenopus laevis. Whole‐cell homogenates of isolated oocytes are used for kinetic analysis of enzymes, with a focus on the initial reaction rate. For each oocyte type studied, the activity signatures of glyceraldehyde 3‐phosphate dehydrogenase (GAPDH) and malate dehydrogenase 1 (MDH1) vary more between the homogenates of single oocytes than between repeat samplings of control homogenates. Unexpectedly, the activity signatures of GAPDH and MDH1 strongly co‐vary between oocytes of each type and change in strength of correlation during oogenesis. Therefore, variability of the kinetic behavior of these housekeeping enzymes between "identical" cells is physiologically programmed. Based on these findings, we propose that single‐cell profiling of enzyme kinetics will improve understanding of how metabolic state heterogeneity is related to heterogeneity revealed by omics methods including proteomics, epigenomics, and metabolomics. [ABSTRACT FROM AUTHOR]

The maintenance and regeneration of adult tissues rely on the self-renewal of stem cells. Regeneration without over-proliferation requires precise regulation of the stem cell proliferation and differentiation rates. The nature of such regulatory mechanisms in different tissues, and how to incorporate them in models of stem cell population dynamics, is incompletely understood. The critical birth-death (CBD) process is widely used to model stem cell populations, capturing key phenomena, such as scaling laws in clone size distributions. However, the CBD process neglects regulatory mechanisms. Here, we propose the birth-death process with volume exclusion (vBD), a variation of the birth-death process that considers crowding effects, such as may arise due to limited space in a stem cell niche. While the deterministic rate equations predict a single non-trivial attracting steady state, the master equation predicts extinction and transient distributions of stem cell numbers with three possible behaviours: long-lived quasi-steady state (QSS), and short-lived bimodal or unimodal distributions. In all cases, we approximate solutions to the vBD master equation using a renormalized system-size expansion, QSS approximation and the Wentzel–Kramers–Brillouin method. Our study suggests that the size distribution of a stem cell population bears signatures that are useful to detect negative feedback mediated via volume exclusion. [ABSTRACT FROM AUTHOR]

We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems. Author summary: Single-cell sequencing data are snapshots of biological processes, making it challenging to infer dynamic relationships between cell types. RNA velocity attempts to bypass this challenge by treating the unspliced RNA content as a proxy for spliced RNA content in the near future, and using this "extrapolation" to build directional relationships. However, the method, as implemented in several software packages, is not yet reliable enough to be actionable, in part due to the large number of arbitrary, user-set hyperparameters, as well as fundamental incompatibilities between the biophysics of transcription in the living cell and the models used throughout the velocity workflows. In this study, we review these issues, and use existing results from the fields of stochastic modeling and fluorescence transcriptomics to develop an alternative theoretical framework. We show that our framework can facilitate the development and inference of physically consistent models for sequencing data, as well as the unification of single-cell analyses to self-consistently treat variation due to cell type dynamics and identities, the stochasticity inherent to single-molecule processes, and the uncertainty introduced by sequencing experiments. [ABSTRACT FROM AUTHOR]

Background: According to Waddington's epigenetic landscape concept, the differentiation process can be illustrated by a cell akin to a ball rolling down from the top of a hill (proliferation state) and crossing furrows before stopping in basins or "attractor states" to reach its stable differentiated state. However, it is now clear that some committed cells can retain a certain degree of plasticity and reacquire phenotypical characteristics of a more pluripotent cell state. In line with this dynamic model, we have previously shown that differentiating cells (chicken erythrocytic progenitors (T2EC)) retain for 24 h the ability to self-renew when transferred back in self-renewal conditions. Despite those intriguing and promising results, the underlying molecular state of those "reverting" cells remains unexplored. The aim of the present study was therefore to molecularly characterize the T2EC reversion process by combining advanced statistical tools to make the most of single-cell transcriptomic data. For this purpose, T2EC, initially maintained in a self-renewal medium (0H), were induced to differentiate for 24H (24H differentiating cells); then, a part of these cells was transferred back to the self-renewal medium (48H reverting cells) and the other part was maintained in the differentiation medium for another 24H (48H differentiating cells). For each time point, cell transcriptomes were generated using scRT-qPCR and scRNAseq. Results: Our results showed a strong overlap between 0H and 48H reverting cells when applying dimensional reduction. Moreover, the statistical comparison of cell distributions and differential expression analysis indicated no significant differences between these two cell groups. Interestingly, gene pattern distributions highlighted that, while 48H reverting cells have gene expression pattern more similar to 0H cells, they are not completely identical, which suggest that for some genes a longer delay may be required for the cells to fully recover. Finally, sparse PLS (sparse partial least square) analysis showed that only the expression of 3 genes discriminates 48H reverting and 0H cells. Conclusions: Altogether, we show that reverting cells return to an earlier molecular state almost identical to undifferentiated cells and demonstrate a previously undocumented physiological and molecular plasticity during the differentiation process, which most likely results from the dynamic behavior of the underlying molecular network. [ABSTRACT FROM AUTHOR]

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Engineered T cells have been shown to be highly effective in cancer immunotherapy, although T cell exhaustion presents a challenge for their long-term function. Additional T-cell sources must be exploited to broaden the application of engineered T cells for immune defense and reconstitution. Unlimited sources of pluripotent stem cells (PSCs) have provided a potential opportunity to generate precise-engineered therapeutic induced T (iT) cells. Single-cell transcriptome analysis of PSC-derived induced hematopoietic stem and progenitor cells (iHSPC)/iT identified the developmental pathways and possibilities of generating functional T cell from PSCs. To date, the PSC-to-iT platforms encounter several problems, including low efficiency of conventional T subset specification, limited functional potential, and restrictions on large-scale application, because of the absence of a thymus-like organized microenvironment. The updated PSC-to-iT platforms, such as the three-dimensional (3D) artificial thymic organoid (ATO) co-culture system and Runx1/Hoxa9-enforced iT lymphopoiesis, provide fresh perspectives for coordinating culture conditions and transcription factors, which may greatly improve the efficiency of T-cell generation greatly. In addition, the improved PSC-to-iT platform coordinating gene editing technologies will provide various functional engineered unconventional or conventional T cells. Furthermore, the clinical applications of PSC-derived immune cells are accelerating from bench to bedside. [ABSTRACT FROM AUTHOR]

Résumé: La mégacaryopoïèse est le processus de différenciation qui aboutit à la production de plaquettes à partir de cellules souches hématopoïétiques (CSH). Ce processus se distingue des autres différenciations hématopoïétiques par plusieurs aspects : (1) la mégacaryopoïèse peut dériver directement de la CSH, surtout en cas de stress, (2) la mégacaryopoïèse et l'érythropoïèse sont deux lignées proches avec un progéniteur commun, (3) les mégacaryocytes sont en majorité des cellules polyploïdes géantes avec une polyploïdisation qui fait partie intégrante du système de différenciation et non dépendante uniquement d'un stress, (4) les plaquettes ont pour origine la fragmentation du cytoplasme du mégacaryocyte ; leur production dépend donc à la fois du nombre de ces mégacaryocytes et de leur taille, (5) le processus de fragmentation est actif et dépend de la protrusion de longues extensions cytoplasmiques appelées proplaquettes dans les sinusoïdes vasculaires de la moelle osseuse qui vont se rompre sous l'effet du flux sanguin, d'abord en préplaquettes, puis en plaquettes. Ces dernières années ont été marquées par des progrès considérables dans la compréhension des différents aspects de la mégacaryopoïèse ainsi que sur le rôle de la thrombopoïétine et de son récepteur MPL (pour myeloproliferative leukemia protein) dans la régulation de la production plaquettaire. Ces connaissances ont permis une meilleure compréhension de nombreuses pathologies, en particulier du mécanisme des thrombopénies et des thrombocytoses héréditaires ou acquises. Elles ont débouché sur de nouvelles thérapies, notamment le développement en clinique des agonistes du récepteur de la thrombopoïétine. Cependant, des données totalement inattendues ont été récemment obtenues, principalement par la technique de transcriptome à l'échelle unicellulare, et qui ont montré que la mégacaryopoïèse n'est pas un processus linéaire, conduisant ainsi à une hétérogénéité fonctionnelle des mégacaryocytes. Si la majorité des mégacaryocytes de la moelle osseuse sont impliqués dans la formation des plaquettes, il en existe deux autres types, l'un étant impliqué dans la formation de la niche hématopoïétique et régulant la quiescence des CSH, l'autre dans la réaction immune, avec des fonctions proches de celles des monocytes ou des cellules dendritiques. Par ailleurs, si la moelle est le principal site de la mégacaryopoïèse, le poumon en est un autre, le parenchyme pulmonaire hébergeant une différenciation mégacaryocytaire qui aboutirait essentiellement à des mégacaryocytes immuns. D'autres mégacaryocytes, d'origine essentiellement médullaire, seraient situés dans la circulation pulmonaire et impliqués dans la production plaquettaire. Ces données ouvrent de nouvelles perspectives quant au rôle des mégacaryocytes dans la régulation de l'hématopoïèse et dans leur rôle dans l'immunité innée, en particulier lors d'infections pulmonaires. Classically, megakaryopoiesis is the differentiation process that results in platelet production from hematopoietic stem cells (HCS). This process compared to other hematopoietic cell differentiations is quite original by several aspects: (1) megakaryopoiesis can derive directly from the HSC, especially in case of stress, (2) megakaryopoiesis and erythropoiesis are two close cell lineages with a common progenitor, (3) megakaryocytes are mostly giant cells whose polyploidization is an integral part of the differentiation process and not dependent on a stress, (4) platelets originate from the fragmentation of the megakaryocyte cytoplasm with a production that depends both on the number and the size of megakaryocytes, (5) the process of fragmentation is active and depends on extrusion of long cytoplasmic extensions, called proplatelets into the vascular sinusoids of the bone marrow where they will break under the blood flow forces into preplatelets and then in platelets. These recent years have been marked by major advances in the understanding of these different aspects of megakaryopoiesis as well as on the role of thrombopoietin and its receptor called MPL in the regulation of platelet production. This knowledge has allowed a better understanding of many pathologies, in particular the mechanism of hereditary or acquired thrombocytopenia and thrombocytosis and has led to new therapies such as the clinical development of thrombopoietin receptor agonists. Recently, however, completely unexpected data have been obtained, questioning the function of megakaryocytes. Although the predominant function is the platelet production, megakaryocytes are heterogeneous, some of them are involved inside the hematopoietic niche in the regulation of the HSC quiescence and others in the immune response with functions close to those of monocytes or dendritic cells. Furthermore, the marrow is not the only site of megakaryopoiesis, but the lung is a second site with the development of an entire megakaryocyte differentiation, which may principally lead to immune megakaryocytes. In addition, megakaryocytes essentially of marrow origin located in the lung circulation are also present and involved in platelet production. These data open new perspectives on the role of megakaryocytes in the regulation of hematopoiesis and in the innate immunity, particularly during pulmonary infection. [ABSTRACT FROM AUTHOR]

In order to support bone marrow regeneration after myeloablation, hematopoietic stem cells (HSCs) actively divide to provide both stem and progenitor cells. However, the mechanisms regulating HSC function and cell fate choice during hematopoietic recovery remain unclear. We herein provide novel insights into HSC regulation during regeneration by focusing on mitochondrial metabolism and ATP citrate lyase (ACLY). After 5‐fluorouracil‐induced myeloablation, HSCs highly expressing endothelial protein C receptor (EPCRhigh) were enriched within the stem cell fraction at the expense of more proliferative EPCRLow HSCs. These EPCRHigh HSCs were initially more primitive than EPCRLow HSCs and enabled stem cell expansion by enhancing histone acetylation, due to increased activity of ACLY in the early phase of hematopoietic regeneration. In the late phase of recovery, HSCs enhanced differentiation potential by increasing the accessibility of cis‐regulatory elements in progenitor cell‐related genes, such as CD48. In conditions of reduced mitochondrial metabolism and ACLY activity, these HSCs maintained stem cell phenotypes, while ACLY‐dependent histone acetylation promoted differentiation into CD48+ progenitor cells. Collectively, these results indicate that the dynamic control of ACLY‐dependent metabolism and epigenetic alterations is essential for HSC regulation during hematopoietic regeneration. Synopsis: The metabolic mechanisms driving hematopoietic stem cell (HSC) activation and recovery of the blood system after myeloablation remain unclear. This study reports ATP citrate lyase (ACLY)‐dependent histone acetylation and changes in chromatin accessibility as dynamic determinants of HSC function during bone marrow regeneration. HSC gene expression, metabolic status and histone acetylation differ between early and late stages post 5‐FU treatment in mice.Early after 5‐FU treatment, increased mitochondrial membrane potential, ACLY expression and global H3K27ac levels enhance HSC expansion and in vivo engraftment potential.Increased HSC differentiation capacity at late post‐5‐FU stages is associated with increased accessibility of progenitor cell‐related genes.Suppression of ACLY‐mediated metabolism maintains HSC identity during late regeneration. [ABSTRACT FROM AUTHOR]

Introduction: Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials., Methods: Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research., Results: Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability., Discussion: AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data., Highlights: There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery., (© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: Mature blood cells arise from hematopoietic stem cells in the bone marrow by a process of differentiation along one of several different lineage trajectories. This is often represented as a series of discrete steps of increasing progenitor cell commitment to a given lineage, but as for differentiation in general, whether the process is instructive or stochastic remains controversial. Here, we examine this question by analyzing single-cell transcriptomic data from human bone marrow cells, assessing cell-to-cell variability along the trajectories of hematopoietic differentiation into four different types of mature blood cells. The instructive model predicts that cells will be following the same sequence of instructions and that there will be minimal variability of gene expression between them throughout the process, while the stochastic model predicts a role for cell-to-cell variability when lineage commitments are being made. Results: Applying Shannon entropy to measure cell-to-cell variability among human hematopoietic bone marrow cells at the same stage of differentiation, we observed a transient peak of gene expression variability occurring at characteristic points in all hematopoietic differentiation pathways. Strikingly, the genes whose cell-to-cell variation of expression fluctuated the most over the course of a given differentiation trajectory are pathway-specific genes, whereas genes which showed the greatest variation of mean expression are common to all pathways. Finally, we showed that the level of cell-to-cell variation is increased in the most immature compartment of hematopoiesis in myelodysplastic syndromes. Conclusions: These data suggest that human hematopoietic differentiation could be better conceptualized as a dynamical stochastic process with a transient stage of cellular indetermination, and strongly support the stochastic view of differentiation. They also highlight the need to consider the role of stochastic gene expression in complex physiological processes and pathologies such as cancers, paving the way for possible noise-based therapies through epigenetic regulation. [ABSTRACT FROM AUTHOR]

Network inference is a notoriously challenging problem. Inferred networks are associated with high uncertainty and likely riddled with false positive and false negative interactions. Especially for biological networks we do not have good ways of judging the performance of inference methods against real networks, and instead we often rely solely on the performance against simulated data. Gaining confidence in networks inferred from real data nevertheless thus requires establishing reliable validation methods. Here, we argue that the expectation of mixing patterns in biological networks such as gene regulatory networks offers a reasonable starting point: interactions are more likely to occur between nodes with similar biological functions. We can quantify this behaviour using the assortativity coefficient, and here we show that the resulting heuristic, functional assortativity, offers a reliable and informative route for comparing different inference algorithms. [ABSTRACT FROM AUTHOR]

Hematopoietic stem cells (HSCs) rely on local interactions in the bone marrow (BM) microenvironment with stromal cells and other hematopoietic cells that facilitate their survival and proliferation, and also regulate their functions. HSCs and multipotent progenitor cells differentiate into lineage-specific progenitors that generate all blood and immune cells. Megakaryocytes (Mks) are hematopoietic cells responsible for producing blood platelets, which are essential for normal hemostasis and blood coagulation. Although the most prominent function of Mks is platelet production (thrombopoiesis), other increasingly recognized functions include HSC maintenance and host immune response. However, whether and how these diverse programs are executed by different Mk subpopulations remains poorly understood. This Perspective summarizes our current understanding of diversity in ontogeny, functions and cell-cell interactions. Cumulative evidence suggests that BM microenvironment dysfunction, partly caused by mutated Mks, can induce or alter the progression of a variety of hematologic malignancies, including myeloproliferative neoplasms (MPNs) and other disorders associated with tissue scarring (fibrosis). Therefore, as an example of the heterogeneous functions of Mks in malignant hematopoiesis, we will discuss the role of Mks in the onset and progression of BM fibrosis. In this regard, abnormal interactions between of Mks and other immune cells might directly contribute to fibrotic diseases. Overall, further understanding of megakaryopoiesis and how Mks interact with HSCs and immune cells has potential clinical implications for stem cell transplantation and other therapies for hematologic malignancies, as well as for treatments to stimulate platelet production and prevent thrombocytopenia. [ABSTRACT FROM AUTHOR]

As the importance of cell heterogeneity has begun to be emphasized, single-cell sequencing approaches are rapidly adopted to study cell heterogeneity and cellular evolutionary relationships of various cells, including stem cell populations. The hematopoietic stem and progenitor cell (HSPC) compartment contains HSC hematopoietic stem cells (HSCs) and distinct hematopoietic cells with different abilities to self-renew. These cells perform their own functions to maintain different hematopoietic lineages. Undeniably, single-cell sequencing approaches, including single-cell RNA sequencing (scRNA-seq) technologies, empower more opportunities to study the heterogeneity of normal and pathological HSCs. In this review, we discuss how these scRNA-seq technologies contribute to tracing origin and lineage commitment of HSCs, profiling the bone marrow microenvironment and providing high-resolution dissection of malignant hematopoiesis, leading to exciting new findings in HSC biology. [ABSTRACT FROM AUTHOR]

Understanding how cells change their identity and behaviour in living systems is an important question in many fields of biology. The problem of inferring cell trajectories from single-cell measurements has been a major topic in the single-cell analysis community, with different methods developed for equilibrium and non-equilibrium systems (e.g. haematopoeisis vs. embryonic development). We show that optimal transport analysis, a technique originally designed for analysing time-courses, may also be applied to infer cellular trajectories from a single snapshot of a population in equilibrium. Therefore, optimal transport provides a unified approach to inferring trajectories that is applicable to both stationary and non-stationary systems. Our method, StationaryOT, is mathematically motivated in a natural way from the hypothesis of a Waddington's epigenetic landscape. We implement StationaryOT as a software package and demonstrate its efficacy in applications to simulated data as well as single-cell data from Arabidopsis thaliana root development. Author summary: Many important biological phenomena involve populations of cells that undergo changes in behaviour over time to achieve a desired state or function. Modern experimental technologies are able to measure aspects of cell state but cannot observe a cell at more than a single instant in time, since the cell is necessarily destroyed in the measurement process. Therefore, the relationship between the present and future states of a cell, which we call its trajectory, must be inferred from observable data. Since biological processes are naturally noisy, we model cells as evolving following a stochastic dynamical system with growth. We show that for datasets drawn from a population of cells in equilibrium and when estimates of cell growth rates are available, cellular trajectories can be estimated by solving an optimal transport problem. We validate our method using simulated data and demonstrate an application to transcriptomic data from Arabidopsis thaliana root development. [ABSTRACT FROM AUTHOR]

Thrombopoiesis had long been a challenging area of study due to the rarity of megakaryocyte precursors in the bone marrow and the incomplete understanding of its regulatory cytokines. A breakthrough was achieved in the early 1990s with the discovery of the thrombopoietin receptor (TpoR) and its ligand thrombopoietin (TPO). This accelerated research in thrombopoiesis, including the uncovering of the molecular basis of myeloproliferative neoplasms (MPN) and the advent of drugs to treat thrombocytopenic purpura. TpoR mutations affecting its membrane dynamics or transport were increasingly associated with pathologies such as MPN and thrombocytosis. It also became apparent that TpoR affected hematopoietic stem cell (HSC) quiescence while priming hematopoietic stem cells (HSCs) towards the megakaryocyte lineage. Thorough knowledge of TpoR surface localization, dimerization, dynamics and stability is therefore crucial to understanding thrombopoiesis and related pathologies. In this review, we will discuss the mechanisms of TpoR traffic. We will focus on the recent progress in TpoR membrane dynamics and highlight the areas that remain unexplored. [ABSTRACT FROM AUTHOR]

Significance Statement: Kidney-derived thrombopoietin (TPO) increases myeloid cell and platelet production during antibody-mediated chronic kidney disease (AMCKD) in a mouse model, exacerbating chronic thromobinflammation in microvessels. The effect is mirrored in patients with extracapillary glomerulonephritis associated with thromboinflammation, TGF β -dependent glomerulosclerosis, and increased bioavailability of TPO. Neutralization of TPO in mice normalized hematopoiesis, reduced chronic thromboinflammation, and ameliorated renal disease. The findings suggest that TPO is a relevant biomarker and a promising therapeutic target for patients with CKD and other chronic thromboinflammatory diseases.Neutralization of TPO in mice normalized hematopoiesis, reduced chronic thromboinflammation, and ameliorated renal disease. The findings suggest that TPO is a relevant biomarker and a promising therapeutic target for patients with CKD and other chronic thromboinflammatory diseases., Background: Chronic thromboinflammation provokes microvascular alterations and rarefaction, promoting organ dysfunction in individuals with various life-threatening diseases. Hematopoietic growth factors (HGFs) released by the affected organ may sustain emergency hematopoiesis and fuel the thromboinflammatory process., Methods: Using a murine model of antibody-mediated chronic kidney disease (AMCKD) and pharmacological interventions, we comprehensively monitored the response to injury in the circulating blood, urine, bone marrow, and kidney., Results: Experimental AMCKD was associated with chronic thromboinflammation and the production of HGFs, especially thrombopoietin (TPO), by the injured kidney, which stimulated and skewed hematopoiesis toward myelo-megakaryopoiesis. AMCKD was characterized by vascular and kidney dysfunction, TGF β -dependent glomerulosclerosis, and microvascular rarefaction. In humans, extracapillary glomerulonephritis is associated with thromboinflammation, TGF β -dependent glomerulosclerosis, and increased bioavailability of TPO. Analysis of albumin, HGF, and inflammatory cytokine levels in sera from patients with extracapillary glomerulonephritis allowed us to identify treatment responders. Strikingly, TPO neutralization in the experimental AMCKD model normalized hematopoiesis, reduced chronic thromboinflammation, and ameliorated renal disease., Conclusion: TPO-skewed hematopoiesis exacerbates chronic thromboinflammation in microvessels and worsens AMCKD. TPO is both a relevant biomarker and a promising therapeutic target in humans with CKD and other chronic thromboinflammatory diseases., (Copyright © 2023 by the American Society of Nephrology.)

A mathematical model of an amperometric biosensor response for uncompetitive inhibition detection is discussed. The model is based on a system of reaction–diffusion equations containing a nonlinear term related to Michaelis–Menten kinetics of the enzymatic reaction. Two highly accurate and easily accessible analytical methods are used to solve the nonlinear differential equations that describe the diffusion coupled with a reaction term. Semi-analytical expressions for the concentrations of substrate, inhibitor, and product in addition to the corresponding current response are derived. The effects of various parameters such as layer thickness, bulk substrate concentration, Michaelis–Menten constant, maximum reaction velocity on current, and the sensitivity and resistance of a biosensor are investigated. A direct comparison with a numerical solution of the governing system is presented to confirm the accuracy of the derived approximate analytical solutions. [ABSTRACT FROM AUTHOR]

Gene regulatory network is a complicated set of interactions between genetic materials, which dictates how cells develop in living organisms and react to their surrounding environment. Robust comprehension of these interactions would help explain how cells function as well as predict their reactions to external factors. This knowledge can benefit both developmental biology and clinical research such as drug development or epidemiology research. Recently, the rapid advance of single-cell sequencing technologies, which pushed the limit of transcriptomic profiling to the individual cell level, opens up an entirely new area for regulatory network research. To exploit this new abundant source of data and take advantage of data in single-cell resolution, a number of computational methods have been proposed to uncover the interactions hidden by the averaging process in standard bulk sequencing. In this article, we review 15 such network inference methods developed for single-cell data. We discuss their underlying assumptions, inference techniques, usability, and pros and cons. In an extensive analysis using simulation, we also assess the methods' performance, sensitivity to dropout and time complexity. The main objective of this survey is to assist not only life scientists in selecting suitable methods for their data and analysis purposes but also computational scientists in developing new methods by highlighting outstanding challenges in the field that remain to be addressed in the future development. [ABSTRACT FROM AUTHOR]

In embryonic stem cells (ESCs), the transcription factor Nanog maintains the stemness of ESCs despite exhibiting heterogeneous expression patterns under varied culture conditions. Efficient fine‐tuning of Nanog expression heterogeneity could enable ESC proliferation and differentiation along specific lineages to be regulated. Herein, by employing a stochastic modeling approach, we show that Nanog expression heterogeneity can be controlled by modulating the regulatory features of a Nanog transcript‐specific microRNA, mir‐296. We demonstrate how and why the extent of origin‐dependent fluctuations in Nanog expression level can be altered by varying either the binding efficiency of the microRNA–mRNA complex or the expression level of mir‐296. Moreover, our model makes experimentally feasible and insightful predictions to maneuver Nanog expression heterogeneity explicitly to achieve cell‐type‐specific differentiation of ESCs. [ABSTRACT FROM AUTHOR]

Lifelong blood production is maintained by bone marrow (BM)-residing hematopoietic stem cells (HSCs) that are defined by two special properties: multipotency and self-renewal. Since dysregulation of either may lead to a differentiation block or extensive proliferation causing dysplasia or neoplasia, the genomic integrity and cellular function of HSCs must be tightly controlled and preserved by cell-intrinsic programs and cell-extrinsic environmental factors of the BM. The BM had been long regarded an immune-privileged organ shielded from immune insults and inflammation, and was thereby assumed to provide HSCs and immune cells with a protective environment to ensure blood and immune homeostasis. Recently, accumulating evidence suggests that hemato-immune challenges such as autoimmunity, inflammation or infection elicit a broad spectrum of immunological reactions in the BM, and in turn, influence the function of HSCs and BM environmental cells. Moreover, in analogy with the emerging concept of "trained immunity", certain infection-associated stimuli are able to train HSCs and progenitors to produce mature immune cells with enhanced responsiveness to subsequent challenges, and in some cases, form an inflammatory or infectious memory in HSCs themselves. In this review, we will introduce recent findings on HSC and hematopoietic regulation upon exposure to various hemato-immune stimuli and discuss how these challenges can elicit either beneficial or detrimental outcomes on HSCs and the hemato-immune system, as well as their relevance to aging and hematologic malignancies. [ABSTRACT FROM AUTHOR]

We investigated how the shape of polymeric vesicles, made by the exact same material, impacts the replication activity and metabolic state of both cancer and non-cancer cell types. First, we isolated discrete geometrical structures (spheres and tubes) from a heterogeneous sample using density-gradient centrifugation. Then, we characterized the cellular internalization and the kinetics of uptake of both types of polymersomes in different cell types (either cancer or non-cancer cells). We also investigated the cellular metabolic response as a function of the shape of the structures internalized and discovered that tubular vesicles induce a significant decrease in the replication activity of cancer cells compared to spherical vesicles. We related this effect to the significant up-regulation of the tumor suppressor genes p21 and p53 with a concomitant activation of caspase 3/7. Finally, we demonstrated that combining the intrinsic shape-dependent effects of tubes with the delivery of doxorubicin significantly increases the cytotoxicity of the system. Our results illustrate how the geometrical conformation of nanoparticles could impact cell behavior and how this could be tuned to create novel drug delivery systems tailored to specific biomedical application. [ABSTRACT FROM AUTHOR]

Posttranscriptional regulation plays a fundamental role in the biology of embryonic stem cells (ESCs). Many studies have demonstrated that multiple mRNAs are coregulated by one or more RNA-binding proteins (RBPs) that orchestrate mRNA expression. A family of RBPs, which is known as the Pumilio-FBF (PUF) family, is highly conserved among different species and has been associated with the undifferentiated and differentiated states of different cell lines. In humans, two homologs of the PUF family have been found: Pumilio 1 (PUM1) and Pumilio 2 (PUM2). To understand the role of these proteins in human ESCs (hESCs), we first assessed the influence of the silencing of PUM1 and PUM2 on pluripotency genes and found that the knockdown of Pumilio genes significantly decreased the OCT4 and NANOG mRNA levels and reduced the amount of nuclear OCT4, which suggests that Pumilio proteins play a role in the maintenance of pluripotency in hESCs. Furthermore, we observed that PUM1-and-PUM2-silenced hESCs exhibited improved efficiency of in vitro cardiomyogenic differentiation. Through an in silico analysis, we identified mRNA targets of PUM1 and PUM2 that are expressed at the early stages of cardiomyogenesis, and further investigation will determine whether these target mRNAs are active and involved in the progression of cardiomyogenesis. Our findings contribute to the understanding of the role of Pumilio proteins in hESC maintenance and differentiation. [ABSTRACT FROM AUTHOR]

Measuring information transmission from stimulus to response is useful for evaluating the signaling fidelity of biochemical reaction networks (BRNs) in cells. Quantification of information transmission can reveal the optimal input stimuli environment for a BRN and the rate at which the signaling fidelity decreases for non-optimal input probability distributions. Here we present sparse estimation of mutual information landscapes (SEMIL), a method to quantify information transmission through cellular BRNs using commonly available data for single-cell gene expression output, across a design space of possible input distributions. We validate SEMIL and use it to analyze several engineered cellular sensing systems to demonstrate the impact of reaction pathways and rate constants on mutual information landscapes. Sarkar et al. present a method called sparse estimation of mutual information landscapes (SEMIL) that quantifies information transmission landscapes through cellular biochemical reaction networks across a space of input distributions using single-cell gene expression data. This study suggests that mutual information landscapes can be used as a performance metric for biochemical reaction networks. [ABSTRACT FROM AUTHOR]

Epithelial-to-mesenchymal transition (EMT) is a fundamental cellular process and plays an essential role in development, tissue regeneration, and cancer metastasis. Interestingly, EMT is not a binary process but instead proceeds with multiple partial intermediate states. However, the functions of these intermediate states are not fully understood. Here, we focus on a general question about how the number of partial EMT states affects cell transformation. First, by fitting a hidden Markov model of EMT with experimental data, we propose a statistical mechanism for EMT in which many unobservable microstates may exist within one of the observable macrostates. Furthermore, we find that increasing the number of intermediate states can accelerate the EMT process and that adding parallel paths or transition layers may accelerate the process even further. Last, a stabilized intermediate state traps cells in one partial EMT state. This work advances our understanding of the dynamics and functions of EMT plasticity during cancer metastasis. Author summary: Epithelial-mesenchymal transition (EMT) is a basic biological process, in which epithelial cells undergo multiple biochemical changes, lose cell-cell junctions and polarization, and become a mesenchymal phenotype with migratory and invasive properties. Recent studies have illustrated the existence and importance of the partial EMT states. It has become increasingly apparent that the EMT has strong differentiation plasticity. This plasticity is heavily implicated in cancer cell invasion and metastasis. However, it is still unclear how the number of intermediate states changes the EMT process. Here, we use a hidden Markov model to describe the EMT process. By fitting with the experimental data, we find that unobservable microstates exist within the observable macrostates: epithelial, partial EMT, and mesenchymal. Additionally, we find that increasing the number of states between the start and end of EMT or including alternative transition avenues via parallel paths or transition layers can accelerate the EMT process. This study suggests a non-trivial function of the EMT plasticity during cancer metastasis. [ABSTRACT FROM AUTHOR]

Background: NANOG functions as the gateway for the generation of pluripotent stem cells (PSCs) in mice and humans. NANOG is a transcription factor highly expressed in pig pre-implantation embryos, indicating that it is a conserved pluripotency-associated factor. However, pig NANOG reporter PSCs have yet to be established, and the regulation of pluripotency by NANOG is not fully understood in this animal. Methods: In this study, pig NANOG tdTomato knock-in reporter positive PC-iPS cells were established using CRISPR/Cas9. The resulting cell line was treated with several cytokines and their corresponding inhibitors to identify pathways that regulate NANOG expression. The pathways examined were LIF (leukemia inhibitory factor)/IL6 (interleukin 6)-STAT3, FGF (fibroblast growth factor)/ERK, IGF1 (insulin-like growth factor 1)/PIP3 (phosphoinositide 3-kinase)-AKT, Activin A/SMAD, and BMP4 (bone morphogenetic proteins)/SMAD. Results: Our experiments showed that the Activin A/SMAD pathway is directly associated with activation of NANOG expression in the pig, as is also the case in mice and humans. Activin A directly regulates the expression of pig NANOG via SMAD2/3; inhibition of this pathway by SB431542 resulted in inhibition of NANOG expression. Conclusions: Our results show that Activin A plays an important regulatory role in NANOG-mediated pluripotency in pig iPS cells. Activin A treatment may be therefore an effective method for de novo derivation of authentic embryonic stem cells (ESCs) from pig pre-implantation embryos. [ABSTRACT FROM AUTHOR]

Non-small cell lung cancer (NSCLC) is a complex disease in need of new methods of therapeutic intervention. Recent interest has focused on using microRNAs (miRNAs) as a novel treatment method for various cancers. miRNAs negatively regulate gene expression post-transcriptionally, and have become attractive candidates for cancer treatment because they often simultaneously target multiple genes of similar biological function. One such miRNA is miR-146a-5p, which has been described as a tumor suppressive miRNA in NSCLC cell lines and tissues. In this study, we performed RNA-Sequencing (RNA-Seq) analysis following transfection of synthetic miR-146a-5p in an NSCLC cell line, A549, and validated our data with Gene Ontology and qRT-PCR analysis of known miR-146a-5p target genes. Our transcriptomic data revealed that miR-146a-5p exerts its tumor suppressive function beyond previously reported targeting of EGFR and NF-κB signaling. miR-146a-5p mimic transfection downregulated arachidonic acid metabolism genes, the RNA-binding protein HuR, and many HuR-stabilized pro-cancer mRNAs, including TGF-β, HIF-1α, and various cyclins. miR-146a-5p transfection also reduced expression and cellular release of the chemokine CCL2, and this effect was mediated through the 3ʹ untranslated region of its mRNA. Taken together, our work reveals that miR-146a-5p functions as a tumor suppressor in NSCLC by controlling various metabolic and signaling pathways through direct and indirect mechanisms. [ABSTRACT FROM AUTHOR]

To better understand the mechanisms behind cells decision-making to differentiate, we assessed the influence of stochastic gene expression (SGE) modulation on the erythroid differentiation process. It has been suggested that stochastic gene expression has a role in cell fate decision-making which is revealed by single-cell analyses but studies dedicated to demonstrate the consistency of this link are still lacking. Recent observations showed that SGE significantly increased during differentiation and a few showed that an increase of the level of SGE is accompanied by an increase in the differentiation process. However, a consistent relation in both increasing and decreasing directions has never been shown in the same cellular system. Such demonstration would require to be able to experimentally manipulate simultaneously the level of SGE and cell differentiation in order to observe if cell behavior matches with the current theory. We identified three drugs that modulate SGE in primary erythroid progenitor cells. Both Artemisinin and Indomethacin decreased SGE and reduced the amount of differentiated cells. On the contrary, a third component called MB-3 simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment. Using single-cell analysis and modeling tools, we provide experimental evidence that, in a physiologically relevant cellular system, SGE is linked to differentiation. [ABSTRACT FROM AUTHOR]