Reciprocal transcription factor networks govern tissue-resident ILC3 subset function and identity.

Innate lymphoid cells (ILCs) are guardians of mucosal immunity, yet the transcriptional networks that support their function remain poorly understood. We used inducible combinatorial deletion of key transcription factors (TFs) required for ILC development (RORγt, RORα and T-bet) to determine their necessity in maintaining ILC3 identity and function. Both RORγt and RORα were required to preserve optimum effector functions; however, RORα was sufficient to support robust interleukin-22 production among the lymphoid tissue inducer (LTi)-like ILC3 subset, but not natural cytotoxicity receptor (NCR)+ ILC3s. Lymphoid tissue inducer-like ILC3s persisted with only selective loss of phenotype and effector functions even after the loss of both TFs. In contrast, continued RORγt expression was essential to restrain transcriptional networks associated with type 1 immunity within NCR+ ILC3s, which coexpress T-bet. Full differentiation to an ILC1-like population required the additional loss of RORα. Together, these data demonstrate how TF networks integrate within mature ILCs after development to sustain effector functions, imprint phenotype and restrict alternative differentiation programs.

Ouroboros: cross-linking protein expression perturbations and cancer histology imaging with generative-predictive modeling.

SUMMARY: Imagine if we could simultaneously predict spatial protein expression in tissues from their routine Hematoxylin and Eosin (H&E) stained images, and create tissue images given protein expression profiles thus enabling virtual simulations of how protein expression alterations impact histology in complex diseases like cancer. Such an approach could lead to more informed diagnostic and therapeutic decisions for precision medicine at lower costs and shorter turnaround times, more detailed insights into underlying disease pathology as well as improvement in predictive and generative performance. In this study, we investigate the intricate correlation between protein expressions obtained from Hyperion mass cytometry and histopathological microstructures in conventional H&E stained glioblastoma (GBM) samples, unveiling morphological patterns and cellular-level spatial alterations associated with protein expression changes. To model these complex relationships, we propose a novel generative-predictive framework called Ouroboros for producing H&E images from protein expressions and simultaneously predicting protein expressions from H&E images. Our comprehensive sample-independent validation over 9920 tissue spots from 4 GBM samples encompassing visual image analysis, quantitative analysis, subspace alignment and perturbation experiments shows that the proposed generative-predictive approach offers significant improvements in predicting protein expression from images in comparison to baseline methods as well as accurate generation of virtual GBM sample images. This proof of concept study can contribute to advancing our understanding of histological responses to protein expression perturbations and lays the foundations for further developments in this area. AVAILABILITY AND IMPLEMENTATION: Implementation and associated data for the proposed approach are available at the URL: https://github.com/Srijay/Ouroboros.

Murine AGM single-cell profiling identifies a continuum of hemogenic endothelium differentiation marked by ACE.

In vitro generation and expansion of hematopoietic stem cells (HSCs) holds great promise for the treatment of any ailment that relies on bone marrow or blood transplantation. To achieve this, it is essential to resolve the molecular and cellular pathways that govern HSC formation in the embryo. HSCs first emerge in the aorta-gonad-mesonephros (AGM) region, where a rare subset of endothelial cells, hemogenic endothelium (HE), undergoes an endothelial-to-hematopoietic transition (EHT). Here, we present full-length single-cell RNA sequencing (scRNA-seq) of the EHT process with a focus on HE and dorsal aorta niche cells. By using Runx1b and Gfi1/1b transgenic reporter mouse models to isolate HE, we uncovered that the pre-HE to HE continuum is specifically marked by angiotensin-I converting enzyme (ACE) expression. We established that HE cells begin to enter the cell cycle near the time of EHT initiation when their morphology still resembles endothelial cells. We further demonstrated that RUNX1 AGM niche cells consist of vascular smooth muscle cells and PDGFRa+ mesenchymal cells and can functionally support hematopoiesis. Overall, our study provides new insights into HE differentiation toward HSC and the role of AGM RUNX1+ niche cells in this process. Our expansive scRNA-seq datasets represents a powerful resource to investigate these processes further.

Foxm1 regulates neural progenitor fate during spinal cord regeneration.

Xenopus tadpoles have the ability to regenerate their tails upon amputation. Although some of the molecular and cellular mechanisms that globally regulate tail regeneration have been characterised, tissue-specific response to injury remains poorly understood. Using a combination of bulk and single-cell RNA sequencing on isolated spinal cords before and after amputation, we identify a number of genes specifically expressed in the spinal cord during regeneration. We show that Foxm1, a transcription factor known to promote proliferation, is essential for spinal cord regeneration. Surprisingly, Foxm1 does not control the cell cycle length of neural progenitors but regulates their fate after division. In foxm1-/- tadpoles, we observe a reduction in the number of neurons in the regenerating spinal cord, suggesting that neuronal differentiation is necessary for the regenerative process. Altogether, our data uncover a spinal cord-specific response to injury and reveal a new role for neuronal differentiation during regeneration.

Complexities in the role of acetylation dynamics in modifying inducible gene activation parameters.

High levels of histone acetylation are associated with the regulatory elements of active genes, suggesting a link between acetylation and gene activation. We revisited this model, in the context of EGF-inducible gene expression and found that rather than a simple unifying model, there are two broad classes of genes; one in which high lysine acetylation activity is required for efficient gene activation, and a second group where the opposite occurs and high acetylation activity is inhibitory. We examined the latter class in more detail using EGR2 as a model gene and found that lysine acetylation levels are critical for several activation parameters, including the timing of expression onset, and overall amplitudes of the transcriptional response. In contrast, DUSP1 responds in the canonical manner and its transcriptional activity is promoted by acetylation. Single cell approaches demonstrate heterogenous activation kinetics of a given gene in response to EGF stimulation. Acetylation levels modify these heterogenous patterns and influence both allele activation frequencies and overall expression profile parameters. Our data therefore point to a complex interplay between acetylation equilibria and target gene induction where acetylation level thresholds are an important determinant of transcriptional induction dynamics that are sensed in a gene-specific manner.

HOX paralogs selectively convert binding of ubiquitous transcription factors into tissue-specific patterns of enhancer activation.

Gene expression programs determine cell fate in embryonic development and their dysregulation results in disease. Transcription factors (TFs) control gene expression by binding to enhancers, but how TFs select and activate their target enhancers is still unclear. HOX TFs share conserved homeodomains with highly similar sequence recognition properties, yet they impart the identity of different animal body parts. To understand how HOX TFs control their specific transcriptional programs in vivo, we compared HOXA2 and HOXA3 binding profiles in the mouse embryo. HOXA2 and HOXA3 directly cooperate with TALE TFs and selectively target different subsets of a broad TALE chromatin platform. Binding of HOX and tissue-specific TFs convert low affinity TALE binding into high confidence, tissue-specific binding events, which bear the mark of active enhancers. We propose that HOX paralogs, alone and in combination with tissue-specific TFs, generate tissue-specific transcriptional outputs by modulating the activity of TALE TFs at selected enhancers.

Classifying cells with Scasat, a single-cell ATAC-seq analysis tool.

ATAC-seq is a recently developed method to identify the areas of open chromatin in a cell. These regions usually correspond to active regulatory elements and their location profile is unique to a given cell type. When done at single-cell resolution, ATAC-seq provides an insight into the cell-to-cell variability that emerges from otherwise identical DNA sequences by identifying the variability in the genomic location of open chromatin sites in each of the cells. This paper presents Scasat (single-cell ATAC-seq analysis tool), a complete pipeline to process scATAC-seq data with simple steps. Scasat treats the data as binary and applies statistical methods that are especially suitable for binary data. The pipeline is developed in a Jupyter notebook environment that holds the executable code along with the necessary description and results. It is robust, flexible, interactive and easy to extend. Within Scasat we developed a novel differential accessibility analysis method based on information gain to identify the peaks that are unique to a cell. The results from Scasat showed that open chromatin locations corresponding to potential regulatory elements can account for cellular heterogeneity and can identify regulatory regions that separates cells from a complex population.

A unified framework for estimating parameters of kinetic biological models.

BACKGROUND: Utilizing kinetic models of biological systems commonly require computational approaches to estimate parameters, posing a variety of challenges due to their highly non-linear and dynamic nature, which is further complicated by the issue of non-identifiability. We propose a novel parameter estimation framework by combining approaches for solving identifiability with a recently introduced filtering technique that can uniquely estimate parameters where conventional methods fail. This framework first conducts a thorough analysis to identify and classify the non-identifiable parameters and provides a guideline for solving them. If no feasible solution can be found, the framework instead initializes the filtering technique with informed prior to yield a unique solution. RESULTS: This framework has been applied to uniquely estimate parameter values for the sucrose accumulation model in sugarcane culm tissue and a gene regulatory network. In the first experiment the results show the progression of improvement in reliable and unique parameter estimation through the use of each tool to reduce and remove non-identifiability. The latter experiment illustrates the common situation where no further measurement data is available to solve the non-identifiability. These results show the successful application of the informed prior as well as the ease with which parallel data sources may be utilized without increasing the model complexity. CONCLUSION: The proposed unified framework is distinct from other approaches by providing a robust and complete solution which yields reliable and unique parameter estimation even in the face of non-identifiability.

The small inhibitor WM-1119 effectively targets KAT6A-rearranged AML, but not KMT2A-rearranged AML, despite shared KAT6 genetic dependency.

BACKGROUND: The epigenetic factors KAT6A (MOZ/MYST3) and KMT2A (MLL/MLL1) interact in normal hematopoiesis to regulate progenitors' self-renewal. Both proteins are recurrently translocated in AML, leading to impairment of critical differentiation pathways in these malignant cells. We evaluated the potential of different KAT6A therapeutic targeting strategies to alter the growth of KAT6A and KMT2A rearranged AMLs. METHODS: We investigated the action and potential mechanisms of the first-in-class KAT6A inhibitor, WM-1119 in KAT6A and KMT2A rearranged (KAT6Ar and KMT2Ar) AML using cellular (flow cytometry, colony assays, cell growth) and molecular (shRNA knock-down, CRISPR knock-out, bulk and single-cell RNA-seq, ChIP-seq) assays. We also used two novel genetic murine KAT6A models combined with the most common KMT2Ar AML, KMT2A::MLLT3 AML. In these murine models, the catalytic activity of KAT6A, or the whole protein, can be conditionally abrogated or deleted. These models allowed us to compare the effects of specific KAT6A KAT activity inhibition with the complete deletion of the whole protein. Finally, we also tested these therapeutic approaches on human AML cell lines and primary patient AMLs. RESULTS: We found that WM-1119 completely abrogated the proliferative and clonogenic potential of KAT6Ar cells in vitro. WM-1119 treatment was associated with a dramatic increase in myeloid differentiation program. The treatment also decreased stemness and leukemia pathways at the transcriptome level and led to loss of binding of the fusion protein at critical regulators of these pathways. In contrast, our pharmacologic and genetic results indicate that the catalytic activity of KAT6A plays a more limited role in KMT2Ar leukemogenicity, while targeting the whole KAT6A protein dramatically affects leukemic potential in murine KMT2A::MLLT3 AML. CONCLUSION: Our study indicates that inhibiting KAT6A KAT activity holds compelling promise for KAT6Ar AML patients. In contrast, targeted degradation of KAT6A, and not just its catalytic activity, may represent a more appropriate therapeutic approach for KMT2Ar AMLs.

Rapid functional impairment of natural killer cells following tumor entry limits anti-tumor immunity.

Immune cell dysfunction within the tumor microenvironment (TME) undermines the control of cancer progression. Established tumors contain phenotypically distinct, tumor-specific natural killer (NK) cells; however, the temporal dynamics, mechanistic underpinning and functional significance of the NK cell compartment remains incompletely understood. Here, we use photo-labeling, combined with longitudinal transcriptomic and cellular analyses, to interrogate the fate of intratumoral NK cells. We reveal that NK cells rapidly lose effector functions and adopt a distinct phenotypic state with features associated with tissue residency. NK cell depletion from established tumors did not alter tumor growth, indicating that intratumoral NK cells cease to actively contribute to anti-tumor responses. IL-15 administration prevented loss of function and improved tumor control, generating intratumoral NK cells with both tissue-residency characteristics and enhanced effector function. Collectively, our data reveals the fate of NK cells after recruitment into tumors and provides insight into how their function may be revived.

CD82 expression marks the endothelium to hematopoietic transition at the onset of blood specification in human.

During embryonic development, all blood progenitors are initially generated from endothelial cells that acquire a hemogenic potential. Blood progenitors emerge through an endothelial-to-hematopoietic transition regulated by the transcription factor RUNX1. To date, we still know very little about the molecular characteristics of hemogenic endothelium and the molecular changes underlying the transition from endothelium to hematopoiesis. Here, we analyzed at the single cell level a human embryonic stem cell-derived endothelial population containing hemogenic potential. RUNX1-expressing endothelial cells, which harbor enriched hemogenic potential, show very little molecular differences to their endothelial counterpart suggesting priming toward hemogenic potential rather than commitment. Additionally, we identify CD82 as a marker of the endothelium-to-hematopoietic transition. CD82 expression is rapidly upregulated in newly specified blood progenitors then rapidly downregulated as further differentiation occurs. Together our data suggest that endothelial cells are first primed toward hematopoietic fate, and then rapidly undergo the transition from endothelium to blood.

Pro- and anti-tumour activities of CD146/MCAM in breast cancer result from its heterogeneous expression and association with epithelial to mesenchymal transition.

CD146, also known as melanoma cell adhesion molecule (MCAM), is expressed in numerous cancers and has been implicated in the regulation of metastasis. We show that CD146 negatively regulates transendothelial migration (TEM) in breast cancer. This inhibitory activity is reflected by a reduction in MCAM gene expression and increased promoter methylation in tumour tissue compared to normal breast tissue. However, increased CD146/MCAM expression is associated with poor prognosis in breast cancer, a characteristic that is difficult to reconcile with inhibition of TEM by CD146 and its epigenetic silencing. Single cell transcriptome data revealed MCAM expression in multiple cell types, including the malignant cells, tumour vasculature and normal epithelium. MCAM expressing malignant cells were in the minority and expression was associated with epithelial to mesenchymal transition (EMT). Furthermore, gene expression signatures defining invasiveness and a stem cell-like phenotype were most strongly associated with mesenchymal-like tumour cells with low levels of MCAM mRNA, likely to represent a hybrid epithelial/mesenchymal (E/M) state. Our results show that high levels of MCAM gene expression are associated with poor prognosis in breast cancer because they reflect tumour vascularisation and high levels of EMT. We suggest that high levels of mesenchymal-like malignant cells reflect large populations of hybrid E/M cells and that low CD146 expression on these hybrid cells is permissive for TEM, aiding metastasis.

Pol II promoter prediction using characteristic 4-mer motifs: a machine learning approach.

BACKGROUND: Eukaryotic promoter prediction using computational analysis techniques is one of the most difficult jobs in computational genomics that is essential for constructing and understanding genetic regulatory networks. The increased availability of sequence data for various eukaryotic organisms in recent years has necessitated for better tools and techniques for the prediction and analysis of promoters in eukaryotic sequences. Many promoter prediction methods and tools have been developed to date but they have yet to provide acceptable predictive performance. One obvious criteria to improve on current methods is to devise a better system for selecting appropriate features of promoters that distinguish them from non-promoters. Secondly improved performance can be achieved by enhancing the predictive ability of the machine learning algorithms used. RESULTS: In this paper, a novel approach is presented in which 128 4-mer motifs in conjunction with a non-linear machine-learning algorithm utilising a Support Vector Machine (SVM) are used to distinguish between promoter and non-promoter DNA sequences. By applying this approach to plant, Drosophila, human, mouse and rat sequences, the classification model has showed 7-fold cross-validation percentage accuracies of 83.81%, 94.82%, 91.25%, 90.77% and 82.35% respectively. The high sensitivity and specificity value of 0.86 and 0.90 for plant; 0.96 and 0.92 for Drosophila; 0.88 and 0.92 for human; 0.78 and 0.84 for mouse and 0.82 and 0.80 for rat demonstrate that this technique is less prone to false positive results and exhibits better performance than many other tools. Moreover, this model successfully identifies location of promoter using TATA weight matrix. CONCLUSION: The high sensitivity and specificity indicate that 4-mer frequencies in conjunction with supervised machine-learning methods can be beneficial in the identification of RNA pol II promoters comparative to other methods. This approach can be extended to identify promoters in sequences for other eukaryotic genomes.

Respectful Modeling: Addressing Uncertainty in Dynamic System Models for Molecular Biology.

Although there is still some skepticism in the biological community regarding the value and significance of quantitative computational modeling, important steps are continually being taken to enhance its accessibility and predictive power. We view these developments as essential components of an emerging 'respectful modeling' framework which has two key aims: (i) respecting the models themselves and facilitating the reproduction and update of modeling results by other scientists, and (ii) respecting the predictions of the models and rigorously quantifying the confidence associated with the modeling results. This respectful attitude will guide the design of higher-quality models and facilitate the use of models in modern applications such as engineering and manipulating microbial metabolism by synthetic biology.

Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models.

In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. What differentiates this approach is the integration of an orthogonal-based local identifiability method into the unscented Kalman filter (UKF), rather than using the more common observability-based method which has inherent limitations. It also introduces a variable step size based on the system uncertainty of the UKF during the sensitivity calculation. This method identified 10 out of 12 parameters as identifiable. These ten parameters were estimated using the UKF, which was run 97 times. Throughout the repetitions the UKF proved to be more consistent than the estimation algorithms used for comparison.