Fetal liver macrophages contribute to the hematopoietic stem cell niche by controlling granulopoiesis.

During embryogenesis, the fetal liver becomes the main hematopoietic organ, where stem and progenitor cells as well as immature and mature immune cells form an intricate cellular network. Hematopoietic stem cells (HSCs) reside in a specialized niche, which is essential for their proliferation and differentiation. However, the cellular and molecular determinants contributing to this fetal HSC niche remain largely unknown. Macrophages are the first differentiated hematopoietic cells found in the developing liver, where they are important for fetal erythropoiesis by promoting erythrocyte maturation and phagocytosing expelled nuclei. Yet, whether macrophages play a role in fetal hematopoiesis beyond serving as a niche for maturing erythroblasts remains elusive. Here, we investigate the heterogeneity of macrophage populations in the murine fetal liver to define their specific roles during hematopoiesis. Using a single-cell omics approach combined with spatial proteomics and genetic fate-mapping models, we found that fetal liver macrophages cluster into distinct yolk sac-derived subpopulations and that long-term HSCs are interacting preferentially with one of the macrophage subpopulations. Fetal livers lacking macrophages show a delay in erythropoiesis and have an increased number of granulocytes, which can be attributed to transcriptional reprogramming and altered differentiation potential of long-term HSCs. Together, our data provide a detailed map of fetal liver macrophage subpopulations and implicate macrophages as part of the fetal HSC niche.

Multimodality imaging and transcriptomics to phenotype mitral valve dystrophy in a unique knock-in Filamin-A rat model.

AIMS: Degenerative mitral valve dystrophy (MVD) leading to mitral valve prolapse is the most frequent form of MV disease, and there is currently no pharmacological treatment available. The limited understanding of the pathophysiological mechanisms leading to MVD limits our ability to identify therapeutic targets. This study aimed to reveal the main pathophysiological pathways involved in MVD via the multimodality imaging and transcriptomic analysis of the new and unique knock-in (KI) rat model for the FilaminA-P637Q (FlnA-P637Q) mutation associated-MVD. METHODS AND RESULTS: Wild-type (WT) and KI rats were evaluated morphologically, functionally, and histologically between 3-week-old and 3-to-6-month-old based on Doppler echocardiography, 3D micro-computed tomography (microCT), and standard histology. RNA-sequencing and Assay for Transposase-Accessible Chromatin (ATAC-seq) were performed on 3-week-old WT and KI mitral valves and valvular cells, respectively, to highlight the main signalling pathways associated with MVD. Echocardiographic exploration confirmed MV elongation (2.0 ± 0.1 mm vs. 1.8 ± 0.1, P = 0.001), as well as MV thickening and prolapse in KI animals compared to WT at 3 weeks. 3D MV volume quantified by microCT was significantly increased in KI animals (+58% vs. WT, P = 0.02). Histological analyses revealed a myxomatous remodelling in KI MV characterized by proteoglycans accumulation. A persistent phenotype was observed in adult KI rats. Signalling pathways related to extracellular matrix homeostasis, response to molecular stress, epithelial cell migration, endothelial to mesenchymal transition, chemotaxis and immune cell migration, were identified based on RNA-seq analysis. ATAC-seq analysis points to the critical role of transforming growth factor-ß and inflammation in the disease. CONCLUSION: The KI FlnA-P637Q rat model mimics human myxomatous MVD, offering a unique opportunity to decipher pathophysiological mechanisms related to this disease. Extracellular matrix organization, epithelial cell migration, response to mechanical stress, and a central contribution of immune cells are highlighted as the main signalling pathways leading to myxomatous MVD. Our findings pave the road to decipher underlying molecular mechanisms and the specific role of distinct cell populations in this context.

Creld1 regulates myocardial development and function.

CRELD1 (Cysteine-Rich with EGF-Like Domains 1) is a risk gene for non-syndromic atrioventricular septal defects in human patients. In a mouse model, Creld1 has been shown to be essential for heart development, particularly in septum and valve formation. However, due to the embryonic lethality of global Creld1 knockout (KO) mice, its cell type-specific function during peri- and postnatal stages remains unknown. Here, we generated conditional Creld1 KO mice lacking Creld1 either in the endocardium (KOTie2) or the myocardium (KOMyHC). Using a combination of cardiac phenotyping, histology, immunohistochemistry, RNA-sequencing, and flow cytometry, we demonstrate that Creld1 function in the endocardium is dispensable for heart development. Lack of myocardial Creld1 causes extracellular matrix remodeling and trabeculation defects by modulation of the Notch1 signaling pathway. Hence, KOMyHC mice die early postnatally due to myocardial hypoplasia. Our results reveal that Creld1 not only controls the formation of septa and valves at an early stage during heart development, but also cardiac maturation and function at a later stage. These findings underline the central role of Creld1 in mammalian heart development and function.

Computational approaches for classification and prediction of P-type ATPase substrate specificity in Arabidopsis.

As an extended gamut of integral membrane (extrinsic) proteins, and based on their transporting specificities, P-type ATPases include five subfamilies in Arabidopsis, inter alia, P4ATPases (phospholipid-transporting ATPase), P3AATPases (plasma membrane H(+) pumps), P2A and P2BATPases (Ca(2+) pumps) and P1B ATPases (heavy metal pumps). Although, many different computational methods have been developed to predict substrate specificity of unknown proteins, further investigation needs to improve the efficiency and performance of the predicators. In this study, various attribute weighting and supervised clustering algorithms were employed to identify the main amino acid composition attributes, which can influence the substrate specificity of ATPase pumps, classify protein pumps and predict the substrate specificity of uncharacterized ATPase pumps. The results of this study indicate that both non-reduced coefficients pertaining to absorption and Cys extinction within 280 nm, the frequencies of hydrogen, Ala, Val, carbon, hydrophilic residues, the counts of Val, Asn, Ser, Arg, Phe, Tyr, hydrophilic residues, Phe-Phe, Ala-Ile, Phe-Leu, Val-Ala and length are specified as the most important amino acid attributes through applying the whole attribute weighting models. Here, learning algorithms engineered in a predictive machine (Naive Bays) is proposed to foresee the Q9LVV1 and O22180 substrate specificities (P-type ATPase like proteins) with 100 % prediction confidence. For the first time, our analysis demonstrated promising application of bioinformatics algorithms in classifying ATPases pumps. Moreover, we suggest the predictive systems that can assist towards the prediction of the substrate specificity of any new ATPase pumps with the maximum possible prediction confidence.

Unravelling evolution of Nanog, the key transcription factor involved in self-renewal of undifferentiated embryonic stem cells, by pattern recognition in nucleotide and tandem repeats characteristics.

Nanog, an important transcription factor in embryonic stem cells (ESC), is the key factor in maintaining pluripotency to establish ESC identity and has the ability to induce embryonic germ layers. Nanog is responsible for self-renewal and pluripotency of stem cells as well as cancer invasiveness, tumor cell proliferation, motility and drug-resistance. Understanding the underlying mechanisms of Nanog evolution and regulation can lead to future advances in treatment of cancers. Recent integration of machine learning models with genetics has provided a powerful tool for knowledge discovery and uncovering evolutionary pathways. Herein, sequences of 47 Nanog genes from various species were extracted and two datasets of features were computationally extracted from these sequences. At the first dataset, 76 nucleotide acid attributes were calculated for each Nanog sequence. The second dataset was prepared based on the 10,480 repeated nucleotide sequences (from 5 to 50bp lengths). Then, various data mining algorithms such as decision tree models were applied on these datasets to find the evolutionary pathways of Nanog diversion. Attribute weighting models were highlighted features such as the frequencies of AA and GC as the most important genomic features in Nanog gene classification and differentiation. Similar findings were obtained by tree induction algorithms. Results from the second database showed that some short sequence strings, such as ACTACT, TCCTGA, CCTGA, GAAGAC, and TATCCC can be effectively used to identify Nanog genes in various species. The outcomes of this study, for the first time, unravels the importance of particular genomic features in Nanog gene evolution paving roads toward better understanding of stem cell development and human targeted disorder therapy.

Machine Learning Based Classification of Microsatellite Variation: An Effective Approach for Phylogeographic Characterization of Olive Populations.

Finding efficient analytical techniques is overwhelmingly turning into a bottleneck for the effectiveness of large biological data. Machine learning offers a novel and powerful tool to advance classification and modeling solutions in molecular biology. However, these methods have been less frequently used with empirical population genetics data. In this study, we developed a new combined approach of data analysis using microsatellite marker data from our previous studies of olive populations using machine learning algorithms. Herein, 267 olive accessions of various origins including 21 reference cultivars, 132 local ecotypes, and 37 wild olive specimens from the Iranian plateau, together with 77 of the most represented Mediterranean varieties were investigated using a finely selected panel of 11 microsatellite markers. We organized data in two '4-targeted' and '16-targeted' experiments. A strategy of assaying different machine based analyses (i.e. data cleaning, feature selection, and machine learning classification) was devised to identify the most informative loci and the most diagnostic alleles to represent the population and the geography of each olive accession. These analyses revealed microsatellite markers with the highest differentiating capacity and proved efficiency for our method of clustering olive accessions to reflect upon their regions of origin. A distinguished highlight of this study was the discovery of the best combination of markers for better differentiating of populations via machine learning models, which can be exploited to distinguish among other biological populations.

Precision assessment of some supervised and unsupervised algorithms for genotype discrimination in the genus Pisum using SSR molecular data.

For the first time, prediction accuracies of some supervised and unsupervised algorithms were evaluated in an SSR-based DNA fingerprinting study of a pea collection containing 20 cultivars and 57 wild samples. In general, according to the 10 attribute weighting models, the SSR alleles of PEAPHTAP-2 and PSBLOX13.2-1 were the two most important attributes to generate discrimination among eight different species and subspecies of genus Pisum. In addition, K-Medoids unsupervised clustering run on Chi squared dataset exhibited the best prediction accuracy (83.12%), while the lowest accuracy (25.97%) gained as K-Means model ran on FCdb database. Irrespective of some fluctuations, the overall accuracies of tree induction models were significantly high for many algorithms, and the attributes PSBLOX13.2-3 and PEAPHTAP could successfully detach Pisum fulvum accessions and cultivars from the others when two selected decision trees were taken into account. Meanwhile, the other used supervised algorithms exhibited overall reliable accuracies, even though in some rare cases, they gave us low amounts of accuracies. Our results, altogether, demonstrate promising applications of both supervised and unsupervised algorithms to provide suitable data mining tools regarding accurate fingerprinting of different species and subspecies of genus Pisum, as a fundamental priority task in breeding programs of the crop.

Prediction of hepatitis C virus interferon/ribavirin therapy outcome based on viral nucleotide attributes using machine learning algorithms.

BACKGROUND: Hepatitis C virus (HCV) causes chronic hepatitis C in 2-3% of world population and remains one of the health threatening human viruses, worldwide. In the absence of an effective vaccine, therapeutic approach is the only option to combat hepatitis C. Interferon-alpha (IFN-alpha) and ribavirin (RBV) combination alone or in combination with recently introduced new direct-acting antivirals (DAA) is used to treat patients infected with HCV. The present study utilized feature selection methods (Gini Index, Chi Squared and machine learning algorithms) and other bioinformatics tools to identify genetic determinants of therapy outcome within the entire HCV nucleotide sequence. RESULTS: Using combination of several algorithms, the present study performed a comprehensive bioinformatics analysis and identified several nucleotide attributes within the full-length nucleotide sequences of HCV subtypes 1a and 1b that correlated with treatment outcome. Feature selection algorithms identified several nucleotide features (e.g. count of hydrogen and CG). Combination of algorithms utilized the selected nucleotide attributes and predicted HCV subtypes 1a and 1b therapy responders from non-responders with an accuracy of 75.00% and 85.00%, respectively. In addition, therapy responders and relapsers were categorized with an accuracy of 82.50% and 84.17%, respectively. Based on the identified attributes, decision trees were induced to differentiate different therapy response groups. CONCLUSIONS: The present study identified new genetic markers that potentially impact the outcome of hepatitis C treatment. In addition, the results suggest new viral genomic attributes that might influence the outcome of IFN-mediated immune response to HCV infection.

Prediction of lung tumor types based on protein attributes by machine learning algorithms.

Early diagnosis of lung cancers and distinction between the tumor types (Small Cell Lung Cancer (SCLC) and Non-Small Cell Lung Cancer (NSCLC) are very important to increase the survival rate of patients. Herein, we propose a diagnostic system based on sequence-derived structural and physicochemical attributes of proteins that involved in both types of tumors via feature extraction, feature selection and prediction models. 1497 proteins attributes computed and important features selected by 12 attribute weighting models and finally machine learning models consist of seven SVM models, three ANN models and two NB models applied on original database and newly created ones from attribute weighting models; models accuracies calculated through 10-fold cross and wrapper validation (just for SVM algorithms). In line with our previous findings, dipeptide composition, autocorrelation and distribution descriptor were the most important protein features selected by bioinformatics tools. The algorithms performances in lung cancer tumor type prediction increased when they applied on datasets created by attribute weighting models rather than original dataset. Wrapper-Validation performed better than X-Validation; the best cancer type prediction resulted from SVM and SVM Linear models (82%). The best accuracy of ANN gained when Neural Net model applied on SVM dataset (88%). This is the first report suggesting that the combination of protein features and attribute weighting models with machine learning algorithms can be effectively used to predict the type of lung cancer tumors (SCLC and NSCLC).