dataesr/scientific-paragraphs-categorization huggingface dataset:
To obtain insights on how different cell types mediate interactions among pathways we applied a force-based visualization technique. First, we built a bipartite network with two node types for stromal cells and relevant molecular processes and edges weighted by the interactions described above. Second, we run a standard under-damped dynamic with elastic attraction and electric repulsion among nodes to find their equilibrium configuration. In order to characterize ctrl cluster 2 into sub-clusters of genes enriched in specific BM endothelial functions a database from Langen et al. (  2017 ) was downloaded and compared against genes present in cluster 2. This dataset divides endothelial cells into three main categories, sinusoidal endothelium cells (type L), specialized capillary subtype (type H), and specialized endothelial cell subtype (type E).
A heatmap was obtained using the pheatmap package on R with the option ward. D2 as a clustering method on row scaled data. To characterize genes present in ctrl cluster 1 specific to MSC functions, we performed a meta-analysis in which gene expression of leptin-receptor-positive (Lepr+) mesenchymal stromal cells from  Tikhonova et al. (2019)  were joined to the gene expression of Nes low , Nes high , and Ng2 samples from our study. All datasets were joined in a matrix and normalized together using DESeq2. Normalization and regularization were done as described before obtaining gene expression data for the different cell types. Comparisons were done for those genes present in cluster 1 and a heatmap was performed using the heatmap package on R with the option ward. D2 as a clustering method on row scaled data.
Secretome analysis Data was analyzed using the Bioconductor version 2. 13 (http://www. bioconductor. org) running on R version 3. 0. 2 (available from www. R. project. org). Protein intensity values were quantile normalized and log2 transformed. Differences in protein expression between groups was assessed using an empirical Bayes moderated t-statistics test from the ''limma'' package. P values were adjusted.
Resource for multiple testing correction using the Benjamnini-Hocberg method. PCA was made by using the expression matrix of normalized and regularized genes using the prcomp function on R. The results were visualized on a 3D map using the plot3d function from the rgl package on R. Ligand-receptor pairs database was downloaded from  Ramilowski et al. (2015)  and compared against protein expression of the secretome from somascan arrays and gene expression from the statistically significant comparisons of AML versus ctrl RNaseq. Two lists of pairs for each cell type were obtained with the secretome as ligand and cell gene expression as receptor. To generate the dynamic figure, we used ligand-receptor pairs which were found coherently up or down-regulated in any of the stromal types. We constructed a bipartite graph representing cell-specific ligand-receptor connectivity and used force-driven dynamics on the network nodes to visualize the structure.
ACKNOWLEDGMENTS The authors would like to thank members of the  Biological Resource Unit, Flow Cytometry, Computational Biology, Proteomics  and  Advanced Sequencing  core facilities at the  Francis Crick Institute  for their valuable help. We would like to thank  Mrs. Shohreh Beski  ( Obstetric Department, Royal London Hospital, London, UK ) and her team as well as  Mrs. Jashu Patel  for helping in the collection of umbilical cord blood. We are grateful to the  ASH-EHA TRTH joint program  for the valuable help in supporting and improving the project. D. P. was supported by a non-clinical research fellowship from  EHA. This work was supported by  The Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001045 ), The  UK Medical Research Council ( FC001045 ), and the  Wellcome Trust  ( FC001045 ).
Cost reduction of high-throughput technologies has enabled the monitoring of the same biological sample across multiple omics studies and multiple timepoints. The goal is to combine longitudinal multi-omics data to detect temporal relationships between molecules and interactions between omics layers. This can finally lead to uncover new regulation mechanisms and interactions that could be responsible for causing complex phenotype or disease. However multi-omics integration of diverse omics data is still challenging due to heterogeneous data and designs. Moreover, interpretation of multi-omics models is the key to understand biological systems. We propose a generic analytic and integration framework for multi-omics longitudinal datasets that consists of multi-omics kinetic clustering and multi-layer network-based analysis.
Introduction Cost reductions of DNA sequencing in addition to other high-throughput multi-omics technologies have revolutionized many research fields ranging from personalized medicine  [6, 12]  to systems biology  [10, 45]. These innovations have led to new biological insights and a better understanding of living organisms  [15, 25, 41]. Thus, enabling the assessment of most biological layers, this democratisation of highthroughput technologies has created large datasets representing different biomolecules that necessitate specific processing and statistical methods  [14, 32]. Multi-omics trials typically collect different types of biomolecules (mRNA, proteins, metabolites, etc. ) from the same biological samples with the ultimate goal of highlighting the interaction between biological layers that could be responsible for causing complex phenotype or diseases  [12].
However, this does not directly elucidate interaction across multiple omic layers  [49]. To connect these layers, a first approach require prior knowledge of across omic molecular networks such as publicly-available databases  [20]. This approach is based on a legacy limited to model organisms and may not reflect the current biological condition. A second method use multivariate data-driven methods that statistically infers correlations between molecules based on multi-omics data. However, this approach may have many possible solutions. A combination of the two methods could improve multi-omics network construction. The ultimate goal of multi-omics networks is to connect phenotypes to biological mechanisms and their regulators. Analysis of the interactome identifies direct neighbors and modules linked to a phenotype.
Data-driven network reconstruction For gene expression data, we relied on Gene Regulatory Network inference. This class of method tries to reverse engineer complex regulatory mechanisms in the organisms and infer relationship between genes. ARACNe  [27]  is a co-expression-based inference algorithm which identify most likely TF -targeted genes interaction by estimating mutual information, a similarity distance between pairs of transcript expression profile. We used ARACNe algorithm on gene expression profile to infer potential TF-targeted genes interaction from the gene expression dataset.
Knowledge-driven network Some kind of interactions cannot be revealed by inference methods and has to be experimentally determined (for example, Protein-Protein interactions, ChIP). We then relied on reviewed interactions found in specialized databases to connect molecules of the same type and also get cross-layered interactions (binding, enzyme, regulation) From the measured molecules in the dataset, we collected all possible interactions through targeted databases. To maximize cross-layered connectivity, we also included nonmeasured proteins or metabolites which were directly connected to measured molecules. Protein Interactions Physical or functional Protein-Protein Interaction (PPI) is one kind of interaction that is difficult to predict and PPI network inference algorithms for MS data are still in their infancy  [51].
For human proteomic data, we relied on the BioGRID database  [5]  which records more than 1. 8 million proteins and genetics interactions from major model organisms. This database collects experimentally determined physical protein-protein interactions and also connects transcriptomics and proteomics layers with regulatory relationship (TF -gene interactions). For other model organisations, we can rely on more specialised or custom databases  [21]. Metabolite Interactions We used the KEGG Pathway database  [19]  which records a collection of manually drawn metabolic pathways representing molecular interaction, reaction and relation networks for human and other model organisms. We used KEGG to link metabolite compounds involved in the same reactions. We also connect metabolites to genes and/or proteins if they are involved in the same biochemical enzymatic reaction thanks to KEGG Orthology database that links genes to high-level functions.
Random Walk As described in  [44] , in an undirected graph G = (V, E), the random walk (RW) starts from a node (v 0 ), called seed, and simulate a particle that randomly moves from one node v t to another v t+1 following the probability distribution: ∀x, y ∈ V, ∀t ∈ N P(v t+1 = y|v t = x) = 1 d(x) if(x, y) ∈ E 0 otherwise, (1) where d(x) is the degree of the node x in the graph G. Valdeolivas et al. also added the possibility to restart at the initial node to avoid dead ends in multi-layered networks. When a steady state is reached, the algorithm gives a probability score to each node of the network which represents the proximity of that node and the seed. We then used the R package RandomWalkRestartMH  [43]  to apply random walk with restart algorithm on multi-omics network with three main purposes to guide interpretation.
We modelled the expression of every molecule with LMMS including the variations of the three replicates for each of the three timepoints. We built multi-omics clusters of expression profiles based on the direction of variation between each step. mRNA and proteins networks were built with ARACNe and BioGrid interaction databases, respectively. Protein coding and TF regulated information were used to connect these layers where UniProtID were converted into Gene Symbols (https://www. uniprot. org/uploadlists/) to ensure proper matching. We also included metabolite reactions from KEGG connected to protein enzymes to metabolites. We performed ORA with mRNA, translatome products and proteins against GO:BP, GO:MF and GO:CC terms for both clusters and entire set of molecules. Finally, we performed RW on this multi-omics network.
Finally, we discarded nontargeted metabolite as they were not annotated and performed longitudinal clustering with multiple block PLS. For network reconstruction, we used ARACNe on genes and TF. We used the Protein-Protein Interaction database for Maize (PPIM)  [53]  to identify protein-coding genes and extracted direct neighbor interactions. PPIM contains more than 2. 7 millions interactions between protein, TF and gene interactions. These interactions are either predicted or experimentally determined from public databases such as UniProt  [7] , BioGrid  [5] , DIP  [46] , IntAct  [31]  and MINT  [24]  or using to text mining. We mainly focused on validated interactions and predicted ones qualified as high-confidence interactions that represents 155,845 interactions with top 5% highest decision scores  [53].
Case study 1: HeLa Cell Cycling study In this example, we studied the HeLa cell cycling from Aviner et al. 1. This dataset was composed of 3 omic layers and 3 timepoints. 2. (C) Multi-omics network layout: represents the connection between entities and their different types of interactions. The network was composed of 4 layers: a gene layer build from mRNA expression, a PPI layer build from measured proteins and BioGRID known interactions, a metabolite layer from KEGG pathways and GO term layer from enrichment analysis.
Pre-processing, modelling and clustering of time profile HeLa cell dataset was assembled into a single table focused on proteins. After missing value removal, this dataset was composed of 6,785 mRNA, 4,102 translation products and 5,023 proteins (Table  1 ). 448 mRNA, 2,672 translation products and 4,295 proteins remained after the Fold-Change filtering step. Finally, once all molecules were modelled over time and noisy profiles were removed, 446 mRNA, 2,318 translation products and 4,237 proteins remained.
Multi-layered network reconstruction The first layer to be reconstructed was the gene inference network from the mRNA. We used the ARACNe algorithm to build a network by kinetic cluster but also to build a entire network composed of all the mRNA. Suppl. Table  1  shows statistics about the sub-networks such as the number of connected / disconnected nodes and edges. The second layer was the PPI network. Proteins were connected to each other using the BioGRID interaction database. As for the genes, PPI sub-networks were built for each kinetic cluster but we also built a entire PPI network composed of all the proteins. In addition, we included BioGRID proteins which were directly connected to the measured ones (first degree neighbours). Number of nodes and edges are detailed in Suppl. Table  1.
These first 2 layers were combined thanks to 2 types of links. First, protein-coding information linked 126 genes to their corresponding proteins. Second, TF-regulated information from TF2DNA and TTRUST databases linked 57 proteins to 403 genes (16,846 interactions). In addition, KEGG pathway database was also used to link protein enzymes to metabolite reactions. This new layer was composed of 1,595 metabolites connected to 2,213 proteins (12,694 interactions).
Pre-processing, modelling and clustering of time profile The maize dataset included a gene expression assay composed of 41,716 transcripts identified using the B73 maize reference genome (AGPv3. 20), with 2,254 transcripts identified as TF-coding genes and a targeted metabolite assay with 45 molecules measured. 1,606 maize genes, 123 TF and all metabolites were differentially expressed between aphid and control groups for at least 1 time points. 331 genes, 36 TF and 29 metabolites were filtered after the 2 Fold-Change filtering step. Once all the molecules were modelled and noisy profiles were removed 1,208 genes, 31 TF and 29 metabolites remained in the exposed group (Table  6 ).
Multi-layered network reconstruction The first layer to be reconstructed was the gene inference network from the mRNA and TF using ARACNe. We used unmodelled filtered data from aphid and control samples to build this first layer. As the first case study, we build sub-network for each cinetic cluster and a global network composed of all the mRNA/TF. Networks composition is detailed in Suppl. Table  4. The second layer was the PPI network. Filtered PPIM database was used to build protein-protein interaction network from measured protein-coding genes. We also included proteins which were directly connected to the measured protein coding genes (first degree neighbours). Gene kinetic cluster subnetworks and global network were extended with PPI interactions. The third network contained metabolic reactions.
). In order to identify cellular mechanisms with multi-omics regulation, we propose to use a combination of both data-driven and knowledge-driven integration methods from longitudinal multi-omic data to highlight interactions not only between molecules of the same biological type but also between omic layers linked to cellular mechanisms. Multi-omics network integration in literature Multi-omics networks are the key to illustrate topological relationships between different molecular species  [23]. The HetioNet network is currently one of the most impressive multi-omics networks. Composed of 18 different types of nodes (genes, SNPs, proteins, compounds, tissues, diseases) and 19 different types of edges between these layers  [13]. Its main objective is to provide a natural representation of the human system hierarchy and to propose genetic-disease association prediction.
Although very comprehensive, some relationships evolve in time and the addition of these dynamic interactions as well as specific expression data should be very valuable to study complex diseases mechanisms. On a smaller scale, MetPriCNet  [48]  is a 3-level network composed of genes, metabolites, diseases and associations between each cross-layer pair. Its objective is to prioritize candidate metabolites for known diseases or phenotypes. Although very useful for therapeutic target prediction, these networks should be connected to expression data to further address the needs of personalized medicine. In another paradigm, OmicsNet  [52]  offers to build customized multi-omics networks from a list of molecules of interest (genes, proteins, metabolites, TF, miRNA). It does not provide association prediction but the connection between the nodes is ensured by several databases (STRING, miRBase, KEGG, ENCODE, etc.
). Its main advantage is to propose in a web browser a user friendly 3D representation of the resulting network. Like other networks, it is specifically designed for the human organism and still too few integration networks target other model organisms. Interpretation guidelines for multi-omics networks: Advantages and Limitations Ranging from expression modelling over time, multivariate clustering to network reconstruction, in the following section we discuss the pros and cons of the choices made at each stage of this methodology. 1. Experimental design: Each experimental design is different, both in type of data, number of replicates, condition and timepoints. Regardless of the normalization step specific to each technology, our previous timeOmics framework can indeed accommodate several design variations to identify highly associated temporal profiles between multiple and heterogeneous datasets.
Network inference: We relied on ARACNe algorithm for gene inference network. More mathematically advanced alternatives exist. [50]  recently applied Gaussian graphical model with graphical lasso to infer gene interactions in 15 specific types of human cancer using RNA-Seq expression data from The Cancer Genome Atlas. Nevertheless, ARACNe is a well-cited, reliable coexpression-based inference method and it provides an easy to use implementation. As mentioned by  [9] , the inference problem is NP-complete and only the combination of the results of several methods would bring us closer to the truth. Moreover, the choice of the inference method can be directed by the type of omics data. With microbiome abundance data, co-occurrence network inference is often applied to identify synergism or competition relationships between species and their environment  [26, 28].
Knowledge-based interaction: The proposed hybrid multi-omics networks were composed of expression data and knowledge links. Therefore, known links can be limited by incomplete databases. In the first case study, we mixed BioGRID, TTRUST and TF2DNA to connect proteins to genes. However, more specialised databases could also be added to include more links. In the second case study, maize is not as well-resourced as humans and PPIM covers all known to date interactions for this organism. In addition, these links represent potential interactions that may append in the studies and the networks are referred to as maps. An additional integration step would consist in constructing a dynamic signal flow where the directionnality and intensity of the interactions would be represented in the form of flows or pathways.
Contribution of network-based integration on public datasets The reanalysis of multi-omics data with new methods can lead to a better understanding of the system. Both cases proposed in this paper are no exception. With the study of HeLa cells during the cell cycle, the authors were able to highlight functional groups, expressed by proteins, which may be over or under-regulated at different phases of the cycle. Combined with the other omics layers, they were able to compare the dynamics of an mRNA, its translation product and the associated protein and thus highlight the regulation mechanism during the cycle. Nevertheless, our methodology, based on network-based multi-omics integration could, in addition to answering the same questions, offer the advantage of going even further. From a key function (Cell division, Chromosome segmentation, Telomere maintenance) it is possible to identify the molecules involved but also to know the events, interactions, regulation, which have allowed its expression.
The analysis of two multi-omics longitudinal studies in two previously published datasets demonstrates that new multi-layered interactions can be unravelled. Eventually, and with highly controlled experimental designs, we expect that dynamic networks can be built to model an entire biological system. Therefore, this methodology will open new avenues for exploration and interpretation from multi-omic studies. Tables  Table 1:  HeLa cell cycling study: Initial number of mRNA, translation products and proteins, and remaining molecules after Fold-Change and noisy modelled profile filtering. A threshold of 2-FC was applied to mRNA and 3-FC to both proteins and translation products. Raw       2. (C) Multi-omics network layout: represents the connection between entities and their different types of interactions.
Therefore, we looked at inhibitor-induced tcDAC2_DIns2 thermal stabilization using the most potent inhibitors characterized in our in vitro inhibition study. All inhibitors tested increased the thermal stability of the enzyme (Figure  S5C ). The highest increase was observed with the pan-HDAC inhibitor QSN (18 C), followed by the inhibitors TB56 and TB75 (13 C). Co-crystallization experiments with all tested inhibitors yielded crystals only in the presence of QSN and TB56. The crystals of both complexes showed good diffraction, and complete crystallographic datasets could be collected at 1. 75 A (QSN) and 2. 3 A ˚(TB56) resolution. Structure determination was performed by molecular replacement using our initial homology model of tcDAC2, and both tcDAC2/QSN and tcDAC2/TB56 structures were refined through manual building and automated refinement (Table  S2 ).
Interestingly, superposition of tcDAC2 with the hsHDAC1/MTA1 (PDB code 4bkx) and hsHDAC3/NCOR (PDB code 4a69) complexes shows that insertion 2, but also insertion 1, would clash with the binding of either MTA1 or NCOR homologs (Figure  4D ). This latter feature, in addition to the absence of an arginine in the tcDAC2 L6 loop and the observed monomeric form of this enzyme, further supports the functional difference between tcDAC2 and the isozymes of the HDAC1-3 family.
Gene targeted deletion mediated by CRISPR-Cas9 Null mutants were also obtained by the CRISPR/Cas9 methodology using a T. cruzi Cas9-GFP expressing strain  (Romagnoli et al. , 2018). For this purpose, sgRNA sequences were designed using the Eukaryotic Pathogen CRISPR guide RNA/DNA Design Tool  (Peng and Tarleton, 2015). DNA templates were generated by using PCR to amplify the sgRNA scaffold sequence from plasmid pX330  (Peng and Tarleton, 2015)  with specific forward primers that contain the T7 promoter and a 20-bp target-specific sequence (Figure  S8 ). The sgRNA was obtained by in vitro transcription using the MEGAshortscript T7 kit according to the manufacturer's instructions. CRISPR-Cas9 mediated knock out was obtained by transfection of 5 3 10 6 Cas9-GFP expressing cells with 20 mg of specific sgRNA using the U-033 program in an Amaxa Nucleofector device as previously described  (Romagnoli et al.