dataesr/scientific-paragraphs-categorization huggingface dataset:
ESR2 Estrogens control development and cell differentiation by binding and activating their nuclear receptors, estrogen receptor α (ESR1) and β (ESR2). Mice lacking Esr2 exhibit both male and female infertility with no evidence of DSD  (132) (133) (134). Consistent with these observation, a heterozygous missense variant (p. K314R) in ERS2 was identified in a 46,XX 16. 5 year-old-girl who presented with primary amenorrhea  (135). Clinical evaluation revealed streak gonads, absent puberty, no breast development, infantile uterus and osteoporosis. This variant is absent from public databases and it is predicted to impair the interaction of ESR2 with nuclear coactivator 1 (NCoA1). Functional studies showed that the variant significantly impaired ESR2 signaling and exhibited dominant negative activity over the wild type  (135).
A heterozygous 3bp deletion resulting in the loss of a single amino acid p. Asn181del within the DNAbinding domain was found by exome sequencing. This variant has an allelic frequency of 3:2000 46,XY men of South Asian origin (https://gnomad. broadinstitute. org/variant/14-64735621-CATT-C?dataset=gnomad_r2_1). Although rare, this allelic frequency in healthy 46,XY men excludes this variant as the cause of the phenotype. The second case was a XY female with clitoromegaly, urogenital sinus and absent uterus. Since FSH and LH levels were within normal range rather than elevated it suggests that the individual may have had androgen insensitivity. Information on the gonads or gonadal hormones was unavailable. This girl carried a rare heterozygous missense variant p. G84V. No somatic anomalies were observed.
Heterozygous carriers are healthy. Data from the gnomAD database indicates that HMCS2 is not a conserved gene in human populations (pLI =0) unlike other genes known to be involved in testis determination, which are intolerant to variation (e. g. DMRT1, SOX9, NR5A1). These data do not provide compelling evidence in favor of causality of HMGCS2 variants in human DSD.
Conclusions The widespread availability of exome and genomic sequencing is revolutionizing our understanding of the genetic causes of rare congenital disorders, including DSD. However, the generation of large genomic datasets also raises questions concerning the interpretation of the data. The first point to consider is the interpretation of variants in genes that are well-established as a cause of DSD. Aside from in silico tools that can provide information on the effects of a variant on protein function, the interpretation of these variants can be aided by simply determining their allelic frequencies in publicly available population genetics datasets. Using population genetics data is a very powerful tool to exclude the possibility that a gene or a specific variant is responsible for DSD.
There is a need to consider the wealth of human population genetics data that is now available for different populations worldwide, which often includes the karyotype of the individual carrying each variant. This data can be rapidly used to exclude either a gene or specific variants as causal (e. g. ESR2, MAMLD1). Databases such as GnomAD also offer tools that determine if a variant is likely to be pathogenic based on observed population genetics data. Determining the ancestry of the DSD family therefore, is essential for the interpretation of the population genomic datasets. In parallel with this, for genes that are established to cause DSD such as MAP3K1 and MAMLD1, there is a need for simple, robust and biologically relevant functional assays to determine variant pathogenicity. In the absence of functional data the contribution of missense variants in genes such as MAP3K1 and MAMLD1 will continue to be classified as VUS.
A good example of where functional studies can aid interpretation of pathogenicity are missense variants reported in GATA4, where variants located within the N-terminal ZF are pathogenic and those outside are not pathogenic  (21, (70) (71) (72) (73). Variant interpretation will be improved if variants that are identified in routine diagnostic screening are made available publicly through databases such as ClinVar (https://www. ncbi. nlm. nih. gov/clinvar/), which reports the relationships between human variations and phenotypes, together with supporting evidence. As more DSD variants are added to such publicly available databases, not only will the accuracy in the interpretation of pathogenicity of such variants improve but also genotypephenotype relationships may be established. The second point to consider is growing number of new genes which are reported to cause DSD.
Funding Sources This work is funded in part by a research grant from the  European Society of Pediatric Endocrinology , and by the  Agence Nationale de la Recherche  ( ANR ),  ANR-10-LABX-73   REVIVE ,  ANR-17-CE14-0038-01 ,  ANR-19-CE14-0022  and  ANR-19-CE14-0012.
A trend towards a slowing of clinical decline measured by the ALS Functional Rating Scale-Revised (ALSFRS-R,  [58] ) was also detected. Importantly, clinical decline was slower in patients with a SOD1 mutation associated to a fast progressing phenotype. This highlights the importance of considering the rate of disease progression in ALS CTs to effectively stratify patients. Despite these positive effects, adverse events (AE) related to the IT lumbar puncture, such as headache, post-lumbar puncture syndrome and back pain, were observed in most participants and CSF pleocytosis occurred in some of them  [57]. To confirm the safety and to evaluate efficacy of Tofersen, a phase 3, randomized, double-blind, placebo-controlled clinical trial (NCT02623699) named VALOR was initiated in May 2019. The trial includes adult patients with confirmed diagnosis of ALS due to all type of SOD1 mutations and with all severity levels but having forced vital capacity > 50%.
ASOs for C9orf72-fALS in clinical trial Similar to SOD1, different groups developed ASOs-based approaches as treatments for C9orf72linked ALS (reviewed by  [50] ). BIIB078 (Biogen), is an ASO that selectively targets expansioncontaining C9orf72 transcripts. Proof-of-concepts studies demonstrated how single, intracerebroventricular (ICV), administration of a C9orf72-ASO produces sustained reductions in RNA foci and dipeptide-repeat proteins and ameliorate behavioural deficits in C9orf72 mice  [59]. A phase 1 clinical trial (NCT03626012) assessing safety, tolerability, PK and the effect on clinical function of BIIB078 is ongoing. This is the first-in-human gene therapy study for this ALS form, which aims to enrol about 90 adult participants carrying C9orf72 mutation and having a diagnosis of probable or definite ALS.
The single intravenous (IV) administration of the AAV9-SMN (Zolgensma®, Novartis) in SMA type I patients, was reported as safe and well tolerated  [67] [68]. However, the impact of age, disease severity and progression rate on the treatment were demonstrated  [66, 69] , underscoring the importance of early intervention  [69]. Currently, Novartis is also testing the possibility to treat older patients with SMA type 2 and type 3 by a single IT infusion. This phase 1/2 trial (NCT03381729) is based on pre-clinical work in animals  [70] , demonstrating that direct injection to the CSF mediates higher therapeutic effects with reduced viral load compared to IV delivery. However, pre-clinical pathology findings affecting the dorsal root ganglia, an area that is highly targeted with gene therapy, has led the FDA to put a partial hold on this CT to allow further review.
ACKNOWLEDGMENTS M. G. B. and G. Q. are supported by the  Association Francaise contre les Myopathies (AFM) , the  Association Institut de Myologie (AIM) , the  Sorbonne Université , the  Institut National de la Santé et de la Recherche Médicale  (INSERM). M. C. is supported by the  ANR  grant  N • ANR-19-CE18-0014-01 (Magenta 2020 ).
By identifying a Core transcriptional Regulatory Circuit upstream of the full TRN, CRCmapper  [15]  helps understand how a few TFs only are sufficient to fully reprogram a cell and durably change its phenotype. In addition, ''hierarchy" is sometimes confused with ''connectivity". Indeed, numerous methods for mapping TRNs use ''in-degree", ''in betweenness" or ''centrality degree" as metrics to identify ''hub" genes, which are often considered ''master regulators" of transcription. In-and out-degree degree are respectively defined as the number of edges arriving to a node and the number of edges leaving a node. In a random network, the in-and out-degrees would follow a Poisson distribution, as most nodes would have roughly the same number of links. In TRNs however, the in-degree distribution, is best approximated by an exponential, showing that most genes are regulated by few TFs, and the out-degree, by a power law distribution, showing that most TFs regulate a few genes  [5, 53, 54].
An influence score for dynamic Bayesian networks that attempts to estimate both the sign and relative magnitude of interactions among variables was used in Banjo (Bayesian Network Inference with Java Objects)  [63]. When faced with limited quantities of observational data, the authors found that combining this influence score with moderate data interpolation reduced a significant portion of false positive interactions in the recovered networks. Another Bayesian inference tool, LeMoNe  [64]  infers TRNs using a centroid clustering approach to assign genes and conditions to modules and subsequently assigns a regulatory program to the gene sets. The authors show that reliably detecting condition-specific or combinatorial regulation is particularly difficult in a single optimum, but can be achieved using ensemble averaging.
TF regulatory information Early work using Chromatin Immuno-precipitation (ChIP) technology helped progresses in TRN mapping  [74, 75]. Methods integrating TF-gene physical interaction knowledge are particularly interesting compared to approaches based on expression only, because, as mentioned in 2. 2), TF RNA levels are not very good surrogates of their activities. One of the first tool integrating TF regulatory information is RegulonDB  [34]. The latest version of this well-maintained relational database including TF-genes interactions information in E. coli, RegulonDB v9. 0, integrates interactions with small RNAs  [28]. Another pioneer algorithm to infer TRNs by using TF regulatory information is the GRAM algorithm  [26]. It was applied to 106 TF ChIP-Chip data and 500 expression profiles to construct a genome-wide TRN in yeast Saccharomyces cerevisiae (S.
cerevisiae). Other methods, such as DISTILLER (Data Integration System To Identify Links in Expression Regulation)  [35] , combine expression data with motifs, ChIP-chip or ChIP followed by sequencing (ChIP-seq) data, to search for co-regulated modules. Supervised (or semi-supervised) methods, such as SEREND (SEmi-supervised REgulatory-Network Discoverer)  [36] , DeMAND (Detecting Mechanism of Action by Network Dysregulation)  [37]  and SIRENE (Supervised Inference of REgulatory NEtworks)  [33]  have also been developed to integrate expression with TF binding information. The first two use models based on logistic regression to predict the probability that genes belong to the same regulon, and SIRENE splits network inference into multiple binary classification problems for each TF, training Support Vector Machine (SVM)-based classifiers.
Single-cell RNA-seq data Recent advances in microfluidics have opened opportunities for generating single-cell RNA-seq (scRNA-seq) data sets. Computational biology approaches to map TRNs from these data have to adjust to their inherent stochastic variation and sparsity. These approaches are designed to map TRNs at the level of unique cells  [62, [84] [85] [86]  or to help reconstruct dynamic cell trajectories or putative TF order of action  [87] [88] [89]. SCENIC (Single-Cell regulatory Network Inference and Clustering)  [62]  identifies sets of genes that are co-expressed with TFs, using GENIE3  [39]  and the faster variant of it, GRNBoost  [40]. Also, to reduce false positive and indirect targets found with GENIE3, SCENIC performs cis-regulatory motif analysis on each co-expression module, and only modules with significant motif enrichment of the correct upstream regulator are retained.
A recent benchmark was performed on six scRNA-seq network inference methods, primarily developed for bulk RNA-seq, based on their ability to infer similar networks when applied to two independent data sets for the same biological condition  [90]. For networks with up to 100,000 links, GENIE3 resulted to be the most reproducible algorithm and, together with GRNBoost2, showed higher intersection with ground-truth biological interactions. However, even the best performing methods showed reproducibility scores that were below 54%, indicating that further improvements are still needed in the design of network inference methods for scRNA-seq data. Other benchmarks including both methods targeting bulk RNA-seq data and methods specifically designed for scRNA-seq data  [91, 92] , also concluded a rather lack of accuracy of these methods, even for approaches specifically developed for single-cell data, and that methods that do not require pseudo-time-ordered cells are generally more accurate.
Chromatin conformation Recent evidences suggest a reciprocal interplay between finescale 3D genome structure and transcription  [96]. 3D structure high-throughput methods such as chromosome conformation capture (Hi-C) or Chromatin Interaction Analysis by Paired-End Tag sequencing (ChIA-PET), have revealed Topologically Associating Domains (TADs)  [97, 98]  and insulated neighborhoods  [99]  mediated by specific proteins, such as CTCF, for insulation and regulation of transcription. ChIA-PET is a method enriching for chromatin interactions involving a specific protein. It was used to examine RNA polymerase-II-associated chromatin interactions in human cells  [100]. Interacting genomic sites were defined as nodes of a TRN and connected as per their connectivity in the ChIA-PET data set. The resulting graph consists of a giant network, organized into chromatin communities (Fig.
Imputation may result in increased homogeneity, especially for marks that are highly cell-type specific, and is less accurate for broad chromatin marks, such as H3K27ac, than for ChIP-seq data with sharp peaks. Rather than providing clear transcriptional regulatory circuitry maps for a large number of cell-types, the major interest of this study is to predict causal variants and traitrelevant tissues. Resolution of such resource should soon be improved with increasing availability of single-cell datasets and more tissue types or environmental perturbations related samples.
Enhancer information Enhancer information was recently used to anchor maps of Core transcriptional Regulatory Circuitry (CRC)  [15] , the small network of TFs that was shown to be most upstream of multiple celltype's gene expression programs. In the cell-types in which they have been identified, the TFs forming the CRC bind to eachother's DNA regulatory sequences (Fig. 2L )  [8, 9, 15, 18, 19, 21]. These core TFs also co-bind the regulatory sequences of most cell identity genes in a given cell-type  [15]  and contribute to the formation of super-enhancers (SEs) in the vicinity of their targets, including their own genes  [15, 103]. Since core TFs are not known for most cell-types, ChIP-seq targeting H3K27ac, the best chromatin mark to identify active enhancers  [104] , can be used as a surrogate to identify SEs with the ROSE program  [103, 105].
Predicted CRCs contain already characterized master TFs, protooncogenes, and terminal TFs of signaling pathways, and are largely supported by ChIP-seq data and functional tests in corresponding cell-types  [15]. The CRCmapper program  [15]  has been designed so that users just need to input a ChIP-seq track to get a CRC map of a sample of interest. The program performs DNA motif analysis of the sum of the promoter and the enhancer sequences within SEs (not the full SE) to predict regulatory interactions, uses a graph algorithm to compute all maximal cliques from a graph, and identifies the most representative fully interconnected loop of TFs as the CRC. For a refined analysis, gene expression data, such as RNA-seq, and DNA accessibility data, such as Assay for Transposase Accessible Chromatin followed by sequencing (ATAC-seq), can be passed to the program, to respectively, improve enhancer to gene associations, and reduce the search space for motifs within the individual enhancers that compose a SE.
To best use ATAC-seq information, the corresponding peaks should be passed to the program instead of the peaks output from MACS. CRCmapper was successfully used to map core TRNs in normal  [20, 106]  or cancer samples  [107, 108]. Importantly, as SEs cluster in the cell to form phase-separated condensates, these can be targeted with drugs to rewire transcriptional addictions in cancer  [109] [110] [111].
It is also important to keep in mind when making predictions from specific data sets that chromatin accessibility is cell-type specific and condition dependent  [15, 101]. Finally, reproducibility still often remains a challenge for these approaches  [90]  and integration of multilayers of data asks the question of convergence of the results  [11]  and of some data types being much more informative than others. Multiple technological and analytical developments should help improve current mapping of TRNs. New developments in identification of TF and transcription co-regulator binding motifs  [112, 113] , in indirect TF binding  [114, 115] , and availability of high-quality motif databases  [116] [117] [118] , should allow better resolution of TRN mapping. Improvement in chromatin conformation identification techniques  [119]  may also allow better association of enhancers to the genes they regulate.
Regression, bulk transcriptomics, tested on [39] org/packages/release/bioc/ single-cell RNA-seq html/GENIE3. html GRNBoost Gene Regulatory Networks Boost http://arboreto. readthedocs. Regression, bulk transcriptomics, tested on [40] io single-cell RNA-seq TIGRESS Trustful Inference of Gene REgulation using http://cbio. ensmp. fr/tigress Regression, bulk transcriptomics [42] Stability Selection LiPLike Linear Profile Likelihood https://gitlab. com/ Regression, bulk transcriptomics [41] Gustafsson-lab/liplike Banjo Bayesian Network Inference with Java Objects Source code and simulated Bayesian inference, bulk transcriptomics [63] data are available upon request LeMoNe http://bioinformatics. psb. Bayesian inference, bulk transcriptomics [64] ugent. be/software TWNs Transcriptome-Wide Networks https://github.
com/battle- Bayesian inference, splicing isoforms, bulk [66] lab/twn_tsn transcriptomics NIR Network Identification by multiple Regression ODEs, bulk transcriptomics [67] Inferelator freely available upon request ODEs, bulk transcriptomics [68] GINsim Gene Interaction Network simulation suite http://ginsim. org/ Logical modelling, bulk transcriptomics [70 71] GNA Genetic Network Analyzer http://www-helix. inrialpes. Piecewise linear equations, bulk transcriptomics [72] fr/gna Network Network Deconvolution http://compbio. mit. edu/nd/ Network deconvolution, bulk transcriptomics [73] Deconvolution index. html RegulonDB http://regulondb. ccg. unam. TF regulatory information, relational database, [34 28] mx bulk transcriptomics GRAM Genetic Regulatory Modules TF regulatory information, bulk transcriptomics [26] DISTILLER Data Integration System to Identify Links in TF regulatory information, bulk transcriptomics [35] Expression Regulation SEREND SEmi-supervised REgulatory Network Discoverer http://sb.
org/#scenic Single-cell RNA-seq, based on GENIE3 and [62] Clustering GENEBoost SCINET Single-Cell Imputation and NETwork https://github. com/ Single-cell RNA-seq and a reference global [85] construction shmohammadi86/SCINET interactome PIDC Partial Information Decomposition and Context Single-cell RNA-seq, information theory, bulk [86] transcriptomics, partial information decomposition SCNS toolkit Single-Cell Network synthesis http://scns. stemcells. cam. ac. Single-cell RNA-seq, boolean logical rules [87] uk/ SCODE scRNA-seq performed on differentiating cells by https://github. com/ Single-cell RNA-seq via regulatory dynamics [88] integrating the transformation of linear ODEs hmatsu1226/SCODE based on ODEs and linear regression CSHMM-TF Continuous-State Hidden Markov Models TF https:// Single-cell RNA-seq and TF-gene interaction, [89] github.
com/jessica1338/ Continuous-State Hidden Markov Models CSHMM-TF-for-time-series- scRNA-Seq MARINa Master Regulator Inference Algorithm http://califano. Differential expression and protein-protein [93] c2b2. columbia. edu/marina- interactions license GNAT http://mostafavilab. stat. ubc. Bulk transcriptomics using hierarchy of tissues, [95] ca/gnat/ Gaussian Markov Random Fields CRCmapper Core transcriptional Regulatory Circuitry https://github. com/ mapper ViolaineSaint-Andre/ CRCmapper.
Patient Cohort We conducted a monocentric, observational, retrospective study based on prospectively collected data at the French referral centre for CA (Henri Mondor Hospital, Creteil, France). All patients with confirmed amyloidosis (light chain (AL), hereditary transthyretin (ATTRv) or wild-type transthyretin (ATTRwt)), with cardiac involvement and available echocardiographic data were included. Biomarkers, multimodality imaging, and biopsies when required were used to confirm the diagnosis as previously described  [10, 11]. Patients gave informed consent for anonymous publication of scientific data. This study complied with the 1975 Declaration of Helsinki and was approved by our local ethics committee (Créteil) and the French Comité National de l'Informatique et des Libertés (CNIL number 1431858).
Echocardiography Evaluation Echocardiograms were performed and analysed by cardiologists blinded to clinical data using the Vivid 7 system (GE Vingmed, Horten, Norway). LV dimensions and interventricular septal thickness (IVST) were measured according to ASE recommendations from the parasternal long-axis view and were used to calculate LV mass index (LVMi)  [9]. LV ejection fraction (LVEF) was calculated using the Simpson's biplane method. Early (Em) and Late (Am) transmitral diastolic peak flow velocities were measured with pulsedwave Doppler recordings, and diastolic septal and lateral mitral annulus velocities with tissue Doppler imaging from the apical four-chamber view. Their mean (e') and the ratio between early diastolic mitral peak flow velocity and e' (E/e') were calculated. Mean peak systolic velocity was calculated from mean lateral and septal peak systolic velocity (s').
Left atrial volume was calculated using 2D four and two-chamber apical views. Tricuspid regurgitation systolic jet velocity was measured with continuous wave Doppler. LV peak systolic global longitudinal strain (GLS) was computed offline from the standard three LV apical views using 2D speckle tracking analysis through automated function imaging (AFI, EchoPAC version 203, GE healthcare). Patients with missing data on echocardiograms, significant mitral valve disease or prosthetic mitral valve were excluded from analysis.
15 in univariate analysis were included in a multivariate logistic regression model. We assessed collinearity between IVST and LVMi, and between LVEF and GLS and only included IVST and GLS in the multivariate analysis model. p values less than 0. 05 were considered statistically significant. Uni-and multivariate analyses were performed globally, and for each CA type (AL, ATTRv, and ATTRwt). Missing data were handled using case-complete analysis. Cumulative event curves were plotted using the Kaplan-Meier survival method according to diastolic function grade. Differences between curves were tested by log-rank analysis. Survival was censored at last contact with the patient. Ten patients were excluded as they were lost of follow-up after the first diagnostic assessment. Statistical analyses were performed using STATA software version v14.