dataesr/scientific-paragraphs-categorization huggingface dataset:
PRSS1 variants PRSS1 variants (and related references) were derived primarily from the Database of Genetic Risk Factors in Chronic Pancreatitis (will be described as the CP Database throughout the manuscript)  [19, 20]  and complemented by data from the Human Gene Mutation Database (HGMD)  [21, 22]. Three variants were excluded from further consideration: one somatic variant (i. e. , p. G191R identified in four tumors  [23] ; registered in the CP database) and two variants obtained via personal communications (registered in HGMD). Cross-reference examination and keyword search ("PRSS1" plus "variant" or "mutation") in PubMed identified 6 additional variants (Fig. 1 ). Variant nomenclature employed here was in accordance with  [24] ; NM_002769. 5 and GRCh37/hg19 chromosome 7 sequence were used as reference PRSS1 mRNA and DNA sequences, respectively.
Herein, we would like to emphasize two points. First, multiple variant nomenclature errors have been corrected by the CP Database  [20]. Second, NG_001333. 2 (Homo sapiens T cell receptor beta locus on chromosome 7) has been used by many investigators and the CP Database as reference PRSS1 DNA sequence, resulting in alternate names for five common 5' variants (see subsections 3. 2. 1 and 3. 4). Functionality, a prerequisite for pathological relevance, is closely bound up with the type of variant or the variant's location within the genomic sequence of the gene in question. This is of particular importance for classifying PRSS1 variants because both GoF and GoP variants are of pathological relevance whereas loss-of-function (LoF) variants are benign in relation to CP  [10]. We therefore classified them by variant type and pathological mechanism within the framework of our recently proposed seven-category framework (Fig.
Clinical genetic data Clinical genetic data refer to whether or not the variant in question has been found in subjects with CP or other diseases, or in controls. Data from some original reports were reinterpreted according to our working definitions of HCP, familial CP (FCP) and idiopathic CP (ICP), in line with our previous publications  [17, 26]. For variants that were reported in 3 studies, all studies were cited. For variants that were reported in >3 studies, only the first three publications or the first publication plus two other selected publications were cited along with the CP Database  [20]  (N. B. the CP Database collated comprehensively the related original reports and extracted the numbers of CP carriers and non-CP carriers for each variant). For any variant, a publication that possibly overlapped with a prior one from the same group (as annotated by Ref.
Results A total of 100 distinct germline PRSS1 variants, all reported in peer-reviewed papers, were derived from a combination of data from the CP Database, HGMD and literature search (Fig. 1 ). Mindful of the existence of two discrete pathological mechanisms underlying CP-related PRSS1 variants, these were divided into five subcategories for classificatory purposes (Fig. 1 ). Before going into the details, we reiterate that some PRSS1 variants have so far only been reported in subjects with diseases other than CP; these variants were classified with respect to their relevance to CP rather than the non-CP diseases.
Uncommon 5 0 variants Except for c. -338T > G, which was identified in 3 of 65 Chinese patients with pancreatic cancer but without pancreatitis  [54] , all six uncommon 5' variants have been reported only once, either in CP patients or controls (Table  3 ). Herein, it should be noted that whereas c. -338T > G is very rare in combined gnomAD populations, it occurs as a low frequency variant in the Japanese and Korean populations according to their respective population-specific databases (i. e. , 0. 01468 in accordance with the 8. 3 K Japanese Population Reference Panel (8. 3KJPN) project and 0. 0109 in accordance with the Korean Reference Genome Database (KRGDB); Table  3 ). Four variants (c. -184G > A, c. -173C > T, c. -147C > T and c. -30_-28delTCC) were subjected to a promoter reporter gene assay but only c.
454 þ 157G > C in the original reports  [92, 93]. g These variants were incorrectly described as exon 3 variants in the original report and were identified in the same patient  [94]. Whether the two variants were in cis or in trans is unknown. h These two variants were detected together. They are most likely to have been generated simultaneously  [100]  given their occurrence at adjacent positions and their absence in gnomAD. i The two variants were annotated here to be in cis since (i) they are in complete LD (R 2 ¼ 1. 0) using the LDpair Tool  [36]  in the context of all populations and (ii) they were detected together in all six carriers. predicted to encode a product comprising only 19 amino acids (Supplementary Fig. 3 ), is likely to be subject to significant degradation by nonsense-mediated mRNA decay  [57].
00006362 (0. 0002613, South Asian) Benign a Data from some original reports were reinterpreted according to our working definitions of HCP, FCP and ICP  [17, 26]. b Most of these data were taken from Masson et al. [10]. Segregation data were additionally provided for variants reported in single HCP families. c For variants reported in >3 unrelated studies, only the first three publications or the first publication and two other selected publications were cited along with the CP Database  [20]. d In accordance with gnomAD v2. 1. 1  [35]. low penetrance, de novo). It should also be emphasized that in silico prediction tools tend to have relatively poor overall performance in relation to missense variants  [63]. Their utility would be even more limited for the prediction of functional consequences of PRSS1 variants owing to the existence of two possible pathological mechanisms.
First, it employed in silico tools to aid the classification of 5 0 and 3', nonsense, silent and intronic variants but not missense variants. This decision was made on the basis of balancing the biological plausibility of a given type of variant being pathologically relevant to CP or not with the reliability (and feasibility) of the in silico prediction tools on the other. For example, SpliceAI is a highly accurate tool for predicting the potential effects of single nucleotide substitutions or small indel variants on splicing. Therefore, a negative prediction for any silent or intronic PRSS1 variant strongly supports a "benign" classification. Even in the case of a positive prediction (i. e. , predicted to affect splicing), from a mechanistic viewpoint, the aberrantly spliced transcript would most likely lead to a LoF rather than a GoF or GoP.
Financial support This work was supported by the Institut National de la Sant e et de la Recherche M edicale (INSERM), the Association des Pancr eatites Chroniques H er editaires and the Association Ga etan Saleün, France and the National Natural Science Foundation of China (no. 82120108006 [Z. L. ]). N. P. received a one-year visiting PhD student scholarship from the China Scholarship Council, the Ministry of Education of China (no. 202006190267). The funding sources did not play any role in the study design, collection, analysis or interpretation of the data or in the writing of the report. Author contributions E. M. and W. B. Z. contributed to the study design, data collection, performed variant classification and contributed to manuscript writing. N. P. collected data and performed variant classification.
Acknowledgments We thank  Virginie Dangles-Marie ,  Marie Dutreix ,  Carsten Janke ,  Magda Magiera ,  Michael Hahne ,  Nathalie Berthault  and  Isaac Dumoulin  for advice and stimulating discussions. We also warmly thank  Magda Magiera  for careful reading of the manuscript. We thank  Isabelle Grandjean  for providing us with PGK-FLPeR and Pgk-cre mice and for her invaluable help. We thank all our colleagues from the  Animal facilities Platform  ( Institut Curie ) for their advices and rigourous work. Funding: This work was supported by  Fondation ARC pour la Recherche sur le Cancer  (https://www. fondation-arc. org/), Ligue contre le Cancer, Comité de l'Essonne  (http://www. ligue-cancer. net/), Institut Curie  (http://curie. fr/), Centre National de la Recherche Scientifique  (http://www.
| Data collection The general characteristics of the patients were extracted from the medical records and listed in Table  3. Operating time was defined as the onset of GA to the end of surgery, that is, included the time of hookwire placement. This was recorded by two independent readers unaware of patient's group allocation and the other reader's findings. The data were retrieved from two data sources: Qbloc surgical process management software® (Evolucare) and the anesthesia records. In case of discrepancies in operating time between the two sources, the shorter time was selected for each of the two groups H+ and H-. Relevant data on intra-and postoperative complications and histological findings were retrieved from the clinical medical records.
The human gut microbiome is a complex ecosystem holding promises as a target for human health. Recent studies have shown that environmental and host factors explain less than 20% of the variation in microbial composition  [1] [2] [3] [4]  , suggesting significant roles for stochastic factors and ecological rules in gut microbiome assembly (reviewed elsewhere  [5] [6] [7]. A factor known to influence species distribution and diversity of ecosystems is the history of community assembly  8. Indeed, members' appearance order in the ecosystem influences the evolution of the ecosystem, a phenomenon known as the priority effe, which has been studied in animal models  9, 10  and in vitro  11  but is underexplored in human due to the lack of datasets that are both large-scale and longitudinally dense.
Unsupervised partitioning of the human gut microbiome across lifespan and populations In the absence of large public data from single cohorts with reduced technical variability, pooled public data are increasingly used to study gut microbiome ecology at various taxonomic levels. We thus exploited the public "curatedMetagenomicData" (CMD, version 3) database  26  of 18,726 fecal metagenomes, including 86 studies across the lifespan, as well as different populations and health conditions (Supplementary Fig. 1a ), with inherent large variations in technical parameters. To identify partitions of the human gut microbiome, i. e. , possible ecological states, we used the Dirichlet Multinomial Mixtures (DMM) partitioning method since it was extensively used in prior attempts in gut microbiome studies  18, [28] [29] [30] [31] [32] [33].
2 ) suggesting that low abundant genera introduced noise in partitioning and reduced the number of partitions. Despite library size variations, 84. 7% of samples retained more than 80% of reads when filtered to contain only the most abundant 30 genera. We kept this dataset representing 87. 1% of total reads for further analyses to delineate a wider variety of partitions. Second, we identified 24 partitions using DMM on five CMD subsampled training sets of 3233 metagenomes obtained by combining selections of 30 samples for each sex, age category, and region of birth to balance age categories and alleviate the overrepresentation of North Americans and Europeans in CMD (Supplementary Fig. 1b ). For up to 100 possible partitions (k = 1-100) and using BIC and Laplace's index as goodness-of-fit measures, a consensus of all five subsampled sets resulted in 24 partitions, referred to as DMM(k24) (Supplementary Fig.
3 ). The DMM(k24) partitions were more homogeneous (based on theta index, see methods) than DMM(k1) (i. e. , no partition scenario), except for partitions m18, m19, and m22 (Supplementary Fig. 4a ). Next, 81% of the samples that were left out during subsampling were then classified into a partition with >80% confidence when using the DMM(k24) model (Supplementary Fig. 4b ). Partitions m4, m10, m12, m14, m15, m16, m17, m23, and m24, which showed significant alpha or beta-diversity differences between the training and remaining set, were over-represented in the training set compared to the remaining set (Chi-square test, p < 0. 05). The whole DMM(k24) set, comprising all samples, remained homogeneous by partitions, as indicated by the few alpha (Shannon index) and beta diversity (Jensen-Shannon Distance) differences with the remaining set of samples not used for DMM modeling (Supplementary Data 1).
Human gut microbiome local partitions are ordinated within global branches We intended to characterize those partitions further. We observed an average variation of up to threefold in the Shannon diversity index across the partitions (Fig. 1a  and Supplementary Data 2) but did not find any significant correlations between the top five genera with the highest cumulative weight contribution in the model component and alpha-diversity (Fig. 1b ). Here, weight contributions to the DMM model are related to genera relative abundance. Of note, two of the least diverse partitions, m19 and m22, were enriched in infant samples (Fig. 1c  and Supplementary Fig. 5 ). We estimated associations between each genus's respective alpha weight in the DMM model, and Shannon estimated alpha diversity of gut microbiome partitions, using Spearman correlations.
We applied the Potential of Heat-diffusion for Affinity-based Trajectory Embedding (PHATE) algorithm to the CMD dataset to further explore this possibility. PHATE is a visualization method conceived to discover latent structures, such as transitions, in high dimensional data while conserving global and local structures of the data, which has previously been applied to gut microbiome data to detect "branches"  27. The PHATE analysis resulted in several branches (Fig. 1d-h ), i. e. , continuums of microbiome configurations connecting local ecological states, on which we mapped the centroids of the partitions previously identified. The 24 partitions were distributed along branches indicating congruence between two independent approaches (DMM-based partitioning and PHATE-visualization). Metagenomes and DMM(k24) partitions were sorted similarly following Shannon's alpha diversity gradient along each branch, with the least diverse partitions at the tips of the branches (m19, m16, m24, m21) (Fig.
Global and local partitioning can be replicated in the 16S rRNA dataset of the American Gut Project Given the large amount of available 16S rRNA gene amplicon data with associated metadata in public databases, we reasoned that confirming the validity of our approach on a 16S rRNA dataset would facilitate further investigations aiming to decipher the links between branches or partitions with health and environmental factors. Thus, we applied the same partitioning and ordination approach to the American Gut Project dataset (see methods), which consists of ca. 16,000 16S rRNA gene amplicon sequencing-based fecal samples associated with multiple demographic, lifestyle, health, dietary variables (Supplementary Fig. 1b ), and which includes longitudinal sampling from a subset of individuals. Using DMM-based modeling at the genus level (Supplementary Fig.
10a ), the best model fit in the AGP database was obtained with 19 partitions. Similar to the CMD partitions, we showed high intrapartitions homogeneity (except for partition 19) (Supplementary Fig. 10b ) based on high prediction confidence (Supplementary Fig. 10c ) and consistent Shannon alpha diversity between the training and remaining sets (Supplementary Fig. 10d ). We further sought to verify whether AGP partitions were analogous to CMD's. Because the AGP cohort is mainly composed of adult samples (>18 years with prevalence >90%) (Supplementary Fig. 1 ) and is less heterogeneous in terms of analytical parameters, population demographics (age, country of residence), we reasoned that the CMD adult-associated partitions should capture AGP partitions. We compared the partitions between the two datasets using a hierarchical clustering approach (based on common dominant genera (Fig.
2 ). We did not observe database-specific clustering and instead noticed the pairing of partitions originating from the heterogeneous (i. e. , CMD) and single cohort (i. e. , AGP), suggesting that analogous partitions could be retrieved regardless of the cohort and technical parameters used (Fig. 2a ). Then, we measured the Jensen-Shannon distance between DMM-based partitions alpha weights from both datasets. There was no significant difference between CMD and AGP datasets (PERMANOVA, p > 0. 05). However, we detected an Akkermansia-enriched branch consisting of a single partition (M13) in the AGP dataset (Fig. 2a ) that was not detected in the CMD dataset. Then, we generated a PHATE-map of the AGP dataset, which resulted in the generation of three main branches. Similar to the CMD dataset, Shannon's alpha diversity decreased towards the tip of the three branches (Fig.
Local partitions and branches exhibit varying levels of stability The ordination of local partitions within global branches prompted us to hypothesize that those partitions represent different states of ecological succession, which we tested with longitudinal data. We analyzed data originating from 745 participants of the AGP cohort, from whom at least two samples were collected over time with 12. 5 (IQR [1. 1-73. 0]) days apart on average. We assigned each of the associated 2,998 gut microbiome profiles to a partition and a branch and reported the changes between two-time points (Supplementary Fig. 11 ). Individuals assigned to Bacteroides and Prevotella branches tended to remain in their initial branches over time while individuals assigned to Clostridiales or Akkermansia branches tended to transition to Bacteroides and, to a lesser extent, to Prevotella branches.
Global and local variations are differently associated with host and environmental factors Last, we interrogated the AGP dataset (ca 16. 000 fecal samples, see methods) to identify associations between branches/partitions and factors relating to the host including dietary habits, lifestyle, region of birth, age, BMI, bowel movement frequency, sex, diseases and antibiotic history). Given the high dimensional nature of the data, we fitted a multinomial logistic regression across 100 predictors (i. e. , factors) collected through the AGP main questionnaire (FDR < 0. 1) (Supplementary Data 4). To gain insights on factors that could explain the decreased diversity of the communities along the branches, we used the most central and diverse partition (M1) as the reference in this logistic regression, which was primarily composed of female participants consuming vegetables in high frequency (daily), with low exposure to antibiotics (Fig.
Furthermore, host-associated predictors model weight variations appeared to be specific to branch. For instance, along the Prevotella branch, from root to tip, we observed a model weight increase for BMI and a model weight decrease for age, while along the Bacteroides branch, we observed a model weight increase for bowel movements per day (Fig. 4d ). We also observed specific association with branches in a global manner that were not associated with partition position within branches. Specifically, sex (being male), region of birth (e. g. , Australia & New Zealand, central and southern Asia) had a high coefficient weight for predicting partitions in the Prevotella branch, lung disease, and exercise frequency were most specific to the Bacteroides branch (Fig. 4d ). Hence, the ability to predict a partition from AGP metadata was associated to overall observed branches structure.
Multiple studies have reported different microbiota states, using either supervised (alpha-diversity) or unsupervised (enterotypes) analysis. Using Dirichlet multinomial mixture models (DMM), ten states have been identified in a study of infants with developing gut microbiome from birth to four years  30  , while (2-5) are typically detected in adults  28. By contrast, Lathi et al. identified 32 gut microbiome partitions using specific, dominant features in adults  36  with the same approach we also used in our study. Then, while most studies analyze partitions independently, we further mapped them into branches, using a recently developed approach, PHATE, a non-linear dimensionality-reduction method that accounts for both local and global structures in a dataset  27. Using a single (AGP) or multiple cohorts (CMD), we showed that similar numbers of partitions converge into two previously identified configurations (Bacteroides and Prevotella enriched) in adults and are centered around Clostridiales-enriched partitions, which were also enriched in Bifidobacterium, and characterized by a higher health index  35.
The main difference between the two datasets was the detection in CMD of some partitions exclusively detected in infants, which were enriched in aerotolerant genera or Bifidobacterium, a common marker of the gut microbiome in early life, which is consistent with the underrepresentation of infants in the AGP dataset. Notably, multiple branches were detected in infants, which is consistent with a higher intersubject gut microbiome variability observed in this population compared to adults  39. Therefore, using complementary approaches, we identified various microbial states connected within branches, which differed in microbial composition, function, and potentially health status. Previous studies identified a low-diversity Bacteroides microbiome state associated with obesity and systemic inflammation  18, 19  , which may be reflected in our study by the Bacteroides-enriched partition with the lowest diversity and associated with higher disease prevalence.
This hypothesis was supported by a network analysis performed without a priori knowledge (without coordinates of partitions) on a large dataset of subjects with time-series data (>700 subjects), which showed that indeed, partition switches occurred most frequently between neighboring partitions. We confirmed previous findings of a low rate of switching between the Bacteroides and Prevotella branches  25, 28, 41. Some partitions were more prone to shift to a specific partition over time, that were non-random inter-partition switches. This result suggests partitions may be connected through low-entropy ecological paths, which deserves further exploration with controlled time series studies. In addition, specific partitions appeared more stable than others, implying that some partitions may be stable ecological attractors while others would be transient states of the human gut microbiome.
Specific associations were found within branches such as the association between the lowest diversity partition of the Prevotella branch with specialized diets. In line with our results, associations with diet were found to be partition-specific in a previous study  1  , suggesting that insight into more refined structures allows for the identification of associations that may be obscured at the global level. Integrating partitions within a branch allowed us to better disentangle both microbiome-environment and microbiome-host associations. Our study has several strengths, including the large size and highly heterogeneous population, which allowed us to uniquely partition highly variable gut microbiomes, and reproducing the findings from a heterogenous dataset to a homogeneous one. Furthermore, we showed the use of complementary and independent approaches to partitioning the gut microbiome by using both large cross-sectional (n > 10,000) and longitudinal data.