dataesr/scientific-paragraphs-categorization huggingface dataset:
Aim: The human gut Bifidobacterium community has been studied in detail in infants and following dietary interventions in adults. However, the variability of the distribution of Bifidobacterium species and intra-species functions have been little studied, particularly beyond infancy. Here, we explore the ecology of Bifidobacterium communities in a large public dataset of human gut metagenomes, mostly corresponding to adults. Methods: We selected 9. 515 unique gut metagenomes from curatedMetagenomicData. Samples were partitioned by applying Dirichlet's multinomial mixture to Bifidobacterium species. A functional analysis was performed on > 2. 000 human-associated Bifidobacterium metagenome-assembled genomes (MAGs) paired with participant gut microbiome and health features.
Over the last decade, large cross-sectional cohorts (> 1,000 subjects) have been established and studied to disentangle the specific associations between intrinsic and extrinsic factors and the gut microbiome. For instance, the abundance of Bifidobacterium has been shown to depend on genetics, specifically lactase persistence/nonpersistence  [11]  , and dietary factors, such as carbohydrates  [11, 12]. Despite their significant relevance for identifying the major factors underlying gut microbiome variation, some single cohorts may display a lack of gut microbiome variation as a function of health status, lifestyle, age, and taxonomic and functional resolution. The use of public databases containing large amounts of human gut microbiota shotgun metagenomic data spanning different ages, countries, health statuses, and lifestyles has greatly increased in recent years, providing new insight into the association of the gut microbiota with the host, environmental factors, and the reconstruction of metagenome-assembled genomes (MAGs)  [13] [14] [15] [16] [17] [18].
One recent study characterized the strain dynamics, pangenome, and genomic diversity of the main Bifidobacterium species from the human gut in early life with MAGs  [6]. However, functional pangenomic analyses of Bifidobacterium in the adult human gut are lacking. We performed an exploratory analysis of the ecology of Bifidobacterium based on data from a public database containing human gut microbiome data, mostly for adult subjects. We first confirmed the previously reported associations with health, age, and other factors. We then identified Bifidobacterium partitions of the gut differing in terms of the abundance of Bifidobacterium, species composition, gut microbiome features, and health status. Finally, using MAG-based pangenomic analysis, we showed that the prevalence of some functional features of some Bifidobacterium species differed between health-associated Bifidobacterium partitions.
Pooled metagenomic studies dataset We extracted taxonomic data from the curatedMetagenomicData (cMD) R package (Pasolli et al. ) (version 3. 0, release 2021), which consists of manually curated metadata together with all the taxonomic read counts aggregated per species with MetaPhlAn3, for 86 studies (17,959 samples). Gut metagenomes with more than five million reads were retained, and one duplicate study (referred to as "LeChatelierE_2013") was excluded. The read counts for the samples were sum-collapsed by genus. The resulting feature table was rarefied to a depth of 1. 000. 000 counts per sample for alpha diversity analysis. Filtering for origin (stools), with the selection of one fecal sample per subject (highest number of reads), resulted in 9,515 unique samples (61 studies). This dataset was used for a global description of the abundance of Bifidobacterium and the prevalence of Bifidobacterium species across different metadata curated in cMD: age, lifestyle, antibiotic use status, and health status.
Bifidobacterium-based clustering of the gut microbiome Samples were partitioned by applying Dirichlet's Multinomial Mixture (DMM) modeling to the microbiota data  [24]  for 32 detected Bifidobacterium species with counts across cMD. We filtered the 9. 515 datasets as follows to obtain a final dataset relating to 5. 329 subjects for DMM: (1) We retained individuals who had not had antibiotic treatment as declared in the cMD (antibiotic use = no) (N = 216) or without information (N = 3. 571) to prevent bias in the diversity calculation; (2) We excluded subjects with a total count < 500, to overcome Bifidobacterium underdetection issues (N = 19); (3) We excluded subjects with no Bifidobacterium species total reads count as a DMM standard (N = 380). DMM models were calculated for different numbers (k) of clusters, k ∈ [1,30], and evaluated with the Bayesian information criterion (BIC) and five different seeds.
Quality was ensured by selecting the MAGs with completeness > 80%. Bifidobacterium species with at least 30 associated MAGs were selected, giving a total of six species. 820 MAGs were assigned to B. longum, 700 to B. adolescentis, 339 to B. bifidum, 178 to B. pseudocatenulatum, 54 to B. catenulatum, and 34 to B. dentium. Pairwise distances were calculated for all MAGS within each species, with Mash v2. 347 and the default sketch size. Hierarchical clustering was then performed for each species with the "ward. D2" method and the "pheatmap" R package (1. 0. 12).
Analysis of pooled metagenomic studies recapitulates major findings of human gut Bifidobacterium ecology We used the "curatedMetagenomicData" (cMD, version 3) database (Pasolli et al. ) to study the ecology of the Bifidobacterium community in the human gut microbiome. The cMD provides standardized, curated human microbiome data with several pieces of metadata per participant. We selected only gut metagenomes (one per subject) and obtained 9. 515 unique samples [Table  1 ]. This dataset contains predominantly data for adults with a westernized lifestyle. Individuals under the age of 19 years accounted for less than 8% of this dataset [newborns (2. 9%), children (3. 4%), and school-age (1. 4%)] [Table  1  and Supplementary Figure  1]. Given the multiple differences in analytical procedures between the studies included in the cMD, we first investigated whether our analysis of the cMD database could reproduce published findings for Bifidobacterium in humans, such as differences according to age, lifestyle, antibiotic use, and health status.
03%, IQR 0%-0. 2%) than in healthy individuals (median 3. 4%, IQR 0. 6%-10. 7%) (Kruskal Wallis, P < 0. 001) [Supplementary Figure  2 ]. In particular, B. adolescentis was more prevalent in healthy individuals (70%) than in those with the disease, particularly for IBD and metabolic diseases (40%-50%), whereas the opposite pattern was found for B. dentium [Supplementary Figure  3 ]. Finally, the relative abundance of Bifidobacterium was lower in subjects with recent antibiotic intake (Mann-Whitney, P < 0. 001) [Supplementary Figure  2 ]. Despite the considerable analytical differences between studies, this dataset reproduced the major associations previously reported between Bifidobacterium and age  [27]  , lifestyle  [28]  , health status  [1, 29, 30]  , and antibiotic use  [31] [32] [33]  in individual cohorts.
A pangenomic analysis of Bifidobacterium reveals functions associated with gut ecology and health Finally, we investigated whether intra-species functions were associated with Bifidobacterium partitions as a surrogate for a more diverse gut microbiome and a higher prevalence of healthy subjects. We selected 2,263 MAGs constructed from an extensive dataset  [16]  included in the cMD database. Therefore, we could pair participants' Bifidobacterium partitions with their Bifidobacterium MAG content. An analysis of 11,673 unique OGs functionally distinguished MAGs from different Bifidobacterium species on the basis of OGs prevalence (chi-squared test, FDR < 0. 05 within species) [Supplementary Table  2 ]. The most significant OGs were that for asparagine synthase (COG0367), which was detected in 99% of the MAGs assigned to B.
Bifidobacterium is a common member of the human gut microbiome, with different species co-existing in the host at different ages  [36]. A number of studies have shown that various metabolic, immune, and intestinal disease states coincide with the depletion of Bifidobacterium from the gut microbiota  [1]. Here, we studied the variability of the gut microbiome as a function of the resident Bifidobacterium community, using a public database compiling curated metagenomics-based studies, mostly performed in adults from Western countries. We first checked that we could reproduce the previously reported findings of a lower abundance of Bifidobacterium species in antibiotic users  [31] [32] [33]  and individuals with diseases  [1]  or adopting a nonwesternized lifestyle  [28]  , the differential prevalence of most Bifidobacterium species depending on age  [10]  , the high prevalence of B.
longum throughout the human lifespan  [27]  , and the higher prevalence of B. adolescentis in healthy subjects than in those with diseases  [29, 30]. We then studied the variability of Bifidobacterium community composition with the Dirichlet multinomial mixtures (DMM) method, which has been used for gut microbiome clustering on the basis of composition in many studies  [24, [37] [38] [39] [40] [41] [42]. We identified partitions enriched in different combinations of Bifidobacterium species in a database containing predominantly adult data. The partitions that were more prevalent were characterized by a higher abundance of B. longum and B. adolescentis, whereas the partition corresponding to the non-detection of Bifidobacterium was the least prevalent. A specific analysis of three individual cohorts of adults (~1,000 subjects) confirmed the detection of several partitions, indicating an effect of between-subject variability rather than technical differences between studies.
e. , functional variability within species). We studied Bifidobacterium-assigned MAGs retrieved from an extensive dataset for the human gut microbiome  [16]  , which could be assigned to metadata and gut microbiome features. EggNOG analysis confirmed known functional differences between the most prevalent Bifidobacterium species, such as the specificity of alpha-L-arabinofuranosidase to B. longum, involved in the metabolism of arabinans, arabinoxylans, and arabinogalactans  [27, 59]  , glycoside hydrolases of host carbohydrate metabolism (mucin, human milk oligosaccharide) for B. bifidum  [60]  , and glycoside hydrolase GH 43 for B. pseudocatenulatum  [61]. Asparagine synthetase was found to be highly specific to B. adolescentis  [62]. The association between species function and Bifidobacterium partitions revealed a difference in the functional features of B.
48, 49  ). In the case of the eclipsing binary HIP 84586 = HD 155555, we revised the companion mass derived from the PMa taking into consideration that the star is a close binary and summing the masses of the two components. If we consider as significant a PMa detected with a SNR > 3 by Kervella et al. 37  , the following additional targets are likely to have planetary like companions: HIP 560, HIP 10679, HIP 84586, HIP 88399. Figure  2  illustrates the constraints the we can derive for these objects combining data from the PMa, from RUW E, and HCI. The case of HIP 88399 has been described in detail by Mesa et al. 42  that included in their analysis also the information from RVs  38. We may consider that in this system there is very likely a planet at a distance of ∼ 6 au and a mass of ∼ 4 M Jupiter , with uncertainties of a factor of two or less in both quantities.
η Tel is not included in the list of members by Gagne et al. 73  and Couture et al. 17. This is possibly due to the low-accuracy RV of the fast-rotating early type star but the properties of the very wide comoving object HIP 95270 (classified as a bona fide member in all the studies) and of the BD companion η Tel B are fully compatible with membership and we then kept the object in the sample. Companions to these stars were drawn from the following data sets: • Visual companions from Gaia DR3 catalogue  80  (Gaia in Table  3 ). Gaia provide separate entries for objects with separation larger than about 0. 7 arcsec, and it is sensitive down to the substellar regime at separation larger than a few arcsec. We considered companions objects with full (5-elements) astrometric solution within 10 arcmin from each stars, whose relative projected velocity is below the escape velocity • Additional visual binaries were considered from Mendez et al.
Data used are from various sources  18, 40-42, 87, 88. In the future, important contributions are also expected from JWST  89  • Radial velocity variations (SB in Table  3 ). The relevant data were from Trifonov et al. 90  and Grandjean et al. 38. We found no data in the Amber catalogue based on FEROS data  91. We also examined the RVs measured by Gaia  92  that are available for 15 stars. All these stars have variable RVs, but in most cases the RV variations can be explained by pulsation and/or activity. • HIP 84586 = V824 Ara = HD 155555 is a renowned eclipsing binary (EB in Table  3 ). The adopted solution is from Tokovinin  82. We also inspected the time series in the TESS archive that are available for 15 of the stars but we did not find evidences for additional companions. The parameters for the substellar companions were obtained as follows: 51 Eri b  51, 93  ; AF Lep b  24  ; β Pic b and c  52, 53, 94, 95  ; HIP 88399 b  42  ; PZ Tel B  19, 96  ; η Tel B  54.
Acknowledgements R. G. , S. D. , V. D. , D. M. , E. R. acknowledge support from the  PRIN-INAF 2019  " Planetary systems at young ages (PLATEA) " and  ASI-INAF  agreement n. 2018-16-HH. 0. SPHERE is an instrument designed and built by a consortium consisting of  IPAG (Grenoble, France) ,  MPIA (Heidelberg, Germany) ,  LAM (Marseille, France) ,  LESIA (Paris, France) ,  Laboratoire Lagrange (Nice, France) ,  INAF-Osservatorio di Padova (Italy) ,  Observatoire de Genève (Switzerland) ,  ETH Zurich (Switzerland) ,  NOVA (Netherlands) ,  ONERA (France)  and  ASTRON (Netherlands) , in collaboration with ESO. SPHERE was funded by  ESO , with additional contributions from  CNRS (France) ,  MPIA (Germany) ,  INAF (Italy) ,  FINES (Switzerland)  and  NOVA (Netherlands). SPHERE  also received funding from the  European Commission Sixth and Seventh Framework Programmes  as part of the  Optical Infrared Coordination Network for Astronomy (OPTICON)  under grant number  RII3-Ct-2004-001566 for FP6  ( 2004-2008 ), grant number  226604  for  FP7  ( 2009-2012 ) and grant number  312430 for FP7  ( 2013-2016 ).
This work has made use of the  SPHERE Data Center , jointly operated by  OSUG/IPAG (Grenoble) ,  PYTHEAS/LAM/CeSAM (Marseille) ,  OCA/Lagrange (Nice)  and  Observatoire de Paris/LESIA (Paris). This research has made use of the SIMBAD database, operated at  CDS, Strasbourg, France. This work has made use of data from the  European Space Agency  (ESA) mission Gaia (https://www. cosmos. esa. int/gaia), processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa.
Tree-based analyses (maximum likelihood) revealed 13 genetic clusters, representing as many as 23 putative species indicated by barcode index numbers (BINs) from the Barcode of Life Datasystems (BOLD) database. We found evidence for geographically-isolated cryptic species, e. g. , within Stereotydeus belli and S. punctatus, as well as unique genetic groups occurring in sympatry (e. g. , Nanorchestes spp. in QMM). Collectively, these data confirm high genetic divergence as a consequence of geographic isolation over evolutionary timescales. From a conservation perspective, additional targeted sampling of understudied areas in the Ross Sea region should be prioritised, as further diversity is likely to be found in these short-range endemic mites.
Available molecular tools for detecting potential species-level differences include barcode index numbers (BINs), which are based on a clustering algorithm  [29]  and integrated into the Barcode of Life Datasystems (BOLD) database. Unique BINs can provide evidence to support putative species identifications and can also be used to quantify diversity where morphology is unclear for understudied or morphologically cryptic taxa. Genetic markers for species-level assessments include the 5' region of the mitochondrial cytochrome c oxidase subunit I (COI-5P) gene  [30] , which is the most commonly used DNA-based marker, particularly for Antarctic taxa such as Collembola  [23, 31, 32]. Unfortunately, this standard "DNA barcoding" region has seen limited sequencing success for mites, e. g. ,  [33] , and previous Antarctic studies of the prostigmatid genus Stereotydeus have used the COI-3P region as an alternative  [18, 23, 34, 35].
Collectively, these studies have provided evidence of local speciation, including two new species descriptions for S. nunatakis and S. ineffabilis in Victoria Land  [36]. However, the difficulty of obtaining specimens from a wider range of remote and isolated locations has limited the geographic coverage of available studies. Further, the absence of COI-5P sequences for Antarctic mites has limited a more widespread comparison with other taxa in Antarctica and elsewhere. Obtaining COI-5P sequences from Antarctic mite taxa would facilitate comparison with existing databases such as the Barcode of Life Datasystems (BOLD) database  [37] , as well as assist in identifying potentially distinct species and population genetic structure. One Antarctic mite taxon that would particularly benefit from further attention is Nanorchestes (Endeostigmata), a widespread genus of small-bodied mites that are also found globally  [38].
Sequencing was carried out in forward and reverse directions using an ABI 3130 at the UoW or an ABI 3730x1 sequencer at the CCDB. Sequences were uploaded to the Barcode of Life Datasystems (BOLD) database. Specimen collection details, photographs, primers used and full sequence data are available from the BOLD database under dataset DS-ANTMIT (http: //www. boldsystems. org/index. php/Public_SearchTerms?query=DS-ANTMIT, accessed on 26 February 2023). Following DNA extraction, the exoskeletons for some individual specimens were carefully removed from the extract solution and permanently slide-mounted in polyvinyl alcohol (PVA) mounting medium (BioQuip Products, Rancho Dominguez, CA, USA) for morphological assessment. The slide-mounted specimens were then identified with the aid of a compound microscope using the available taxonomic literature e.
Data Analyses In addition to our newly generated sequences, 491 publicly available COI-5P sequences of at least 400 bp in length were downloaded from GenBank in July 2022 for the genera Nanorchestes, Eupodes and Rhagidia. No previous sequences were available for Stereotydeus or Coccorhagidia. None of the existing public records were from Antarctica or the sub-Antarctic. To simplify the analyses, we reduced these public records to one sequence per unique barcode identification number (BIN) or named species (n = 26). The final sequence alignment of 156 sequences, each ranging from 404 to 658 bp in length, included our new sequences (n = 130) as well as the 26 representative publicly available sequences (n = 11 Eupodes, n = 10 Nanorchestes, n = 5 Rhagidia). All sequences were aligned in Geneious Prime v2021.
Based on the maximum-likelihood phylogenetic tree, monophyletic clades were considered as "genetic clusters", or putative species, if members of the group had already been taxonomically identified to species level (slide-mounted specimens) or otherwise based on location, as there was a clear genetic distinction between regions. A barcode gap analysis  [52]  was then performed on these putative species groups using ape v5. 6. 2  [53] , spider v1. 5. 0  [54]  and ggplot2 v3. 3. 5  [55]. Following the genetic analyses, all specimens that occurred within the same BIN as the morphologically identified specimens were also attributed to this taxonomic name following protocols in deWaard et al. [56].
Discussion We assessed the genetic diversity of Antarctic mites from across a wide geographic and taxonomic range in the Ross Sea region. We obtained 130 standard COI-5P DNA barcode sequences sensu  [30]  from individual mites representing the four genera Nanorchestes, Stereotydeus, Coccorhagidia and Eupodes. The sequences were grouped into 13 distinct genetic clusters and a total of 23 unique BINs, some of which were found at only a single location. We also identified locations where multiple clusters or BINs occurred in sympatry, highlighting the potential conservation priorities for protecting sites with high levels of diversity, in keeping with the Antarctic Specially Protected Area priorities outlined by the Protocol on Environmental Protection to the Antarctic Treaty  [57]. Nanorchestes was the most genetically diverse and widespread genus in our dataset, with six major genetic clusters distributed across ten locations, covering all three ACBRs in the Ross Sea region  [11, 12]  as well as Lauft Island and Macquarie Island.
Supplementary Materials: The following supporting information can be downloaded at: https: //www. mdpi. com/article/10. 3390/genes14030606/s1, Table  S1 : List of 19 mite species known from the Ross Sea region and from which area they occur: North Victoria Land (NVL), South Victoria Land (SVL) or the Queen Maud Mountains (QMM); Table  S2 : Extended collection information for each sequence in the phylogenetic tree, including BOLD codes used in the ANTMIT dataset; Table  S3 : Results from the barcode gap analysis;.
Acknowledgments: We are very grateful to two anonymous reviewers for their thoughtful and supportive comments and to  L. Gerrish  for producing Figure 1. We thank  C. Beet ,  D. Cowan ,  U. Nielsen ,  L Sancho ,  A. Green ,  P. Roudier ,  M. Stevens ,  H. Morgan ,  B. Storey ,  M. Shaver-Adams ,  N. Fierer ,  M. Diaz  and  G. Schellens  for assistance and company in the field and/or laboratory.
H. W. , I. D. H. and B. J. A. in S. V. L. and Q. M. M. ; and the Antarctic Circumpolar Expedition (ACE) for funding to SLC (for Mt. Siple) and the Australian Antarctic Program for funding to SLC (for Macquarie Island). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: All original photographs, specimen and sequence data are available online from the Barcode of Life DataSystems (BOLD) database (www. boldsystems. org (accessed on 26 February 2023)) under dataset DS-ANTMIT (http://www. boldsystems. org/index. php/Public_ SearchTerms?query=DS-ANTMIT (accessed on 26 February 2023).
Image acquisition and analysis Images for hiPSC-derived motoneurons were acquired using ImageXpress micro imaging system (Molecular Devices ® ) and analysed using MetaXpress for automated quantification following the instructions from the software. The percentage of ISL1 and TUJ1 positive cells, a transcription factor specific for motoneurons and the pan-neuronal class III betatubulin, respectively, was automatically determined by counting the number of cells expressing ISL1 or TUJ1 over the total number of cells counterstained with Hoechst, using, for both, the metaXpress software tool 'Find Round Objects' following the instructions from the software (MetaXpress). Neurite length was determined by using the metaXpress tool 'fibres bioapplication module' and normalised to the total number of ISL1-positive cells (supporting information Material and Method 5).