dataesr/scientific-paragraphs-categorization huggingface dataset:
Future changes in reef fish vulnerability We finally estimated how climate change could affect the global patterns of functional vulnerability in shallow-water reef fishes over the coming 60 years (2041-2070 compared to the period 1981-2015). To do so, we used species distribution models  44  (see supplementary information) to forecast habitat suitability for 2320 species after integrating >12 million records of species occurrences extracted from open access databases (GBIF  45  , OBIS  46  ) and local SCUBA transects (Reef Life Survey  47, 48  , SERF  49  , GASPAR  50  ). We used the SSP1-2. 6 climate change scenario (Shared Socioeconomic Pathways combined with the Representative Concentration Pathway 2. 6  23  ) that is inherent to the Paris Agreement that aims to sustainably limit global warming to less than 2 °C51.
We chose twelve fish traits: age and length at maturity, maximum age, the asymptotic mass and length, the Von Bertalanffy growth coefficient (from FISHLIFE  52  ), mobility, period of activity, schooling, vertical position, diet, and reef association (from the GAS-PAR traits database  50  ). Under the 2 °C warming scenario (i. e. SSP1-2. 6), the forecasted functional vulnerability of fish communities on shallow-water coral and rocky reefs for the period 2041-2070 is expected to be lower in the tropics (Fig. 4a ) where species richness is expected to remain high (Supplementary Fig. 11 ). As for the North Sea (case study 1), functional vulnerability is expected to be negatively related to species richness (Fig. 2b ), yet with a threshold in species richness -of about 800 species-above which functional vulnerability rebounds towards higher values (Fig.
While we assessed functional vulnerability by considering mean trait values for each species, we were unable to account for potential spatiotemporal variation in trait values due to insufficient data. Traits can be highly plastic and can vary in response to environmental (local adaptation) or ecological pressures (phenotypic plasticity) which in turn partly determine the level of functional vulnerability within communities. Finally, we argue that trait-based assessments of ecosystem vulnerability are important to help decision-makers and conservation policy. However, we raise the critical issue that taxonomic biases in trait data availability must be alleviated to avoid further conservation biases orientated towards emblematic and well-studied groups. Nonetheless, trait information is continuously growing  72  for an increasing number of clades, therefore encouraging the confident use of trait-based integrative approaches for conservation perspectives.
Virtual communities framework The first step of the framework is to generate a multi-dimensional trait space that represents the functional similarity among all species  [79] [80] [81]  , to characterize the functional structure of a given community. The second step is to compute a distance matrix that quantifies the dissimilarity between all pairs of species based on their trait combinations. Here, we used the flexible Gower pairwise distance since it can handle multiple data types (from continuous to categorical traits) and is less sensitive to missing data than other distances  81. We used the compu-te_dist_matrix() function in the funrar R package  82. Finally, we performed a Principal Coordinates Analysis using the pcoa() function (ape R package) to transform pairwise species distances into a multidimensional space  83.
Adaptation of the algorithm to occurrence and habitat suitability data Because presence/absence information is more accessible in ecological surveys, we developed a second algorithm to assess functional vulnerability when species abundance data are not available. While the statistical procedure was the same as the one developed in the abundance-based approach, the rarefaction curve of the observed community is here compared to the rarefaction curves of only two virtual communities characterized by opposite functional redundancy distributions (Supplementary Fig. 1 ) and species extinction were used as disturbance. To account for datasets containing probabilities of presence (or habitat suitability) usually computed from presence/ absence data, we developed a third version of the algorithm. Disturbances are here characterized by a diminution of 0.
Case study 1: Temporal vulnerability of North Sea fish communities We applied our quantitative framework to estimate the functional vulnerability of the North Sea fish communities. We gathered abundance data from the International Bottom Trawl Survey (IBTS), an annual monitoring campaign that uses stratified random sampling to survey fish and invertebrate communities in 1°longitude × 0. 5°latitude cells covering the entire North Sea each year in winter (Fig. 2a ). Every year, at least two hauls were performed within each cell with a 3-m vertical opening bottom trawl (GOV trawl) with a 10-mm mesh codend during 30 min at an average speed of 4 knots. In each survey, fishes were identified and counted, and the resulting abundances were standardized to numbers of individuals per hour of trawling.
The fish abundance data set included 168 taxa across 154 survey cells over 38 years (1983-2020), which represents a total of 13,433 hauls. Prior to statistical analyses, species abundances (across all survey cells and years) were log10(x + 1) transformed. We calculated the yearly mean abundance of each species at the scale of the North Sea to obtain one regional community per year from 1983 to 2020. Abundance data were downloaded from the International Council for Exploitation of the Sea data portal (ICES; https://datras. ices. dk/Data_products/Download/ Download_Data_public. aspx). For this case study, each simulated disturbance corresponded to a decrease of 5% of the total abundance in the observed community. In combination with species abundance, and for each fish taxa, eight traits encompassing life history, habitat use and trophic ecologychosen because they are documented to mediate species influences on ecosystem functioning  39, 84  -were collected from the PANGAEA traits database:  85  age at maturity, asymptotic length, fecundity, offspring size, habitat (water column position), trophic level, feeding mode, and spawning type.
Case study 3: Future changes in functional vulnerability of reef fishes In this last study case, we applied our algorithm to estimate how climate change could affect the functional vulnerability of global reef fish communities in 60 years (2041-2070 compared to the period of reference 1981-2015). To do so, we estimated the functional vulnerability of contemporary and future communities using species distribution models of 2320 species. The species distribution modeling framework is detailed in the supplementary information. In brief, we integrated > 12 million records of species presence from open access databases (GBIF  45  , OBIS  46  ) and local SCUBA transects (Reef Life Survey  47, 48  , SERF  49  , GASPAR 50 ) (Supplementary Fig. 12 ). We extracted the environmental conditions of species' presences for 6 environmental variables: sea surface temperature (SST; minimum and maximum), pH (minimum), sea surface salinity (SSS; minimum), net primary productivity (NPP; mean), degree heating weeks (mean; DHW  86  ) and gravity of human impacts within 500 km 2 (mean) and used these variables as covariates.
Note that DHW had by far the lowest explanatory power of all variables on average across all species such that our results are likely to be robust to excluding degree heating week projections (if temperature change is already included as SST-min and SST-max). Finally, dispersal rates of species were constrained to 10 km per year, corresponding approximately to observed realized latitudinal range shifts of marine species  91. All species maps were produced on a consistent 0. 25°global grid that represents coastal and reef systems. For this last case study, 12 traits were chosen: length at maturity, age at maturity, maximum age, the asymptotic mass and length, the Von Bertalanffy growth coefficient (from FISHLIFE  52  ), mobility, period of activity, schooling, vertical position, diet, and reef association (from the GASPAR traits database  50  ).
3 ). Several analyses were performed to quantify the effect of missing trait data (i. e. , Not Available, or NA in traits datasets) on final vulnerability values. To do so, the spatio-temporal distribution of NAs was firstly investigated and vulnerability values were recomputed by removing all species for which a NA was observed for at least one trait. We then compared the new vulnerability values to those obtained from the entire trait dataset (i. e. , with all NAs). Additionally, we re-ran the analyses considering implemented traits tables (i. e. , with all NAs replaced by estimated values), which were obtained using a random Forest technique (missForest: MissForest R package; Stekhoven and Bürhlmann  92  ). For all case studies, vulnerability values presented a very low sensitivity to the NA items present in traits datasets, as their deletion and/or imputation did not substantially affect spatiotemporal patterns of vulnerability (Supplementary Figs.
Acknowledgements This research is supported by the  Fondation pour la Recherche sur la Biodiversité (FRB)  and  Electricité de France (EDF)  in the context of the  CESAB  project ' Causes and consequences of functional rarity from local to global scales ' (FREE) and by  FRB  and  FFP (France Filière Pêche)  in the context of the  CESAB  project  'Climate change effects on exploited marine communities ' (MAESTRO). This research was also funded through the  2017-2018 Belmont Forum and BiodivERsA REEF-FUTURES project under the BiodivScen ERA-Net COFUND program  along with the  French National Research Agency , the  Natural Sciences and Engineering Research Council  (Grant No. RGPBB/525590 ), the  Canada Research Chairs Program , the  Ocean Frontier Institute , and the  Research Council of Norway  (no.
Data availability Abundance data of the North Sea fish communities (case study1) are available through https://datras. ices. dk/Data_products/Download/ Download_Data_public. aspx and at https://figshare. com/s/9d3cd-1d6f68a73dcea11 (input table for analyses). Traits data of the North Sea fish communities are available through the PANGAEA traits database at: https://doi. org/10. 1594/PANGAEA. 900866. Presence/absence and traits data for marine mammals (case study 2) are available at: https://figshare. com/articles/Input_data_for_Global_vulnerability_of_ marine_mammals_to_global_warming_/11323304. All fish species occurrences used for SDM models (GASPAR, SERF, RLS, OBIS & OBIS) and associated forecasted habitat suitability data (case study 3) are available at [https://figshare. com/s/2c5fb050f2d2b9e01170].
Traits data used for case study 3 (GASPAR & FISHLIFE) are available at: [https://figshare. com/s/2c5fb050f2d2b9e01170]. Projection of environmental variables from the CMIP6 Earth system model are available through https://esgf-node. llnl. gov/projects/cmip6/. Source data are provided with this paper: [https://doi. org/10. 6084/m9. figshare. 20099219]. Background map of Fig. 2a was obtained through the maps r package (v. 3. 3. 0). Background map shapefiles for Figs. 3a, b, 4a, c are available on the NOAA website: https://www. ngdc. noaa. gov/mgg/ shorelines/data/gshhg/latest/. Code availability Codes to run vulnerability analyses are available at: Case study 1: https://figshare. com/s/9d3cd1d6f68a73dcea11 Case study 2: https://figshare. com/s/5dcefa52f529ad34dc02 Case study 3: https://figshare.
Complex genetic predispositions accelerate the chronic degeneration of midbrain substantia nigra neurons in Parkinson's disease (PD). Deciphering the human molecular makeup of PD pathophysiology can guide the discovery of therapeutics to slow the disease progression. However, insights from human postmortem brain studies only portray the latter stages of PD, and there is a lack of data surrounding molecular events preceding the neuronal loss in patients. We address this gap by identifying the gene dysregulation of live midbrain neurons reprogrammed in vitro from the skin cells of 42 individuals, including sporadic and familial PD patients and matched healthy controls. To minimize bias resulting from neuronal reprogramming and RNA-seq methods, we developed an analysis pipeline integrating PD transcriptomes from different RNA-seq datasets (unsorted and sorted bulk vs.
singlecell and Patch-seq) and reprogramming strategies (induced pluripotency vs. direct conversion). This PD cohort's transcriptome is enriched for human genes associated with known clinical phenotypes of PD, regulation of locomotion, bradykinesia and rigidity. Dysregulated gene expression emerges strongest in pathways underlying synaptic transmission, metabolism, intracellular trafficking, neural morphogenesis and cellular stress/immune responses. We confirmed a synaptic impairment with patch-clamping and identified pesticides and endoplasmic reticulum stressors as the most significant gene-chemical interactions in PD. Subsequently, we associated the PD transcriptomic profile with candidate pharmaceuticals in a large database and a registry of current clinical trials. This study highlights human transcriptomic pathways that can be targeted therapeutically before the irreversible neuronal loss.
Instead, holistic omics analysis of patientderived tissue can accelerate the discoveries of innovative therapeutic directions  5. Recently, a few iPSC studies used RNAseq to reveal cellular mechanisms underlying PD  31, 32, [35] [36] [37] [38]. However, the number of individuals compared in each study remains relatively low (≤3 controls, ≤3 PD), and it is not clear whether these findings can be generalized to the broader PD community. Here, we compared the transcriptome profiles of PD to healthy controls with midbrain neurons reprogrammed from donors' fibroblasts from 42 individuals (25 healthy, 17 PD patients). We found that variance in neuronal reprogramming and RNA-seq methods could substantially bias transcriptome profiles. Therefore, we developed an analysis pipeline integrating a range of RNA-seq datasets (unsorted and sorted bulk vs.
single-cell and Patch-seq) and neuronal reprogramming strategies (induced pluripotency vs. direct conversion). Gene expression signatures consistent across independent methods and datasets will arguably be more disease-relevant. Furthermore, notwithstanding the disproportionate clinical preponderance of sporadic PD over familial PD (8:1), the large majority of pre-clinical research focuses on the familial forms of the disease. Thus, we used the integrated analysis pipeline to identify a PD signature common to both genetic forms of PD (LRRK2, PARK2 and SNCA) and the sporadic form (broad range of low penetrance risk variants). Defining the molecular perturbations in PD patients' midbrain neurons can help guide the design and evaluation of much-needed PD therapeutics. Identifying a molecular profile resistant to bias from RNA-seq, cellular reprogramming methods, and patient stratifications will maximize clinical relevance.
Transcriptomic data of midbrain neurons reprogrammed from PD patients and healthy controls We investigated the molecular mechanisms dysregulated in PD through transcriptomic analysis of human midbrain neurons reprogrammed from the fibroblasts of a large and diverse cohort of 42 individuals (Fig. 1 ; Supplementary Table  1 ). Our cohort comprises 10 sporadic PD patients (including four young onset, <40 years old), 7 familial PD patients with mendelian variants in PD genes (PARK2, LRRK2, SNCA), and 25 matched healthy subjects. We analysed a total of 5,359 single cells and 80 bulk RNA-seq samples. These raw sequencing data were combined from three unpublished transcriptomic datasets (sc-iPS-PatchSeq, bulk-iPS-Dopa, bulk-iN-Dopa) and three recently published datasets (sc-iPS-10XSeq  38  , bulk-iPS-Mixed  31  , bulk-iN-Mixed  39  ).
The midbrain neuronal cells were derived from fibroblasts using a range of reprogramming strategies (Sendai viral, retroviral or episomal) and neural differentiation protocols (induced pluripotency or direct conversion) (Fig. 1B ). Half of the bulk transcriptomic samples (n = 44/80) originated from reprogrammed induced neurons (iNs) generated using two different conversion protocols (bulk-iN-Dopa and bulk-iN-Mixed; see Methods for details). The other half (n = 36/80) were derived from iPSCs using either an embryoid body-based (bulk-iPS-Dopa) or monolayer-based (bulk-iPS-Mixed) neural induction. The bulk-iPS-Dopa and bulk-iN-Dopa datasets were optimized to generate higher proportions of dopaminergic neurons (see Methods for details). The single-cell RNA-seq data were collected either with Patch-seq, from electrophysiologically mature neurons (AP Types 4 + 5 as previously described in  40, 41  ) (sc-iPS-PatchSeq), or with the high-throughput 10X Genomics Chromium system  38  (sc-iPS-10XSeq).
Sample inclusion criteria for RNA-seq datasets Quality assessment at the level of the individual sample (bulk RNAseq) or cell (single-cell RNA-seq) is an essential step in identifying outliers, filtering out spurious samples, and optimizing the reproducibility of biologically relevant results. We excluded cells or samples with low read mapping, a low number of detected genes, and a low number of expressed housekeeping genes (Supplementary Fig. 1 ; see Methods for details). The average read mapping rate across all 80 bulk transcriptomes was 85%, with 78 of 80 libraries showing >75% alignment, indicating the high quality of these samples. We also quantified the number of total genes and housekeeping genes detected and excluded any sample with <1000 genes detected or with less than 65 housekeeping genes expressed (out of a curated list of 98 housekeeping genes  42  ; Supplementary Table  2 ).
For single-cell transcriptome data, we only retained cells with an overall read alignment (mapping) rate of at least 50% 43 (Supplementary Fig. 1 ). In addition, to exclude potential cell doublets or multiplets from single-cell analyses, we removed cells with exceptionally high gene or UMI detection (i. e. , >8000 genes or >37,500 unique molecular identifiers detected). Quality control metrics for each sample are detailed in Supplementary Table  3. Published PD iPSC datasets that failed to pass these quality control criteria were not included in this study.
Neuronal reprogramming methods sway the overall transcriptomic profiles To identify the main sources of variation between transcriptomic samples, we compared the datasets with hierarchical clustering and principal components analysis (PCA). To compare single-cell and bulk RNA-seq samples, we generated artificial bulk transcriptomes for the Patch-seq and the 10X Chromium datasets (sc-iPS-PatchSeq, sc-iPS-10XSeq) by summing up the read counts across all cells sequenced from each subject. We then performed PCA clustering with the transcriptomes of 86 (n = 80 bulk and six artificial single-cell bulk) reprogrammed neuronal samples that passed all quality control (QC), 19 postmortem adult human substantia nigra samples from the Gene-Tissue Expression (GTEx) project, and four samples of commercially available iPSC-derived human midbrain floorplate dopaminergic neurons (iCell Dopa-Neurons, Fujifilm Cellular Dynamics).
This analysis revealed a distinct clustering of samples based on the derivation method independently of disease status (i. e. , neurons generated from iPSCs clustered separately from neurons generated by direct conversion (iNs)) (Fig. 2A ). Hierarchical clustering analysis based on Euclidean distance-based similarity revealed that GTEx adult substantia nigra and striatum tissue grouped into a distinct cluster separate from in vitro-engineered datasets (Fig. 2B ), suggesting a combination of technical and endogenous differences between post-mortem adult brain tissue and live in vitro-engineered neuronal tissue. Nevertheless, the Euclidean distance with adult brain tissue was relatively small overall, highlighting the quality of in vitro-engineered tissues despite their limitations  20. Altogether, our study shows that the technical variance resulting from the methods used to obtain the neuronal tissue (post-mortem vs.
Scoring differential mRNA expression in PD within unique transcriptomics datasets Identifying the molecular differences between bioengineered midbrain neurons from PD and healthy controls may provide valuable mechanistic insights into the early stages of PD pathogenesis (prior to neurodegeneration). First, we performed differential gene expression testing between PD and matched control samples in each independent dataset, as outlined in Fig. 1. However, the choice of bioinformatic methods for transcriptomics analysis has not reached an unequivocal consensus yet, and the poor concordance between differential expression results across methods remains a challenge  [44] [45] [46] [47]. For example, using either raw read counts or normalized expression values (e. g. , TPM/CPM), or simply choosing different statistical analysis packages can lead to substantially different results from the same dataset.
Furthermore, differential gene expression calls that are exclusively based on arbitrary binary cut-offs of P-value (significant/non-significant) and/or fold-change lead to loss of information, bias, and lower statistical power  [48] [49] [50]. Therefore, rather than only relying on single P-values for inferring each gene's significance in PD, we implemented an analytical pipeline to determine multivariate Fig. 1  Patient and sample distributions of six transcriptomic datasets of Parkinson's disease reprogrammed neurons. A The transcriptome data analyzed in the present study were collected across six different datasets and originate from the reprogrammed neurons of n = 25 healthy individuals, n = 7 patients with known mutations in PD genes, and n = 10 patients with a sporadic form of the disease (n = 42 unique subjects total).
B Overview of tissue culture trajectories used in six reprogramming studies to generate in vitro midbrain-like neurons from PD patient-and healthy subject-derived fibroblasts (see Methods for details). Pie charts on the left summarize the number of subjects from which neurons were derived, color-coded by disease phenotype and mutation type as shown in (A). Neuron cultures were generated via induced pluripotent stem cell (iPSC) technology (n = 4 studies) or by direct conversion of fibroblasts into induced neurons (iNs, n = 2 studies), and neuronal transcriptome data was obtained by single-cell or bulk RNA-seq (n = 2 and 4 studies, respectively). The trajectory color reflects the combinatory choice of the reprogramming and sequenced method used (orange: single-cell iPSC neuron datasets; blue: bulk iPSC neuron datasets; green: bulk iN datasets).
Single-cell RNA-seq was performed on (i) functionally mature (AP Types 4 + 5) single neurons collected after patch-clamp recording (PatchSeq, n = 44) or on (ii) wild-type and isogenic SNCA-A53T neurons harvested using 10X Chromium technology  38. The two iN datasets enriched for successfully reprogrammed (i. e. , PSA-NCAM-positive) neurons using fluorescence-activated cell sorting (FACS). The bulk-iPS-Dopa and bulk-iN-Dopa studies used an optimized differentiation protocol to generate a high proportion of midbrain dopaminergic neurons. Pie charts on the right indicate the number of cells or samples sequenced per disease state and/or mutation type. A total of 5,359 single neurons and 80 neuronal bulk samples were analyzed. gene dysregulation scores. In particular, we used both raw counts and normalized TPM expression values as input and combined the log2 fold changes and P-values from multiple statistical tests (logit method).