dataesr/scientific-paragraphs-categorization huggingface dataset:
Using the same significance cutoff (p<0. 05, q<0. 2, n=32 samples), we found that 13 nasopharyngeal cytokines were differently regulated between the critical and non-critical COVID-19 cases (Supplemental Fig. 3C, D ). Interestingly, only two cytokines (CCL2/MCP-1, VEGF) overlapped with the plasma dataset (Fig. 3E ), whereas other nasal cytokines including FLT3-L, EGF, CXCL1/GROα, PDGF-AA, IL-7 and TGF-α were significantly increased with worsening disease severity (Supplemental Fig. 3C, D ). Taken together, these results suggest that cytokine responses are compartmentalized during SARS-CoV-2 infection and are regulated, similar to spike-specific antibodies, in a tissue-dependent fashion. As certain cytokines are known to negatively regulate antibody responses (ie type I interferons;  Hensley et al.
, 2020; Zou et al. , 2020). In order to gain insight into how viral load may influence immune responses, we performed multidimensional scaling (MDS) which is a way of visualizing the level of similarity of individual cases of a dataset (in this case viral load, cytokines and antibody response characteristics). From the MDS projection and correlation matrix of our plasma dataset (Fig. 4C ; Supplemental Fig. 4A ), we could see that viral load was positively associated with the systemic inflammatory response (IL-6, TNFα, MIP-3β) and several regulatory cytokines (IL-10, IL-1RA) but not with the anti-viral interferon response (IFNα2) (Fig. 4C, D ). These results are in line with several reports of SARS-CoV-2dependent induction of hyper-inflammation as well as the critical role for interferon responses in controlling initial infection  (Chen et al.
, 2020; Fajnzylber et al. , 2020; Hadjadj et al. , 2020). Interestingly, plasma viral load was positioned distinctly from pseudo-neutralization activity (that formed a tight cluster with systemic spike-specific IgG and IgA) (Fig. 4C ). Nevertheless, viral load was positively correlated with these virus-specific antibody responses (Fig. 4D ; Supplemental Fig. 4A ) indicating a role for viral load in driving spike-specific humoral immunity. A similar MDS projection derived from the nasopharyngeal dataset generated a markedly different pattern. In the nasopharynx, SARS-CoV-2 viral loads were more closely associated with spike-specific IgG and IgA responses. However, in contrast with the plasma, viral loads were not positively associated with any inflammatory or regulatory cytokines and showed strong negative correlations with IL-33, IFNγ, IFNλ3, IFNβ and IFNα2 (Fig.
, 2020). However, the main striking result from our study was significant elevations in critical patients of a cluster of plasma cytokines and growth factors that did not have an obvious role in anti-viral immunity. Insight into their implication in severe COVID-19 came from the integration of plasma datasets with the nasal microbiome which revealed positive associations with opportunistic bacterial genera such as Prevotella and Streptococcus, and negative associations with key mucosal cornerstone genera such as Corynebacterium and Dolosigranulum. This hypothesis was supported by additional associations between presence of nasopharyngeal Staphylococcus genus and plasma inflammatory cytokines such as IL-6. Whether such nasal dysbiosis drives systemic inflammation will require further study, but this hypothesis has further support from a recent study documenting a similar mechanism in the infected intestine  (Giron et al.
, 2020) , we analyzed immune responses between 8 to 12 days after onset of first symptoms for all patients and before the initiation of any antiviral or anti-inflammatory treatment. Healthy controls were asymptomatic adults, matched with cases on age (+/-5 years), with a negative SARS-CoV-2 RT-PCR testing at time of inclusion. The study conforms to the principles outlined in the Declaration of Helsinki, and received approval by the appropriate Institutional Review Board (Cochin-Port Royal Hospital, Paris, France; number AAA-2020-08018). Epidemiological, demographic, clinical, laboratory, treatment, and outcome data were extracted from electronic medical records using a standardized data collection form. Chest Moderate cases were defined as clinical symptoms associated with dyspnea and radiological findings of pneumonia on thoracic CT scan, and requiring a maximum of 3 L/min of oxygen, stable for at least the following 24 hours.
3ug/mL. The SBG revelation enzyme concentration was 150pM. For the IFNβ assay, the 710322-9 IgG1, kappa, mouse monoclonal antibody (PBL Assay Science) was used as a capture antibody after coating paramagnetic beads (0. 3 mg/mL), the 710323-9 IgG1, kappa, mouse monoclonal antibody and "Crystal Miner" software and SARS CoV-2 viral concentration (cp/mL) were finally calculated considering the extracted volume of plasma (140 µL).
Sequence processing and statistical analysis After removing reads containing incorrect primer or barcode sequences and sequences with more than one ambiguous base, we recovered from 42 samples a total of 8. 075. 384 reads (192. 271 reads on average). The bioinformatics analysis was performed as previously described  (Volant et al. , 2020). Briefly, amplicons were clustered into operational taxonomic units (OTU) with VSEARCH (v1. 4) and aligned against the SILVA database. The input amplicons were then mapped against the OTU set to get an OTU-abundance table containing the number of reads associated with each OTU. The normalization, statistical analyses and multiple visualization were performed with SHAMAN (SHiny application for Metagenomic Analysis (shaman. c3bi. pasteur. fr) based on R software.
IFNα2 and IFNγ) were associated with the presence of Dolosigranulum genus in nasal microbial communities (Fig. 6A, B; Supplemental Fig. 6A), whereas IFNλ3 was linked to overall microbial diversity (Shannon, Simpson diversity indices) suggesting genus-specific as well as community-driven regulation of mucosal cytokine production. The previously identified association of viral load and spike-specific antibody responses remained, whereas associations of potential nasal pathobionts (including Prevotella, Streptococcus, Peptostreptococcus and Clostridial genera) with disease severity-associated nasopharyngeal cytokines was revealed (Fig. 6A, B; Supplemental Fig. 6A). MDS projections after integrating nasal microbiome profiles into the plasma datasets revealed intriguing associations of Staphylococcus and Peptostreptococcus genera with viral load, spike-specific responses, neutralization capacity and inflammatory cytokines (IL-6, TNFα, MIP-3β) (Fig.
Acknowledgments This study was supported by an  Institut Pasteur Covid-19 research grant  and by a grant (CoVarImm) from the  Agence National de la Recherche  ( ANR-flash Covid19 ) awarded to DD and JPD, and by the  Laboratoire d'Excellence ''Milieu Intérieur " (grant no. ANR-10-LABX-69-01 ) and the  Fonds IMMUNOV, for Innovation in Immunopathology. NS  is a recipient of the  Pasteur-Roux-Cantarini Fellowship. We thank the  UTechS CB of the Center for Translational Research, Institut Pasteur  for supporting Luminex and Simoa analysis. We thank  Laurence Motreff  and  Laurence Ma ,  Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France , supported by  France Génomique  ( ANR-10-INBS-09-09 ), IBISA and the Illumina COVID-19 Projects' offer for microbial sequencing. We thank  Amine Ghozlane ,  Bioinformatics and Biostatistics HUB, Institut Pasteur, Paris , for the assistance with the 16S rRNA sequencing data analysis.
Patients Present cohort: Individuals with BCL11A-IDD were identified through collaborating clinicians' direct clinical practice, the Deciphering Developmental Disorders Study,  10  GeneMatcher  19  and by contact with submitters of molecularly diagnosed individuals in publicly available databases: Decipher  20  and ClinVar. 21 For the latter, submitting diagnostic laboratories kindly facilitated contact with referring clinicians within the terms and conditions each provider so permitted. Patients previously reported by co-authors are included in the present cohort with updated phenotypic information: P4,  22  P8 and P24, 23 P17, P30, P35, P36. 5  All patients had been previously identified as having a likely pathogenic or pathogenic variant in BCL11A. Details of the prior genetic analyses performed are provided in Supplementary Data.
Variant annotation Given the discrepancy between BCL11A transcript assemblies for GRCh37 and GRCh38, variants with chromosomal positions annotated to GRCh37 were mapped to GRCh38 using Assembly Converter (Ensembl release 103 -February 2021). All variants (SNVs and CNVs) were annotated to RefSeq transcripts using Variant Effect Predictor with genome build GRCh38. p13 (Ensembl). CNVs were visualized as a custom track in Ensembl (GRCh38. p13). BCL11A has multiple annotated transcripts; for the purposes of this manuscript we selected 3 transcript isoforms with evidence of expression in brain tissue  17   Variants were classified based on predicted effects on the different isoforms and on predictions of nonsense mediated decay escape (NMDe). 27 PTVa, premature termination codon (PTC) in all 3 isoforms: PTVa1, possible escape or decreased efficiency of NMD; PTVa2, likely NMD in all 3 isoforms.
Statistical analysis The frequencies of each clinical manifestation were calculated using as the denominator the number of individuals for whom clear evidence of presence/absence of each manifestation was available (i. e. patients where the data were not available were excluded from the calculation). Categorical variables were described as n (%), and continuous variables were described as median and ranges. Statistical tests were performed using the Graphpad Prism software v. 8. 0. 1. Graphical representations were generated using Graphpad Prism and R v. 4. 0. 3 (ggplot2 v. 3. 3. 3, circlize v. 0. 4. 13). Unless otherwise indicated, for comparison of frequencies between groups, Fisher's exact test was used, at a significance threshold of p<0. 05. For comparison over timepoints, 2-way ANOVA was applied.
Cohort overview We present 42 individuals with heterozygous variants in BCL11A [18 females (43%) and 24 males (57%)] classified as pathogenic or likely pathogenic. Of these, 37 are previously unreported; 5 have been reported previously,  5, 22  but are presented here with detailed update of the phenotype. Median age at molecular diagnosis was 7 years (range 1-19 y). Median age at last assessment was 9 years (range 2-23 y) (Supplementary Table  S1 ). We also analyzed data from 17 previously published patients  5, [12] [13] [14] [15] [16]  and from 16 records deposited in publicly available databases, for a total of 75 patients with BCL11A-IDD. Median age at last assessment of the previously published individuals was 7 +5/12 years (range 3-15 y). Median age at last assessment of the patients in publicly available databases was 5 +11/12 years (range 3 +7/12 -7 +5/12 y).
The latter possibility also seems less likely considering recent evidence for altered NMD mechanisms in rare variants. Using allele-specific expression, Teran et al. (  2021 ) created predictive models of NMD efficiency, finding that rare and ultra-rare variants (MAF <0. 001%) are less likely to escape NMD. 37 Variants classified as pathogenic in ClinVar were more likely to exhibit allelic imbalance due to NMD. Allelic imbalance affecting rare variants was determined by variant location, allele frequency, and conservation, remaining consistent across tissue types. Further investigations will be required to test these hypotheses for patientspecific BCL11A variants. Interestingly, in the gnomAD database (v. 2. 1. 1)  38 , which excludes known severe pediatric disease, there are only 8 variants potentially truncating at least one isoform recorded (Supplementary Table  S7 ).
Acknowledgments We would like to acknowledge the patients and their families. We would also like to acknowledge the  Deciphering Developmental Disorders (DDD) Study. The DDD study presents independent research commissioned by the  Health Innovation Challenge Fund  [grant number  HICF-1009-003 ]. This study makes use of DECIPHER (http://decipher. sanger. ac. uk), which is funded by  Wellcome. See Nature PMID: 25533962 or www. ddduk. org/access. htmlfor full acknowledgement. We thank  Daniel Perrett  of the DECIPHER team for assistance in generating Figure 1 and Supplementary Figure S1A. Part of the data presented here was provided through access to the data and findings generated by the  100 , 000   Genomes  Project, which is funded by the  NIHR  and  NHS England  (full acknowledgement on https://www.
The higher degree of Denisovan introgression into Ayta Magbukon can be observed despite the group's detectable $10%-20% East Asian recent admixture, in contrast with the less admixed Papuan and Australian populations (Figures 3B-3D and S5A-S5D). These results are consistent when using alternative datasets, testing individuals instead of populations, accounting for ascertainment bias, or correcting for Neanderthal ancestry in these populations (Figures S5F-S5O; see STAR Methods). The difference in the degree of Denisovan admixture is clearer when we masked out East-Asian-related admixture in Negritos (Figures  3B, 3C,  and S5E ; see STAR Methods). The masking step results in a distinctively higher point estimate of Denisovan ancestry, with non-overlapping confidence intervals, in most Negritos than in Papuans or Australians.
Wall et al. 18  found slightly longer mean tract lengths in an Aeta group (likely the Ayta Mag-antsi and Mag-indi in our dataset) than in Papuans, suggesting separate Denisovan introgression events into Aeta and Papuans. Note, however, that the mean tract lengths found by Wall et al. 18  are $15 times shorter than those found by Jacobs et al. 16  and, in our estimates, at least partly attributed to different inference approaches, making direct comparisons difficult.
Data and code availability All requests to access the genotype and sequence data presented in this work are managed through the Data Access Committee of the official data repositories at the European Genome-phenome Archive (https://ega-archive. org/ega/home; Database: EGAS00001005408 and EGAS00001005407). The data generated in this study can be obtained for the purposes of academic research on genetic origins and diversity following approval by the Data Access Committee. All simulation scripts used in this study are available at https://github. com/jammc313/Philippine_Denisovan_Ancestry.
D and f4-ratio statistics on genotyped data All Philippine ethnic groups carry Neanderthal ancestry that is similar to the levels found in all worldwide populations outside of Africa (Figure  S3C ). This was determined using the test f4-ratio (Chimp;AltaiNeanderthal,Mbuti,X) / (Chimp;AltaiNeanderthal,Mbuti, Vindi-jaNeanderthal). The analysis was restricted to the sites that are non-polymorphic or homozygous ancestral in the Denisovan genome (to correct for inflation of the estimate due to the presence of Denisovan ancestry in some populations). The comparable levels of Neanderthal ancestry in all populations are in line with previous findings indicating that Neanderthal introgression is likely to have been a single event that occurred when anatomically modern humans migrated out of Africa 2,3.
Among Negritos, Ayta Magbukon displayed the highest estimated levels of Denisovan ancestry in a worldwide panel of populations, with Ayta Magbukon exhibiting higher estimates than Papuans, Australians, or any Oceanian populations (Figures  2A, 2B , and S5A-S5D; Data S1A). These findings are consistent when we ran the D tests with different outgroups (Chimp, Juhoansi, or YRI), when we ran with Phil_HO or Phil_AsiaPacific_315K datasets, or with our Philippine dataset merged with the Estonian Genome Diversity Project dataset, or when we correct for Neanderthal ancestry in target populations with f4-ratio (Mbuti;Neanderthal,East-Asian,X:Mbuti; Neanderthal,EastAsian,Denisova) (Figures  S5F-S5L ; Data S1B and S1C). The findings are also consistent when we restrict the Phil_1KGP_SGDP_1. 92M dataset to a panel of SNPs that are polymorphic only or ascertained for Europeans or Africans or to the H3frica SNP panel (Figures  S5M-S5O ).
RFMix-based masking of East Asian ancestry Considering that Philippine Negritos are admixed with Cordilleran-related ancestry (Figure  1D ), we implemented RFMix to produce a new dataset where we removed the Cordilleran-related alleles (and hence retain only the Negrito-related genome-regions) among Philippine Negritos. RFMix  64  was implemented on a shapeit-phased  94, 95  subset of the Phil_1KGP_SGDP_1. 92M dataset using the flag -fb 1 -e 1 -n 10 -u 1, with 15 Cordillerans as a reference for least admixed East Asian and 15 Papuans as a reference for least admixed Australasian. We find that Northern Negritos of the Philippines consistently exhibit higher Denisovan ancestry, with nonoverlapping confidence intervals, relative to Australians or Papuans or to a southern Negrito, Mamanwa (Figure  S5E ).
There is no change in the levels of Denisovan ancestry in Papuans and Australians before and after masking (the data points are overlapping), which indicates that the increase in Denisovan ancestry among Philippine Negritos is solely related to removal of confounding East Asian admixture. We also performed the test D(Mbuti;Denisovan,Negrito,Papuan/Australian), both in the unmasked and masked versions of the dataset, with matched sample size between populations and inclusion of least admixed individuals (Figures  3B  and 3C ). We consistently find that Ayta Magbukon exhibit higher Denisovan ancestry relative to Papuans or Australians, and is more pronounced when we mask away East Asian-related ancestry.
S' method For identifying archaic introgressed sequences without the use of reference archaic genomes, we utilized the recently developed Sprime (S') method  15. The method is designed to detect divergent haplotypes that are in strong LD and that are of very low frequency or absent in outgroup African population (who are known not to harbor archaic ancestry). The S' method was demonstrated to have better power relative to other archaic reference-free methods, and is appropriate for large-scale genomic data  15. For our analysis, we utilized 15 SGDP Sub-Saharan African individuals as the outgroup population (which includes the Juhoansi, Mbuti, Khomani, Yoruba, and Biaka individuals), and 5 newly sequenced Ayta Magbukon and 15 SGDP Papuan individuals as the test populations. Following retrieval of archaic sequences using S', we leveraged the sequences to the reference Altai Denisovan and Vindija Neanderthal genomes.
Admixture Graph Analysis We constructed admixture graphs using the qpGraph tool of the AdmixTools v5. 0 software package to determine which populationhistory model best fits the data that accounts for the number of Denisovan introgression events into Philippine Negritos and Papuans. Using the Phil_1KGP_SGDP_Ancient_Transv_317K dataset, we incorporarted in the model the chimpanzee as an outgroup, French to represent West Eurasians, Balangao Cordilleran and Liangdao 2 to represent East Asians, and Australian, Papuan, and Ayta Magbukon to represent Australasians (Figures  6A-6C ). The qpGraph tool was ran with the following parameters: outpop (NULL), blgsize (0. 05), diag (0. 0001), hires (YES), and lsqmode (YES). A model is rejected when the worst fitting f statistical test has a significant Z score of > | 3 |.
The parameters fixed across all models are shown in Table  S1 , and parameters that differed between models are shown in Table  S2. All scripts used to generate data are available at https://github. com/jammc313/Philippine_Denisovan_Ancestry. The null model (Figure  7F ) includes an admixture event coming from an unsampled Denisovan population that diverged from the 'Altai' Denisovan 300 kya, into the shared ancestral population of Papuan and Ayta (4%, 48 kya). An alternative model (Alt1) follows an identical setup (Figure  7K ), plus an additional admixture event (2%) coming from a distinct and unsampled Denisovan population, (also diverged from the Altai Denisovan 300 kya), into the (ancestors of) an Ayta population (40 kya), resulting in a total of 4% + 2% = 6% Denisovan admixture in the (ancestral) Ayta population.
ACKNOWLEDGMENTS We are grateful to all of the volunteers and indigenous cultural communities who participated in this research. The project is recognized by and implemented in partnership with the  National Commission for Culture and the Arts (NCCA)  of the Philippines, in accordance with the provisions of Philippine Republic Act 7356 or the Law Creating the  NCCA. The project was reviewed and approved by the  Regional Ethical Review Board in Uppsala, Sweden  ( Dnr 2016/103 ). We are thankful to  Felipe Mendoza de Leon , Jr. , NCCA chairperson 2010-2016, for his support. We also thank the regional offices of the  National Commission for Indigenous Peoples (NCIP), Local Government Units , and/or regional cultural organizations for facilitating fieldwork. See SI Appendix 10 of Larena et al. 11 for the complete list of acknowledgments.
The PCA distribution did not exhibit any significant trend or difference between the groups. In order to determine the metabolites that contributed to the differences between C0, C2, and C3 groups, an orthogonal partial least squares discriminant analysis (OPLS-DA) was then performed on the NMR dataset. The 2D OPLS-DA score plots of fecal NMR profiles among the four groups showed that C0, C2, C3 groups could be distinguished clearly with good model fitness and predictability (R 2 Y = 0. 993 and Q 2 = 0. 835, p-value = 6. 35 × 10 -7 ) (Figure  1B ); however, the fecal profiles of the C1 and C0 groups overlapped partially with each other (Figure  S1 ) indicating that drug treatment started to affect the patient fecal metabolome after the second cycle of treatment. Metabolite fluctuations between C0 and C1, reporting differences of the metabolites concentrations between the two groups and represented in the heatmap, showed the same behavior (Figure  3 ).
NMR Spectroscopy NMR data were recorded using Bruker 600MHz AVANCE III Spectrometer equipped with a BBFO + probe and a Sample Jet autosampler, which enabled the storage of 5 racks of 96 NMR tubes at 5 • C. The sample temperature was controlled at 300 K during experiments. Spectra were recorded using the 1D Nuclear Over Hauser Effect spectroscopy pulse sequence (trd-90 • -t1-90 • -tm-90 • -taq) with a relaxation delay (trd) of 24 s, a mixing time (tm) of 4 ms, and a t1 of 4 µs. The sequence enables optimal suppression of the water signal that dominates the spectrum. We collected 128 free induction decays (FIDs) of 65,532 data points using a spectral width of 12,019. 230 kHz and an acquisition time of 2. 726 s. The spectra were automatically phased and baseline corrected and referenced to the internal standard (TSP; δ = 0.
NMR Data Processing NMR data were further processed using NMRPROCFLOW v1. 2  [80]. Basically, 1D NMR spectrum baselines were further adjusted using the global correction method. The spectral region with the residual water peak (4. 5-5 ppm) was excluded from the data. To align the set of spectra, we chose the alignment method based on a least squares algorithm. The spectral region from 0. 5 to 9 ppm was binned using an intelligent bucketing method with a resolution factor of 0. 5. The resulting dataset was then normalized using the constant sum normalization method. Finally, normalized data were exported to SIMCA-P 14 (Umetrics, Umea, Sweden) prior to statistical analysis.
Metabolite Identification Metabolites were identified based on their respective chemical shifts using a library provided with Chenomx NMR Profiler version 8. 5 (Chenomx NMR Suite 8. 5, Chenomx Inc. , Edmonton, AB, Canada) and also using NMR databases (Madison Metabolomics Consortium Database)  [81]  and HMDB (Human Metabolomics Database) [82] and quantified using TSP signal as a reference. Following initial designation, metabolite identifications were confirmed conducting 13 C and 2D NMR experiments (COSY and HSQC). The statistical analyses were performed on the matrix of the quantified metabolites.