dataesr/scientific-paragraphs-categorization huggingface dataset:
Transformants were isolated on SD -Ura -Leu and subsequently replica plated onto media containing 5-fluoroorotic acid (SD + 5-FOA), which is toxic to cells expressing URA3 and will thus select for recombination between the two HIS3-3' sequences. Recombination was confirmed by testing for growth on SD -Leu media. Proper deletion of LEU2 and a part of HIS3 was confirmed by PCR. Strain identity was validated by sequencing the barcodes inserted at the ura3Δ locus  (Cubillos et al, 2009). In total, we obtained 10 wild yeast strains with the genotype MATa hoΔ:: hphMX6 ura3Δ::kanMX4 his3Δ1 leu2Δ0 (Dataset EV8).
Synthetic genetic array (SGA) analysis Synthetic genetic array analysis was performed as described previously  (Baryshnikova et al, 2010) , with the exception that 5% mannose was added to the YPD plates used in the first steps of SGA analysis to facilitate pinning of the wild isolates. In brief, the 10 SGA-compatible kanMX-marked wild strains (Dataset EV8, MATa hoΔ::hphMX6 ura3Δ::kanMX4 his3Δ1 leu2Δ0), and a S288C negative control strain (DMA1, MATa his3Δ::kanMX ura3Δ0 leu2Δ0 met15Δ0 or DMA809, MATa hoΔ::kanMX his3Δ0 ura3Δ0 leu2Δ0 met15Δ0; Dataset EV8) were crossed to a collection of 1,474 natMX-marked temperaturesensitive mutants of essential genes (MATa xxx-ts::natMX4 can1Δ:: STE2pr-SpHIS5 lyp1Δ his3Δ1 leu2Δ0 ura3Δ0 met15Δ0;  (Costanzo et al, 2016) ). Each cross was performed in four technical replicates, and for six wild strains (Y14273, Y14274, Y14275, Y14276, Y14277 and Y14280; see Dataset EV8 for strain information) and the S288C control, we performed an additional biological replicate, also containing four technical replicates.
In a series of subsequent pinning steps, diploid cells were selected and sporulated, and colonies consisting of pools of around 60,000 haploid segregant progeny  (Parts et al, 2014)  carrying both natMX and kanMX selection markers were isolated. The final selection step for haploid progeny carrying both markers was performed at both 26 and 34°C. Plate images were processed with gitter v1. 0. 3 (Wagih & Parts, 2014) and normalised with SGAtools  (Wagih et al, 2013). Briefly, this process includes processing plate image files to detect the grid of colonies, quantifying the colony sizes, filtering out missing values and any technical replicates that accounted for at least 90% of the variation in the signal, log 2 -transforming and calculating the average and standard error of the mean of the remaining technical replicates (reported in Dataset EV1).
For genotype trees, we calculated the distance between strains as the number of called genetic variants that are present in either strain, but not the other one. For phenotype trees, we calculated the distance between strains as 1 minus the Pearson's correlation of their suppression profiles. To test for consistency of suppression within complexes and pathways, we considered multiple functional annotation datasets. The sources for these datasets were: protein complexes (the Complex Portal  (Meldal et al, 2019) , downloaded June 6, 2018), KEGG pathway annotation  (Kanehisa et al, 2016) , co-expression (gene partners with a co-expression score > 1;  (Huttenhower et al, 2006) ) and subcellular localisation  (Huh et al, 2003). For each annotation that groups multiple genes, we calculated the average correlation of suppression values across wild strains between all possible allele pairs.
To evaluate the significance of these values for a complex with N alleles, we sampled the matching number of N(N-1)/2 allele pairs 1,000 times, while also matching the number of pairs that came from the same gene. We calculated the P-value of enrichment as the frequency of observing statistics from permuted data more extreme than the real value and used the false discovery rate correction to adjust the P-values. We called a gene set consistent above chance if the average correlation was above 0. 25, and the corrected P-value below 0. 25. To assess whether there is additional deleterious mutation load for genes that can be suppressed, we downloaded the SIFT  (Ng & Henikoff, 2001)  annotations from the Saccharomyces Genome Resequencing Project 2 (Bergstro ¨m et al, 2014) website http://www. moseslab.
Random sporulation assay A total of 102 wild strain × TS allele combinations were selected for confirmation assays (Dataset EV3). Between 3 and 20 different TS alleles were tested for each of the 10 wild strains, for a total of 78 different TS alleles corresponding to 56 different essential genes. The selected crosses spanned a wide range of suppression scores and included eight crosses with a negative suppression value. As controls, we crossed each selected TS allele to a reference S288C strain, and each wild strain was crossed to a wild-type S288C reference strain, giving a total of 78 S288C TS allele controls, and 10 S288C × wild strain controls. All 190 strain pairs were crossed and sporulated. Sporulated cells were plated onto two agar plates that selected for haploid MATa spores that carried the TS allele (SD -His/Arg/Lys +CAN/LYP/NAT).
One plate was incubated at 26°C and one at 34°C. After 3 days plates were imaged, colony size and number were determined using CellProfiler  (Carpenter et al, 2006). We calculated the difference between the number of colonies and the colony area at 26°C and 34°C for each TS allele-wild strain combination and compared the values for the S288C control to those of the wild strain crosses (Dataset EV3). Images that contained < 100 colonies at 26°C were excluded from the analysis, and all images with < 30 colonies were excluded from colony size determination. A TS allele-wild strain pair was considered to show suppression when either the number or the average size of the colonies of the wild cross was substantially larger than that of the control cross (TS allele × S288C) at 34°C (Dataset EV3).
Colonies were scraped from the agar plates, and genomic DNA was isolated from the pools using the Qiagen DNeasy Blood & Tissue kit. Samples were sequenced using Illumina sequencing. For each bulk segregant sequencing sample, we performed read mapping and variant calling under the Varathon framework (https:// github. com/yjx1217/Varathon). Briefly, the raw reads were trimmed by trimmomatic v0. 38  (Bolger et al, 2014)  and subsequently mapped to the yeast reference genome (SGD R64-1-1) using bwa v0. 7. 17  (Li & Durbin, 2009). The resulting read alignment was further processed by samtools v1. 9  (Li et al, 2009) , picard tools 2. 18. 25 (https://broadinsti tute. github. io/picard/) and GATK3 v3. 6 for sorting, duplicate removal, INDEL realignment and indexing. Variant calling was carried out by freebayes v1.
To call QTLs in the remaining 19 samples without ploidy issues, and with strong or moderate suppression scores in the screen, we used Selection QTL Mapper (https:// github. com/PMBio/sqtl), which implements the approach used for bulk segregant analysis mapping described in  Parts et al (2011). Briefly, this approach first estimates reference allele frequencies in each sample using a probabilistic model that includes allele frequencies as latent variables, sequencing reads as observations and the recombination rate parameter to couple frequencies at nearby sites. The posterior allele frequency distributions were then combined across biological replicates according to Bayes rule and used to identify a broad set of QTL regions that had at least 12% frequency change between permissive and restrictive temperatures, and were at least 1kb long, using parameters "af_lenient=0.
8, sd_lenient=3, af_stringent=0. 12, sd_stringent=5, length_cutoff=1000, peak_cut-off=0. 03". A stricter set with allele frequency change of at least 0. 20 was used for all but reproducibility analyses. Sites within 30kb of the TS allele or an SGA selection marker were not considered as QTL candidates. All whole-genome sequencing data are publicly available at NCBI's Sequence Read Archive (http://www. ncbi. nlm. nih. gov/sra), under accession number PRJNA673501. Variant frequencies are listed in Dataset EV4.
For instance, the set of genes that were co-expressed with the query gene, but not in the same complex or pathway, were further ranked by whether they co-localised (highest rank) or not (lowest rank) with the query. The sources for these datasets were protein complexes (the Complex Portal  (Meldal et al, 2019) , downloaded June 6, 2018), KEGG pathway annotation  (Kanehisa et al, 2016) , co-expression (gene partners with a co-expression score > 1;  Huttenhower et al, 2006)  and subcellular localisation  (Huh et al, 2003). We manually added suppressor candidate genes with genetic interactions or other known functional connections to the query gene that were not captured by our computational prediction and also included known general modifier genes MKT1 and HAP1.
Suppressor candidate validation To validate the predicted suppressor genes, we introduced 50 potential suppressor alleles into the reference genetic background. First, kanR or nrsR ("natR") targeting guide RNA (gRNA) sequences were cloned into the pML104 or pML107 plasmid vectors, which carry Cas9 and either URA3 or LEU2 (Dataset EV8,  Laughery et al, 2015). Second, for nonessential suppressor gene candidates, we amplified the genes including ~400 bp upstream of the start codon and ~400 bp downstream of the stop codon from the various wild strains by PCR and co-transformed the PCR fragment and the pML104-kanR1136 and pML107-kanR468 plasmids (Dataset EV8) into a strain carrying a deletion allele of the suppressor gene (MATa xxxD::kanMX4 his3D1 leu2D0 ura3D0 met15D0; Euroscarf). The gRNAs will cut the kanMX4 cassette at two places and the homology of the promoter and terminator sequences of the PCR product to the genomic sequences flanking the double-stranded DNA breaks will promote repair via homologous recombination and integration of the PCR product into the genome.
For essential genes, we used a similar strategy using a set of haploid strains in which the essential gene of interest was deleted in the genome but present on a plasmid (MATa xxxD::natR_kanR(Cterm) his3D1 leu2D0 ura3D0 + [XXX, URA3]; van Leeuwen et al, 2020), and the plasmids pML104-natR412 and pML107-natR854 (Dataset EV8) that carry gRNAs that target natR. Transformants were initially selected on SD -Ura -Leu and then propagated on YPD. Within 3 days of growth on YPD, the vast majority of yeast strains had lost the gRNA plasmids and properly replaced the suppressor candidate deletion allele with the wild allele, which we confirmed by PCR. For essential genes, we streaked the allele-swapped strains on SDall + 5-FOA to remove the plasmid carrying the essential gene. Next, we crossed the allele-swapped strains to the corresponding TS mutant and sporulated the resulting diploids.
Data availability The datasets (and computer code) produced in this study are available in the following databases: • Whole-genome sequencing data: NCBI's Sequence Read Archive, accession number PRJNA673501 (https://www. ncbi. nlm. nih. gov/ bioproject/PRJNA673501/). • Raw colony size data: Figshare (http://dx. doi. org/10. 6084/m9. f igshare. 14170787) • Computer code: GitHub (https://github. com/lp2/2021-MolSysBio-.
;Albert et al, 2014Albert et al, , 2018;;Parts et al, 2014). To test the phenotypic consequence of the candidate genes, we replaced their open reading frame and ~100-400 bp surrounding region with the wild version in the reference strain background and tested for suppression of the corresponding TS allele. In total, we tested 50 suppressor gene candidates from 31 mapped loci of various strength (Dataset EV7, Fig EV4)and identified causal genes for 17 loci (55%, Dataset EV7, Figs 4A and.
Acknowledgements We thank  M. Costanzo ,  J. Hou ,  S. Soyk ,  G. Tan ,  M. Taschner ,  B. € Unl€  u and  A. Vjestica  for critical reading of the manuscript, discussions, reagents and technical assistance. We also thank  B. Andrews  and  C. Boone  whose laboratories the work was incepted in, and whose vision shaped the direction of this project. LP was supported by  Wellcome  ( 206194 ),  IT Centre of Excellence EXCITE  ( TK148 ) and a  Marie Curie International Outgoing Fellowship  ( 328541 ). JL was supported by the  Swiss National Science Foundation  ( PCEGP3_181242 ). GL was supported by the  Foundation for Medical Research  ( EQU202003010413 ). CP holds a  Ramon y Cajal fellowship  ( RYC-2017-22959 ).
Overall, 35 of the 39 (90%) protein-coding transcripts associated with these DMRs were expressed in brain according to the GTEx track in the UCSC browser (https://genome. ucsc. edu). We therefore looked more closely at expression of these genes in disease-relevant cell types, namely human fetal cerebral excitatory and inhibitory neurons  27  , and found that in these two cell types, the genes associated with the 50 DMRs are expressed at significantly higher levels than other genes, and this is more pronounced in excitatory neurons (Supplementary Fig. 1 ). Twenty of the associated genes encoded proteins whose function, if disrupted, would be predicted to lead to one or more phenotypic Fig. 1  Overall genome-wide DNA methylation in the TET3 cohort samples. a Principal components analysis of the TET3 discovery and validation cohorts (samples 1-16, Table  1 ; n = 3 TET3 (BA), blue; n = 8 TET3 (MA), red; n = 1 TET3 benign (MA), yellow; and n = 4 family member controls, green).
3a ) and the hierarchical clustering (Supplementary Fig. 2b ) along with the MVP score (Fig. 3b )-clearly points toward the variant present in sample 22 being pathogenic. Previously unreported individuals with pathogenic variants identified using the TET3 episignature Using DNA methylation profiling of whole blood, we have identified and confirmed eight additional individuals from five families with pathogenic variants in TET3 (Supplementary Data 2) in our signature validation (Fig. 2c, d ) and testing (Fig. 3a, b ) cohorts. All individuals harbor mono-allelic variants and were referred to our study due to the presence of a suspected pathogenic TET3 variant. Importantly and distinct from the other affected individuals, Individual 5-1 (Supplementary Data 2; sample 27 from Table  1 ) was identified exclusively via DNA methylation profile by using the TET3 episignature to screen the EpiSign database which contains over 1000 samples from individuals without a previous genetic diagnosis (Fig.
To determine whether BEFAHRS could also be differentiated, we used a multi-class prediction model to compare the TET3-deficient samples with samples from 46 other neurodevelopmental conditions with 38 distinct DNA methylation episignatures in the EpiSign database  10  and from additional controls (Fig. 3c ). In addition to the TET3 and related samples (all samples in Table  1 ) and the 64 unrelated age-matched and sex-matched controls used for signature discovery (probe selection), this plot also includes 549 additional unrelated controls and over 1000 samples from other syndromes used to train (75% of these samples) and test (25% of these samples) the classifier. While the TET3-deficient samples were all analyzed using the EPIC array, which contains over 850,000 probes, many of the other samples were analyzed using Illumina's 450K array, which contains about half as many probes.
We therefore categorized the variants as pathogenic and non-Fig. 3  Classification of samples using the TET3 episignature. a Multidimensional scaling (MDS) plot of the TET3 signature discovery, validation, and testing samples, including one previously unknown sample identified from the EpiSign database, using the episignature from the second round of signature discovery. The discovery and validation pathogenic samples (n = 3 TET3 (BA), light blue; n = 8 TET3 (MA), light red) along with matched controls (n = 55, light green) were used to identify a TET3 episignature, which was then used to classify the remaining samples, including the VUS's of the testing cohort (n = 2 TET3 VUS (BA), orange; n = 8 TET3 VUS (MA), purple; n = 1 TET3 benign (MA), yellow; n = 1 EpiSign screen, black; n = 4 family member controls, green).
The 66 samples used for signature identification are shown as partially transparent circles, and the remaining samples are opaque. See Table  1  for descriptions of samples. TET3 VUS (MA) samples are numbered according to Table  1. b Methylation variant pathogenicity (MVP) plot of the same samples. c The TET3 episignature from the final round of signature discovery applied to samples from 46 other neurodevelopmental conditions, which exhibit 38 different DNA methylation episignatures in our EpiSign database (some syndromes share signatures). For each syndrome and for control samples, 75% of samples were used to train the classifier (blue) and 25% were used for testing (gray). VUS, variants of uncertain significance; TET3 VUS (BA), samples with biallelic TET3 VUS's; TET3 VUS (MA), samples with mono-allelic TET3 VUS's; TET3 benign (MA), the benign variant that did not reduce catalytic activity in vitro  8  ; EpiSign screen, an unknown sample identified by screening the Episign database; family member controls, family members of affected individuals lacking TET3 variants; TET3 (BA), samples with bi-allelic pathogenic TET3 variants; TET3 (MA), samples with mono-allelic pathogenic TET3 variants; matched controls, age-matched and sex-matched controls.
DATA AVAILABILITY The summarized, anonymized data for each subject is described in the study. The data sets generated and/or analyzed during the current study are not publicly available due to institutional and ethics restrictions. Deidentified data can be available from the corresponding authors on reasonable request. Software used in this study is publicly available and detailed analytical methodology is as previously reported  10. The analysis of the expression of DMR-associated genes in fetal neurons utilized the freely available resource https://descartes. brotmanbaty. org/bbi/humangene-expression-during-development/; raw data for this resource are available at dbGaP (accession number phs002003. v1. p1), and processed data are available at Gene Expression Omnibus (GEO; GSE156793)  27.
CODE AVAILABILITY All code related to the fundamental analysis of the data is from publicly available R packages as described in the text. Packages and version numbers are: R 3. 6. 2, minfi 1. 32. 0, MatchIt 3. 0. 2, limma 3. 42. 2, gplots_3. 0. 4, e1071 1. 7-3, DMRcate 2. 0. 7. Additional details are included in the "Methods" section. Code for minfi can be found at http://bioconductor. org/packages/release/bioc/html/minfi. html. The analysis of the expression of DMR-associated genes in fetal neurons was performed in R version 3. 6. 1. The full code is available at https://github. com/hansenlab/ mdem_overlap/blob/main/beck_fahrner_analysis. R.
In addition, the "Testing" cohort included an individual (labeled "Episign screen"), who was initially identified using the TET3 episignature to screen a database of over 1000 undiagnosed individuals and later found to have a nonsense variant in TET3 on prior negative exome sequencing. All variants were verified using Mutalyzer (https://mutalyzer. nl). Each cohort included a set of unrelated age-and sexmatched controls (4-5 controls for every TET3-deficient sample) as described. These controls were not matched for ethnicity but are mostly Western European, as were the majority of TET3-deficient individuals on whom DNA methylation arrays were performed.
A Kolmogorov-Smirnov test was used to compare the two distributions. For distribution analysis, probes that were missing data for one or more samples were excluded, leaving 776,533 complete cases. DNA methylation signature detection was performed three times as described in the results. For each round of detection the training sample set consisted of TET3 pathogenic samples and a set of age-matched and sex-matched controls selected from our database of previously analyzed samples  10  using the MatchIt R package version 3. 0. 2  36. Each time, principal component analysis (PCA) was performed to ensure none of the selected controls were outliers. Methylation levels (beta values) were logit transformed to M-values and the transformed values used for linear regression modeling using the limma package version 3.
Comparison of DMR-associated gene expression to the expression of other genes in neurons We downloaded the "Expression Matrix by Cell Type" from the freely available website https://descartes. brotmanbaty. org/bbi/human-geneexpression-during-development/. This matrix contains the expression (transcripts per million; TPM) of 63,561 genes (protein-coding and nonprotein-coding), in 172 cell types identified using single-cell RNA-seq during fetal development  27. Raw data for this resource are available at dbGaP (accession number phs002003. v1. p1), and processed data are available at Gene Expression Omnibus (GEO; GSE156793)  27. We obtained the expression of each of the genes associated with our 50 DMRs in cerebral excitatory neurons and cerebral inhibitory neurons. In cases where a DMR overlapped more than one gene, we retained the gene with the highest expression.
Final remarks In summary, our results extend previous findings on high-order interdependencies of the ageing brain using a novel framework that incorporated whole brain modelling and connectome datasets along the lifespan. Whole-brain models have enhanced our understanding of the brain across different conditions, and therefore provide a highly promising avenue of research in the field of ageing neuroscience. In this context, our work constitutes the first step towards mechanistic explanations on how functional high-order interdependencies in the human brain are affected by the age-connnectome degeneration. Future work should validate our modelling approach in the presence of other forms of brain degeneration, such as the interplay between aging and pathologies like Alzheimer or Parkinson diseases.
Using these quantities, we calculated the following metrics for redundancy and synergy, for each subject k, interacting region m, and interaction order n: R m n (k) = 1 N + n,m i1 • • • in-1 Ω + (k) (X m , X i1 ,. , X in-1 ) , (10) S m n (k) = 1 N - n,m i1 • • • in-1 Ω - (k) (X m , X i1 ,. , X in-1 ) , (11) where N + n,m and N - n,m represent the number of n-plets with positive and negative O-information values, respectively. Finally, the average of these quantities over all subjects and regions can be also calculated as R n (k) = 1 M M m=1 R m n (k) , (12) S n (k) = 1 M M m=1 S m n (k). (13) The code to compute all these metrics is available at https://github. com/brincolab/High-Order-interactions. September 15, 2021 14/21.
Acknowledgments M. G. was partially supported by  CONICYT-PFCHA/ Doctorado Nacional/ 2019-21190577. R. C. acknowledges financial support from  Fondecyt Iniciación 2018 Proyecto 11181072. P. M. was funded by the  Wellcome Trust  (grant no. 210920/Z/18/Z ). F. R. acknowledges the support of the  Ad Astra Chandaria Foundation. P. O. is funded by  Fondecyt Regular  grant  1211750  and  ANID-Basal  Project  FB0008. J. M. C acknowledges financial support from  Ikerbasque (The Basque Foundation for Science)  and from the  Ministerio Economia, Industria y Competitividad (Spain)  and  FEDER  (grant  DPI2016-79874-R ), as well as from the  Department of Economic Development and Infrastructures of the Basque Country  ( Elkartek Program ,  KK-2018/00032 ,  KK-2018/00090 , and  KK-2021/00009 ). The  Centro Interdisciplinario de Neurociencia de Valparaíso (CINV)  is a  Millennium Institute  supported by Grant  ICN09-022  ( ICM-ANID ).