dataesr/scientific-paragraphs-categorization huggingface dataset:
All returned empty spectra, illustrating that no contamination was introduced during the lab work. The triplicate data files obtained from the MALDI were merged in R using the packages MALDIquant and MALDI-quantForeign  (Gibb and Strimmer, 2012)  to smooth the intensity of the peaks (applying a moving average function), remove the baseline (using the TopHat method) and align the spectra (SuperSmoother, signal to noise ratio of 3). The three replicates were then summed into a single spectrum and the baseline removed once more using the TopHat approach. The obtained. msd files were analysed in the open source MS tool mMass (http://www. mmass. org/, Strohalm et al. , 2010)  with a signal to noise ratio set to 3. The presence of nine peptide markers was assessed and taxonomic identifications were made through comparisons with existing databases  (Welker et al.
g. weathering stages 4-5, Cervid/Saiga and Rhinocerotidae). The biomolecular preservation of the bone fragments was assessed further through calculation of the glutamine deamidation values (following the methodology of  Wilson et al. , 2012). Because we used two different MALDI instruments (a Bruker Autoflex in Leipzig and a Bruker Ultraflextreme in Kent), we first cross-compared three control samples between both machines and between all Kent runs. This comparison indicates a low amount of inter-and intrainstrument variation (within the range of 0. 05) justifying the direct comparison of all obtained values, regardless of instrument or run (for raw data see Supporting Information Table  S7 ). We calculated a t-test value that showed no significant difference in calculated deamidation values for either the AmBic or acid protocol obtained on the MALDI instruments from either Leipzig or Kent (see Supporting Information Tables  S7  and S8 ).
Faunal accumulation dynamics Animal carcasses, as well as stone artefacts and bone tools made by Neanderthals, accumulated on a flood plain setting at Salzgitter-Lebenstedt during an interstadial more than 51 000 years ago. This chronological position is established by 10 radiocarbon-dated bones which were pre-selected for good collagen preservation by NIR screening. The added value of fast, non-destructive NIR pre-screening has also been Table  6. Bone weathering of the bone fragments sampled for ZooMS across the different spatial zones (A-D) at Salzgitter-Lebenstedt. The division in sections A-D follows the methodology of  Pastoors (2001). Weathering data modified from  Behrensmeyer (1978)  illustrated at other Palaeolithic sites  (Fewlass et al. , 2019b; Talamo et al. , 2021b)  and is an effective tool to apply at sites where collagen preservation is variable (e.
While in this study we only recorded the specific skeletal part using general categories (e. g. long bone, flat bone, cranial, rib), to fully realize the potential of the ZooMSidentified fauna it is crucial to develop a method of element quantification using standard zooarchaeological metrics such as minimum number of elements (MNE), individuals (MNI) and anatomical units (MAU). Such an approach will allow for further in-depth integration with the morphological dataset in order to investigate aspects of carcass transport or selective destruction at Palaeolithic sites, and is of crucial importance for future integrated zooarchaeological analyses. Detailed taphonomic studies of the unidentifiable bone fraction can further clarify patterns of movement of material at a site, especially when detailed spatial data are available (cfr.
Acknowledgments. We would like to thank  Babette Ludowici  and  Michaela Scheffler  for facilitating access to the Salzgitter-Lebenstedt fauna stored at the  Braunschweigisches Landesmuseum in Wolfenbüttel (Lower Saxony, Germany). We thank  Huan Xia  and  Dorothea Mylopotamitaki  for assistance in the palaeoproteomics lab in Leipzig. This project was funded through the  Max Planck society. Karen Ruebens  (grant agreement No. 745662 ) and  Geoff M. Smith  (grant agreement No. 101027850 ) received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie scheme. F. Welker  has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No. 948365 ).
Animal management and samples collection Comprehensive information about the pig feeding trial with 200 growing pigs has been described by  Blavi et al. (2021). In brief, the study involved 6-weeks old growing pigs with an average initial body weight of 11. 5 ± 0. 98 kg that were assigned to a randomized complete block design. Pig fecal matter was obtained on day 26 and 116 of the experiment (direct sampling from the rectum; 8 fecal samples per treatment group) as described previously  (Brinck et al. , 2023). Sampling time points were selected to get data on the effects of dietary Cu both during the initial animal growth stage (day 26) and shortly before animals would normally be sent for slaughter (day 116), when microbiomes were suspected to be most selected by the dietary Cu treatments.
16S rRNA gene amplicon sequencing and data processing The primer pair 515F (GTGCCAGCMGCCGCGGTAA) and 806R (GGAC TACHVGGGTWTCTAAT) were used to amplify the V4 region of the bacterial 16S rRNA gene by PCR  (Walters et al. , 2016). At Novogene Bioinformatics Technology Co. Ltd. (UK), a small-fragment library was created, followed by paired-end sequencing with the Novaseq PE250. The DADA2 pipeline  (Callahan et al. , 2016)  in R version 4. 0. 5 was used to process the 16S rRNA gene amplicon sequences. Filtering and trimming of the reads were done using the default settings of DADA2. The paired-end reads were subsequently combined, and an amplicon sequence variant (ASV) database was built using the core sample inference algorithm and trained error models of DADA2 followed by the removal of chimeras.
S1C ). All qPCR reactions were carried out in technical triplicates, with a nontemplate control included in each run. A threshold cycle (C T ) of 31 was used as the detection limit for the individual PCR reactions. C T values higher than 31 were set to 0 and only genes detected in all triplicates were regarded as positive. Relative gene copy numbers were calculated with the formula: relative gene copy numbers = 10 (31-Ct)/(10/3) as described previously  (Looft et al. , 2012) , where Ct refers to HT-qPCR results. HT-qPCR data were normalized among samples by dividing the relative gene copy numbers by the corresponding 16S rRNA gene copy numbers.
Statistical analyses R version 4. 0. 5 (2021-03-31) was used for data exploration, graphics, and all statistical analyses. Bacterial community composition was examined with "microeco" version 0. 10. 1 and "ggplot2" version 3. 3. 3 R packages  (Liu et al. , 2021; Wickham, 2016). Alpha diversity of samples was displayed with Chao1 and Shannon measures, and statistical differences between treatment groups and time points were assessed by the Kruskal-Wallis rank sum test  (Kruskal and Wallis, 1952)  combined with Dunn's test for Multiple comparisons. Beta diversity of samples was displayed using Nonmetric multidimensional scaling (NMDS) based on Bray-Curtis dissimilarity metrics. Differences in bacterial community composition among treatment groups and time points were investigated by perMANOVA.
To examine the potential effects of Cu on the bacterial community structure and MRG/MGE composition, data from pigs with the same treatment and sampling time point were aggregated. MRG/MGE profiles were analyzed using the "vegan" R package version 2. 6-2  (Oksanen et al. , 2022). The function adonis was used to analyze variations in MRG/MGE compositions across treatment groups and time points using permutation multivariate analysis of variance (perMANOVA;  Anderson, 2001). NMDS ordination using Bray-Curtis dissimilarity index was used to display dissimilarities of MRG/MGE compositions across treatments and time points. The "pheatmap" R package version 1. 0. 12 was used to create a heatmap of relative gene abundances. Significant differences in the relative abundance of MRG or MGE classes and also in individual genes were assessed by a oneway Analysis of Variance (ANOVA) test using the "dplyr" R package version 1.
The gene products of MMAA and MMAB convert intracellular vitamin B 12 (cobalamin, Cbl) into its cofactor form (adenosylcobalamin, AdoCbl), which is used by methylmalonyl-CoA mutase (MMUT) for the breakdown of methylmalonyl-CoA to succinyl-CoA as Article https://doi. org/10. 1038/s42255-022-00720-8 whereas those of 60 individuals had MMUT activity similar to controls (other MMA) (Fig. 1b ). In the MMUT-deficient samples, we searched the WGS dataset for disease-causing variations in the MMUT gene and identified the molecular cause of disease in 148 out of 150 individuals (Supplementary Table  1 ). Pathogenic variants constituted 165 missense alleles, 105 truncating alleles, 21 splicing alleles, 2 alleles with in-frame deletions and 3 alleles containing copy-number variants (Fig. 1c, d  and Extended Data Fig.
Surviving patients with MMA are affected by long-term complications that mainly include neurological abnormalities (movement disorder and intellectual impairment), kidney failure and anemia/ neutropenia  7. Even though the (dys)function of MMUT has been studied extensively  [8] [9] [10] [11]  , the main metabolic disturbances and pathomechanisms in MMA remain an open question and curative treatment options are not available. Technological advances in genomics and mass spectrometry, leveraging datasets of whole molecule classes (omics), have recently led to a paradigm shift in their use as diagnostic tools. For example, single-layer WGS has achieved diagnostic rates of 30-50% in rare disease cohorts  [12] [13] [14]  , while dual-layer combination with RNA sequencing (RNA-seq) can improve this by 10-35%  [15] [16] [17] [18].
Article https://doi. org/10. 1038/s42255-022-00720-8 multi-omics factor analysis  23  , integrating both genetic data layers and proteotyping data, identified mitochondrial metabolic pathways and, in particular, the electron transport chain and the TCA cycle to be enriched in MMUT-deficient samples (Fig. 3a ). In more detail, the proteins SLC16A3, CS, MDH2 and OGDH were found to be the main drivers of this particular factor's variance in the proteotyping data within the TCA-associated gene sets (Fig. 3b ). Linear discriminant analysis of genes shared between transcriptomics and proteotyping indicated MMUT as the strongest and SUCLA2, OGDH and PDHB to be top drivers of separation between MMUT-deficient and control samples (Fig. 3c ). Further, gene set enrichment analysis utilizing sample stratification by disease severity, both by CSS and PI+, also identified oxidative phosphorylation and the TCA cycle as over-represented pathways in the proteomics (CSS and PI+) and transcriptomics (CSS) datasets (Fig.
6a ). Using cross-linking immunoprecipitation (IP) followed by immunoblotting, we found that IP of over-expressed MMUT, MMAB, MMAA and MCEE enabled detection of endogenous MMUT and MMAB (Extended Data Fig. 9b ), a result confirmed by reciprocal detection of endogenous MMAB and MMUT (Extended Data Fig. 9c ), indicating that these pathway members were indeed part of a complex. In contrast, IP following expression of EV, VLCAD and ACO2 did not result in the detection of endogenous MMUT and MMAB (Extended Data Fig. 9b ), demonstrating that the above-detected interactions are specific. To examine a potential complex with TCA cycle and anaplerotic enzymes, we performed IP coupled to mass spectrometry of the four MMUT pathway proteins (MMUT, MMAA, MMAB and MCEE) and two negative controls (EV and VLCAD).
Discussion In this study, we used an integrated multi-modal approach to diagnose and uncover pathomechanisms of the IEM like MMA. Unique to this investigation was the relatively large set of patient samples and corresponding phenotypes available for such a rare genetic disease and the ability to coordinate aliquots from the same samples to generate data at three molecular layers. The results of our study will encourage future endeavors to use our approach in any setting of an inborn monogenic disease. Moving forward, the datasets derived from our study can be further exploited, for example, by applying network contextualization tools  34  , integrating multi-omics and flux modeling  35  and reconstructing genome-scale metabolic networks  36  , continuing to refine the pipeline of a multi-modal study of IEMs.
Our findings reinforce the value of comprehensive and complementary datasets to increase diagnostic yield and the understanding of the pathophysiological underpinnings of disease. Our multi-modal profiling allowed the identification of causative genetic variation in 84% of the cohort, including causative factors in the samples without MMUT deficiency. We were able to widen the set of genes beyond the classical MMA genes MMUT, MMAA and MMAB. For example, the identification of ACSF3 damaging variants in our cohort is particularly notable as they have recently been linked to combined malonic and MMA  37. The phenotype of patients with combined malonic and MMA was indistinguishable from the remainder of patients with MMA with normal MMUT activity, highlighting the fact that IEMs present with widely overlapping phenotypes and that they should be studied with large gene panels or with WGS approaches to avoid biases toward known genes and to augment the chances of diagnosis.
While the ability of clinical phenotypic information to predict a molecular diagnosis was limited, phenotypic variables, both clinical and biochemical, enabled sample stratification by disease severity and consequently identification of multi-level alterations of metabolic genes/proteins that were not apparent following examination of single omics layers. Such a move away from 'data silos' into true integrative and mechanism-based, multi-layered analysis remains challenging, as it requires new analytical and statistical methods to combine these disparate datasets  38. In this capacity, multi-omics factor analysis  23  highlighted the disruption to transcripts and proteins of the TCA cycle and related pathways, a finding verified by the correlation with phenotypic data utilizing both PI activity and a CSS.
Cohort selection Patient samples were referred to our center initially for enzymatic or genetic diagnostic purposes. For this study, we selected affected individuals (n = 210) based on the presence of methylmalonic acid in urine or plasma. Patient samples were accompanied by a questionnaire filled by the referring physician (Supplementary Document 1) containing data on the patients' clinical and biochemical presentation. Phenotype data are provided in the Source Data for Fig. 2. For the analyses shown in Fig. 2 , nine phenotypic variables (hypothermia, hyperventilation, irritability, somnolence, vomiting, dehydration, feeding difficulties, responsive to acute treatment and estimated glomerular filtration rate) were excluded due to their nonspecific nature, whereas the analyses in Supplementary Fig.
Full MS survey scans were acquired at a resolution of 60,000 with automatic gain control (AGC) target of 3 × 10 6 and a maximum injection time of 45 ms over a scan range of m/z 375-1,500. A data-dependent top-12 method was used for HCD MS/MS with a normalized collision energy of 28 at a resolution of 15,000 and a fixed first mass of m/z 100. Precursor ions were isolated in a 1. 4-Th window and accumulated to reach an AGC target value of 1 × 10 5 with a maximum injection time of 22 ms. Precursor ions with a charge state of 1 and 6 as well as isotopes were excluded for fragmentation. Dynamic exclusion was set to 15 s. DDA raw files were processed with Proteome Discoverer (v. 2. 2) using a human UniProt database (release 201804) together with iRT peptides (Biognosys) and common contaminants. The processing workflow consisted of SequestHT  45  and Amanda  46  nodes coupled with Percolator  47.
DIA-MS setup and data analysis For DIA analysis, samples were measured on a Q-Exactive HF mass spectrometer (Thermo Fisher Scientific). Mobile phase A consisted of HPLC-grade water with 0. 1% (v/v) formic acid and mobile phase B consisted of HPLC-grade ACN with 20% (v/v) HPLC-grade water and 0. 1% (v/v) formic acid. Peptide separation was carried out on an ES806, 2 μm, 100 Å, 150 μm internal diameter × 150 mm, C18 EASY-Spray column (Thermo Fisher Scientific) at a temperature of 50 °C. For LC-MS/ MS analyses, 2 μg each sample were loaded onto the column via an Easy-nLC 1200 system (Thermo Fisher Scientific). Samples were loaded at 4 μl min -1 with 100% mobile phase A for 5 min. Peptide elution was performed using the following gradient (1) 2% to 8% mobile phase B in 4 min; (2) 8% to 32% mobile phase B in 49 min; (3) 32% to 60 % mobile phase B in 1 min; and (4) ramp to 98% mobile phase B in 1 min at 2 μl min -1.
For DIA acquisition on a Q-Exactive HF mass spectrometer, we applied a DIA method published elsewhere  48. In short, we performed an MS1 scan over a mass range of m/z 400-1210 at a resolution of 120,000 with an AGC target value of 3 × 10 6 and with a maximum injection time of 50 ms. For MS/MS scans, the resolution was at 30,000 with an AGC target value of 1 × 10 6 and with 'Auto' maximum injection time. Precursor ions were isolated within a 15-Th window and fragmented by HCD with normalized collision energy 28. A total of 54 MS/MS scan windows were defined, interspersed every 18 scans with an MS1 scan. DIA data analysis was performed in Spectronaut v. 12 (Biognosys) using standard parameters. For identification, a Q value cutoff of 0. 01 was applied on the precursor as well as on the protein level.
The MS1 area was selected for quantification. Quantification parameters were set to mean peptide quantity for major group quantity, the top three peptides were selected for protein quantity calculation. Data filtering was set to Q value sparse, with no imputation. Cross-run normalization was set to local. The protein report for downstream analysis contained information report about PG. ProteinAccessions, PG. ProteinDescriptions, PG. ProteinNames. PG. Qvalue and PG. Quantity.
Quality assessment of WGS, RNA-seq and DIA-MS data Overall quality assurance tests revealed a mean of high-quality aligned genomic reads of 8. 7 × 10 8 at a median genomic coverage of > 38-fold (Extended Data Fig. 1b ). A median of 3. 74 million single-nucleotide variations were called using the Genome Analysis Toolkit  49  and Deep-Variant  50. RNA-seq data showed a median Phred score of > 36. 3 at three and more cycles (Extended Data Fig. 1c ), while proteomics data showed a high reproducibility with 2,218 proteins detected in at least 75% of samples (Extended Data Fig. 1d ). For 9 of the 230 samples RNA extraction yielded insufficient nucleic acid to proceed with transcriptome sequencing; hence, these datasets were excluded from all further analysis (transcriptomics data of sample IDs 22, 54, 59, 78, 89, 109, 123, 207 and 221).
Selection of primary fibroblasts for polar metabolomics To select cell lines for metabolomics, we opted for a balanced design with ten MMUT-deficient cell lines and ten control lines. MMUT-deficient lines were picked to show over-expression of GLUD1 and under-expression of OGDH, whereas the control lines were chosen to show the reverse pattern. We fitted a mixed-effects model with PI+ as a response, two fixed effects for GLUD1 and OGDH expression and a random effect with the same covariance structure as the proteomics data after column and row normalization. From the MMUT-deficient and control cell lines, we chose ten with the lowest predicted value of PI+ and ten with the highest predicted value, respectively. The top-ten ranked MMUT-deficient (MMA014, MMA092, MMA042, MMA067, MMA093, MMA104, MMA013, MMA030, MMA138 and MMA036) and the last-ten ranked control primary fibroblasts (MMA219, MMA221, MMA227, MMA222, MMA213, MMA230, MMA226, MMA228, MMA225 and MMA215) were selected and cultured as described above.
Polar metabolomics in patient-derived fibroblasts Untargeted metabolite profiling was performed using flow injection analysis on an Agilent 6550 QTOF instrument (Agilent) using negative ionization, 4 GHz high-resolution acquisition and scanning in MS1 mode between m/z 50-1,000 at 1. 4 Hz 51. The solvent was 60:40 isopropanol:water supplemented with 1 mM NH 4 F at pH 9. 0, as well as 10 nM hexakis(1H, 1H, 3H-tetrafluoropropoxy)phosphazine and 80 nM taurochloric acid for online mass calibration. The seven batches were analyzed sequentially. Within each batch, the injection sequence was randomized. Data were acquired in profile mode, centroided and analyzed with MatLab (Mathworks). Missing values were filled by recursion in the raw data. Upon identification of consensus centroids across all samples, ions were putatively annotated by accurate mass and isotopic patterns.
Starting from the HMDB v. 4. 0 database, we generated a list of expected ions, including deprotonated, fluorinated and all major adducts found under these conditions. All formulas matching the measured mass within a mass tolerance of 0. 001 Da were enumerated. As this method does not employ chromatographic separation or in-depth MS2 characterization, it is not possible to distinguish between compounds with the identical molecular formula. The confidence of annotation reflects level 4 but, in practice, in the case of intermediates of primary metabolism, it is higher because they are the most abundant metabolites in cells. The resulting data matrix included 1,809 ions that could be matched to deprotonated metabolites listed in HMDB. All m/z peaks that remained unmatched or were associated with adducts or heavy isotopomers were discarded.
Metabolomics in mouse tissues The mouse body fluid and tissue samples derived from five female Mmut-ki/wt and five female Mmut-ko/ki mice were collected as described above and prepared as previously published  52. Sample analysis using LC-MS was performed as previously published  53. Ions were annotated to metabolites based on exact mass to the KEGG database  54  considering [M-H+] and 0. 01 Da mass accuracy.
Western blotting Protein extraction and Western blotting was performed as described previously  59. Primary antibodies used were probing for the following proteins: MMUT (Abcam, ab67869, 1:1,000 dilution, mouse host), OGDH (Atlas Antibodies, HPA020347, 1:500 dilution, rabbit host), GLUD (Abcam, ab166618, 1:2,000 dilution, rabbit host) and β-actin (Sigma, A1978, 1:5,000 dilution, mouse host). Secondary antibodies used were anti-rabbit HRP (Santa Cruz, sc-2357, 1:5,000 dilution, mouse host) and anti-mouse HRP (Santa Cruz, sc-516102, 1:5,000 dilution, goat host).
Affinity-capture mass spectrometry The 293T cells were grown in DMEM (Gibco) supplemented with 10% fetal bovine serum (Gibco) and antibiotics (GE Healthcare). Transient transfection of each pCDNA3-C-FLAG-LIC construct was performed at least three separate times using Lipofectamine 3000 (Thermo Fisher Scientific) according to manufacturer's instructions. At 48 h after transfection, cells were crosslinked using 0. 5% paraformaldehyde (PFA, Sigma-Aldrich) in PBS (Gibco) for 10 min at RT, the reaction was quenched with 1. 25 M glycine/PBS (Sigma-Aldrich) for 10 min at 4 °C, cells were centrifuged for 5 min at 2,000g at 4 °C and the pellet resuspended in lysis buffer (1% Nonidet P-40, 0. 5% deoxycholine, 150 mM NaCl and 50 mM Tris-HCl, pH 7. 5; all Sigma-Aldrich). Pre-cleared cell extracts were immunoprecipitated with anti-FLAG M2 (F3165, Sigma-Aldrich), anti-MMUT (ab67869, Abcam) or anti-MMAB (HPA039017, Sigma-Aldrich) using Dynabeads Protein G (Thermo Fisher Scientific) according to the manufacturer's instructions.