A 15-day-old girl was found dead inside a cabinet at her home in northeast Brazil after her own mother confessed to killing her. Eduarda de Oliveira provided the Alagoas Civil Police five different versions of Ana de Oliveira's disappearance before she finally told family members of the baby's whereabouts Tuesday. Little Ana was stuffed into a plastic bag that was hidden in a wooden cabinet in the laundry room of Eduarda's residence in Novo Lino. The 22-year-old mother first told investigators that Ana's death was an accident. She alleged that her daughter choked while she was breastfeeding her and that she was unable to revive her.  Eduarda then admitted to suffocating Ana because the baby's cries and the noise from a bar near the family home had deprived her of sleep on two consecutive nights. Eduarda was arrested for concealing a body and has not been charged with murder. Authorities were led on a citywide search Friday after Eduarda reported that Ana was ripped out of her hands by four men. The mother-of-two claimed she was approached by the suspects in black vehicle while she was waiting with her five-year-old son for his school bus to arrive. She then changed her story and reported that a blonde woman sitting in the back of the car had aimed a gun at her and forced her to hand over Ana through the car window before the driver sped off. Eduarda later retracted the version of the event and claimed the four men flashed knives at her and kidnapped the baby.  Her story unraveled even more as she claimed she was intercepted by two men on foot on a highway and snatched Ana away from her. Eduarda then told investigators that she forgot to lock the front gate to her home and that two hooded men broke into the residence Friday around midnight and sexually abused gunpoint while her children were sleeping. Surveillance cameras inspected by the police discredited each of her accounts. Police used a sniffer dog and searched the home Monday but were unable to locate the baby. Investigators are looking into whether another person helped Eduarda hide Ana's body. 'The mother may have become desperate and asked for help from a family member to do this,' Alagoas Civil Police chief Igor Diego said, according to Brazilian news outlet G1.  'And taking advantage of this moment when there was no one in the house, no one on the street, because the child's mother was in another region of Novo Lino, this person may have taken the body and put it in the closet,' Diego added. 'These are things that the investigation will still work on.' Major terror attack 'was just HOURS away' before it was foiled by the special forces and police:... Victim of acid attack 'plotted by his ex-partner who teamed up with a gang' dies in hospital six... 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No one seems to have shared their thoughts on this topic yetLeave a comment so your voice will be heard first. {{message}} This website is using a security service to protect itself from online attacks The action you just performed triggered the security solution There are several actions that could trigger this block including submitting a certain word or phrase You can email the site owner to let them know you were blocked Please include what you were doing when this page came up and the Cloudflare Ray ID found at the bottom of this page Metrics details Bioethanol is a sustainable energy alternative and can contribute to global greenhouse-gas emission reductions by over 60% Its industrial production faces various bottlenecks including sub-optimal efficiency resulting from bacteria Broad-spectrum removal of these contaminants results in negligible gains suggesting that the process is shaped by ecological interactions within the microbial community we survey the microbiome across all process steps at two biorefineries over three timepoints in a production season Leveraging shotgun metagenomics and cultivation-based approaches we identify beneficial bacteria and find improved outcome when yeast-to-bacteria ratios increase during fermentation We provide a microbial gene catalogue which reveals bacteria-specific pathways associated with performance We also show that Limosilactobacillus fermentum overgrowth lowers production with one strain reducing yield by ~5% in laboratory fermentations Temperature is found to be a major driver for strain-level dynamics Improved microbial management strategies could unlock environmental and economic gains in this US $ 60 billion industry enabling its wider adoption we describe and compare the microbial populations of the industrial bioethanol process across all unitary steps at two major biorefineries in Brazil Sampled at 3 time points over a single production season we use a combination of shotgun metagenomics and cultivation-based methods to interrogate the microbiome at multiple taxonomic and functional levels and identify ecological factors underpinning community dynamics and bioconversion efficiency We link increased temperatures with growth of specific bacteria that impede yeast viability and fermentation performance strains from the same bacterial species showed distinct effects most likely driven by metabolic differences that can be beneficial or detrimental to ethanol yield Our findings motivate the adoption of higher resolution methods to holistically monitor the microbial communities in industrial-scale fermentation processes in order to maintain performance We also suggest strategies towards the development of strategies to control the growth of undesirable microbes which will make bioethanol production more cost-effective and thus encourage greater adoption of this renewable energy source A Outline of study sampling strategy, repeated 3 times in a single production season at 2 independent mills. Fresh media [1] are fed into fermentation vessels for 3 h. Samples were collected at 0–1.5 h [2], 1.5–3 h [3] and post-feeding [4]. Biomass is then centrifuged [5] and acid-washed [6] before re-entering the vessels. Vector images were obtained from Flaticon (www.flaticon.com) and figure as created using Adobe Illustrator B Metagenomic profiling of microbial communities grouped by relative batch performance (columns) per mill (row) Higher numbers of bacteria (blue) or eukaryotes (red) are not linked to better production performance high-performing batch) did not contain sufficient DNA for sequencing Error bars show variation across multiple samples For samples containing more than one datapoint C Eukaryote-to-bacteria ratios across the production process eukaryotic populations increased in the high-performing batches (orange square) during fermentation steps (thick line) and decreased in lower-performing batches (light and dark brown Grey line denotes equal proportion of eukaryotes and bacteria (i.e The y-axis indicates the fold change in the eukaryote to bacteria ratio Higher fold values indicate a greater eukaryote abundance and lower fold values a higher prokaryote abundance D Genes associated with changes in fermentation performance involved in metabolism or membrane transport differed between low- (brown) and high-performing (orange) batches (FDR < 0.1 for both mills) Increase in genes linked to bacteria-specific pathways lipopolysaccharide biosynthesis and the phosphotransferase (PTS) system Gene modules are ordered by decreasing relative abundance KEGG Module IDs are provided in parentheses The centre line denotes the median value (50th percentile) while the box contains the 25th–75th percentiles of dataset The whiskers mark the 5th and 95th percentiles and values beyond these upper and lower bounds are considered outliers Source data are provided as a Source Data file Measurements commonly used by the industry to assess bioethanol production process quality were collected at each sampling to describe each fermentation batch (Supplementary Data 1) leading to possible confounders without proper data available To facilitate evaluation and fair comparison of performance across the different batches we devised a composite metric that accounted for ethanol yield quality of biological catalysts as well as potential inhibitors of fermentation performance (Methods) This enabled the three batches from each biorefinery to be ranked by performance: low suggesting that changes were also occurring within the bacterial community these observations point to a central role of microbial community dynamics in driving the success of the bioconversion process A Taxonomic profiling of bacterial populations detected across process steps fermentum (dark blue) strains comprise the majority of bacteria across samples Bacterial communities in the high-performing batch of Mill A and low-performing batch of Mill B undergo less change compared to others the 10 most abundant species are shown; beige colour represents other bacteria Average values were used in process steps where multiple samples were collected amylovorus decreases by the end of fermentation C Bacterial species associated with changes in fermentation performance High-performing batches showed increases in L while other lactic acid bacteria including L L.buchneri and L.plantarum decreased (bottom row) (FDR < 0.1 in both mills) Species are ordered by decreasing relative abundance while the box contains the 25th to 75th percentiles of dataset A Correlation matrix of industrial fermentation parameters that showed strong associations throughout the production season (all sampling timepoints) Size and colour of points show correlation strength and direction Low ethanol yield is associated with high acidity titres (dark red and increased bacterial cell count is linked to lower viability of yeast cells (orange Cross denotes correlations with FDR > 0.1 B Bacterial species associated with fermentation performance parameters fermentum is linked to higher acidity titres (Spearman’s ρ = 0.64 and increased bacterial cell count (D; Spearman’s ρ = 0.63 Its presence in the fermentation process also suggests that this environment may be an untapped source for novel industrially-relevant enzymes A Ethanol yield from pairwise fermentations of industrial yeast strain PE-2 with 3 L fermentum industrial isolates (blue) and the 5 most abundant bacteria in the bioethanol production microbiome compared to standalone fermentation (white) Final ethanol yields were compared by multiple t-test (statistical significance analysis with alpha value of 0.05) based on the alignment of 40 bacterial marker genes fermentum strains broadly cluster into 4 groups; strain (C) belongs to a different clade than strains (A Internal nodes are labelled with bootstrap values from 500 resamplings with the triangular root replaced for visual clarity C Metabolite profiles measured from the supernatant of the 3 L Strains A and B showed similar production of acetate right) produced no ethanol and almost twice as much lactate as strains A and B it seems that the inhibition of yeast growth is not solely driven by organic acid production by the bacteria but probably by the lack of essential nutrients in the media that was previously cultivated with bacteria suggesting competition for nutrients among them Final ODs (Supplementary Fig. 5) and specific growth rates (Supplementary Fig. 6) presented divergent absolute values between the different spent media produced by the 3 bacterial strains with strain C resulting in lower growth rate but higher OD values in fermented molasses supplemented with sugars This raises the question whether the inhibitory effect of such strain is not somewhat related with the production of another inhibitory molecule These results suggest that this strain might be fitter for faster growth during the fermentation and environmental conditions might play a big role in defining which dominant strain would overtake the L fermentum ratio at each stage of fermentation Blue line denotes equal proportion of the two bacteria (i.e amylovorus at the end of the bioprocess (right of blue line) B Vat temperature at each stage of fermentation Temperature decreases as the bioprocess progresses while the box contains the 25th – 75th percentiles of dataset Error bars denote variation across experimental replicates (*p < 0.05) This finding aligns with the results observed In this study, the gene annotations and metabolic characteristics of three Lactobacillus fermentum strains are presented, using metagenome-assembled genomes (MAGs) from various samples (Supplementary Fig. 9B The findings indicate a high level of conservation in genes and metabolic pathways among these strains The study also reinforces previous observations that certain L fermentum strains are associated with decreased performance demonstrating a link between specific bacterial strains and their biological impact we have used high-resolution metagenomic sequencing and physiological data from laboratory experiments combined with in-depth surveying of two independent biorefineries to build a comprehensive map of the microbial ecology underlying industrial-scale bioethanol production We provide insights into how selective pressures imposed by the fermentation process shape microbiome functionality and ultimately The unidirectional nature of the fermentation setup provided a unique system to study the interactions within a complex microbial community and how it changes in response to quantifiable changes in its environment We demonstrate in vitro that higher temperatures encourage rapid growth of L which could drive the dominance of bacteria over S cerevisiae populations that may already be under heat stress along with increased competition for limited resources likely reduces viability of the yeast cells This interplay motivates the adoption of sequencing technologies in place of rough estimations such as total cell counts to evaluate and predict bioconversion outcome more accurately and thus improve process control of this industry our metagenomic approach also enables the discovery of microbial species and functions not previously associated with fermentation performance We show that bacteria not only hinder but also enhance the industrial production of bioethanol impact on industrial productivity is likely to be mediated by particular strains rather than entire species This suggests that current microbial management approaches aimed at removing all bacteria from the bioconversion process may be counterproductive and in fact reduce overall ethanol yields monitoring of the bacterial community provides strategic first steps towards improving overall performance of the industrial process In addition to ensuring optimal fermentation activity by S tighter control of conditions such as vat temperature may also facilitate the retention of a microbiome that supports sustainable production of bioethanol all chemicals and reagents used were purchased from Sigma-Aldrich (St We sampled two independent ethanol mills (named Mill A and Mill B) in the production season of 2017 Both mills are located in the State of São Paulo Brazil in a region with the prevalence of the humid subtropical climate (Cfa) with an annual precipitation of around 2000 mm The mills were completely independent from each other and have raw material sourced from different producers and sugarcane fields Both mills operated via fed-batch fermentations (Melle-Boinot setup) and had a similar ethanol production capacity with a daily output of ca The following steps of the ethanol production process were sampled: (1) Fermentation broth (Feeding line with fresh fermentation media); (2) start (0–1.5 h after feeding has commenced); (3) middle (1.5−3 h); (4) end of fermentation (after cessation of feeding); (5) yeast cream after separation of the wine (which is sent to distillation centrifugation); and (6) biomass after acid wash treatment (sulphuric acid pH 2.5 for 1 h) samples were collected from different vats at different stages of fermentation in a single day Samples were collected directly from the production process and diluted 1x in a sterile Phosphate Buffered Saline (PBS) solution with glycerol (50%) The samples were readily frozen in dry ice until final storage in ultrafreezer (−80 °C) Each mill had several vessels operating in the same fermentation step the data was converted into monthly averages The industrial performance calculation was obtained by the product of the multiplication of the parameters directed correlated with process performance (i.e divided by the product of the multiplication of the parameters inversely correlated with process performance (i.e The score is obtained by multiplying ethanol yield (Ethyield) and yeast viability (Yeastviab) values and dividing its product by the product obtained from the multiplication of bacterial cell counts (Baccounts) and wine acidity titre (Acidtitre) values Saccharomyces cerevisiae strain PE-2 was kindly provided by Prof Strains of Lactobacillus amylovorus and Lactobacillus fermentum were isolated from stored industrial samples Lactobacillus buchneri and Zymomonas mobilis were purchased from ATCC (Manassas Industrial samples were serially diluted in sterile PBS and plated in Man Rogosa Sharpe (MRS) Agar media containing cycloheximide (0.1% v.v−1) to inhibit yeast growth Plates were incubated at either 30 °C or 37 °C statically A loopful of an isolated colony was grown in liquid MRS in the same conditions and stored at −80 °C (see section ‘DNA extraction of bacterial isolates’ below) Yeast strains were cultured in Yeast Potato Dextrose (YPD) media and Zymomonas mobilis was cultured at Trypsin Soy Broth (TSB) media All cultivations were performed statically Pure isolates were grown overnight in adequate media and conditions (please see section Isolation and maintenance of industrial strains) cells were pelleted via centrifugation (>10,000 g for 4 min.) and genomic DNA was extracted using the MasterPure™ Gram Positive DNA Purification Kit (Lucigen Corporation DNA extraction of metagenomic samples was performed using the DNeasy Powerlyzer Powersoil Kit (QIAGEN Extraction was not possible for sample collected from the starting broth (Step 1) in Mill A on 17/11/2017 All DNA extraction quantifications were performed with Qubit Fluorometer (Thermo Fischer Scientific Shotgun metagenomics and genome sequencing of isolates were performed on the NextSeq 500 using NextSeq High Output v2 Kit (300 Cycles) (Illumina USA) by the Sequencing Core Facility at The Novo Nordisk Foundation Center for Biosustainability (Technical University of Denmark The library preparation was performed using the KAPA HyperPlus Library Prep Kit (Roche and the indexing kit used was the Dual Indexed PentAdapters Quantity and quality control were performed using Qubit dsDNA HS Assay Kit (Invitrogen USA) and DNF-473 Standard Sensitivity NGS Fragment Analysis Kit (1 bp - 6000 bp; Agilent The sequencing reads length were 150 base pair paired-end (2 × 150 bp) dual indexed and flow cell loading was 1.3 pM The sequencing chemistry used was 2-channel sequencing-by-synthesis (SBS) technology Two modifications were made in the source code before compiling IDBA_UD: (1) in file src/basic/kmer.h kNumUint64 was changed from 4–8 to allow maximum kmer length beyond 124; (2) in file src/sequence/short_sequence.h kMaxShortSequence was set to 512 to support longer read length Assembly statistics are provided in Supplementary Data The longest DNA sequence for each cluster was used to generate the resulting catalogue of 297,115 non-redundant gene sequences with median length 336 bp species names have been edited in the main text to reflect updates in taxonomic classification fermentum strains was compared using their gene name annotations as provided by Prokka all strains were cultured in their optimal media and conditions (see above “Strains” and “Isolation of industrial strains and maintenance” sections) the biomass was calculated via optical density (OD; 600 nm wavelength) All cells were pelleted via centrifugation (3400 × g cells were diluted in SM diluted in sterile Milli-Q H2O (10x final sugar concentration of 18 g/L) for an OD value of 1.0 Strains were later diluted in fresh SM media in specific wells in the 96 deep-well plate to a final OD value of 0.1 All the pairwise cultivations were performed statically The fermentations were performed in triplicate The carbohydrate titre and composition (sucrose glucose and fructose) and fermentation metabolites (glycerol and acetic acid) were determined by high-performance liquid chromatography (HPLC) (UltiMate 3000 The analites were separated using an Aminex HPX-87H ion exclusion column (Bio-Rad USA) and were isocratically eluted at 30 °C using a 5 mM sulphuric acid solution as mobile phase The detection was performed refractrometrically Ethanol yield was calculated according using the following equation: Lactobacilli supernatant metabolite profile was analysed via HPLC after 48 h of growth (please see section Fermentation experiments for a detailed description of the HPLC method) A pre-inoculum of lactobacilli stored at −80 °C was grown in MRS for 24 h After that the OD from these cultures was measured and fresh MRS media was inoculated with a fixed OD of 0.1 and incubated statically at 37 °C the cells were separated via centrifugation and the supernatant was sent for further analysis To investigate the impact of lactic acid bacteria metabolites on the growth of S and C) were cultivated in diluted molasses (20 g l−1 TRS) with an initial inoculation OD of 0.5 the spent growth media was collected by centrifugation and filtered through a 0.22 µm filter to produce the media used for the microplate assay (referred to as “diluted molasses previously fermented by each of the L cerevisiae PE-2 was evaluated using a Tecan Infinite® 200 PRO microplate reader at a temperature of 30 °C for 24 h with an initial OD of 0.1 in 200 µL of the following media: (1) diluted molasses (20 g l−1 TRS) (2) diluted molasses previously fermented by each of the L fermentum strains; (3) diluted molasses previously fermented by each of the L fermentum strains supplemented with a mixture of sugars (to restore its initial sugar composition) and (4) diluted molasses spiked with key bacterial metabolites (i.e. to restore the composition produced in the diluted molasses previously fermented by each of the L to investigate the impact of yeast growth metabolites on lactic acid bacteria growth cerevisiae PE-2 was cultivated in diluted molasses (20 g l−1 TRS) with an initial inoculation OD of 0.5 the spent growth media was collected by centrifugation and filtered through a 0.22 µm filter to produce the media used for the microplate assay (referred to as “diluted molasses previously fermented by S and C) was evaluated using a Tecan Infinite® 200 PRO microplate reader at a temperature of 30°C for 150 h with an initial OD of 0.1 in 200 µL of the following media: (1) diluted molasses (20 g l−1 TRS) (2) diluted molasses previously fermented by S cerevisiae; (3) diluted molasses previously fermented by S cerevisiae supplemented with a mixture of sugars (to restore its initial sugar composition) and (4) diluted molasses spiked with key yeast metabolites (i.e. to restore the composition produced in the diluted molasses previously fermented by S The industrial molasses sample used in this study was obtained from an ethanol production plant and was diluted in distilled water to a concentration of 20 g L−1 TRS and sterilised through a 0.22 µm filter The final sugar concentration was determined using an ion exchange column HPX-87C (Bio-Rad) at 85 °C with H2O as the mobile phase and a flow rate of 0.6 l min−1 for glucose The microbial metabolites concentrations were obtained using an ion exchange column HPX-87H (Bio-Rad) at 60 °C with 5 mM H2SO4 as the mobile phase and a flow rate of 0.6 l min−1 for lactic acid Community compositions were compared using Bray-Curtis distance on species relative abundance and Permutational Multivariate Analysis of Variance (PERMANOVA) with 999 permutations and the Bray-Curtis method was applied by providing Mill/Process step/Date as function Spearman’s correlation coefficient was calculated for each pair of industrial metadata variables and between metadata variables and species abundances False discovery rate (FDR) was calculated using Benjamini-Hochberg (BH) method Statistical analyses for fermentation experiments were performed using the software GraphPad Prism 8 In this section, we present the comprehensive and detailed methodology for the analysis of metagenomic raw data64 addressing every aspect from initial quality control to the final annotation and classification stages/MAG production: Quality control and trimming: The process starts with paired end reads of 150 bp FastQc (v0.12.1) inspects the raw reads for quality control providing a preliminary assessment of potential issues in the sequence data Subsequent trimming is performed using bbduk (v39.00) This step involves the removal of adaptor sequences and the exclusion of sequences with a Phred score <33 and a minimum read length of 100 bp The parameters set for this process include qin = 33 for input quality offset hdist = 1 for Hamming distance in error correction the contigs undergo quality control using Quast These tools are chosen to minimize biases and maximize accuracy with each providing a unique approach to binning The outputs from these tools are aggregated using DASTool (v1.1.6) which refines the bins by dereplicating contigs and selecting high-quality candidates Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article Homo-and heterofermentative lactobacilli differently affect sugarcane-based fuel ethanol fermentation Identification of lactic acid bacteria isolated from different stages of malt whisky distillery fermentations Diversity of lactic acid bacteria of the bioethanol process Antimicrobial susceptibility of lactobacillus species isolated from commercial ethanol plants Bacterial contaminants of fuel ethanol production Modeling bacterial contamination of fuel ethanol fermentation Yeast selection for fuel ethanol production in Brazil Complex yeast-bacteria interactions affect the yield of industrial ethanol fermentation Biofilm formation and ethanol inhibition by bacterial contaminants of biofuel fermentation Evaluation of optimization techniques for parameter estimation: application to ethanol fermentation considering the effect of temperature Process Synthesis for Fuel Ethanol Production 1st edn Well-to-wheels energy use and greenhouse gas emissions of ethanol from corn sugarcane and cellulosic biomass for US use Ethanol production in Brazil: the industrial process and its impact on yeast fermentation Scientific challenges of bioethanol production in Brazil Conventional and nonconventional strategies for controlling bacterial contamination in fuel ethanol fermentations Ethanol production in Brazil: a bridge between science and industry Effects of acetic acid and lactic acid on the growth of Saccharomyces cerevisiae in a minimal medium Microbial diversity in sugarcane ethanol production in a Brazilian distillery using a culture-independent method Weak acid adaptation: the stress response that confers yeasts with resistance to organic acid food preservatives Resolving bacterial contamination of fuel ethanol fermentations with beneficial bacteria—an alternative to antibiotic treatment Microbial contamination of commercial corn-based fuel ethanol fermentations Effects of lactobacilli on yeast-catalyzed ethanol fermentations Highly thermostable xylanase production from a thermophilic geobacillus sp strain WSUCF1 utilizing lignocellulosic biomass A synthetic medium to simulate sugarcane molasses Lactobacillus buchneri strain NRRL B-30929 converts a concentrated mixture of xylose and glucose into ethanol and other products A comparative view of metabolite and substrate stress and tolerance in microbial bioprocessing: from biofuels and chemicals Engineered phage with antibacterial CRISPR–Cas selectively reduce E Portal UNICA. UNICA http://www.unica.com.br/ (2019) Good or bad bioethanol from a greenhouse gas perspective – what determines this The environmental exposures and inner- and intercity traffic flows of the metro system may contribute to the skin microbiome and resistome SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth Ab initio gene identification in metagenomic sequences CD-HIT: Accelerated for clustering the next-generation sequencing data SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies NG-meta-profiler: fast processing of metagenomes using NGLess Kraken: ultrafast metagenomic sequence classification using exact alignments Bracken: estimating species abundance in metagenomics data and domain prediction at the metagenomic scale functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses Sensitive protein alignments at tree-of-life scale using DIAMOND KEGG: kyoto encyclopedia of genes and genomes Genome analysis prokka: rapid prokaryotic genome annotation Prodigal: Prokaryotic gene recognition and translation initiation site identification Fast genome-wide functional annotation through orthology assignment by eggNOG-mapper ResFinder 4.0 for predictions of phenotypes from genotypes antiSMASH 6.0: improving cluster detection and comparison capabilities proGenomes: a resource for consistent functional and taxonomic annotations of prokaryotic genomes Reference sequence (RefSeq) database at NCBI: current status proGenomes2: an improved database for accurate and consistent habitat taxonomic and functional annotations of prokaryotic genomes T-coffee: a novel method for fast and accurate multiple sequence alignment SequenceMatrix: concatenation software for the fast assembly of multi-gene datasets with character set and codon information Molecular evolutionary genetics analysis (MEGA) for macOS Disentangling the impact of environmental and phylogenetic constraints on prokaryotic within-species diversity Genomes onLine database (GOLD) v.8: overview and updates Growthcurver: An R package for obtaining interpretable metrics from microbial growth curves A simple scaled down system to mimic the industrial production of first generation fuel ethanol in Brazil a package of R functions for community ecology Computational methods for chromosome-scale haplotype reconstruction MEGAHIT: An ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences MetaQUAST: evaluation of metagenome assemblies Improved metagenomic analysis with Kraken 2 Download references The authors would like to acknowledge Marcos Vinícius Thais Granço and Rafael Alves for their support on acquiring industrial samples and metadata René Schneider for providing access to laboratory equipment for processing industrial samples We acknowledge the support of Georges Neto on the processing of samples from the first sampling time point batch The authors would also like to thank Oleksandr M The research was supported by funding from The Novo Nordisk Foundation under NFF grant number: NNF10CC1016517 (F.S.O.L. acknowledges support by the European Molecular Biology Organisation (ALTF 137-2018) and National Health and Medical Research Council of Australia (APP1166180) acknowledges funding from São Paulo Research Foundation (FAPESP) funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) These authors contributed equally: Felipe Senne de Oliveira Lino The Novo Nordisk Foundation Center for Biosustainability Tobias Svend-Aage Beyer-Pedersen & Morten Otto Alexander Sommer School of Chemistry and Molecular Biosciences Leibniz Institute for Natural Product Research and Infection Biology Escola de Engenharia de Alimentos da Universidade de Campinas Departamento de Engenharia Química da Escola Politécnica da Universidade de São Paulo Thamiris Guerra Giacon & Thiago Olitta Basso collected and processed industrial samples and metadata prepared the metagenomics samples for sequencing critically analysed bioinformatics results designed and performed laboratory scale fermentations designed the metabolic interaction experiments critically analysed the laboratory scale fermentation results wrote the manuscript with input from all authors The authors declare no competing interests Nature Communications thanks Steven Singer reviewer(s) for their contribution to the peer review of this work Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations Download citation DOI: https://doi.org/10.1038/s41467-024-49683-2 Anyone you 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