Biology subjects

Hackmann, T.

Publications and source records attributed to Hackmann, T..

7 recordsLinked to original sources

bioRxiv2026

A timeline for evolution of metabolism in early microbes

The metabolism of early microbes shaped our planet's chemistry and enabled more life to evolve. However, it remains uncertain when many forms of metabolism arose and which organisms carried them out. Here we combine machine learning, metabolic networks, and phylogenomics of microbes to date 14 forms of metabolism and 6 other traits. We find that metabolism evolved in three waves, starting >3.4 billion years ago (Ga) with fermentation and sulfur compound respiration. A second wave began ~3.2 Ga with aerobic respiration and growth, while a third began ~1.4 Ga with further specialization. We compared these predictions with independent dates from the geochemical record, and agreement with conservative dates was good (CCC = 0.81 to 0.89). Several forms of metabolism are invisible in the geochemical record, and our approach dates these forms, too. Our results reconcile different lines of evidence for microbial evolution and provide a fuller picture of early Earth.

evolutionary biology↗

bioRxiv2025

Setting new boundaries of 16S rRNA gene identity for prokaryotic taxonomy

The 16S ribosomal RNA (rRNA) gene is frequently sequenced to classify prokaryotes and identify new taxa. If sequences from two strains share less than [~]99% identity, the strains are usually classified as different species. Classification thresholds for genera and other ranks have also been proposed, but they are based on dated datasets. Here we update these thresholds by determining sequence identity of the 16S rRNA gene for nearly 18,000 type strains. This represents 86.5% of all strains validly published, and it involved making more than 161 million pairwise sequence alignments. In 90% of all cases, sequences from the same species shared a minimum of 97.2 to 100% identity. The corresponding values for genus were 90.3 to 99.0%, 79.6 to 94.1% for family, 73.1 to 90.1% for order, 72.2 to 86.3% for class, and 69.7 to 83.6% for phylum. We propose these values serve as thresholds for classifying new prokaryotic taxa. A major change from previous guidelines is recognizing that these boundaries overlap. This overlap has already been observed for relative evolutionary divergence, a metric correlated with 16S rRNA gene identity. Together with other metrics, 16S rRNA gene identity allows classification of prokaryotes from species to phylum.

microbiology↗

bioRxiv2023

The phenotype and genotype of fermentative microbes

Fermentation is a major type of metabolism carried out by many organisms. The study of this metabolism cuts across many fields, including cell biology, animal and human health, and biofuel production. Despite this broad importance, there has been no systematic study of fermentation across many organisms. Here we explore the phenotype and genotype of fermentative prokaryotes in order to gain insight into this metabolism. We assembled a dataset containing phenotypic records of 8,350 organisms (type strains) plus 4,355 genomes and 13.6 million genes. Fermentation was widespread, being found in 30% of all organisms and across the tree of life. Fermentative organisms were more likely than non-fermentative ones to have certain phenotypic traits. Some traits (such as oxygen insensitivity) were expected, but others (such as long cells) were surprising. Fermentative organisms also had a distinct genotype, with 9,450 gene functions and 337 metabolic pathways being more common in them. In a related analysis, we identified end products (metabolites) for 1,455 organisms fermenting 100 substrates. We found 55 products were formed in nearly 300 combinations, showing fermentation is more complex than previously realized. Additionally, we built metabolic models for 406 organisms and predicted which products they form. These models did not predict all products accurately, revealing gaps in our knowledge of metabolic pathways. Our study paints a full picture of fermentation while showing there is still much to learn about this type of metabolism. The microbiology community can explore resources in this work with an interactive tool (https://github.com/thackmann/FermentationExplorer).

microbiology↗

bioRxiv2022

The enzyme Rnf completes the pathway for forming propionate during fermentation in Prevotella

Propionate is a microbial metabolite that is formed in the gastrointestinal tract, and it affects host physiology as a source of energy and signaling molecule. Despite the importance of propionate, the biochemical pathways responsible for its formation are not clear in all microbes. For the succinate pathway used during fermentation, a key enzyme appears to be missing--one that can oxidize ferredoxin and reduce NAD. Here we show that Rnf [ferredoxin--NAD(+) oxidoreductase (Na(+)-transporting)] is this key enzyme in two abundant bacteria of the rumen (Prevotella brevis and Prevotella ruminicola). We found these bacteria form propionate, succinate, and acetate with the classic succinate pathway. At first, this pathway appears unbalanced, forming reduced ferredoxin and oxidized NAD in excess. If this continued unabated, fermentation would halt within 1.5 s. We found these bacteria solve this problem by oxidizing ferredoxin and reducing NAD with Rnf. This is demonstrated using growth experiments, genomics, proteomics, and enzyme assays. Genomic and phenotypic data suggest many bacteria use Rnf similarly. We cataloged fermentation products of >1,400 species of prokaryotes, and nearly 10% formed propionate, succinate, and acetate. Over 40% of species carrying out this fermentation also had genes for Rnf. This work shows Rnf is important to propionate formation in many bacteria from the environment, and it provides fundamental knowledge for manipulating fermentative propionate production.

microbiology↗

bioRxiv2020

Using neural networks to mine text and predict metabolic traits for thousands of microbes

Microbes can metabolize more chemical compounds than any other group of organisms. As a result, their metabolism is of interest to investigators across biology. Despite the interest, information on metabolism of specific microbes is hard to access. Information is buried in text of books and journals, and investigators have no easy way to extract it out. Here we investigate if neural networks can extract out this information and predict metabolic traits. For proof of concept, we predicted two traits: whether microbes carry one type of metabolism (fermentation) or produce one metabolite (acetate). We collected written descriptions of 7,021 species of bacteria and archaea from Bergeys Manual. We read the descriptions and manually identified (labeled) which species were fermentative or produced acetate. We then trained neural networks to predict these labels. In total, we identified 2,364 species as fermentative, and 1,009 species as also producing acetate. Neural networks could predict which species were fermentative with 97.3% accuracy. Accuracy was even higher (98.6%) when predicting species also producing acetate. We used these predictions to draw phylogenetic trees of species with these traits. The resulting trees were close to the actual trees (drawn using labels). Previous counts of fermentative species are 4-fold lower than our own. For acetate-producing species, they are 100-fold lower. This undercounting confirms past difficulty in extracting metabolic traits from text. Our approach with neural networks can extract information efficiently and accurately. It paves the way for putting more metabolic traits into databases, providing easy access of information by investigators.

microbiology↗

bioRxiv2020

A new pathway for forming acetate and synthesizing ATP during fermentation in bacteria

Many bacteria and other organisms form acetate during fermentation. Forming acetate from high energy-precursors (acetyl-CoA or acetyl phosphate) is one of the few ways that fermentative bacteria generate ATP. Here we found a biochemical pathway for forming acetate and synthesizing ATP that was unknown in bacteria. We found the bacterium Cutibacterium granulosum formed acetate during fermentation of glucose. With enzymatic assays, we showed it formed acetate using a pathway involving two enzymes. The first enzyme, succinyl-CoA:acetate CoA-transferase (SCACT), forms acetate from acetyl-CoA. The second enzyme, succinyl-CoA synthetase (SCS), synthesizes ATP. This pathway is common in eukaryotes, but it has not been found in bacteria or other organisms. We found two related bacteria (C. acnes and Acidipropionibacterium acidipropionici) also used this pathway. None used the most common pathway for forming acetate in bacteria (involving acetate kinase and phosphotransacetylase). The SCACT/SCS pathway may be used by many bacteria, not just C. granulosum and relatives. When we searched genomes for bacteria known to form acetate, we found over 1/6 encoded this pathway. These bacteria belong to 104 different species and subspecies in 12 different phyla. With this discovery, all five pathways known to form acetate and ATP during fermentation can be found in bacteria. This discovery is important to manipulating fermentation and to the evolution of biochemical pathways.

biochemistry↗

bioRxiv2018

Accurate estimation of microbial diversity with Distanced

Microbes are the most diverse organisms on Earth. Sequencing their DNA suggests thousands of different microbes could be present in a single sample. Errors in sequencing, however, make it challenging to estimate exactly how diverse microbes are. Here we developed a tool that estimates diversity accurately, even in the presence of sequencing errors. We first evaluated two existing tools, DADA2 and Deblur, which work by correcting sequencing errors. We found that these tools estimated within-sample (alpha) diversity poorly. In fact, we obtained better estimates if did not use the tools at all (left errors uncorrected). These tools performed poorly because they changed the relative abundance of different sequences; this is a side effect of correcting errors and discarding up to 90% of sequence reads in the process. Previous evaluations ignored sequence abundance when calculating diversity, overlooking this problem. Our tool, Distanced, differs from existing tools because it does not correct sequencing errors. Instead, it corrects sequence distances, which are used to calculate diversity. It does this correction with Phred quality scores and Bayes theorem. No sequence reads are discarded in the process. In our evaluation, Distanced accurately estimated diversity of bacterial DNA, fungal DNA, and even antibody mRNA. Given its accuracy, Distanced will help investigators answer important questions about microbial diversity. For example, it could answer how important is diversity for the planets ecosystems and human health.

microbiology↗