1. Computational and Systems Biology
  2. Microbiology and Infectious Disease

Lipid discovery enabled by sequence statistics and machine learning

  1. Priya M Christensen
  2. Jonathan Martin
  3. Aparna Uppuluri
  4. Luke R Joyce
  5. Yahan Wei
  6. Ziqiang Guan  Is a corresponding author
  7. Faruck Morcos  Is a corresponding author
  8. Kelli L Palmer  Is a corresponding author
  1. Department of Biological Sciences, University of Texas at Dallas, United States
  2. Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, United States
  3. School of Podiatric Medicine, University of Texas Rio Grande Valley, United States
  4. Department of Biochemistry, Duke University Medical Center, United States
  5. Department of Bioengineering, University of Texas at Dallas, United States
  6. Center for Systems Biology, University of Texas at Dallas, United States
https://doi.org/10.7554/eLife.94929.3
Share this article
Cite this article
  1. Priya M Christensen
  2. Jonathan Martin
  3. Aparna Uppuluri
  4. Luke R Joyce
  5. Yahan Wei
  6. Ziqiang Guan
  7. Faruck Morcos
  8. Kelli L Palmer
(2024)
Lipid discovery enabled by sequence statistics and machine learning
eLife 13:RP94929.
https://doi.org/10.7554/eLife.94929.3
  2. Download BibTeX
  3. Download .RIS

eLife Assessment

This study reports important findings on identifying sequence motifs that predict substrate specificity in a class of lipid synthesis enzymes. It sheds light on a mechanism used by bacteria to modify the lipids in their membrane to develop antibiotic resistance. The evidence is compelling, with a careful application of machine learning methods, validated by mass spectrometry-based lipid analysis experiments. This interdisciplinary study will be of interest to computational biologists and to the community working on lipids and on enzymes involved in lipid synthesis or modification.

https://doi.org/10.7554/eLife.94929.3.sa0

Abstract

Bacterial membranes are complex and dynamic, arising from an array of evolutionary pressures. One enzyme that alters membrane compositions through covalent lipid modification is MprF. We recently identified that Streptococcus agalactiae MprF synthesizes lysyl-phosphatidylglycerol (Lys-PG) from anionic PG, and a novel cationic lipid, lysyl-glucosyl-diacylglycerol (Lys-Glc-DAG), from neutral glycolipid Glc-DAG. This unexpected result prompted us to investigate whether Lys-Glc-DAG occurs in other MprF-containing bacteria, and whether other novel MprF products exist. Here, we studied protein sequence features determining MprF substrate specificity. First, pairwise analyses identified several streptococcal MprFs synthesizing Lys-Glc-DAG. Second, a restricted Boltzmann machine-guided approach led us to discover an entirely new substrate for MprF in Enterococcus, diglucosyl-diacylglycerol (Glc2-DAG), and an expanded set of organisms that modify glycolipid substrates using MprF. Overall, we combined the wealth of available sequence data with machine learning to model evolutionary constraints on MprF sequences across the bacterial domain, thereby identifying a novel cationic lipid.

Introduction

Lipids are ubiquitous and diverse group of amphiphathic compounds that are critical for fundamental biological processes, including the formation of cell membranes, protection, and cargo delivery; energy storage; and cell signaling pathways (Fahy et al., 2011). Chemical modifications to lipids, including changes in fatty acid tails (saturated and unsaturated) (Wang et al., 2018), modifications of head groups (Roy et al., 2009), and alterations to lipid charge (Roy and Ibba, 2008), impact these interactions, as well as the physicochemical properties of membranes. Commonly, bacteria synthesize lipids that are negatively charged or neutral (Sohlenkamp and Geiger, 2016). Some bacteria modify the negatively charged lipids with amino acids to make them positively charged to confer resistance to cationic antimicrobial peptides and cationic antibiotics (Weidenmaier et al., 2005; Joyce and Doran, 2023). The bacterial domain is a rich source of natural lipids that are likely coevolved for specific interactions with host tissues and other cells, yet the full extent of this chemical diversity remains unexplored. Finding bacteria that possess unique lipids can be useful for biotechnology. For example, natural lipids found in bacteria can be used for drug delivery. A study by Gujrati et al., 2014, used low cytotoxic outer membrane vesicles that contained a modified lipopolysaccharide to deliver siRNA to targeted cancer cells. This delivery mechanism had success and demonstrates the possibility of using naturally occurring bacterial lipids for future drug delivery processes to eukaryotic cells.

One enzyme responsible for modifying lipids in bacteria is the multiple peptide resistance factor (MprF). MprF modifies lipids through the transfer of amino acids from charged tRNAs to the head group of the anionic membrane phospholipid, phosphatidylglycerol (PG) (Roy and Ibba, 2008). The MprF protein is comprised of two domains: an N-terminal flippase domain responsible for moving aminoacylated lipids from the inner membrane leaflet to the outer leaflet and a C-terminal synthase domain responsible for the aminoacyl transfer from tRNA to lipids (Ernst et al., 2009; Staubitz et al., 2004). Recently, in Streptococcus agalactiae (Group B Streptococcus, GBS) it was found that MprF can modify two different substrates with lysine (Lys) – the glycolipid glucosyl-diacylglycerol (Glc-DAG) and the phospholipid, PG (Joyce et al., 2022), generating Lys-Glc-DAG, as well as Lys-PG (Joyce et al., 2021; Joyce et al., 2022; Figure 1). An S. agalactiae mprF deletion mutant no longer synthesizes Lys-Glc-DAG or Lys-PG and expression of S. agalactiae mprF in the heterologous host Streptococcus mitis conferred Lys-Glc-DAG and Lys-PG synthesis to S. mitis (Joyce et al., 2022). This was the first time MprF was demonstrated to add lysine onto a neutral glycolipid (Glc-DAG).

Figure 1
Download asset Open asset
Chemical structures of lysine lipids.

Lys-PG, lysyl-phosphatidylglycerol; Lys-Glc-DAG, lysyl-glucosyl-diacylglycerol; Lys-Glc2-DAG, lysyl-diglucosyl-diacylglycerol.

The synthesis of Lys-Glc-DAG by S. agalactiae MprF illustrated a unique lipid substrate and product by the enzyme that had not been characterized before, highlighting the possibility for further exploration into unknown lipids and lipid substrates by MprF of different bacterial species. We sought to investigate the molecular determinants of enzyme specificity and identify other bacterial species that may have this novel specificity. This investigation led us from the standard methodology of BLASTp queries (Camacho et al., 2009), which are based on single residue amino acid substitution frequencies, to a more modern statistical method called a restricted Boltzmann machine (RBM) (Tubiana et al., 2019b), which captures global statistical patterns within natural sequence data. In this work, we develop a general strategy for combining this high-powered statistical model with lipidomic analysis to discover MprF variants with unique enzymatic activity. This process led us to the discovery of an uncharacterized lipid substrate for MprF, diglucosyl-diacylglycerol (Glc2-DAG), which is present in several strains identified by the parameters of the statistical model. These models also provide interpretable and actionable information for use in further enzyme characterization, and we show that this application is a useful companion tool for experimental work.

Results

Simple pairwise sequence statistics identify streptococcal MprF enzymes that synthesize Lys-Glc-DAG

Utilizing a method based on the amino acid sequence of S. agalactiae MprF, we identified four streptococcal MprFs with high sequence identity to S. agalactiae COH1 MprF (WP_000733236.1). A BLASTp analysis found that Streptococcus sobrinus ATCC 27352 MprF (WP_019790557.1) had 61.4% amino acid identity to the S. agalactiae MprF, Streptococcus salivarius ATCC 7073 MprF (WP_002888893.1) had 43.09% amino acid identity, Streptococcus ferus MprF (WP_018030543.1) had 61.88% amino acid identity, and Streptococcus downei (WP_002997695.1) had 61.03% amino acid identity (Table 1). Plasmids were designed to express the S. sobrinus mprF (pSobrinus), S. salivarius mprF (pSalivarius), S. ferus mprF (pFerus), and S. downei mprF (pDownei) genes. The plasmids were transformed into the expression host S. mitis NCTC12261 (SM61). The empty vector (pABG5ΔphoZ, referred to as pABG5 here) was also transformed (Figure 2a). S. mitis does not natively encode mprF but synthesizes lipids (Glc-DAG; PG) that are substrates for S. agalactiae MprF (Adams et al., 2017; Joyce et al., 2019; Joyce et al., 2022), making it an appropriate heterologous host for expressing streptococcal MprFs. Previously, a plasmid expressing S. agalactiae mprF (pGBSMprF) (Joyce et al., 2022) was generated and transformed into S. mitis. Lys-Glc-DAG and Lys-PG presence in SM61 was only possible with the expression of pGBSMprF (Figure 2b; Joyce et al., 2022).

Figure 2
Download asset Open asset
Synthesis of lysine lipids (Lys-PG and Lys-Glc-DAG) in S. mitis expressing mprFs from S. agalactiae, S. salivarus, and S. ferus.

(a) S. mitis NCTC12261 with empty vector control (pABG5) lacks lysine lipids; (b) S. agalactiae mprF (pGBSMprF) produces both Lys-PG and Lys-Glc-DAG; (c) S. salivarius mprF produces only Lys-PG; (d) S. ferus mprF produces only Lys-Glc-DAG. Left panels: total ion chromatograms (TIC); middle panels: mass spectra of retention time 19.5–21.5 min showing Lys-PG and PC; right panels: mass spectra of retention time 26–30 min showing Lys-Glc-DAG. Note: ‘*’ is an extraction artifact due to chloroform used. DAG, diacylglycerol; MHDAG, monohexosyldiacylglycerol; DHDAG, dihexosyldiacylglycerol; PG, phosphatidylglycerol; Lys-PG, lysyl-phosphatidylglycerol; Lys-Glc-DAG, lysyl-glucosyl-diacylglycerol; PC, phosphatidylcholine.

Table 1
Summary of different MprF variants expressed in S. mitis and the lysine lipids they produce.

Percentage of amino acid identity and similarity compared to S. agalactiae COH1 MprF; data obtained from BLASTp. The lipids each strain synthesize are denoted by a checkmark or an x. Lys-PG, lysyl-phosphatidylglycerol; Lys-Glc-DAG, lysyl-glycosyl-diacylglycerol.

Bacteria strains% Amino acid identity (similarity)Lys-PGLys-Glc-DAG
SM61(pGBSMprF)100.0 (100.0)✓✓
SM61(pFerus)61.9 (79.0)X✓
SM61(pSobrinus)61.4 (79.0)X✓
SM61(pDownei)61.0 (79.0)X✓
SM61(pSalivarius)43.1 (66.0)✓X
SM61(pABG5)- (-)XX

S. mitis lipids of strains expressing the plasmids pSobrinus (Table 1), pSalivarius (Figure 2c), pFerus (Figure 2d), and pDownei (Table 1) were analyzed. Lipidomic analysis found that pSobrinus, pDownei, and pFerus conferred synthesis of Lys-Glc-DAG, but not Lys-PG in S. mitis. Notably, pFerus confers synthesis of Lys-Glc-DAG at a similar level to S. agalactiae MprF (Figure 2b and d). In contrast, pSalivarius conferred synthesis of Lys-PG only and not Lys-Glc-DAG. Importantly, S. salivarius MprF also synthesized Lys-PG at a level most similar to S. agalactiae MprF (Figure 2b and c). Lysine-modified lipids synthesized by S. mitis strains expressing streptococcal MprFs used in this study are summarized in Table 1. In general, streptococcal MprF enzymes with higher amino acid percent (>60%) to S. agalactiae MprF conferred Lys-Glc-DAG synthesis to S. mitis, but only S. agalactiae MprF conferred both Lys-Glc-DAG and Lys-PG synthesis in S. mitis.

RBM can provide sensitive, rational guidance for sequence classification

We set out to understand which specific MprF amino acid residues are involved in enzyme specificity for either the PG or Glc-DAG substrate. To this end, we employed an RBM, a probabilistic graphical model which aims to model the probability of data in a dataset through statistical connections between the features of the data and a set of hidden units (Smolensky, 1986).

This class of models is closely related to another model termed the Boltzmann machine. The Boltzmann machine formulation is closely related to the Potts model from physics, which was successfully applied in biology to elucidate important residues in protein structure and function (Morcos et al., 2011), one example being the careful tuning of enzyme specificity in bacterial two-component regulatory systems (Cheng et al., 2014; Jiang et al., 2021). The Boltzmann machine formulation from Morcos et al., 2011, termed direct-coupling analysis, stores patterns in the form of a pairwise coupling matrix and local field matrix (shown in Equation 2), which has been useful in understanding how likely pairs of residues are to interact in physical space. We hypothesize that S. agalactiae MprF and other MprF enzymes that utilize glycolipid substrates have unique patterns of interacting residues relative to those enzymes that use only PG as a substrate.

While modeling how pairs of residues covary is powerful, many important features of enzyme function like allostery or enzyme specificity (which is the purpose of this analysis) may involve more than two residues (Goodey and Benkovic, 2008), and in these situations it can be difficult to connect sets of residues using solely pairwise parameters (Figliuzzi et al., 2018). Therefore, a more global approach is required to address our hypothesis. To this end, an RBM has recently been developed for use in the learning of protein sequence data, with special attention given to producing a model which produces sparse, interpretable, and biologically meaningful representations of these higher-order statistical couplings within the hidden units (Tubiana et al., 2019b). The design of this RBM can be seen in Figure 3, where the model architecture is represented by purple dots and green triangles. The dots are the ‘visible’ layer, which take in input sequences and encode them into the ‘hidden’ layer, where each triangle represents a separate hidden unit. The lines connecting the visible and hidden layers show that each hidden unit can see all the visible units (the statistics are global), but they cannot see any of the other hidden units, meaning the hidden units are mutually independent. This global model with mutually independent hidden units (see the marginal distribution form shown in Equation 3) allows the following useful properties: higher-order couplings between residues (sets of coupled residues instead of pairs); bimodal hidden unit outputs given the training dataset (Equation 4); sparse, interpretable configurations of the hidden units (wμ) (Equation 5); and a compositional representation of the hidden unit weights (Tubiana et al., 2019a), where input sequences are modeled largely through combinations of hidden units instead of single, highly activating units.

Figure 3
Download asset Open asset
Schematic of the restricted Boltzmann machine (RBM) methodology.

An aligned set of protein sequences is first used to learn a hidden unit representation that best describes the statistics of the sequence dataset given restrictions on the hidden unit representation. Then, the individual hidden units can be studied to find particular units which allow useful enzyme classification, and additionally, these weights can be meaningfully interpreted as statistically covarying sequence configurations. Additionally, the classification can be used to create filtered datasets to train more models.

The compositional representation is a critical feature of this method, allowing us to find independently coupled sets of residues which in combination describe these protein sequences, and it is achieved through the particular choices of training parameters. More details on the training parameter choices are found in Materials and methods. In particular, we focus on the scoring of sequences using individual hidden units wμ:

(1) Iμ(v)=∑iwiμ(vi)

where v is an input sequence vectorized through one-hot encoding, to produce a hidden unit activation (a single number) which depends on the input sequence residues at position i and the value of the hidden unit weight matrix for that residue at that position.

We used this methodology to study the sequences in the family of MprF, which is a large protein composed of independent flippase and synthase domains (Ernst et al., 2009; Slavetinsky et al., 2012). In our analysis, we restrict ourselves to the cytosolic region (see yellow box in Figure 4a), also known as the aminoacyl-PG synthase domain, which constituted the Pfam family DUF2156 (now renamed LPG_synthase_C) (Mistry et al., 2021).

Figure 4 with 1 supplement see all
Download asset Open asset
Example of hidden unit analysis and usage.

(a) The structure of PDB:7DUW, with the red colored region being the transmembrane flippase domain and the yellow boxed region the cytosolic domain which we focus on. (b) The activations produced by inputting a sequence into a hidden unit, producing a single number as output which corresponds to a summation of negatively and positively weighted residues. Performed on entire training set (histogram in blue), highlighting sequences corresponding to predominantly positive weighted residues. (c) Hidden unit from a restricted Boltzmann machine (RBM) trained on the Pfam DUF2156 domain. The MSA positions 152 and 212 correspond to residues S684 and R742, respectively. (d) Residues (in yellow) in the MprF cytosolic domain which form the binding pocket for Lys-tRNALys (the ligand analogue L-lysine amide shown in green), from PDB:4V36. LYN, L-lysine amide.

One hidden unit in particular is highlighted, where we find bimodal activation (Figure 4b) of the hidden unit (Figure 4c) when Equation 1 is evaluated with the training sequences. Interpreting the sequence histograms, a positive value activation in Figure 4b corresponds to a sequence containing combinations of the residues listed in the positive portion of the weight diagram in Figure 4c, whereas a negative activation corresponds to residues in the negative portion. For display purposes, the residue weights shown in all of our hidden units correspond to the positions where the magnitude of the visible positions wiμ is greater than some threshold proportional to the largest magnitude position (position 212); the hidden units have values for all possible amino acids in the entire protein length, but they are considerably smaller than this threshold and generally very low.

Two positions in particular, 152 and 212, in the positive portion of the hidden unit correspond to residues 684 and 742, which were experimentally demonstrated to be relevant for aminoacylated-PG production by the Bacillus licheniformis and Pseudomonas aeruginosa cytosolic synthase domain variants (Hebecker et al., 2015); in B. licheniformis the residues which form a complex with the lysine from Lys−tRNALys are shown in Figure 4c. We have highlighted these two aforementioned variants as well as the S. agalactiae variant for demonstration of sequence scoring through Equation 1 in Figure 4a. We used this hidden unit to create a filtered dataset; only sequences that contained this necessary motif were of interest to us, and we subsequently trained a new model using only sequences with a catalytic hidden unit activation greater than 2 (because it cleanly split the bimodal distribution). We outline this general workflow in Figure 3. We also note that the full-length sequences of the sequences included in our new dataset are enriched with sequences which contain the bacterial flippase domain (shown in red in Figure 4a), and the sequences we removed rarely contain the flippase domain (see Figure 4—figure supplement 1 for domain analysis).

RBM hidden units identify plausible sequence residues for functional characterization

After filtering the dataset and training another model, we set out to find hidden units which could describe MprF specificity. To this end, we identify hidden units which are sparse, have high overall magnitude, and involve residues which could be plausibly linked to the lipid-binding specificity (see Materials and methods for more details). Through this search method we identified the two hidden units shown in Figure 5A. They both have high sparsity and high magnitude relative to the majority of other hidden units in the model, and importantly they correspond to residues localized to regions of the catalytic domain that had been previously identified through Autodock experiments using a PG molecule with side chains C5:0/C8:0 (Hebecker et al., 2015) (highlighted in Figure 5A). The scoring of the training set of sequences with these hidden units is shown in Figure 5B, and we see a clear separation between the cytosolic domain sequences which produces Lys-Glc-DAG (from S. agalactiae) and two domain variants which produce only aminoacylated-PG (from P. aeruginosa and B. licheniformis). Importantly, these output activations depend on a greatly reduced subset of the full sequence and do not correspond to clustering on total sequence identity (Figure 5—figure supplement 1).

Figure 5 with 1 supplement see all
Download asset Open asset
Proposed set of hidden units for classifying lipid specificity.

(A) Two hidden units found in a restricted Boltzmann machine (RBM) trained on the filtered Pfam dataset. The hidden unit residues are highlighted in the PDB:4V34 structure, with arrows pointing to their corresponding residue sets. (B) The activations of the hidden units when scoring the sequences used in the training set and sequences from NCBI not used during training (N=23,138). S. agalactiae produces Lys-Glc-DAG and Lys-PG, while B. licheniformis and P. aeruginosa produces only aminoacylated-PG. Q1-Q4 are quadrant labels which we refer to throughout the paper. Lys-PG, lysyl-phosphatidylglycerol; Lys-Glc-DAG, lysyl-glucosyl-diacylglycerol.

Using these two hidden units as sequence classifiers with a potential link to the substrate specificity of cytosolic domain variants, we used Figure 5B to propose sequences in quadrant three (Q3), the quadrant occupied by the S. agalactiae MprF variant with this novel glycolipid-modifying function. We selected a sequence which was distant in terms of sequence identity from the Streptococcus genus (Supplementary file 1) and which has been implicated in human pathologies (Mundy et al., 2000), Enterococcus dispar MprF. We also emphasize that the two hidden units we selected are from a set of 300 total hidden units, and that while other hidden units may differentially classify our three sequences, without the additional link to lipid binding provided by the Autodocking experiment there would be only weak justification for using their activations to make predictions.

Enterococcal MprF enzymes synthesize Lys-Glc-DAG, Lys-PG, and a novel cationic glycolipid Lys-Glc2-DAG

E. dispar ATCC 51266 was identified as a possible candidate for novel lipid synthesis through the analyses described above. Remarkably, our lipidomic analysis found that E. dispar synthesizes a novel cationic lipid, lysyl-diglucosyl(Glc2)-diacylglycerol (Lys-Glc2-DAG) (Figure 1, Figure 6a and b), as well as Lys-Glc-DAG and Lys-PG (Figure 6c, Figure 6—figure supplement 1). The structural identification of Lys-Glc-2-DAG was supported by exact mass measurement ([M+H]+ observed at m/z 1047.730; calculated m/z 1047.731) and tandem MS (Figure 6b). Lys-Glc-2-DAG is unusually polar and charged for a lipid; chromatographically, Lys-Glc2-DAG is highly retentive on a silica-based normal phase column and elutes off the column at the very end of the gradient (Figure 6—figure supplement 1). The use of an amino-based HPLC column led to much improved elution profiles for Lys-Glc2-DAG and other glyco- and lysine lipids (Figure 6).

Figure 6 with 1 supplement see all
Download asset Open asset
Identification of Lys-Glc2-DAG in E. dispar.

(a) Positive ion mass spectrum of Lys-Glc2-DAG species in E. dispar. Major Lys-Glc2-DAG species with carbon atoms (before colon) and double bonds (after colon). (b) MS/MS product ions and fragmentation scheme of the most abundant Lys-Glc2-DAG ion at m/z 1047.73. (c) Extracted ion chromatograms of LC/MS of lysine lipids (Lys-PG, Lys-Glc-DAG, Lys-Glc2-DAG) and Glc2-DAG separated on an amino HPLC column. Glc2-DAG, diglucosyl-diacylglycerol; Lys-PG, lysyl-phosphatidylglycerol; Lys-Glc-DAG, lysyl-glucosyl-diacylgylcerol; Lys-Glc2-DAG, lysyl-diglucosyl-diacylglycerol.

The discovery of Lys-Glc2-DAG in E. dispar led us to analyze other Enterococcus strains: Enterococcus faecium 1,231,410, Enterococcus faecalis T11 and OG1RF Enterococcus gallinarium EG2, Enterococcus casseliflavus EC10, and Enterococcus raffinosus Er676. All Enterococcus strains examined were found to synthesize Lys-PG, Lys-Glc-DAG, and Lys-Glc2-DAG at varying levels (Figure 7a and Table 2). MprF-dependent Lys-PG production by E. faecalis and E. faecium was previously reported (Bao et al., 2012; Roy and Ibba, 2009), but cationic glycolipids production has not been previously reported. E. gallinarium EG2 and E. casseliflavus EC10 encode a single copy of mprF gene, while the other enterococci we tested encode two distinct mprF genes (mprF1 and mprF2) (Bao et al., 2012; Roy and Ibba, 2009). When these MprF sequence variants are plotted using our chosen RBM hidden units, we see that the two single copy strains plot in quadrant two (Q2), while for the two copy sequences, each MprF variant maps to separate coordinates (Figure 7—figure supplement 1). Both E. raffinosus Er676 MprF variants plot to quadrant two (with the lowest Weight 2 value being 0.34). The E. faecium and E. faecalis variants have copies plotting directly in quadrant one and variants plotting farther out from the sequence cluster; we note that the two displaced variants are both lacking the characteristic aspartic acid and glutamic acid amino acids at positions 100 and 48 in the hidden units (Figure 5A), which are the dominant contributions for determining quadrant one occupancy. To clarify which variants were determinants of the novel cationic lipid production, lipidomic analysis on an E. faecalis OG1RF mini-mariner (EfaMarTn) transposon mutant (Dale et al., 2018) with a transposon insertion within OG1RF_10760 mprF (referred to as mprF2 in previous studies [Bao et al., 2012]; OG1RF_10760::Tn) revealed that Lys-PG, Lys-Glc-DAG, and the newly identified Lys-Glc2-DAG were absent when the synthase domain of the gene was disrupted (Figure 7b, Supplementary file 1). This led us to the conclusion that E. faecalis mprF2 is necessary for the synthesis of the three Lys-lipids in E. faecalis. When the empty vector (pABG5) was transformed into OG1RF_10760::Tn, no Lys-lipids synthesis was observed as expected (Figure 7c). This phenotype was complemented by expression of E. faecalis mprF2 from a plasmid (pOGMprF2) (Figure 7d). Additionally, expression of E. faecium mprF1 (EFTG_00601) (pEfMprF1) restored Lys-PG and Lys-Glc2-DAG synthesis to the E. faecalis OG1RF_10760::Tn strain (Figure 7e), while E. faecium mprF2 (EFTG_02430) did not rescue Lys-lipid synthesis (Figure 7f). Taken together, these data indicate that E. faecalis MprF2 (OG1RF_10760) and E. faecium MprF1 (EFTG_00601), like S. agalactiae MprF, act on both phospholipid and glycolipids substrates, additionally synthesizing the novel cationic glycolipid, Lys-Glc2-DAG.

Figure 7 with 1 supplement see all
Download asset Open asset
E. faecalis MprF2 and E. faecium MprF1 confer Lys-Glc2-DAG synthesis.

(a) OG1RF-WT; (b) OG1RF_10760::Tn lacks lysine lipids; (c) OG1RF_10760::Tn+pABG5 lacks lysine lipids; (d) OG1RF_10760::Tn+pOGMprF2 restores lysine lipids; (e) OG1RF_10760::Tn+pEFMprF1 restores lysine lipids; (f) OG1RF_10760::Tn+pEFMprF2 lacks lysine lipids. Expression of OGMprF2 and EFMprF1 in OG1RF_10760 Tn mutant restores Lys-Glc2-DAG synthesis. Shown are the extracted ion chromatograms of lysine lipids (Lys-PG, Lys-Glc2-DAG) and Glc2-DAG separated on an amino HPLC column. Note: Lys-Glc-DAG was found in trace amounts or missing from lipid extractions. Glc2-DAG, diglucosyl-diacylglycerol; Lys-PG, lysyl-phosphatidylglycerol; Lys-Glc2-DAG, lysyl-diglucosyl-diacylglycerol.

Table 2
Table of all strains studied, the quadrant they occupy, and the lipids they synthesize.

** trace amounts Lys-Glc-DAG present in lipid extractions. * indicates heterologous expression of mprF in S. mitis. Lys-Glc-DAG, lysyl-glucosyl-diacylglycerol; Lys-Glc2-DAG, lysyl-diglucosyl-diacylglycerol; Lys-PG, lysyl-phosphatidylglycerol.

Bacterial strainsLys-Glc-DAGLys-Glc2-DAGLys-PG
Q1Bacillus subtilis 168XX✓
Bacillus licheniformis ATCC 14580XX✓
Staphylococcus aureus RN4220XX✓
Exiguobacterium acetylicum UTDF19-27CXX✓
Enterococcus faecalis T11/OG1RF**✓✓✓
Enterococcus faecium 1,231,410✓✓✓
Q2Enterococcus raffinosus Er676✓✓✓
Enterococcus gallinarum EG2✓✓✓
Enterococcus casseliflavus EC10✓✓✓
Q3Streptococcus salivarius* ATCC 7073XX✓
Enterococcus dispar ATCC 51266✓✓✓
Streptococcus agalactiae CJB111/COH1✓X✓
Streptococcus sobrinus* ATCC 27352✓XX
Streptococcus downei* (WP_002997695.1)✓XX
Streptococcus ferus* (WP_018030543.1)✓XX
Q4Ligilactobacillus salivarius ATCC 11741XX✓
Lacticaseibacillus rhamnosus ATCC 7469XX✓
Lactobacillus casei ATCC 393XX✓
Levilactobacillus brevis ATCC 14869X✓✓
Lacticaseibacillus paracasei ATCC 25302X✓✓

Hidden unit 2 is correlated with GlcN-DAG substrate activity in MprF

When we started our RBM analysis, we had substrate specificity information for only the quadrants one and three, and through identifying the novel substrate in E. dispar we expanded our testing to quadrant two by testing Enterococcus strains. To expand our RBM analysis, we tested specific sequences which were found in quadrant four (Q4). We conducted lipid analysis on Ligilactobacillus salivarius ATCC 11741, Levilactobacillus brevis ATCC 14869, Lacticaseibacillus rhamnosus ATCC 7469, Lactobacillus casei ATCC 393, and Lacticaseibacillus paracasei ATCC 25302. All strains tested synthesize Lys-PG (Table 2). Only L. brevis and L. paracasei synthesized Lys-Glc2-DAG, L. paracasei synthesized low levels of Lys-Glc2-DAG. An additional quadrant one (Q1) strain Exiguobacterium acetylicum UTDF19-27C was analyzed by lipidomics. It was found to synthesize only Lys-PG. This MprF falls near Staphylococcus aureus RN4220 and Bacillus subtilis 168 in our plot, both of which synthesize only Lys-PG (Peschel et al., 2001; Slavetinsky et al., 2012) (and experimentally confirmed in this study). This further supports that strains located in quadrant one of Figure 8 are capable of Lys-PG synthesis.

Figure 8
Download asset Open asset
The two proposed hidden unit activations with all of the sequences from Table 2 labeled.

Protein ID of highlighted sequences are listed in Supplementary file 1. (#) indicates the lipid activity was confirmed through heterologous expression.

Summaries of all functional species/variants tested and the Lys-lipids they produced are shown in Table 2, and plotted in the two RBM hidden units in Figure 8. We list for each MprF variant which of the three tested lipids it acts on, and in Figure 8 we indicate whether a variant has only PG as a substrate or if it operates on GlcN-DAG as a substrate irrespective of its PG activity. With this plot, we can see that sequences which plot with positive activations in hidden unit 2 are more likely to have GlcN-DAG as a substrate (8/9), and sequences with negative values predominantly use PG (7/11), with a Fisher’s exact test p-value of 0.028 (Supplementary file 2). Hidden unit 1, however, is not correlated with GlcN-DAG specificity, with a Fisher’s exact p-value of 0.67. Therefore, we conclude that hidden unit 2, and thus the high magnitude sequence positions identified by it, are correlated with GlcN-DAG specificity.

Discussion

The utilization of the S. agalactiae MprF amino acid sequence as a query for BLASTp was a simple method to identify various streptococcal MprFs and further our understanding of MprF specificity. This allowed us to identify three MprFs that can synthesize the highly cationic Lys-Glc-DAG. Importantly, although highly similar to S. agalactiae MprF, this method did not identify other streptococcal MprFs capable of lysine addition to both glycolipid and phospholipid substrates (generating Lys-Glc-DAG and Lys-PG) nor did it lead to the identification of a novel cationic glycolipid found in Enterococcus. Sequence logos (Schneider and Stephens, 1990) may identify important sequence features, but largely in instances where amino acids are highly conserved. When amino acids must interact to produce a specific function, yet the exact identity of those amino acids is not a requirement, methods which model the strength of amino acid coevolution in sequence sets can find important signals which in the sequence logo might appear to be negligible. For this reason we used the RBM method (Tubiana et al., 2019b), which can model statistical connectivity of residues at multiple sites in the protein sequence.

The RBM can be used in a number of ways to study sequence data. First, it allowed us to filter the sequence data using hidden units (Figure 4); the two hidden units which formed the basis of our specificity analysis were not found in models trained on the Pfam dataset when it was initially pulled from Pfam, and this was potentially due to the presence of sequences without relevant catalytic function adding noise to the dataset. The most prominent feature for our purposes is the ability to use the hidden units learned through RBM training to classify sequences. The bimodal nature of this particular model provides a relatively simple interpretation which is straightforward to use for prediction.

We show in Figure 5 that the combination of clustering and interpretable hidden unit structure allows us to identify a small subset of residues within a structure, grounding our statistical clustering in MprF’s structural features. The combination of experimental evidence and evolutionary information provided a strong rationale for selecting organisms for further study, ultimately leading to the discovery of novel cationic lipid biosynthesis in E. dispar (Figure 6, Figure 6—figure supplement 1).

One goal of this study was to find the structural determinants of GlcN-DAG activity by MprF, and we found that hidden unit 2 from our RBM analysis was predictive of this. However, there are notable exceptions such as the E. faecium allele EGTF_00601, which has sequence features placing it in quadrant one yet has GlcN-DAG activity. Therefore, identifying the exact sequence determinants of GlcN-DAG specificity remains to be fully elucidated. One explanation could be our exclusion of the N-terminal flippase domain and the non-cytosolic region of the C-terminal domain in the RBM analysis, where important residue interactions determining specificity could occur and is under further investigation. Additionally, a challenge in experimental validation will be to standardize the method of studying mprF alleles from diverse organisms. When using native organisms, lipids produced may vary during the growth phase of the organisms, the media they are grown in, and whether a membrane stressor is present (i.e. inducible production of specific lipids). For this study we used stationary phase cultures of equal volume and bacteria were grown in their respective standard laboratory media. The use of a heterologous host for expression of mprF alleles is a method of standardization, but may result in not identifying novel lipids that would be found in their native bacteria since the appropriate lipid substrates may not be present (Joyce et al., 2019; Joyce et al., 2021). A combination of both approaches (native organisms and heterologous hosts) may be required. Additionally, spike-in controls might be implemented to help us to more quantitatively understand the output specificity of these MprF variants. Ultimately, mutational studies of the residues identified by the RBM will allow for identification of the residues critical for specificity, though likely the residues will depend in part on the primary sequence being altered.

The general method of RBM-guided exploration has applications in enzyme design, for example, in enhancing methods like directed evolution (Arnold, 2018). Concretely, the residues identified through hidden units can be thought of as templates for mutational exploration, greatly reducing the search space for assessing which residues are critical for specificity determination. Enzymes like MprF that produce lipids of modified charge have potential applications in biotechnology and therapeutics, where the design of lipid nanoparticles with different charge and chemical properties of the head group have important physiological implications (Carrasco et al., 2021). Notably, the identification of Lys-Glc2-DAG opens new research avenues, particularly in the fields of antibiotic resistance and host-pathogen interactions, wherein lipid modification and cationic lipids play key roles. With respect to human health, it would be relevant to investigate whether Lys-Glc2-DAG plays a role in enterococcal resistance to the last-line membrane-active antibiotic, daptomycin.

Materials and methods

Bacterial strains and growth conditions

Request a detailed protocol

See Supplementary files 1 and 3 for a full list of bacterial strains used in this study. S. mitis NCTC12261 (referred to as SM61 here) (Kilian et al., 1989) was grown in Todd Hewitt Broth (THB) at 37°C with 5% CO2. Escherichia coli DH5α (Taylor et al., 1993) and B. subtilis 168 (Kunst et al., 1997) were grown at 37°C with shaking at 225 rpm in lysogeny broth. S. aureus RN4220 (Kreiswirth et al., 1983) was grown at 37°C with shaking at 220 rpm in Trypic Soy Broth. S. agalactiae COH1 ATCC BAA-1176 (Kuypers et al., 1989) and CJB111 (Faralla et al., 2014; Spencer et al., 2021) were grown in THB at 37°C. E. faecium 1,231,410 (Palmer et al., 2010), E. faecalis T11 (McBride et al., 2007) and OG1RF (Gold et al., 1975), E. gallinarium EG2 (Palmer et al., 2010), E. casseliflavus EC10 (Palmer et al., 2010), and E. raffinosus Er676 (Sharon et al., 2023) were grown at 37°C in Brain Heart Infusion (BHI). E. dispar ATCC 51266 (Collins et al., 1991) was grown at 30°C in BHI. E. acetylicum UTDF19-27C was grown in BHI at 37°C. L. salivarius ATCC 11741 (Drucker, 1979), L. rhamnosus ATCC 7469 (Collins et al., 1989), L. casei ATCC 393 (Hansen and Lessel, 1971), and L. paracasei ATCC 25302 (Collins et al., 1989) were grown in Lactobacilli MRS Broth at 37°C and 5% CO2. L. brevis ATCC 14869 (Rogosa and Hansen, 1971) was grown in Lactobacilli MRS Broth at 30°C, 5% CO2. B. licheniformis ATCC 14580 was grown in nutrient broth at 37°C with shaking at 225 rpm (Chester, 1901). A transposon mutant of E. faecalis OG1RF from Dale et al., 2018, with a mini mariner transposon (EfaMarTn) insertion in OG1RF10760 (OG1RF10760::Tn) was grown on BHI supplemented with chloramphenicol at a concentration of 15 μg/mL. The mutant was confirmed to be erythromycin-sensitive. The transposon mutant location was confirmed through Sanger sequencing performed by the Genome Center at the University of Texas at Dallas (Richardson, TX, USA).

Where appropriate for plasmid selection, kanamycin (Sigma-Aldrich) was added. For E. coli, a concentration of 50 μg/mL was used, for S. mitis a concentration of 300 μg/mL, and for E. faecalis a concentration of 500 μg/mL was used.

Routine molecular biology procedures

Request a detailed protocol

Genomic DNA was extracted as done previously in Adams et al., 2015, and Manson et al., 2003. All PCRs used Phusion polymerase (Thermo Fisher Scientific) and Phusion 5x HF buffer (Thermo Fisher Scientific) in a Veriti PCR machine (Applied Biosystems). List of primers used are found in Supplementary file 4. Gibson assemblies were completed using a 2x HI-FI Assembly master mix following the manufacturer’s protocol (New England Biolabs). PCR clean-up was done using the GeneJET PCR Purification Kit (Thermo Fisher Scientific) per the manufacturer’s protocol. Plasmid extractions were performed per manufacturer’s protocol using the GeneJET Plasmid miniprep kit (Thermo Fisher Scientific). All plasmid constructs were confirmed by Sanger sequencing at the Massachusetts General Hospital CCIB DNA Core facility or by Illumina sequence at SeqCenter. DNA concentrations were measured using NanoDrop (Thermo Fisher Scientific) or Quibit 2.0 (Invitrogen by Life Technologies). Optical density at 600 nm (OD600 nm) was measured in a disposable cuvette (Thermo Fisher Scientific) using a spectrophotometer (Thermo Scientific Genesys 30).

Gibson assembly was performed using pABG5 as previously described (Joyce et al., 2022). Transformation of plasmids into E. coli, S. mitis, and E. faecalis was performed as previously described in ; Joyce et al., 2022Joyce et al., 2019; Joyce et al., 2022; Salvadori et al., 2016; Shepard and Gilmore, 1995.

Acidic Bligh-Dyer lipid extractions

Request a detailed protocol

Bacteria were grown for approximately 16 hr overnight in 15 mL of an appropriate culture medium. After growth, OD600 nm measurements were taken, and cells were pelleted at 4280 × g for 5 min at room temperature in a Sorvall RC6+ centrifuge. The supernatant was tipped out, and the cell pellet was washed and resuspended in 1× phosphate buffered saline (PBS). The cells were pelleted again, and all the supernatant was aspirated out. The cell pellet was stored at –80°C until lipid extraction. An acidic Bligh-Dyer lipid extraction was performed as previously reported (Joyce et al., 2019; Joyce et al., 2021). Briefly, cell pellets were resuspended in 0.8 mL of 1x Dulbecco’s PBS (Sigma-Aldrich). Cells were transferred to a 9 mL glass tube with a Teflon-lined cap (Pyrex). 1 mL chloroform (MilliporeSigma) and 2 mL methanol (Thermo Fisher Scientific) were added to create the single-phase Bligh-Dyer. Tubes were vortexed every 5 min for 20 min, then centrifuged at 500 × g for 10 min at room temperature. The supernatant was transferred to a new 9 mL glass tube. 100 µL of hydrochloric acid (Thermo Fisher Scientific) was added followed by 1 mL of chloroform and 0.9 mL of 1x Dulbecco’s PBS to create the two-phase Bligh-Dyer. Tubes were gently mixed and centrifuged at 500 × g for 5 min at room temperature. After centrifuging, the bottom layer was extracted to a new tube and dried under nitrogen gas and stored at –80°C prior to lipidomic analysis. Lipid analyses were repeated in biological triplicate aside, from OG1RF_10760::Tn extractions, which were performed once.

Analysis of lysine lipids by an amino column-based LC-ESI MS: a new method

Request a detailed protocol

An amino column-based normal phase LC-ESI MS (liquid chromatography electrospray ionization-mass spectrometry) was performed using an Agilent 1200 Quaternary LC system coupled to a high-resolution TripleTOF5600 mass spectrometer (Sciex, Framingham, MA, USA). A Unison UK-Amino column (3 µm, 25 cm ×2 mm) (Imtakt USA, Portland, OR, USA) was used. Mobile phase A consisted of chloroform/methanol/aqueous ammonium hydroxide (800:195:5, vol/vol/vol). Mobile phase B consisted of chloroform/methanol/water/aqueous ammonium hydroxide (600:340:50:5, vol/vol/vol/vol). Mobile phase C consisted of chloroform/methanol/water/aqueous ammonium hydroxide (450:450:95:5, vol/vol/vol/vol). The elution program consisted of the following: 100% mobile phase A was held isocratically for 2 min and then linearly increased to 100% mobile phase B over 8 min and held at 100% B for 5 min. The LC gradient was then changed to 100% mobile phase C over 1 min and held at 100% C for 3 min, and finally returned to 100% A over 0.5 min and held at 100% A for 3 min. The total LC flow rate was 300 µL/min. The MS settings were as follows: ion spray voltage (IS)=5000 V, curtain gas (CUR)=20 psi, ion source gas 1 (GS1)=20 psi, de-clustering potential (DP)=50 V, and focusing potential (FP)=150 V. Nitrogen was used as the collision gas for MS/MS experiments. Data acquisition and analysis were performed using Analyst TF1.5 software (Sciex, Framingham, MA, USA).

Generation of mprF expression plasmids

Request a detailed protocol

The S. downei mprF nucleotide sequence (Locus tag HMPREF9176_RS03810) and S. ferus mprF nucleotide sequence (Locus tag A3GY_RS0106165) were used to design synthetic geneblocks (GeneWiz from Azenta Life Sciences). A 20 nucleotide 5’ extension and a 20 nucleotide 3’ extension complementary to the pABG5 plasmid were added to enable Gibson assembly of the inserts with pABG5. Sequences for the geneblocks can be found in Supplementary file 5. The S. salivarius mprF (Locus tag SSAL8618_04345) was amplified from S. salivarius ATCC 7073. A 20 nucleotide 5’ extension and a 20 nucleotide 3’ extension complementary to the pABG5 plasmid was added to the insert. The same protocol was used for generation of pSobrinus, pEFMprF1, pEFMprF2, and pOGMprF2 with slight modifications. S. sobrinus mprF (Locus tag DLJ52_05040) was amplified from S. sobrinus ATCC 27352. E. faecium mprF1 (Locus tag EFTG_00601) and E. faecium mprF2 (Locus tag EFTG_02430) were amplified from E. faecium 1, 231, 410. E. faecalis mprF2 (Locus tag OG1RF_10760) was amplified from E. faecalis OG1RF.

Boltzmann machines and RBM for protein sequences

Formulation of models

Request a detailed protocol

The Boltzmann machine, as described in Morcos et al., 2011, is defined as:

(2) P(v)=1Zexp⁡(∑i<jeij(vi,vj)+∑ihi(vi))

which is a probability distribution defined on the pairwise interactions (the couplings eij) between the positions (vn) in an input sequence (v) and a local field term hi. The pairwise coupling matrix is akin to a covariance matrix, and the local fields are similar to single site frequency measures.

The joint probability distribution of the RBM as described in Tubiana et al., 2019b, is as follows:

(3) P(v,h)=1Zexp⁡(∑i=1Ngi(vi)−∑μ=1MUμ(hμ)+∑i,μhμwiμ(vi))

Here, the vector v again represents the input sequence data, g(vi) is a local field which controls the conditional probability of the input data, Uμ is the hidden unit potential/activation, and the terms hi and wiμ(v) couple the input variables with the hidden unit variables. In this way, interactions between residues are mediated by a relatively smaller set of hidden units which do not interact directly with each other, leading to a bipartite structure as opposed to the Boltzmann machine’s fully connected pair-based formulation.

Particularly important is the potential function U, which in this work is the dRELU function defined as:

(4) Uμ(h)=12γμ,+ h+2+12γμ,− h−2+θμ,+ h++θμ,− h−h+=max(h,0) , h−=min(h,0)

The four parameters γμ,±,θμ,± are learned through the inference procedure, and allow the positive and negative components of the hidden units wμ to be separately gated, which allows learning of bimodal distributions for the activations hμ. Another key feature is the sparsity regularization on the weights applied during model inference:

(5) L2/L1=λ122qN∑μ(∑i|wiμ(vi)|)2

where q is the number of amino acids plus a gap character (21) and the hyperparameter λ12 can be increased to induce sparsity and lower weight magnitude. Equations 4 and 5 together guide the representation of the hidden units to bimodal outputs with interpretable features.

Dataset acquisition and preprocessing

Request a detailed protocol

Multiple sequence alignment used for model training was the DUF2156 domain acquired from Pfam (Mistry et al., 2021) (in later revisions renamed LPG_synthase_C). Starting with this MSA, it was processed to include only amino acid characters and gaps, excluding sequences with ambiguous or non-standard characters. Additionally, sequences with contiguous strings of gaps greater than 20% of the full sequence’s length were removed. This left 11,507 sequences with a length of 298. After the data cleaning process described in Figure 4, a total of 7865 sequences were used to train our final model.

Model training

Request a detailed protocol

The model previously described (Tubiana et al., 2019b) was utilized, specifically the Python 2.7 version freely available on GitHub (https://github.com/jertubiana/ProteinMotifRBM; Tubiana, 2019). Extensive testing of the combinations of the number of hidden units, L2/L1 regularization, and learning rate were performed. Models were trained in triplicate with different random seeds, as the training procedure converges to slightly different weight values depending on their initial random configuration. Training was considered successful when the model consistently produced similar sets of weights across different random seeds, and the weights had the previously mentioned sparsity and magnitude indicating compositional representation. The final model used in this work was trained for 2000 epochs with a learning rate of 0.1, a learning rate decay of 0.33, L2/L1 parameter λ12 set to 1.0, and 300 hidden units.

Model weight selection

Request a detailed protocol

To find hidden units which were relevant to function we computed the L1 norm of each hidden unit individually, then ranked them. The hidden units with the largest magnitude were looked at first, and these were typically the sparsest of all hidden units. Hidden units were chosen which involved sequence coordinates implicated in our function of interest. Specifically, locations identified through Autodock (Hebecker et al., 2015) where the lipid was likely to interact, and a small radius around this region to select a small set of coordinates. We chose the only hidden units which had both overlap with multiple residues in this chosen radius and predictive power (separation) for the three examples we had to start with.

Software accessibility

Request a detailed protocol

DUF2156 domain sequences were retrieved from Pfam33 (Mistry et al., 2021). Raw sequence datasets are available as Supplementary Material.

Data availability

All sequence data used to build the restricted Boltzmann machine model and all information used to create Figure 8 are available online through the Open Science Framework.

The following data sets were generated

References

    1. Kunst F
    2. Ogasawara N
    3. Moszer I
    4. Albertini AM
    5. Alloni G
    6. Azevedo V
    7. Bertero MG
    8. Bessières P
    9. Bolotin A
    10. Borchert S
    11. Borriss R
    12. Boursier L
    13. Brans A
    14. Braun M
    15. Brignell SC
    16. Bron S
    17. Brouillet S
    18. Bruschi CV
    19. Caldwell B
    20. Capuano V
    21. Carter NM
    22. Choi SK
    23. Codani JJ
    24. Connerton IF
    25. Cummings NJ
    26. Daniel RA
    27. Denizot F
    28. Devine KM
    29. Düsterhöft A
    30. Ehrlich SD
    31. Emmerson PT
    32. Entian KD
    33. Errington J
    34. Fabret C
    35. Ferrari E
    36. Foulger D
    37. Fritz C
    38. Fujita M
    39. Fujita Y
    40. Fuma S
    41. Galizzi A
    42. Galleron N
    43. Ghim SY
    44. Glaser P
    45. Goffeau A
    46. Golightly EJ
    47. Grandi G
    48. Guiseppi G
    49. Guy BJ
    50. Haga K
    51. Haiech J
    52. Harwood CR
    53. Hènaut A
    54. Hilbert H
    55. Holsappel S
    56. Hosono S
    57. Hullo MF
    58. Itaya M
    59. Jones L
    60. Joris B
    61. Karamata D
    62. Kasahara Y
    63. Klaerr-Blanchard M
    64. Klein C
    65. Kobayashi Y
    66. Koetter P
    67. Koningstein G
    68. Krogh S
    69. Kumano M
    70. Kurita K
    71. Lapidus A
    72. Lardinois S
    73. Lauber J
    74. Lazarevic V
    75. Lee SM
    76. Levine A
    77. Liu H
    78. Masuda S
    79. Mauël C
    80. Médigue C
    81. Medina N
    82. Mellado RP
    83. Mizuno M
    84. Moestl D
    85. Nakai S
    86. Noback M
    87. Noone D
    88. O’Reilly M
    89. Ogawa K
    90. Ogiwara A
    91. Oudega B
    92. Park SH
    93. Parro V
    94. Pohl TM
    95. Portetelle D
    96. Porwollik S
    97. Prescott AM
    98. Presecan E
    99. Pujic P
    100. Purnelle B
    101. Rapoport G
    102. Rey M
    103. Reynolds S
    104. Rieger M
    105. Rivolta C
    106. Rocha E
    107. Roche B
    108. Rose M
    109. Sadaie Y
    110. Sato T
    111. Scanlan E
    112. Schleich S
    113. Schroeter R
    114. Scoffone F
    115. Sekiguchi J
    116. Sekowska A
    117. Seror SJ
    118. Serror P
    119. Shin BS
    120. Soldo B
    121. Sorokin A
    122. Tacconi E
    123. Takagi T
    124. Takahashi H
    125. Takemaru K
    126. Takeuchi M
    127. Tamakoshi A
    128. Tanaka T
    129. Terpstra P
    130. Tognoni A
    131. Tosato V
    132. Uchiyama S
    133. Vandenbol M
    134. Vannier F
    135. Vassarotti A
    136. Viari A
    137. Wambutt R
    138. Wedler E
    139. Wedler H
    140. Weitzenegger T
    141. Winters P
    142. Wipat A
    143. Yamamoto H
    144. Yamane K
    145. Yasumoto K
    146. Yata K
    147. Yoshida K
    148. Yoshikawa HF
    149. Zumstein E
    150. Yoshikawa H
    151. Danchin A
    (1997) The complete genome sequence of the gram-positive bacterium Bacillus subtilis
    Nature 390:249–256.
    https://doi.org/10.1038/36786
  47. Book
    1. Smolensky P
    (1986)
    Information Processing in Dynamical Systems: Foundations of Harmony Theory
    MIT Press.

Article and author information

Author details

  1. Priya M Christensen

    Department of Biological Sciences, University of Texas at Dallas, Richardson, United States
    Contribution
    Data curation, Formal analysis, Validation, Investigation, Visualization, Writing – original draft, Writing – review and editing
    Contributed equally with
    Jonathan Martin
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4790-9839

  2. Jonathan Martin

    Department of Biological Sciences, University of Texas at Dallas, Richardson, United States
    Contribution
    Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    Contributed equally with
    Priya M Christensen
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0946-3864

  3. Aparna Uppuluri

    Department of Biological Sciences, University of Texas at Dallas, Richardson, United States
    Contribution
    Data curation, Validation, Investigation, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0005-6334-8375

  4. Luke R Joyce

    Department of Immunology and Microbiology, University of Colorado Anschutz Medical Campus, Aurora, United States
    Contribution
    Validation, Investigation, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9346-671X

  5. Yahan Wei

    School of Podiatric Medicine, University of Texas Rio Grande Valley, Harlingen, United States
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  6. Ziqiang Guan

    Department of Biochemistry, Duke University Medical Center, Durham, United States
    Contribution
    Resources, Supervision, Funding acquisition, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing
    For correspondence
    ziqiang.guan@duke.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8082-3423

  7. Faruck Morcos

    1. Department of Biological Sciences, University of Texas at Dallas, Richardson, United States
    2. Department of Bioengineering, University of Texas at Dallas, Richardson, United States
    3. Center for Systems Biology, University of Texas at Dallas, Richardson, United States
    Contribution
    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    faruckm@utdallas.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6208-1561

  8. Kelli L Palmer

    Department of Biological Sciences, University of Texas at Dallas, Richardson, United States
    Contribution
    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    kelli.palmer@utdallas.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7343-9271

Funding

National Institutes of Health (R35GM133631)

  • Jonathan Martin
  • Faruck Morcos

National Science Foundation (MCB-1943442)

  • Faruck Morcos

National Institutes of Health (R01AI178692)

  • Ziqiang Guan
  • Faruck Morcos
  • Kelli L Palmer

Cecil and Ida Green Foundation

  • Kelli L Palmer

American Heart Association

https://doi.org/10.58275/AHA.23POST1013835.pc.gr.161273
  • Luke R Joyce

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

JM and FM acknowledge support from the National Institutes of Health (NIH R35GM133631). FM acknowledges support from the National Science Foundation CAREER award (MCB-1943442). We acknowledge support from the National Institutes of Health grant R01AI178692 (ZG, FM, and KP) and R01AI148366 (ZG and KP). KP acknowledges support from the Cecil H and Ida Green Chair in Systems Biology Science. LRJ acknowledges support from the American Heart Association (23POST1013835).

Version history

  1. Preprint posted:
  2. Sent for peer review:
  3. Reviewed Preprint version 1:
  4. Reviewed Preprint version 2:
  5. Version of Record published:

Cite all versions

You can cite all versions using the DOI https://doi.org/10.7554/eLife.94929. This DOI represents all versions, and will always resolve to the latest one.

Copyright

© 2024, Christensen, Martin et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 754
    views
  • 40
    downloads
  • 0
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Priya M Christensen
  2. Jonathan Martin
  3. Aparna Uppuluri
  4. Luke R Joyce
  5. Yahan Wei
  6. Ziqiang Guan
  7. Faruck Morcos
  8. Kelli L Palmer
(2024)
Lipid discovery enabled by sequence statistics and machine learning
eLife 13:RP94929.
https://doi.org/10.7554/eLife.94929.3

Share this article

https://doi.org/10.7554/eLife.94929

Categories and tags

Further reading

    1. Cancer Biology
    2. Computational and Systems Biology

    Luminal epithelial cells integrate variable responses to aging into stereotypical changes that underlie breast cancer susceptibility

    Rosalyn W Sayaman, Masaru Miyano ... Mark A LaBarge
    Research Article Updated

    Effects from aging in single cells are heterogenous, whereas at the organ- and tissue-levels aging phenotypes tend to appear as stereotypical changes. The mammary epithelium is a bilayer of two major phenotypically and functionally distinct cell lineages: luminal epithelial and myoepithelial cells. Mammary luminal epithelia exhibit substantial stereotypical changes with age that merit attention because these cells are the putative cells-of-origin for breast cancers. We hypothesize that effects from aging that impinge upon maintenance of lineage fidelity increase susceptibility to cancer initiation. We generated and analyzed transcriptomes from primary luminal epithelial and myoepithelial cells from younger <30 (y)ears old and older >55 y women. In addition to age-dependent directional changes in gene expression, we observed increased transcriptional variance with age that contributed to genome-wide loss of lineage fidelity. Age-dependent variant responses were common to both lineages, whereas directional changes were almost exclusively detected in luminal epithelia and involved altered regulation of chromatin and genome organizers such as SATB1. Epithelial expression variance of gap junction protein GJB6 increased with age, and modulation of GJB6 expression in heterochronous co-cultures revealed that it provided a communication conduit from myoepithelial cells that drove directional change in luminal cells. Age-dependent luminal transcriptomes comprised a prominent signal that could be detected in bulk tissue during aging and transition into cancers. A machine learning classifier based on luminal-specific aging distinguished normal from cancer tissue and was highly predictive of breast cancer subtype. We speculate that luminal epithelia are the ultimate site of integration of the variant responses to aging in their surrounding tissue, and that their emergent phenotype both endows cells with the ability to become cancer-cells-of-origin and represents a biosensor that presages cancer susceptibility.

    1. Computational and Systems Biology

    Emergence of power law distributions in protein-protein interaction networks through study bias

    David B Blumenthal, Marta Lucchetta ... Martin H Schaefer
    Research Article Updated

    Degree distributions in protein-protein interaction (PPI) networks are believed to follow a power law (PL). However, technical and study biases affect the experimental procedures for detecting PPIs. For instance, cancer-associated proteins have received disproportional attention. Moreover, bait proteins in large-scale experiments tend to have many false-positive interaction partners. Studying the degree distributions of thousands of PPI networks of controlled provenance, we address the question if PL distributions in observed PPI networks could be explained by these biases alone. Our findings are supported by mathematical models and extensive simulations, and indicate that study bias and technical bias suffice to produce the observed PL distribution. It is, hence, problematic to derive hypotheses about the topology of the true biological interactome from the PL distributions in observed PPI networks. Our study casts doubt on the use of the PL property of biological networks as a modeling assumption or quality criterion in network biology.

    1. Computational and Systems Biology
    2. Immunology and Inflammation

    A new pipeline SPICE identifies novel JUN-IKZF1 composite elements

    Peng Li, Sree Pulugulla ... Warren J Leonard
    Short Report

    Transcription factor partners can cooperatively bind to DNA composite elements to augment gene transcription. Here, we report a novel protein-DNA binding screening pipeline, termed Spacing Preference Identification of Composite Elements (SPICE), that can systematically predict protein binding partners and DNA motif spacing preferences. Using SPICE, we successfully identified known composite elements, such as AP1-IRF composite elements (AICEs) and STAT5 tetramers, and also uncovered several novel binding partners, including JUN-IKZF1 composite elements. One such novel interaction was identified at CNS9, an upstream conserved noncoding region in the human IL10 gene, which harbors a non-canonical IKZF1 binding site. We confirmed the cooperative binding of JUN and IKZF1 and showed that the activity of an IL10-luciferase reporter construct in primary B and T cells depended on both this site and the AP1 binding site within this composite element. Overall, our findings reveal an unappreciated global association of IKZF1 and AP1 and establish SPICE as a valuable new pipeline for predicting novel transcription binding complexes.