Genomics – Page 2 – Carbon Chemist

The genetic legacy of the wild ancestors of modern cattle

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AI-designed DNA sequences regulate cell-type-specific gene expression

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Nature, Published online: 23 October 2024; doi:10.1038/d41586-024-03170-2

Researchers have used artificial-intelligence models to create regulatory DNA sequences that drive gene expression in specific cell types. Such synthetic sequences could be used to target gene therapies to particular cell populations.

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October 23, 2024

A virus-derived enzyme can destroy the membrane structures that protect bacteria

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RESEARCH BRIEFINGS
03 October 2024

Bacteria in the gut have been shown to cause the severe side effects associated with treating blood diseases with genetically dissimilar, and thus immunologically incompatible, blood stem cells (allogeneic haematopoietic cell transplantation). An enzyme derived from viruses that infect these bacteria demonstrates potential for eradication therapy.

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October 3, 2024

Children with Down’s syndrome are more likely to get leukaemia: stem-cells hint at why

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Download the Nature Podcast 25 September 2024

In this episode:

00:46 Unravelling why children with Down’s syndrome are at a higher risk of leukaemia

Children with Down’s syndrome have a 150-fold increased risk of developing leukaemia than those without the condition. Now, an in-depth investigation has revealed that changes to genome structures in fetal liver stem-cells seem to be playing a key role in this increase.

Down’s syndrome is characterized by cells having an extra copy of chromosome 21. The team behind this work saw that in liver stem-cells — one of the main places blood is produced in a growing fetus — this extra copy results in changes in how DNA is packaged in a nucleus, opening up areas that are prone to mutation, including those known to be important in leukaemia development.

The researchers hope their work will be an important step in understanding and reducing this risk in children with Down’s syndrome.

Research Article: Marderstein et al.

News and Views: Childhood leukaemia in Down’s syndrome primed by blood-cell bias

11:47 Research Highlights

How taking pints of beer off the table lowers alcohol consumption, and a small lizard’s ‘scuba gear’ helps it stay submerged.

Research Highlight: A small fix to cut beer intake: downsize the pint

Research Highlight: This ‘scuba diving’ lizard has a self-made air supply

14:12 Briefing Chat

How tiny crustaceans use ‘smell’ to find their home cave, and how atomic bomb X-rays could deflect an asteroid away from a deadly Earth impact.

Science: In the dark ocean, these tiny creatures can smell their way home

Nature: Scientists successfully ‘nuke asteroid’ — in a lab mock-up

Subscribe to Nature Briefing, an unmissable daily round-up of science news, opinion and analysis free in your inbox every weekday.

Never miss an episode. Subscribe to the Nature Podcast on Apple Podcasts, Spotify, YouTube Music or your favourite podcast app. An RSS feed for the Nature Podcast is available too.

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September 25, 2024

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Combinatorial mutagenesis library designs

Combinatorial library 1

Library 1 was designed using a computationally efficient greedy strategy to search for the largest number of single aa substitutions that, when combined, preserve both fold and function even in the highest-order mutants (Fig. 1b). The algorithm used previously published ddPCA data and thermodynamic modelling results for GRB2-SH3, including inferred single aa substitution free energy changes of folding and binding for this protein²³. We showed previously that this model—which assumes that individual inferred folding and binding free energy changes (ΔΔG_f and ΔΔG_b) combine additively in multi-mutants—accurately predicts the effects of double aa substitutions²³. Therefore, this same additive model was used to make predictions about the energetic and phenotypic effects of higher-order mutants explored in the greedy search.

First, the set of candidate single aa mutations was restricted to those with confident free energy changes, defined as those with 95% confidence intervals < 1 kcal mol⁻¹ and whose effects were measured in at least 20 genetic backgrounds (that is, double aa mutations). Candidate mutations were further restricted to those reachable by single-nucleotide substitutions in the wild-type sequence to simplify synthesis of the resulting combinatorial mutagenesis library. The algorithm begins from an arbitrary starting mutation and iteratively selects further mutations at other residue positions until all residues in the protein have been mutated. The heuristic works by selecting further mutations at each step that maximize the fold and function of the current highest-order mutant combination, that is, the geometric mean of model-predicted AbundancePCA and BindingPCA growth rates. This procedure is then repeated for all possible starting mutations.

To visualize and compare the resulting solutions, we also simulated the median AbundancePCA and BindingPCA growth rates of all candidate combinatorial libraries, calculated using a random sample of 10,000 variants. Although the algorithm is not guaranteed to produce the optimal solution at each Hamming distance from the wild-type sequence, the greedy approach nevertheless achieves solutions in which both phenotypes are predicted to be preserved in variants with more than 30 mutations (Extended Data Fig. 1b), beyond which one or both phenotypes are lost. Defining viable libraries as those preserving both molecular phenotypes above 70% of the maximal value (that is, the geometric mean of simulated median AbundancePCA and BindingPCA growth rates) resulted in the largest candidate combinatorial library consisting of all combinations of 34 single aa mutations (Fig. 1 and Extended Data Fig. 1b–d).

Combinatorial library 2

We clustered the contact map (minimal side-chain heavy-atom distance < 5 Å) comprising all GRB2-SH3 surface residues (RSASA ≥ 0.25) existing in secondary structure elements (Extended Data Fig. 4) and selected the following four physically proximal residues for saturation combinatorial mutagenesis: H26, M28, A39 and T44 (see Extended Data Fig. 5).

Combinatorial library 3

This library was designed to include all combinations of 15 single aa substitutions with mild effects (within one-third of the AbundancePCA fitness interquartile range of the wild type²³) in close proximity in the primary sequence and reachable by single-nucleotide substitutions while avoiding mutations in binding interface residues (minimal side-chain heavy-atom distance to the ligand < 5 Å). We used a sliding window approach to determine the number of candidate mutant residues in stretches of 20, 21 and 22 consecutive residues in GRB2-SH3 (Extended Data Fig. 4b). Only one window with a width of 22 aa (starting at residue position 10) includes 15 candidate positions (Extended Data Fig. 4b). The final library consisted of all combinations of the following randomly selected candidate mutations at these positions: D10N, P11A, D14N, G15E, G18C, R20S, R21Q, D23E, F24I, H26L, V27I, M28K, D29E, N30T and S31T (see Fig. 4).

Combinatorial library 4: SRC

This library was designed using the same greedy algorithm from data and thermodynamic modelling results for SRC⁵¹, including inferred single aa substitution free energy changes of folding and activity for this protein. The design includes 15 single aa substitutions reachable by single nt substitution in a 22 aa window, located in the N-lobe of the SRC kinase domain, avoiding mutations in the activation loop, subsetting folding and activity ddGs to confident energies (95% confidence interval < 1 kcal mol⁻¹) and associated with singles observed in at least seven backgrounds. The final library consisted of all combinations of the following randomly selected candidate mutations at these positions: V329G, G344S, F349V, K343M, E331K, V337A, E332A, M341K, S330N, I336L, T338S, S345T, L346V, P333T and Y340S (see Fig. 6).

Mutagenesis library construction and selection assays

Media and buffers used

LB: 10 g l⁻¹ bacto-tryptone, 5 g l⁻¹ yeast extract, 10 g l⁻¹ NaCl. Autoclaved 20 min at 120 °C.
YPDA: 20 g l⁻¹ glucose, 20 g l⁻¹ peptone, 10 g l⁻¹ yeast extract, 40 mg l⁻¹ adenine sulphate. Autoclaved 20 min at 120 °C.
SORB: 1 M sorbitol, 100 mM LiOAc, 10 mM Tris pH 8.0, 1 mM EDTA. Filter sterilized (0.2-mm nylon membrane, Thermo Scientific).
Plate mixture: 40% PEG3350, 100 mM LiOAc, 10 mM Tris-HCl pH 8.0, 1 mM EDTA pH 8.0. Filter sterilized.
Recovery medium: YPD (20 g l⁻¹ glucose, 20 g l⁻¹ peptone, 10 g l⁻¹ yeast extract) + 0.5 M sorbitol. Filter sterilized.
SC-URA: 6.7 g l⁻¹ yeast nitrogen base without aa, 20 g l⁻¹ glucose, 0.77 g l⁻¹ complete supplement mixture drop-out without uracil. Filter sterilized.
SC-URA/MET/ADE: 6.7 g l⁻¹ yeast nitrogen base without aa, 20 g l⁻¹ glucose, 0.74 g l⁻¹ complete supplement mixture drop-out without uracil, adenine and methionine. Filter sterilized.
Competition medium: SC-URA/MET/ADE + 200 μg ml⁻¹ methotrexate (Merck Life Science), 2% DMSO.
DNA extraction buffer: 2% Triton-X, 1% SDS, 100 mM NaCl, 10 mM Tris-HCl pH 8.0, 1 mM EDTA pH 8.0.

Plasmid construction

For libraries 1–3: GRB2 mutagenesis plasmid pGJJ286: wild-type GRB2-SH3 was digested from pGJJ046 (described previously²³) with the restriction enzymes AvrII and HindIII and cloned into the digested plasmid pGJJ191 (described previously²⁴) using T4 ligase (New England Biolabs). AbundancePCA pGJJ046 and pGJJ045 plasmids and BindingPCA pGJJ034 and pGJJ001 plasmids were previously described²³. For library 4: pTB043 plasmid containing full-length SRC was described previously⁵¹. pTB043 is based on the same backbone as the AbundancePCA plasmids. The difference is that full-length SRC is fused to the DHFR[3] fragment at its N terminus and to the DHFR[1,2] fragment at its C terminus, so DHFR is reconstituted following correct folding of SRC, whereas unfolded SRC genotypes result in degradation of the fusion protein.

Libraries construction

Libraries 1–3: libraries were constructed in two steps. First, an IDT primer containing the chosen combination of mutations was assembled by Gibson into the mutagenesis plasmid pGJJ286. Libraries were then cloned into the yeast plasmids AbundancePCA pGJJ045 and BindingPCA pGJJ001 by digestion/ligation. For the first step, the libraries into the mutagenesis plasmid were assembled by Gibson reaction (in-house preparation) of two fragments. The vector fragment was obtained by polymerase chain reaction (PCR) amplification of pGJJ286 with the oligos shown in Supplementary Tables 1 and 2, incubated with DpnI to remove the template and gel purified using QIAquick gel extraction kit (Qiagen). The insert fragment was obtained by mixing equimolar amounts of IDT mutation primer (Supplementary Tables 1 and 2) and a reverse elongation primer (Supplementary Tables 1 and 2) and incubating for one cycle of annealing/extension with Q5 polymerase (New England Biolabs). dsDNA product was then incubated with ExoSAP-IT (Applied Biosystems) to remove the remaining ssDNA and purified with MinElute columns (Qiagen). 100 ng of vector in a molar ratio of 1:5 with the insert was incubated for 3 h at 50 °C with a Gibson mix 2× prepared in-house. The reaction was desalted by dialysis with membrane filters (MF-Millipore) for 1 h and concentrated 4× using a SpeedVac concentrator (Thermo Scientific). DNA was then transformed into NEB 10-beta High Efficiency Electrocompetent E. coli. Cells were allowed to recover in SOC medium (NEB 10-beta Stable Outgrowth Medium) for 30 min and later transferred to LB medium with spectinomycin overnight. A fraction of cells was also plated into spectinomycin + LB + agar plates to estimate the total number of transformants. 100 ml of each saturated E. coli culture was collected the next morning to extract the mutagenesis plasmid library using the QIAfilter Plasmid Midi Kit (QIAGEN). To obtain the final libraries into the yeast plasmids, libraries in pGJJ286 plasmid were digested with NheI and HindIII, gel purified (MinElute Gel Extraction Kit, QIAGEN) and cloned into pGJJ045 or pGJJ034 digested plasmids with T4 ligase (New England Biolabs) by temperature-cycle ligation following the manufacturer’s instructions, 67 fmol of backbone and 200 fmol of insert in a 33.3-μl reaction. The ligation was desalted by dialysis using membrane filters for 1 h, concentrated 4× using a SpeedVac concentrator (Thermo Scientific) and transformed into NEB 10-beta High Efficiency Electrocompetent E. coli cells.

Library 4: this library was constructed in one step by Gibson reaction of two fragments. The vector fragment was obtained by amplification of pTB043 plasmid with the oligos shown in the Supplementary Tables 1 and 2. The second fragment was obtained with ten cycles of PCR using mutated IDT primer as template (Supplementary Tables 1 and 2).

Methotrexate yeast selection assay

The yeast selection assay was previously described²³. The high-efficiency yeast transformation protocol described below (adjusted to a pre-culture of 200 ml of YPDA) was scaled up or down, depending on the number of transformants for each library (Supplementary Table 2). Three independent pre-cultures of BY4742 were grown in 20 ml of standard YPDA at 30 °C overnight. The next morning, the cultures were diluted into 200 ml of pre-warmed YPDA at an OD_600nm = 0.3 and incubated at 30 °C for 4 h. Cells were then collected and centrifuged for 5 min at 3,000g, washed with sterile water and SORB medium, resuspended in 8.6 ml of SORB and incubated at room temperature for 30 min. After incubation, 175 μl of 10 mg ml⁻¹ boiled salmon sperm DNA (Agilent Genomics) and 3.5 μg of plasmid library were added to each tube of cells and mixed gently. 35 ml of plate mixture was added to each tube to be incubated at room temperature for a further 30 min. 3.5 ml of DMSO was added to each tube and the cells were then heat shocked at 42 °C for 20 min (inverting tubes from time to time to ensure homogenous heat transfer). After heat shock, cells were centrifuged and resuspended in approximately 50 ml of recovery media and allowed to recover for 1 h at 30 °C. Cells were then centrifuged, washed with SC-URA medium and resuspended in 200 ml SC-URA. 10 μl was plated on SC-URA Petri dishes and incubated for about 48 h at 30 °C to measure the transformation efficiency. The independent liquid cultures were grown at 30 °C for about 48 h until saturation. Saturated cells were diluted again to OD_600nm = 0.1 in SC-URA/MET/ADE media and allowed to grow four generations until OD_600nm = 1.6 at 30 °C and 200 rpm. A fraction of the culture was then used to inoculate 200 ml of competition media containing methotrexate at a starting OD_600nm = 0.05 and the rest was collected and pellets frozen and stored as input. Cells in competition media were allowed to grow for 3–5 generations (Supplementary Table 2), collected and frozen and stored as output.

DNA extractions and plasmid quantification

The DNA extraction protocol used was previously described²³. The protocol below is for 100 ml of collected culture at OD_600nm ≈ 1.6. Protocols were scaled up or down, depending on the library (Supplementary Table 2). Cell pellets (one for each experiment input/output replicate) were resuspended in 1 ml of DNA extraction buffer, frozen by dry ice/ethanol bath and incubated at 62 °C in a water bath twice. Subsequently, 1 ml of phenol/chloro/isoamyl in a ratio of 25:24:1 (equilibrated in 10 mM Tris-HCl, 1 mM EDTA, pH 8.0) was added, together with 1 g of acid-washed glass beads (Sigma Aldrich) and the samples were vortexed for 10 min. Samples were centrifuged at room temperature for 30 min at 4,000 rpm and the aqueous phase was transferred into new tubes. The same step was repeated twice. 0.1 ml of NaOAc 3 M and 2.2 ml of pre-chilled absolute ethanol were added to the aqueous phase. The samples were gently mixed and incubated at −20 °C for at least 30 min. After that, they were centrifuged for 30 min at full speed at 4 °C to precipitate the DNA. The ethanol was removed and the DNA pellet was allowed to dry overnight at room temperature. DNA pellets were resuspended in 0.6 ml TE 1X and treated with 5 μl of RNase A (10 mg ml, Thermo Scientific) for 30 min at 37 °C. To desalt and concentrate the DNA solutions, the QIAEX II Gel Extraction Kit was used (50 µl of QIAEX II beads, QIAGEN). The samples were washed twice with PE buffer and eluted twice by 125 µl of 10 mM Tris-HCI buffer, pH 8.5. Finally, plasmid concentrations in the total DNA extract (which also contained yeast genomic DNA) were quantified by quantitative PCR using the primer pair oGJJ152–oGJJ153 that binds to the ori region of the plasmids.

Sequencing library preparation

Libraries 1–3: this was shown in ref. ²³. Briefly, the sequencing libraries were constructed in two consecutive PCR assays. The first PCR (PCR1) was designed to amplify the mutated protein of interest and to increase the nucleotide complexity of the first sequenced bases by introducing frame-shift bases between the adapters and the sequencing region of interest (Supplementary Tables 1 and 2). The second PCR (PCR2) was necessary to add the remainder of the Illumina adapter and demultiplexing indexes. PCR2 reactions were run for each sample independently using Hot Start High-Fidelity DNA Polymerase. In this second PCR, the remaining parts of the Illumina adapters were added to the library amplicon. The forward primer (5′ P5 Illumina adapter) was the same for all samples (GJJ_1J), whereas the reverse primer (3′ P7 Illumina adapter) differed by the barcode index (Supplementary Table 3) to be subsequently pooled and demultiplexed after deep sequencing. All samples were pooled in an equimolar ratio and gel purified using the QIAEX II Gel Extraction Kit. The purified amplicon library pools were subjected to Illumina 150-bp paired-end NextSeq500 sequencing at the CRG Genomics Core Facility.

Library 4: the method for preparing the library for sequencing was the same as for the other libraries but in the second PCR step, we used a barcoded index in the forward primer as well (5′ P5 Illumina adapter). The purified amplicon library pool was sequenced with an Illumina paired-end NextSeq2000 machine this time.

Sequencing data processing

FastQ files from paired-end sequencing of all AbundancePCA and BindingPCA experiments were processed with DiMSum v1.3 (ref. ⁵²) using default settings with small adjustments (https://github.com/lehner-lab/DiMSum). Supplementary Table 4 contains DiMSum fitness estimates and associated errors for all experiments. Experimental design files and command-line options required for running DiMSum on these datasets are available on GitHub (https://github.com/lehner-lab/archstabms). Variants with fewer than ten input read counts in any replicate were discarded (‘fitnessMinInputCountAll’ option), that is, only variants observed in all replicates above this threshold were retained. For library 1, we also included fitness estimates that derived from a subset of replicates whose input read counts exceeded this threshold (‘fitnessMinInputCountAny’ option; see Fig. 1).

For library 1, we also included a wild-type-only sample for sequencing using pGJJ046 as template to derive empirical estimates of sequencing error rates. The FastQ file for this sample was processed identically to those of the replicate input/output samples in the first-pass analysis with DiMSum with permissive base quality thresholds (‘vsearchMinQual = 5’ and ‘vsearchMaxee = 1000’). Read counts for all variants were then adjusted by subtracting the expected number of sequencing errors derived from the wild-type-only sample and proportional to the total sequencing library size of each sample. Finally, fitness estimates and associated errors for library 1 were then obtained from the resulting corrected variant counts with DiMSum (‘countPath’ option).

Thermodynamic modelling with MoCHI

We used MoCHI⁴³ (https://github.com/lehner-lab/MoCHI) to fit all thermodynamic models to combinatorial DMS data using default settings with small adjustments. The software is based on our previously described genotype–phenotype modelling approach²³, with extra functionality and improvements for ease of use and flexibility^24,43. Models fit to shallow (double-mutant) libraries and used in the analyses described in this work (for example, combinatorial mutagenesis library designs) were obtained using the original software implementation²³.

We model protein folding as an equilibrium between two states: unfolded (u) and folded (f), and protein binding as an equilibrium between three states: unfolded and unbound (uu), folded and unbound (fu) and folded and bound (fb). We assume that the probability of the unfolded and bound state (ub) is negligible and free energy changes of folding and binding are additive, that is, the total binding and folding free energy changes of an arbitrary variant relative to the wild-type sequence is simply the sum over residue-specific energies corresponding to all constituent single aa substitutions.

We configured MoCHI parameters to specify a neural network architecture consisting of additive trait layers (free energies) for each biophysical trait to be inferred (folding or folding and binding for AbundancePCA or BindingPCA, respectively), as well as one linear transformation layer per observed phenotype. The specified nonlinear transformations ‘TwoStateFractionFolded’ and ‘ThreeStateFractionBound’ derived from the Boltzmann distribution function relate energies to proportions of folded and bound molecules, respectively (see Figs. 2a and 4e,f). The target (output) data to fit the neural network comprise fitness scores for the wild-type and aa substitution variants of all mutation orders. The inclusion of both first-order and second-order (pairwise energetic coupling) model coefficients in the models was specified using the ‘max_interaction_order’ option.

A random 30% of aa substitution variants of all mutation orders was held out during model training, with 20% representing the validation data and 10% representing the test data. Validation data were used to evaluate training progress and optimize hyperparameters (batch size). Optimal hyperparameters were defined as those resulting in the smallest validation loss after 100 training epochs. Test data were used to assess final model performance.

MoCHI optimizes the parameters θ of the neural network using stochastic gradient descent on a loss function ${\mathcal{L}}[\theta ]$ based on a weighted and regularized form of mean absolute error:

$${\mathcal{L}}[\theta ]=1/N\mathop{\sum }\limits_{n=0}^{N-1}\left|{y}_{n}-{\widehat{y}}_{n}\right|{\sigma }_{n}^{-1}+{\lambda }_{2}{\parallel \theta \parallel }^{2}$$

in which y_n and σ_n are the observed fitness score and associated standard error, respectively, for variant n, ŷ_n is the predicted fitness score, N is the batch size and λ₂ is the L₂ regularization penalty. To penalize very large free energy changes (typically associated with extreme fitness scores), we set λ₂ to 10⁻⁶, representing light regularization. The mean absolute error is weighted by the inverse of the fitness error (${\sigma }_{n}^{-1}$) to downweight the contribution of less confidently estimated fitness scores to the loss. Furthermore, to capture the uncertainty in fitness estimates, the training data were replaced with a random sample from the fitness error distribution of each variant. The validation and test data were left unaltered.

Models were trained with default settings, that is, for a maximum of 1,000 epochs using the Adam optimization algorithm with an initial learning rate of 0.05 (except for library 1, for which we used an initial learning rate of 0.005). MoCHI reduces the learning rate exponentially (γ = 0.98) if the validation loss has not improved in the most recent ten epochs compared with the preceding ten epochs. Also, MoCHI stops model training early if the wild-type free energy terms over the most recent ten epochs have stabilized (standard deviation ≤ 10⁻³).

Free energies are calculated directly from model parameters as follows: ΔG_b = θ_bRT and ΔG_f = θ_fRT, in which T = 303 K and R = 0.001987 kcal K⁻¹ mol⁻¹. We estimated the confidence intervals of model-inferred free energies using a Monte Carlo simulation approach. The variability of inferred free energy changes was calculated between ten separate models fit using data from: (1) independent random training–validation–test splits and (2) independent random samples of fitness estimates from their underlying error distributions. Confident inferred free energy changes are defined as those with Monte Carlo simulation-derived 95% confidence intervals < 1 kcal mol⁻¹. Supplementary Table 5 contains inferred binding and folding free energy changes and energetic couplings from all second-order models.

Linear model to predict energetic coupling strength

We built a linear model to predict energetic coupling strength (absolute value of energetic coupling terms) from 12 features (see Fig. 3e), comprising five distance metrics for residue pairs or positions thereof in the protein structure: backbone distance (linear 1D distance separating residue pairs along the primary aa sequence), inter-residue distance (minimal side-chain heavy-atom distance in 3D space), number of core residues (0, 1 or both residues in the pair with RSASA < 0.25), number of binding interface residues (0, 1 or both with minimal side-chain heavy-atom distance to the ligand < 5 Å), number of beta-sheet residues (0, 1 or both in beta strands) and seven features describing the number of chemical bonds or interactions between the atoms of pairs of residues as calculated using the GetContacts software tool (https://getcontacts.github.io/): backbone to backbone hydrogen bonds, side chain to backbone hydrogen bonds, side chain to side chain hydrogen bonds, pi–cation interactions, pi-stacking interactions, salt-bridge interactions and van der Waals interactions. Before running GetContacts, we used PyMOL to fill missing hydrogens (‘h_add’ command), FoldX⁵³ to restore the wild-type proline at position 54 that is mutated in the reference crystal structure (PDB: 2VWF; ‘PositionScan’ command) and removed GAB2 ligand atoms. The training dataset comprised energetic couplings inferred from library 1 and the test set comprised independently inferred energetic couplings from library 3 (see Fig. 3f).

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

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Is bird flu spreading among people? Data gaps leave researchers in the dark

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Avian influenza virus particles as a coloured transmission electron micrograph. — These bird flu virus particles (artificially coloured) were imaged by an electron microscope.Credit: Eye of Science/Science Photo Library

All eyes are on Missouri.

Researchers are anxiously awaiting data from the midwestern state about a mysterious bird flu infection in a person who had no known contact with potential animal carriers of the disease. The data could reveal whether the ongoing US bird flu outbreak in dairy cattle has reached a dreaded turning point: the emergence of a virus capable of spreading from human to human.

Huge amounts of bird-flu virus found in raw milk of infected cows

Thus far, data from the mysterious infection are few and far between: small snippets of the H5N1 virus’s genome sequence and an incomplete infection timeline. Ratcheting up concerns is the fact that no Missouri dairy farms have reported a bird flu outbreak; this might be because there really are no infections, or because the state does not require farmers to test their cows for the virus.

“The fear is that the virus is spreading within the community at low levels, and this is the first time that we’re detecting it,” says Scott Hensley, a viral immunologist at the University of Pennsylvania Perelman School of Medicine in Philadelphia. “There’s no data to suggest that to be the case, but that’s the fear.”

A mystery case

On 6 September, Missouri public-health officials and the US Centers for Disease Control and Prevention (CDC) announced that an adult in the state had developed symptoms including chest pain, nausea, vomiting and diarrhea, and was hospitalized owing to other medical conditions. That person did not become severely ill and has recovered from the infection. Tests revealed it to be H5N1 influenza, often referred to as bird flu.

Since March, when the H5N1 virus was first detected in US dairy cattle, there have been more than a dozen cases of human infection that were traced back to contact with infected animals, including cows and birds. The Missouri case stands out because investigators found no such link and no tie to unprocessed food products, such as raw milk, from potentially infected livestock.

If bird flu sparks a human pandemic, your past immunity could help

This raised the possibility that the virus might have evolved to not only infect humans, but also to spread between people. If so, this increases the risk of it sweeping through human populations, potentially triggering a dangerous outbreak.

But that’s not the only possibility, cautions Jürgen Richt, a veterinary virologist at Kansas State University in Manhattan. “It’s a mystery case,” he says. “So you have to throw your net a little wider. Maybe they cleaned out a bird feeder in the household. Did they go to a state fair? What kind of food did they consume?”

More concerns were raised about the Missouri case on 13 September, when the CDC announced that two people who had close contact with the hospitalized person had also become ill around the same time. One of them was not tested for flu; the other tested negative.

That test result is encouraging but not definitive, says Hensley, because the sample could have been collected when the individual’s viral levels were too low for detection — after they started to recover, for instance. A key next step will be to test all three people for antibodies against the strain of H5N1 bird flu that has been infecting cattle. Such antibodies, particularly in the two contacts, would be definitive evidence of past infection.

Genomic sleuthing

While researchers await the antibody results, they are combing through patchy genome-sequence data from virus samples from the hospitalized person. This could yield any signs that the virus might have adapted to human hosts. The search is a challenge, however: the samples contained very low levels of viral RNA — so little that some researchers have shied away from analysing the sequences altogether.

Bird flu virus has been spreading among US cows for months, RNA reveals

“What I would want to see is higher quality,” says Ryan Langlois, a viral immunologist at the University of Minnesota Medical School in Minneapolis. “I am very leery about interpreting anything from partial sequences.”

But for Hensley, one feature of the sequence fragments immediately leapt out: a single change in the string of amino acids that form a flu protein called hemagglutinin (the ‘H’ in H5N1). That protein sits on the surface of influenza viruses, where it helps the viruses bind to and infect host cells. It is also a target of flu vaccines.

The change that Hensley found creates a site to which a large sugar molecule can bind. That sugar, he says, could then act as an umbrella, shielding the swath of hemagglutinin beneath it. It is a change that his laboratory has studied in other flu strains, and it could affect how the virus binds to host cells — as well as whether vaccines being developed against the H5N1 virus found in cattle can recognize and perform well against the virus detected in Missouri.

Surveillance gaps

Even if the sequences were available, researchers know little about which genetic changes might allow bird flu viruses to better infect humans or to become airborne, says virologist Yoshihiro Kawaoka at the University of Wisconsin–Madison. Previous studies¹^,² had suggested that changes to a gene encoding a protein responsible for copying the viral genome could be crucial for allowing the virus to replicate in mammalian cells. But researchers were unable to sequence that gene from the isolate from Missouri.

Meanwhile, the CDC has issued contracts to five companies in the United States to provide testing services for H5N1 and other emerging pathogens. Testing of cattle also needs to be improved so that public-health officials will know which regions of the country to surveil for infections in humans, says Seema Lakdawala, a virologist at Emory University in Atlanta, Georgia. In the United States, most testing of cattle is regulated at the state level, but only a handful of states have required routine testing on some dairy farms.

Public-health workers still don’t have a good handle on how many US herds have cows infected with H5N1, or whether cattle have immunity after contracting bird flu or can become reinfected, she says.

While researchers wait for more information, Hensley cautions against panic. “This could still be a one-off case and not the sign of something bigger,” he says.

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September 19, 2024

A broader view of the diversity of human gene expression

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RESEARCH BRIEFINGS
18 September 2024

Differences in gene expression between individuals are an important source of variation in traits and diseases. However, most of the research into such differences in humans has focused on individuals of European ancestries, limiting its generalizability. A geographically diverse, open-access gene-expression resource now enables the exploration of variation of gene expression in under-represented populations.

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September 18, 2024

Why does heart disease affect so many young South Asians?

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Epidemiologist Mohammed Ali has been thinking about heart health for much of his life. When he was a child, he remembers two of his uncles in Pakistan having heart attacks at an early age — one died at 39. He came to find that early heart disease is common for those with South Asian ancestry. People from this region, which includes India, Pakistan, Bangladesh, Sri Lanka and the Maldives, make up about one-quarter of the world’s population, but account for up to 60% of all heart disease cases¹.

“Everywhere you look, in every single South Asian group and diaspora, there is this risk which we cannot explain,” says Ali, now at Emory University in Atlanta, Georgia.

From Vikings to Beethoven: what your DNA says about your ancient relatives

It’s a challenge that researchers, clinicians and policymakers are struggling with. In what some physicians refer to as the South Asian paradox, many in this group have heart disease despite the absence of some common risk factors, such as smoking tobacco or having a high body mass index. It’s surprising, moreover, that such a large and diverse group spanning the globe and having a range of diets, socio-economic statuses and behaviours would all share this elevated risk — as well as an above-average risk for type 2 diabetes, which itself contributes to heart-disease risk. Genetics would seem to be a likely culprit, but so far, scant data have been collected for people of South Asian ancestry. This group accounts for less than 2% of genomics-study participants and less than 0.5% of participants in clinical trials focused on cardiovascular disease².

That is starting to change. In February, researchers in India completed an effort to sequence 10,000 genomes across the country. The US National Heart, Lung and Blood Institute launched a US$4-million programme last year to assemble a study group of 10,000 people with East Asian, South Asian and southeast Asian ancestry, or those who identify as Native Hawaiian or as Pacific Islander. And a research programme called OurHealth, which launched in October 2023, aims to quantify risk factors in the South Asian community, with a focus on genetic risks. Several research groups hope that exploring the genomes of people with South Asian ancestry will lead to a fresh understanding of risk factors, opportunities for precision medicine and potential therapeutic targets.

But even as these projects kick off, some are concerned that a focus on the genetics and genomics of a broadly defined population oversimplifies the issue and carries some risk. “What genetics has done is create these new, very persuasive arguments that there are actually biological differences that make us different,” says Projit Bihari Mukharji, a science historian at Ashoka University in Sonipat, India. And that could have unintended consequences, such as further racializing medicine or promoting inaction on combating heart disease and diabetes.

Hearty history

The high rate of cardiovascular disease among South Asians wasn’t always apparent, with a 1941 study noting the opposite. It found that around 1.3% of deaths in a Mumbai hospital were due to cardiovascular disease, compared with 8.4% in the United States³. But by the late 1960s, a concerning picture started to emerge in the South Asian diaspora: in the United States, the United Kingdom, Singapore and other countries, first- and second-generation immigrants had higher rates of diabetes and cardiovascular disease when compared with other racial or ethnic groups in those places⁴. “We used to think this was a migrant Indian phenomenon,” says Viswanathan Mohan, a physician who specializes in diabetes in Chennai, India. But, after India opened its state-run economy to private and foreign investors in 1991, rates of diabetes and cardiovascular disease began to increase there, too — probably because of the increased availability of calorie-dense food and a decrease in physical activity thanks to increased industrialization, says Mohan.

Many epidemiological studies followed. Jitendra Singh, India’s Minister of Science and Technology, said in February that the preponderance of studies showing elevated risks across populations and even after generations living abroad points to a genetic cause. “There’s something to do with the genes, regardless of the environment they live in,” he said at an event announcing the completion of the Indian 10,000 genomes project.

A researcher sat at a table in a marquee explains the SAHELI study to members of the South Asian community of Aurora, Illinois — A researcher discusses a heart-health programme with residents near Chicago, Illinois.Credit: Tessa Crawford/Northwestern University

Despite the limited genetics data for South Asians, scientists are hopeful that more research will lead to fruitful discoveries. Some groups of people in South Asia show strong founder effects — a low rate of genetic diversity that results from a small ancestral population — so often have genotypes that are not found elsewhere.

Some of these genotypes have helped to inform drug research. In Pakistan, researchers found a group of people who have mutations in a gene involved in producing certain triglycerides, a type of fat in the blood⁵. Compared with family members lacking the mutation, individuals with the mutation in both copies of the gene had reduced spikes in lipid levels after eating fatty foods, which could be protective against cardiovascular disease.

“There’s now active drug development bolstered by the fact that there are individuals out there that had two inactivated copies,” says Pradeep Natarajan, a preventive cardiologist at Massachusetts General Hospital and the Broad Institute of Harvard and MIT in Cambridge, who was involved in the study.

Ambitious survey of human diversity yields millions of undiscovered genetic variants

Some studies have also identified risk factors. About 15 years ago, researchers found a mutation in a gene encoding heart proteins in about 4% of people with Indian ancestry⁶. This can lead to cardiomyopathy, a relatively rare disease that weakens heart muscles. The mutation hasn’t been found in other populations, says Kumarasamy Thangaraj, a geneticist at the Center for Cellular and Molecular Biology in Hyderabad, India, who led the study. In the past few years, he and his team have found mutations in a different gene that could add to this risk, and which were also only found in people in India⁷. He hopes to screen people nationwide to identify those who are at risk of cardiomyopathy, and offer them counselling. “We are trying to get local government clearance to screen entire populations.” Thangaraj says.

Identifying people with a high risk of common forms of heart disease might also be possible, if challenging. Over the past decade, researchers and cardiologists have increasingly used polygenic risk scores to predict a person’s chance of developing a certain disease (see Nature 562, 181–183; 2018). These are algorithms that take into account all the genetic variants a person might have that correlate with an elevated or reduced risk of a certain disorder, and then combine them into an overall score.

Polygenic risk scores have proven predictive in some circumstances. But they are only as good as the data on which they are based, and have come up short in calculating risk for South Asian populations. One study⁸ of risk scores for coronary artery disease found that increasing the population diversity in the algorithm’s training data increased its accuracy — and not just for South Asians, but for all others. So adding more genome data from South Asian individuals could improve these models. But some are sceptical that focusing on the genetics of South Asians will improve their health.

Red flags and proper care

Medicine is trying to move away from taking race and ethnicity into account when delivering treatment. Race is a social classification, not a biological one, and has been often used to exclude people from treatment.

Last year, the American Heart Association (AHA) in Dallas, Texas, which creates standards for cardiologists, moved to using a clinical-risk calculator for cardiovascular disease that, in almost all cases, does not include race as a variable. But the calculator retains South Asian heritage as a “risk enhancing factor”⁹.

“We want race-free medicine,” says Sadiya Khan, a cardiologist at Northwestern University in Chicago, Illinois, who was the lead author of the AHA’s risk calculator. But the exception made for South Asians could be beneficial, she argues. “I’m 38 years old and most people would say ‘You’re way too young to be having a heart attack,’” says Khan, who identifies as South Asian. She hopes that the designation will serve as a “red flag” to make sure South Asians get the care they need, while researchers work out what causes this disparity. If clinicians correctly adjust their expectations when treating people from South Asia, it might save lives.

Members of a girls cricket team in India watch their team mate in shin pads practicing her batting technique — A group of girls attend cricket training in Dharoki, India. Exercising regularly can help to improve heart health, particularly in young people of South Asian heritage.Credit: Atul Loke/The New York Times/Redux/eyevine

But the AHA guidance explicitly recognizes that the factors contributing to elevated risk are not entirely genetic. In its calculator, racial heritage is used as a proxy for non-genetic factors that contribute to disease risk, even before birth. For instance, says Nishi Chaturvedi, an epidemiologist at University College London, South Asian women are more likely to develop diabetes during pregnancy, which can predispose their children to the condition. This cannot fully be explained by genetics, suggesting an environmental component¹⁰. Then, throughout early life, South Asian children tend to exercise less than do those from other population groups, and have carbohydrate-heavy diets¹¹. And the time span for South Asians to go from a prediabetic to a diabetic state is much shorter than for white people with similar traits, which makes it difficult for doctors to treat the condition early¹². All of this leads to negative outcomes in heart health. “I support the idea of doing more analysis of genetic risk factors. But I think that’s being done at the cost of looking at whole-of-life determinants of diabetes and cardiovascular disease,” Chaturvedi says.

And many factors contributing to the health disparities are a result of racism. “The intergenerational trauma that South Asians hold from colonialism, the migration experience, the stressors related to settlement and experiencing racism in a new country moves towards biologically altering who we are,” says Ananya Tina Banerjee, an epidemiologist at McGill University in Montreal, Canada, who identifies as South Asian. Researchers who study social and environmental drivers of inequities say that these factors are affecting South Asians in ways that medicine is struggling to measure and treat.

For instance, in one analysis of UK Biobank data, participants with South Asian ancestry were around 2.4 times more likely to experience cardiovascular disease than were participants with European ancestry. But when the researchers adjusted for socio-economic factors, diet, cholesterol, measures of inflammation and behavioural data, that risk fell to around 1.4 times — which the researchers say is likely to be due to genetics¹³.

The social side of risk

Alka Kanaya, a specialist in internal medicine at the University of California, San Francisco, says that, if researchers had a better understanding of non-genetic contributing factors, these could explain an even larger portion of the risk, reducing the share attributable to genetics.

Kanaya leads a study called Mediators of Atherosclerosis in South Asians Living in America (MASALA). Launched in 2010, it now has 2,300 participants in San Francisco, Chicago and New York City, including people with Indian, Pakistani and Bangladeshi heritage. Kanaya and her colleagues are trying to piece together how biological risk factors interact with non-biological ones. “There’s a lot of social, behavioural and socio-economic differences that are going to be explaining the majority of the risk,” she says.

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She and others argue that putting more effort into understanding these social determinants of health could result in more interventions that are both effective and scalable. “We need to go back to the drawing board a little bit and really do some more co-creation with communities,” says Namratha Kandula, a cardiologist at Northwestern University and an investigator with MASALA. Scientists in Chicago are trialling group counselling to help participants to manage their diets and stress, and have set up an exercise programme for mothers and daughters. Banerjee is testing similar interventions in Montreal. She started an women’s exercise group in a mosque and is providing educational workshops for children with a family history of diabetes.

Researchers investigating genetic predispositions and those looking into environmental effects recognize that the two are intertwined. But some worry that the focus on genetics could send the wrong message — that cardiovascular disease is unavoidable, baked into South Asians’ genes from birth. That kind of fatalism is something that Naveed Sattar, who specializes in cardiometabolic medicine at Glasgow University, UK, has seen in his clinic. “They say, ‘Doctor, if it is meant to happen it will happen,’” he says. “But, we shouldn’t be fatalistic by any means.”

Sattar has studied interventions that make him hopeful, even as he deals with his own heart-disease risks. He’s found that dietary interventions helped South Asians to prevent and to reverse type 2 diabetes, and that many drugs used to manage metabolic disease, such as statins and blood pressure medications, are effective in this group. Recently, Sattar found that the blockbuster GLP-1 drugs used to treat diabetes are potentially more effective in South Asians¹⁴.

“It’s not that biology doesn’t affect people’s health, but if you look at biology in isolation, you’re not going to actually make a difference for the vast majority of people, particularly those with the greatest need,” says Kandula.

She and others argue that more attention should be paid to the social and cultural contributors to heart disease among South Asians, but funding for such work is hard to come by.

With increased interest in South Asian cohort studies and the collection of more biological data, Kanaya hopes that social-science-based work will keep pace. “We’re doing a great job funding the multi-omics studies. Now let’s put in enough funding for the social–behavioural stuff, too. Let’s bring them up to par, because we know there’s going to be a really important interplay.”

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September 11, 2024

Famed Pacific island’s population ‘crash’ debunked by ancient DNA

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A close up view of a Moai statue on a bright summer day on Rapa Nui. — Rapa Nui is known for its giant stone figures, called moai.Credit: Sébastien Lecocq/Alamy

More than 800 years ago, Polynesians sailed thousands of kilometres across the Pacific Ocean to one of the most remote islands on Earth, Rapa Nui.

Now, a study of ancient genomes from descendants of these voyagers has answered key questions about the island’s history, dispelling the idea of a population collapse hundreds of years ago, and confirming precolonial contact with Indigenous Americans.

Ancient voyage carried Native Americans’ DNA to remote Pacific islands

The theory that the early Indigenous inhabitants of Rapa Nui — also known as Easter Island — ravaged its ecosystem and caused the population to crash before the arrival of Europeans in the early eighteenth century was popularized in the 2006 book Collapse, by geographer Jared Diamond, but some other scholars have since criticized that theory.

The latest analysis, published on 11 September in Nature¹, “serves as the final nail in the coffin of this collapse narrative”, says Kathrin Nägele, an archaeogeneticist at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. “It’s correcting the image of Indigenous people.”

The study was done with the endorsement of and input from officials and Indigenous community members in Rapa Nui. The authors say that their data could contribute to the repatriation of the remains sampled in the study, which were collected in the nineteenth and twentieth centuries and sit in a Paris museum.

Answers from DNA

After settling Rapa Nui by around ad 1200, ancient Polynesian people developed a flourishing culture famous for its hundreds of colossal stone statues, called moai.

When Europeans first reached the island in 1722, they estimated that it had a population of between 1,500 and 3,000 people and found a landscape denuded of the palm-tree forests that would have once covered the island. By the late nineteenth century, the Indigenous population, called the Rapanui, had dwindled to 110 people, owing to a smallpox outbreak and the kidnap of one-third of the inhabitants by Peruvian slave traders.

The ‘ecocide’ theory, that a pre-contact population of 15,000 or more plundered the once-pristine island’s resources, has been challenged by researchers who have questioned humans’ role in deforestation and its effects on food production, as well as the large estimates for the population.

Indigenous groups look to ancient DNA to bring their ancestors home

Anna-Sapfo Malaspinas, a population geneticist at the University of Lausanne, Switzerland, and Víctor Moreno-Mayar, an evolutionary geneticist at the University of Copenhagen, were hopeful that ancient Rapanui DNA could address the ecocide theory, as well as another lingering question: when ancient islanders mixed with Native Americans.

Their team’s 2014 study of genomes from contemporary Rapanui identified that these people had some Native American ancestry that seemed to have been acquired before European arrival², hinting at voyages to the Americas. But a 2017 study found no signs of Native American ancestry in the genomes of three individuals people who lived in Rapa Nui before 1722³.

To find answers, the researchers turned to human remains in France’s National Museum of Natural History that were collected in the nineteenth and early twentieth centuries. Genome sequences from the teeth or inner-ear bones of 15 individuals, and comparisons with other ancient and modern populations, suggested they were Rapanui, and radiocarbon dating showed that they lived between 1670 and 1950.

No population collapse

Both ancient and modern genomes carry information about how a population’s size has changed over time. When the population is small, segments of DNA shared between individuals — which are inherited from a common ancestor — tend to be longer and more abundant, compared with DNA segments from periods when numbers are higher.

In the genomes of the ancient Rapanui, there were signs of a population bottleneck around the time the island was settled, as would be expected when a founder group arrives. But after that, the island’s population seemed to grow steadily until the nineteenth century.

Divided by DNA: The uneasy relationship between archaeology and ancient genomics

Translating these trajectories into actual population numbers is not straightforward, but further modelling suggested that the genetic data are not consistent with, for example, a drop from 15,000 to 3,000 people before the eighteenth century. “There’s no strong collapse,” says Malaspinas. “We’re quite confident that it did not happen.”

All the ancient Rapanui carried Native American ancestry in their genomes, which the researchers determined had probably resulted from mixing dated to the fourteenth century. The Native American segments most closely resembled DNA from ancient and modern-day inhabitants of the central Andean highlands in South America, but the dearth of ancient and modern human genomes from the Americas makes it impossible to pinpoint the people the ancient Rapanui encountered, adds Moreno-Mayar. Still, the finding that Rapanui encountered Native Americans hundreds of years before Europeans arrived is “a banger result”, says Nägele. “We can look for where this happened and who travelled.”

Community input

Keolu Fox, a genome scientist at the University of California, San Diego, says the finding that Rapanui reached the Americas will come as no surprise to Polynesian people. “We’re confirming something we already knew,” he says. “Do you think that a community that found things like Hawaii or Tahiti would miss a whole continent?”

The researchers received a similar reaction when presenting their initial findings in Rapa Nui. Malaspinas recalls being told that ‘of course we went to the Americas’. She, Moreno-Mayar and other colleagues made multiple trips to the island to consult with officials and residents throughout the study.

From Vikings to Beethoven: what your DNA says about your ancient relatives

Malaspinas and her colleagues got approval for the study from committees that oversee land use and cultural heritage on the island. The researchers sought their permission after sampling the remains in Paris — something Malasipinas now regrets. “I would do things differently if I had started the project today,” she says, adding that her team was prepared to shelve the work if the committees had said no.

Community outreach in Rapa Nui shaped the questions the project tackled, says Malaspinas, such as trying to settle the relationship between ancient and present-day Rapanui. There was also a strong interest in repatriating the remains, something the researchers hope will eventually happen.

Nägele, who works in Polynesia, thinks the researchers did a good job of engaging with people in Rapa Nui. But she adds that scientists should have a stronger role in pressuring foreign institutions to return Indigenous remains to their place of origin.

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September 11, 2024

Tag: Genomics

00:46 Unravelling why children with Down’s syndrome are at a higher risk of leukaemia

11:47 Research Highlights

14:12 Briefing Chat

Combinatorial mutagenesis library designs

Combinatorial library 1

Combinatorial library 2

Combinatorial library 3

Combinatorial library 4: SRC

Mutagenesis library construction and selection assays

Media and buffers used

Plasmid construction

Libraries construction

Methotrexate yeast selection assay

DNA extractions and plasmid quantification

Sequencing library preparation

Sequencing data processing

Thermodynamic modelling with MoCHI

Linear model to predict energetic coupling strength

Reporting summary

A mystery case

Genomic sleuthing

Surveillance gaps

Hearty history

Red flags and proper care

The social side of risk

Answers from DNA

No population collapse

Community input