Biology subjects

Sandhu, M.

Publications and source records attributed to Sandhu, M..

16 recordsLinked to original sources

bioRxiv2026

A 4,000-year genomic history of Bantu speakers from the Congo rainforest

The Congo rainforest was the heartland of the Bantu Expansion, yet its demographic history remains partially understood. We inferred past population dynamics using genomes from 960 sub-Saharan Africans, including four newly sequenced ancient individuals from Iroungou cave and 176 present-day individuals from Gabon. Bantu-speaking populations began differentiating ~4,000 years ago, preceding explosive population growth by a millennium. Distinct southward dispersals contributed to the ancestry of Bantu speakers in Gabon and elsewhere in sub-Saharan Africa, with the ~700-year-old Iroungou individuals providing the closest genetic proxies for the latter. Both Bantu speakers and hunter-gatherers from the Congo rainforest experienced a major demographic crisis ~1,500 years ago, coinciding with an archaeological hiatus. These findings illuminate early Bantu dispersals and the subsequent, unexplained collapse of rainforest populations.

genomics↗

bioRxiv2025

Impact of multi-echo ICA modeling decisions on motor-task fMRI analysis

Multi-echo independent component analysis (ME-ICA) has been demonstrated to improve sensitivity and reliability of task functional magnetic resonance imaging (fMRI) data and, in particular, motor-task data with inherent task-correlated head motion. However, previous work has shown that an overly aggressive ME-ICA denoising approach may unintentionally remove task-related signal, while a more conservative approach may not effectively mitigate noise. While the effects of varied implementations of ME-ICA on signal and noise characteristics have been tested thoroughly in breath-hold data, the effects of similar modeling decisions have not been studied in motor-task data, which present with a more localized neural response. Here, we tested and compared the impacts of three analysis methods using rejected ME-ICA components as regressors in subject-level modeling: Aggressive (simple inclusion of ME-ICA regressors), Moderate (excluding task-correlated ME-ICA regressors from the model), and Conservative (orthogonalization of ME-ICA regressors to the core model and accepted ME-ICA components). We applied these methods to data from healthy and multiple sclerosis populations that included performance of hand-grasp, shoulder-abduction, and ankle-flexion tasks. We found that when the amount of head motion and its correlation with the task was high and the expected task-evoked signal was relatively low, the Conservative method led to significantly higher activation, t-statistics, and test-retest similarity in motor regions compared to the Aggressive method. Future motor-task studies may wish to implement similar models to prevent loss of motor signal, while still mitigating the effects of task-correlated head motion.

bioengineering↗

bioRxiv2025

Fitness Landscape Ruggedness Arises from Biophysical Complexity

The ruggedness of a proteins fitness landscape constrains how it can evolve and how effectively it can be engineered. What determines whether a fitness landscape is rugged, and where in sequence space that ruggedness concentrates, remains poorly understood. Existing measures of ruggedness rely on complete combinatorial datasets and cannot be applied to landscapes in which only a fraction of possible sequences have been characterized. Here, we introduce a graph-based measure of ruggedness based on how quickly fitness differences diffuse across a landscape, which can be applied to sparse datasets. Applying this measure to 64 protein domains and several experimental systems with characterized folding and binding constraints, we show that ruggedness is not uniformly distributed but concentrates at regions where the dominant constraint on fitness changes. These results establish that the composition of individually simple constraints can generate localized ruggedness, providing a mechanistic basis for the complexity of protein fitness landscapes.

evolutionary biology↗

bioRxiv2023

Leveraging ancestral sequence reconstruction for protein representation learning

Protein language models (PLMs) convert amino acid sequences into the numerical representations required to train machine learning (ML) models. Many PLMs are large (>600 M parameters) and trained on a broad span of protein sequence space. However, these models have limitations in terms of predictive accuracy and computational cost. Here, we use multiplexed Ancestral Sequence Reconstruction (mASR) to generate small but focused functional protein sequence datasets for PLM training. Compared to large PLMs, this local ancestral sequence embedding (LASE) produces representations 10-fold faster and with higher predictive accuracy. We show that due to the evolutionary nature of the ASR data, LASE produces smoother fitness landscapes in which protein variants that are closer in fitness value become numerically closer in representation space. This work contributes to the implementation of ML-based protein design in real-world settings, where data is sparse and computational resources are limited.

bioinformatics↗

bioRxiv2023

Origin Of Evolutionary Bifurcation In An Enzyme

Evolution can lead to significantly distinct outcomes depending on the mutational path taken. Evolutionary bifurcation, in which two mutational trajectories segregate, becoming non-interchangeable over time, is the basis of diversification in all kingdoms of life. Here, we present a detailed molecular description of a bifurcation event that rapidly led to the emergence of two distinct enzymes from a common ancestor. When initiated from two starting points that differed by a single amino acid, the laboratory evolution of a phosphotriesterase (PTE) toward arylester hydrolysis resulted in different genetic and phenotypic outcomes. One trajectory led to a >35,000-fold increase in activity via the reorganization of its active site to achieve exquisite enzyme-substrate complementarity. The second trajectory gave rise to an evolved variant with a [~]500-fold increase in activity, but exhibiting an alternative substrate binding mode resulting from the destabilization of an active site loop. While initial mutations tend to dictate mutational accessibility, we rather observed the gradual divergence and specialisation of each trajectory, following the emergence of distinct molecular interaction networks. Intramolecular epistasis underlay pathway bifurcation by promoting unique synergistic interactions within each trajectory, while restricting the fixation of mutation across pathways. Our results illustrate how distinct molecular outcomes can radiate from a common protein ancestor and give rise to phenotypic diversity.

evolutionary biology↗

bioRxiv2023

Identifying the core genome of the nucleus-forming bacteriophage family and characterization of Erwinia phage RAY

We recently discovered that some bacteriophages establish a nucleus-like replication compartment (phage nucleus), but the core genes that define nucleus-based phage replication and their phylogenetic distribution were unknown. By studying phages that encode the major phage nucleus protein chimallin, including previously sequenced yet uncharacterized phages, we discovered that chimallin-encoding phages share a set of 72 highly conserved genes encoded within seven distinct gene blocks. Of these, 21 core genes are unique to this group, and all but one of these unique genes encode proteins of unknown function. We propose that phages with this core genome comprise a novel viral family we term Chimalliviridae. Fluorescence microscopy and cryo-electron tomography studies of Erwinia phage vB_EamM_RAY confirm that many of the key steps of nucleus-based replication encoded in the core genome are conserved among diverse chimalliviruses, and reveal that non-core components can confer intriguing variations on this replication mechanism. For instance, unlike previously studied nucleus-forming phages, RAY doesnt degrade the host genome, and its PhuZ homolog appears to form a five-stranded filament with a lumen. This work expands our understanding of phage nucleus and PhuZ spindle diversity and function, providing a roadmap for identifying key mechanisms underlying nucleus-based phage replication.

microbiology↗

bioRxiv2022

The rugged DNA-binding sequence-fitness landscape of the LacI/GalR Family is a product of asymmetry in the operator:repressor complex

How a proteins function influences the shape of its fitness landscape, smooth or rugged, is a fundamental question in evolutionary biochemistry. Smooth landscapes arise when incremental mutational steps lead to a progressive change in function, as commonly seen in enzymes and binding proteins. On the other hand, rugged landscapes are poorly understood because of the inherent unpredictability of how sequence changes affect function. Here, we experimentally characterize the entire sequence phylogeny, comprising 1158 extant and ancestral sequences, of the DNA-binding domain (DBD) of the LacI/GalR transcriptional repressor family. Our analysis revealed an extremely rugged landscape with rapid switching of specificity even between adjacent nodes. Further, the ruggedness arises due to the necessity of the repressor to simultaneously evolve specificity for asymmetric operators and disfavors potentially adverse regulatory crosstalk. Our study provides fundamental insight into evolutionary, molecular, and biophysical rules of genetic regulation through the lens of fitness landscapes.

biochemistry↗

bioRxiv2021

Dissecting the allosteric networks governing agonist efficacy and potency in G protein-coupled receptors

G protein-coupled receptors (GPCRs) translate binding of extracellular ligands into intracellular responses through conformational changes. Ligand properties are described by the maximum response (efficacy) and the agonist concentration at half-maximal response (potency). Integrating structural changes with pharmacological properties remains challenging and has not yet been performed at the resolution of individual amino acids. We use epinephrine and {beta}2-adrenergic receptor as a model to integrate residue-level pharmacology data with intramolecular residue contact data describing receptor activation. This unveils the allosteric networks driving ligand efficacy and potency. We provide detailed insights into how structural rearrangements are linked to fundamental pharmacological properties at single-residue level in a receptor-ligand system. Our approach can be used to determine such pharmacological networks for any receptor-ligand complex.

biochemistry↗

bioRxiv2021

Flashfm: A Flexible and Shared Information Fine-mapping Approach for Multiple Quantitative Traits

Joint fine-mapping that leverages information between quantitative traits could improve accuracy and resolution over single-trait fine-mapping. Using summary statistics, flashfm (FLexible And SHared information Fine-Mapping) fine-maps signals for multiple traits, allowing for missing trait measurements and use of related individuals. In a Bayesian framework, prior model probabilities are formulated to favour model combinations that share causal variants to capitalise on information between traits. Simulation studies demonstrate that both approaches produce broadly equivalent results when traits have no shared causal variants. When traits share at least one causal variant, flashfm reduces the number of potential causal variants by 30% compared with single-trait fine-mapping. In a Ugandan cohort with 33 cardiometabolic traits, flashfm gave a 20% reduction in the total number of potential causal variants from single-trait fine-mapping. Flashfm is computationally efficient and can easily be deployed across publicly available summary statistics for signals in up to six traits.

genetics↗

bioRxiv2021

The chemotherapeutic CX-5461 primarily targets TOP2B and exhibits selective activity in high-risk neuroblastoma.

Survival in high-risk pediatric neuroblastoma has remained around 50% for the last 20 years, with immunotherapies and targeted therapies having had minimal impact. Here, we identify the small molecule CX-5461 as selectively cytotoxic to high-risk neuroblastoma and synergistic with low picomolar concentrations of topoisomerase I inhibitors improving survival in vivo in orthotopic patient-derived xenograft neuroblastoma mouse models. CX-5461 recently progressed through phase I clinical trial as a first-in-human inhibitor of RNA-POL I. However, we also use a comprehensive panel of in vitro and in vivo assays to demonstrate that CX-5461 has been mischaracterized and that its primary target at pharmacologically relevant concentrations, is in fact topoisomerase II beta (TOP2B), not RNA-POL I. These findings are important because existing clinically approved chemotherapeutics have well-documented off-target interactions with TOP2B, which have previously been shown to cause both therapy-induced leukemia and cardiotoxicity--often-fatal adverse events, which can emerge several years after treatment. Thus, while we show that combination therapies involving CX-5461 have promising anti-tumor activity in vivo in neuroblastoma, our identification of TOP2B as the primary target of CX-5461 indicates unexpected safety concerns that should be examined in ongoing phase II clinical trials in adult patients before pursuing clinical studies in children.

cancer biology↗

bioRxiv2021

Protein and substrate flexibility contribute to enzymatic specificity in human and bacterial methionine adenosyltransferases

Protein conformational change can facilitate the binding of non-cognate substrates and underlie promiscuous activities. However, the contribution of substrate conformational dynamics to this process is comparatively poorly understood. Here we analyse human (hMAT2A) and Escherichia coli (eMAT) methionine adenosyltransferases that have identical active sites but different substrate specificity. In the promiscuous hMAT2A, non-cognate substrates bind in a stable conformation to allow catalysis. In contrast, non-cognate substrates rarely sample stable productive binding modes in eMAT owing to altered mobility in the enzyme active site. Different cellular concentrations of substrate likely drove the evolutionary divergence of substrate specificity in these orthologs. The observation of catalytic promiscuity in hMAT2A led to the detection of a new human metabolite, methyl thioguanosine, that is produced at elevated level in a cancer cell line. This work establishes that identical active sites can result in different substrate specificity owing to the effects of substrate and enzyme dynamics.

biochemistry↗

bioRxiv2021

Polygenic Prediction of Type 2 Diabetes in Continental Africa

ObjectivePolygenic prediction of type 2 diabetes in continental Africans is adversely affected by the limited number of genome-wide association studies (GWAS) of type 2 diabetes from Africa, and the poor transferability of European derived polygenic risk scores (PRS) in diverse ethnicities. We set out to evaluate if African American or multi-ethnic derived PRSs would improve polygenic prediction in continental Africans. Research Design and MethodsUsing the PRSice software, ethnic-specific PRSs were computed with weights from the type 2 diabetes GWAS of the Million Veteran Program (MVP) study. The South African Zulu study (1602 cases and 976 controls) was used as the target data set. Replication and assessment of the best predictive PRS association with age at diagnosis was done in the Africa America Diabetes Mellitus (AADM) study (1031 cases and 738 controls). ResultsThe African American derived PRS was more predictive of type 2 diabetes compared to the European and multi-ethnic derived scores. Notably, participants in the 10th decile of this PRS had a 3.19-fold greater risk (OR 3.19; 95%CI (1.94-5.29), p = 5.33 x10-6) of developing diabetes and were diagnosed 2.6 years earlier compared to those in the first decile. ConclusionsAfrican American derived PRS enhances polygenic prediction of type 2 diabetes in continental Africans. Improved representation of non-Europeans populations (including Africans) in GWAS, promises to provide better tools for precision medicine interventions in type 2 diabetes.

genetics↗

bioRxiv2020

The NK Landscape as a Versatile Benchmark for Machine Learning Driven Protein Engineering

Machine learning (ML) has revolutionized protein biology, solving long-standing problems in protein folding, scaffold generation and function design tasks. A range of architectures have shown success on supervised protein fitness prediction tasks. Nevertheless, in the absence of rational approaches for evaluating which architectures are optimal for specific datasets and engineering tasks, architecture choice remains challenging. Here, we propose a framework for investigating the determinants of success for a range of ML architectures. Using simulated (the NK model) and empirical fitness landscapes, we measure sequence-fitness prediction along six key performance metrics: interpolation within the training domain, extrapolation outside the training domain, robustness to increasing epistasis/ruggedness, ability to perform positional extrapolation, robustness to sparse training data, and sensitivity to sequence length. We show that architectural differences between algorithms consistently affect performance against these metrics across both experimental and theoretical landscapes. Moreover, landscape ruggedness emerges as a primary determinant of accuracy of sequence-fitness prediction. Our methodology and results provide a rational strategy for experimental data sampling, model selection and evaluation rooted in fitness landscape theory; one that we hope will advance sequence-fitness prediction accuracy, with implications for protein engineering and variant functional prediction.

synthetic biology↗

bioRxiv2020

The evolution of human SIRT1 for multi-specificity is enabled by active-site adaptation

Many enzymes that catalyze protein post-translational modifications (PTM) can specifically modify multiple target proteins. However, little is known regarding the molecular basis and evolution of multi-specificity in these enzymes. Here, we used bioinformatics and experimental approaches to investigate the molecular basis and evolution of multi-specificity in the sirtuin-1 (SIRT1) deacetylase. Guided by bioinformatics analysis of SIRT1 orthologues and substrates, we identified and examined key mutations that have occurred during the evolution of human SIRT1. We found that while these mutants maintain high activity toward conserved histone substrates they exhibit a dramatic loss of activity toward acetylated p53 protein that appeared only in Metazoa. These results demonstrate that active site substitutions in SIRT1 are essential to enable the inclusion of p53 substrate. Our results are consistent with a model in which promiscuous ancestral activity can provide an evolutionary starting point from which multispecificity toward a number of cellular substrates can develop.

biochemistry↗

bioRxiv2019

Machine Learning for Prioritization of Thermostabilizing Mutations for G-protein Coupled Receptors

Although the three-dimensional structures of G-protein-coupled receptors (GPCRs), the largest superfamily of drug targets, have enabled structure-based drug design, there are no structures available for 87% of GPCRs. This is due to the stiff challenge in purifying the inherently flexible GPCRs. Identifying thermostabilized mutant GPCRs via systematic alanine scanning mutations has been a successful strategy in stabilizing GPCRs, but it remains a daunting task for each GPCR. We developed a computational method that combines sequence, structure and dynamics based molecular properties of GPCRs that recapitulate GPCR stability, with four different machine learning methods to predict thermostable mutations ahead of experiments. This method has been trained on thermostability data for 1231 mutants, the largest publicly available dataset. A blind prediction for thermostable mutations of the Complement factor C5a Receptor retrieved 36% of the thermostable mutants in the top 50 prioritized mutants compared to 3% in the first 50 attempts using systematic alanine scanning.\n\nStatement Of SignifiganceG-protein-coupled receptors (GPCRs), the largest superfamily of membrane proteins play a vital role in cellular physiology and are targets to blockbuster drugs. Hence it is imperative to solve the three dimensional structures of GPCRs in various conformational states with different types of ligands bound. To reduce the experimental burden in identifying thermostable GPCR mutants, we report a computational framework using machine learning algorithms trained on thermostability data for 1231 mutants and features calculated from analysis of GPCR sequences, structure and dynamics to predict thermostable mutations ahead of experiments. This work represents a significant advancement in the development, validation and testing of a computational framework that can be extended to other class A GPCRs and helical membrane proteins.

biophysics↗

bioRxiv2019

The transferability of lipid-associated loci across African, Asian and European cohorts

The under-representation of non-European samples in genome-wide association studies could ultimately restrict who benefits from medical advances through genomic science. Our aim was therefore to address the fundamental question whether causal variants for blood lipids are shared across populations.\n\nA polygenic score based on established LDL-cholesterol-associated loci from European discovery samples had consistent effects on serum levels in samples from the UK, Uganda and Greek population isolates (correlation coefficient r=0.23 to 0.28 per LDL standard deviation, p<1.9x10-14). Trans-ethnic genetic correlations between European ancestry, Chinese and Japanese cohorts did not differ significantly from 1 for HDL, LDL and triglycerides. In each study, >60% of major lipid loci displayed evidence of replication with one exception. There was evidence for an effect on serum levels in the Ugandan samples for only 10% of major triglyceride loci. The PRS was only weakly associated in this group (r=0.06, SE=0.013). We establish trans-ethnic colocalization as a method to distinguish shared from population-specific trait loci.\n\nOur results provide evidence for high levels of consistency of genetic associations for cholesterol biomarkers across populations. However, we also demonstrate that the degree of shared causal genetic architecture can be population-, trait- and locus-specific. Efforts to implement genetic risk prediction in clinical settings should account for this.

genetics↗