Random variation in reproductive success--genetic drift--profoundly shapes genetic diversity and evolutionary trajectories. The strength of drift depends on the variance in descendant number, σd2, which governs key evolutionary outcomes: for instance, the establishment probability of a beneficial mutation scales inversely with σd2. However, whether σd2 itself evolves over long timescales has remained unclear, because allele-frequency fluctuations depend on drift only through the effective population size, Ne = N/σd2, which blends census population size with descendant-number variance. Here, we disentangle these components by using model-based Bayesian inference combined with joint tracking of (i) frequency fluctuations of neutrally barcoded lineages and (ii) census population sizes across growth cycles in the E. coli Long-Term Evolution Experiment. Analyzing 33 clones spanning the ancestor through 50,000 generations in two replicate populations (Ara-2 and Ara+2), we find that the strength of genetic drift evolved markedly--and divergently--between the two replicate populations. Both census size and σd2 changed substantially through time, with most variation in Ne driven by shifts in σd2 rather than census size. After approximately 2,000 generations, the σd2 of the two populations diverged sharply: Ara+2 generally remained close to a bottleneck-only null expectation, whereas Ara-2 exhibited 1.5-5x stronger drift, consistent with an evolved increase in stochasticity during growth. Because establishment probability scales as s/σd2, a beneficial mutation of given effect is roughly twice as likely to establish in Ara+2 as in Ara-2. Our results demonstrate that the key parameter governing genetic drift can itself evolve, with direct consequences for adaptation.

Sequence verification of plasmids is a fundamental process in synthetic biology. For plasmid sequence verification using next-generation sequencing (NGS) library preparation, Tn5 transposase is widely used. Streamlined sequencing workflow for laboratory-scale applications is important; however, recombinant Tn5 production in-house can be laborious. In this study, we demonstrated that the addition of a large soluble tag was not essential for purification and that the fusion of a His10 tag and protein A was sufficient to yield active Tn5 transposase in adequate amounts. In addition, we evaluated exonuclease-based genomic DNA digestion for plasmid sequencing from an E. coli lysate and the data analysis pipeline of sequences derived from the Illumina iSeq100 platform for de novo assembly, reference mapping, and annotation. This study proposes a simple workflow of an in-house easy-to-purify Tn5-based plasmid sequencing platform using a compact benchtop sequencer (AistSeq).

The biosynthetic production of human milk oligosaccharides through metabolic engineering has gained increasing attention in recent years. Yet, limited research has focused on the microbial synthesis of 3′-sialyllactosamine (3′-SLN), a critical intermediate for assembling sialyl Lewis X (sLex). In this work, a pathway enabling 3′-SLN biosynthesis was constructed in E. coli BL21(DE3). A key α2,3sialyltransferase (α2,3SiaT) from Bibersteinia trehalosi was identified and utilized to enhance the sialylation step toward 3′-SLN formation. To improve flux through the N-acetyllactosamine branch, essential genes were chromosomally integrated and overexpressed, while a CMP-N-acetylneuraminic acid synthesis module was coexpressed to secure an adequate donor supply. Furthermore, the expression balance was optimized through combinatorial adjustment of gene copy numbers and translational strength across modules. The engineered E. coli strain produced 0.78 g/L of 3′-SLN in shake-flask cultivation and further accumulated up to 7.75 g/L during 5-L fed-batch fermentation conditions, confirming the feasibility of microbial synthesis of 3′-SLN.

Liquid–liquid phase separation (LLPS) compartmentalizes biological systems into dynamic, membraneless condensates that regulate diverse cellular functions. Although protein and RNA-mediated LLPS have dominated research, DNA increasingly emerges as an active driver of phase separation rather than a passive structural scaffold. DNA-driven condensates orchestrate critical nuclear processes, from chromatin organization and transcriptional regulation to genome stability and innate immune responses. Yet LLPS principles extend beyond biology: programmable DNA nanostructures now enable synthetic droplets and hydrogels with tunable mechanical properties, opening pathways to biomaterials, diagnostics, and synthetic cells. Here we synthesize current understanding of DNA-mediated LLPS across biological and synthetic domains, emphasizing five underappreciated topics: (1) DNA’s active driving role in LLPS through charge and topology; (2) reversible DNA aggregation and aggregate-to-condensate transitions, distinct from irreversible protein misfolding; (3) non-Fickian transport mechanisms including ballistic wave diffusion triggered by molecular recognition; (4) single-molecule mechanical characterization revealing state-dependent material properties; and (5) the multiscale complexity of cellular DNA condensation shaped by topological constraints and hierarchical organization. We highlight emerging single-molecule technologies, optical tweezers, and scanning probe microscopy that directly measure condensate mechanics and state transitions with unprecedented resolution. This integrated perspective bridges fundamental biophysics of natural DNA condensates with rational engineering principles for programmable synthetic systems, providing a blueprint for therapeutic and biotechnological applications.

Multiple sequence alignment (MSA) data underlies current principles in protein folding and protein-protein interaction prediction, from which large language models (LLMs) in tandem with protein datasets, can predict protein structure. However, what is missing are user-friendly tools that enable researchers to predict and demonstrate coevolution - the principal input which these MSAs infer. Here we present tools to identify and visualize coevolution, through a pipeline (CoEVFold) that uses basic direct coupling algorithms derived from GREMLIN and alignment of sequences from MMSEQs2. The pipeline generates a visual representation of coevolution for a single protein but can also represent coevolution of homomeric or heteromeric protein complexes, as well as coevolution within protein networks. The input for this pipeline can be an amino acid sequence, or user input protein structures from Alphafold their own files or the PDB database. In validation of CoEVFolds capabilities, and utilizing proteins from known prokaryotic and eukaryotic model systems (Bacillus subtilis, Escherichia coli and Saccharomyces cerevisiae), as well as phage proteins, CoEVFold predicts coevolution between proteins known to interact, proteins known to oligomerize, and coevolution in proteins known to be part of a protein complex. Collectively, these suite of tools, named CoEVFold suite, have broad applicability and provide a useful toolkit to those interested in dissecting protein-protein interactions and networks. https://colab.research.google.com/drive/1MSSvNTq7KZ4Lr0XTz89vUuK-J3xOTzwS?usp=sharing  and Github. 

Identifying common function-determining structural motifs among proteins with different folds is a foundational task in computational biology with no go-to solution. Indeed, standard alignment tools like TM-align are ill-suited for matching small, sequence-order-independent motifs, while specialized tools have limited success. Here, we introduce LocAlign, a local structural alignment algorithm based on geometric deep learning. Given two protein structures, LocAlign iteratively predicts atom-level correspondences and a 3D superimposition. By formulating training as a weakly supervised task on pairs of proteins bound to identical ligands, we bypass the need for ground-truth alignments. We find that LocAlign recovers known functional motifs without explicit supervision, identifying high-quality alignments for 87% and 37% of protein pairs with similar and dissimilar folds, respectively. We show that, equipped with confidence scoring and motif-conditioning capabilities, LocAlign supports diverse applications, including functional annotation of the dark proteome and drug off-target screening. LocAlign is thus a potent, versatile framework for protein functional site comparison.

Mechanoresponsive biomaterials are a revolutionary class of materials designed to respond dynamically to mechanical stimuli, providing tissue engineering and regenerative medicine with precise control over biological processes. Through processes including supramolecular interactions, strain stiffening, and force-induced conformational changes, these materials, which include hydrogels, elastomers, and piezoelectric composites, imitate the mechanics exclusive to biological tissues. Additionally, mechanoresponsive systems improve drug delivery by releasing drugs in response to pH changes or mechanical strain using various materials, including magnetic scaffolds and ultrasound-triggered micelles. Despite advancements in numerous arenas of biological sciences, problems with clinical translation, scalability, and long-term biocompatibility still exist. New developments combine technologies like 4D bioprinting to create dynamic, patient-specific scaffolds and artificial intelligence (AI)-assisted design to maximize material qualities. To achieve material innovation with the desired level of biological complexity, future initiatives should focus on multifunctional platforms that combine mechanical, electrical, and biochemical inputs at an advanced level. This review dives into several aspects of mechanoresponsive biomaterials by navigating through the fabrication methods, underlying principles, inception of these in biomedical applications, and progression through the current research settings.

Promoters are essential in transcriptional regulation, with the −10 and −35 boxes playing a critical role in determining their strength. Modulating these regions can effectively fine-tune promoter strength. However, the lack of a clear quantitative relationship between sequence composition and transcriptional output impedes the rational design of promoters. To address this, we developed a synthetic promoter library by varying RNA polymerase binding energies at the −10 and −35 boxes. The library was partitioned into four sublibraries with expression strengths spanning an 80-fold range. Using fluorescence-activated cell sorting FACS followed by sequencing, we identified 20,799 distinct promoters. Analysis of this library uncovered distinct sequence-activity patterns, including a small subset of −35 box sequences that consistently conferred high transcriptional output across diverse −10 partners. Based on this, we developed an artificial intelligence platform that integrates a convolutional neural network for strength prediction (Pearson’s r = 0.84) with a balanced generative adversarial network incorporating a gradient penalty for de novo promoter design. By coupling these models, we achieved a precise design of promoters with user-defined strengths (r = 0.85), establishing a bidirectional framework that links −10/–35 boxes to transcriptional activity through deep learning. This study expands the sequence diversity of functional −10 and −35 boxes in E. coli, provides a predictive platform for rational promoter engineering, and deciphers combinatorial motif interactions governing transcriptional regulation.

Bacteria and archaea encode diverse antiviral defense systems, many of which rely on toxic effector proteins that are activated specifically upon bacteriophage infection. However, the mechanisms by which infection is recognized and coupled to effector activation remain poorly understood for most antiviral systems. Here, we focus on ApeA, a HEPN-domain antiphage protein that confers immunity through cleavage of host tRNAs within their anticodon loops. We show that ApeA proteins form large doughnut-shaped oligomers that are activated upon ligand binding in a conserved protein pocket distinct from the catalytic center. In the Ec2ApeA variant, this pocket specifically recognizes 5′-phosphorylated deoxydinucleotides that likely arise as intermediates of host genome degradation by viral nucleases, thereby enabling Ec2ApeA to achieve a broad protection profile. Together, our results reveal how small-molecule products of virus-induced host cell destruction function as signals that activate bacterial immune defenses.