Biology subjects

Si, T.

Publications and source records attributed to Si, T..

12 recordsLinked to original sources

bioRxiv2026

Charting sex divergence in the human brain across the lifespan

Biological sex fundamentally shapes human brain organization, but sex-specific normative neuroanatomical trajectories across the lifespan remain largely uncharted. Here, we constructed independent, sex-specific lifespan brain charts using structural neuroimaging data from 59,915 healthy individuals (29,760 males and 30,155 females) ranging in age from 266 postconception days to 100 years. By examining 296 structural phenotypes across global, cortical, and subcortical measures, these models revealed widespread sex differences in maturational timing, with males reaching peak milestones later than females. These trajectories demonstrate that sex differences evolve dynamically, with phenotype-specific windows of emergence and maximal separation. Compared with conventional sex-pooled references, sex-specific models achieved superior predictive accuracy and reduced misestimation of individual deviations in healthy populations. Across five neuropsychiatric conditions, sex-specific models improved the detection of extreme deviations and revealed both shared and sex-dependent patterns of disorder-related neuroanatomical abnormalities. These sex-specific charts establish tailored normative references for assessing brain development, ageing, and disease.

neuroscience↗

bioRxiv2026

CombinGym: a benchmark platform for machine learning-assisted design of combinatorial protein variants

Combinatorial mutagenesis is essential for exploring protein sequence-function landscapes in engineering applications. However, while large-scale machine learning benchmarks exist for protein function prediction, they are primarily limited to single-mutant libraries, leaving a critical gap for combinatorial mutagenesis. Here we introduce CombinGym, a benchmarking platform featuring 14 curated combinatorial mutagenesis datasets spanning 9 proteins with diverse functional properties including binding affinity, fluorescence, and enzymatic activities. We evaluated nine machine learning algorithms from five methodological categories (alignment-based, protein language, structure-based, sequence-label, and substitution-based) across multiple prediction tasks, assessing both zero-shot and supervised learning performance using Spearmans {rho} and Normalized Discounted Cumulative Gain metrics. Our analysis reveals the substantial impact of measurement noise and data processing strategies on model performance. By implementing hierarchical dataset splits (0-vs-rest, 1-vs-rest, 2-vs-rest, and 3-vs-rest scenarios), we demonstrate the value of lower-order mutation data for empowering machine learning models to predict higher-order mutant properties. We validated this capacity through both in silico simulation (improving fluorescence brightness of an oxygen-independent fluorescent protein) and experimental validation (engineering enzyme substrate specificity), achieving a substantial increase in specific activity. All datasets, benchmarks, and metrics are available through an interactive website (https://www.combingym.org), facilitating collaborative dataset expansion and model development through integration with automated biofoundry platforms.

synthetic biology↗

bioRxiv2025

Decoding Prokaryotic Whole Genomes with a Product-Contextualized Large Language Model

Genomes encode the instructions for life, yet their interpretation requires models capable of capturing long-range genome-wide functional context at scale. Existing genomic foundation models have primarily focused on sequence-level representations, providing powerful tools for biological prediction while leaving complementary opportunities for function-level genome modeling. Here, we present GenSyntax, a function-level genome representation learning framework that models prokaryotic genomes as ordered sequences of gene-product descriptors. By treating gene products as semantic units, GenSyntax transforms annotated replicons into interpretable "genetic paragraphs" that capture genome-wide functional context. We trained GenSyntax on 49,250 annotated prokaryotic genomes and evaluated its ability across genome-scale tasks, including plasmid host prediction, gene-product disambiguation, genome contig ordering and gene essentiality prediction. Compared with general-purpose large language models and representative genomic foundation models, GenSyntax achieved competitive performance across multiple benchmarks, with independent external evaluations supporting its generalization beyond the original training datasets. GenSyntax embeddings also captured signals associated with microbial phenotypes, and gene essentiality predictions enabled exploratory design of minimal genomes. Together with the lightweight GenSyntax-Tiny model, GenSyntax provides a complementary, post-annotation framework for function-level analysis of prokaryotic genomes.

genomics↗

bioRxiv2025

Autonomous liquid-handling robotics scripting through large language models enables accessible and safe protein engineering workflows

Laboratory automation accelerates synthetic biology, but robot programming remains a barrier to broader participation. Large language model (LLM)-based automation must address four interconnected requirements in this field: sequence-level biosecurity, community standards, context-dependent biological behavior, and distributed collaboration across laboratories. Here we present LabscriptAI, an execution-aware agent framework that couples authoring and runtime control loops to translate natural-language instructions into platform-specific robot scripts and support bounded recovery under deterministic authorization and human oversight. On a 90-task liquid-handling benchmark, LabscriptAI achieved a 96.7% simulation-pass rate, the highest among the evaluated LLM-based and commercial baselines. We demonstrated its capabilities through sequence screening at designated workflow checkpoints; standardized cell-free characterization of 854 green fluorescent protein (GFP) designs from 171 student teams, coupled to data deposition; containment-aware engineering of a formaldehyde-converting enzyme, yielding 6.7-fold higher ethylene glycol titers in a two-stage cascade; and quality-assured preparation of 531 genetic parts for the 2025 iGEM Distribution Kit distributed worldwide. Together, these results establish LabscriptAI as a biosecurity-aware, community-integrated framework connecting protocol authoring, controlled execution, and traceable experimental outputs across laboratory platforms.

bioengineering↗

bioRxiv2025

Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine 1-7. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.

neuroscience↗

bioRxiv2024

Degradation of indole-3-acetic acid by plant-associated microbes

Plant-associated microbiota affect pant growth and development by regulating plant hormones homeostasis. Indole-3-acetic acid (IAA), a well-known plant hormone, can be produced by various plant-associated bacteria. However, the prevalence of microbes with the capacity to degrade IAA in the rhizosphere has not been systematically studied. In this study, we analyzed the IAA degradation capabilities of bacterial isolates from the roots of Arabidopsis and rice. Using genomics analysis and in vitro assays, we found that 21 out of 189 taxonomically diverse bacterial isolates possess the ability to degrade IAA. Through comparative genomics and transcriptomic assays, we identified iac-like or iad-like operon in the genomes of these IAA degraders. Additionally, the regulator of the operon was found to be highly conserved among these strains through protein structure similarity analysis. Some of the IAA degraders could utilize IAA as their sole carbon and energy source. In planta, most of the IAA degrading strains mitigated Arabidopsis seedling root growth inhibition (RGI) triggered by exogenous IAA. Importantly, we observed increased colonization preference of IAA degraders from soil to root according to the frequency of the biomarker genes in metagenome-assembled genomes (MAGs) collected from different habitats, suggesting that there is a close association between IAA degraders and IAA-producers. In summary, our findings further the understanding of the functional diversity and roles of plant-associated microbes.

microbiology↗

bioRxiv2023

Functional connectome through the human life span

The lifespan growth of the functional connectome remains unknown. Here, we assemble task-free functional and structural magnetic resonance imaging data from 33,250 individuals aged 32 postmenstrual weeks to 80 years from 132 global sites. We report critical inflection points in the nonlinear growth curves of the global mean and variance of the connectome, peaking in the late fourth and late third decades of life, respectively. After constructing a fine-grained, lifespan-wide suite of system-level brain atlases, we show distinct maturation timelines for functional segregation within different systems. Lifespan growth of regional connectivity is organized along a primary-to-association cortical axis. These connectome-based normative models reveal substantial individual heterogeneities in functional brain networks in patients with autism spectrum disorder, major depressive disorder, and Alzheimers disease. These findings elucidate the lifespan evolution of the functional connectome and can serve as a normative reference for quantifying individual variation in development, aging, and neuropsychiatric disorders.

neuroscience↗

bioRxiv2023

A novel f-divergence based generative adversarial imputation method for scRNA-seq data analysis

Comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data can enhance our understanding of cellular diversity and aid in the development of personalized therapies for individuals. The abundance of missing values, known as dropouts, makes the analysis of scRNA-seq data a challenging task. Most traditional methods made assumptions about specific distributions for missing values, which limit their capability to capture the intricacy of high-dimensional scRNA-seq data. Moreover, the imputation performance of traditional methods decreases with higher missing rates. We propose a novel f -divergence based generative adversarial imputation method, called sc-f GAIN, for the scRNA-seq data imputation. Our studies identify four f -divergence functions, namely cross-entropy, Kullback-Leibler (KL), reverse KL, and Jensen-Shannon, that can be effectively integrated with the generative adversarial imputation network to generate imputed values without any assumptions, and mathematically prove that the distribution of imputed data using sc-f GAIN algorithm is same as the distribution of original data. Real scRNA-seq data analysis has shown that, compared to many traditional methods, the imputed values generated by sc-f GAIN algorithm have a smaller root-mean-square error, and it is robust to varying missing rates, moreover, it can reduce imputation bias. The flexibility offered by the f -divergence allows the sc-f GAIN method to accommodate various types of data, making it a more universal approach for imputing missing values of scRNA-seq data.

bioinformatics↗

bioRxiv2023

Mapping Neurophysiological Subtypes of Major Depressive Disorder Using Normative Models of the Functional Connectome

Major depressive disorder (MDD) is the most burdensome psychiatric disorder characterized by remarkably heterogeneous clinical phenotypes. It remains challenging to delineate the heterogeneity of neurobiological abnormalities underlying the clinical variance and, on this basis, to identify neurophysiological subtypes of MDD patients. Here, using a large multisite resting-state functional MRI data from 1,148 MDD patients and 1,079 healthy controls, we generated lifespan normative models of functional connectivity strengths, mapped the heterogeneity of patients individual deviations, and identified neurobiological MDD subtypes. MDD patients showed positive deviations mainly in the default mode and subcortical areas, and negative deviations widely distributed over the cortex. However, there was a great inter-subject heterogeneity as indicated by that no more than 3.14% of patients deviated from the normative range for any brain region. Two neurophysiological MDD subtypes were identified. Subtype 1 showed severe deviations with positive deviations in the default mode, limbic, and subcortical areas, and negative deviations in the sensorimotor, dorsal and ventral attention areas, while subtype 2 showed a moderate but conversed deviation pattern. The severe-deviation subtype had older age, higher medicated proportion, and higher Suicide item score, while the moderate-deviation subtype showed higher Work and Activities and Depressed Mood item scores. Moreover, the baseline deviations in the severe-deviation subtype were predictive of 6-month antidepressant treatment effects in a subsample. To our knowledge, the current study is the largest multisite analysis of neurophysiological MDD subtyping to date and the findings shed light on our understanding of the biological mechanisms underlying the intersubject heterogeneity of clinical phenotypes, which are informative for the development of personalized treatments for this disorder.

neuroscience↗

bioRxiv2022

Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments

Protein engineering aims to find top functional sequences in a vast design space. For such an expensive "black-box" function optimization problem, Bayesian optimization is a principled sample-efficient approach, which is guided by a surrogate model of the objective function. Unfortunately, Bayesian optimization is computationally intractable with the vast search space. Even worse, it proposes sequences sequentially, making it incompatible with batched wet-lab measurement. Here, we report a scalable and batched method, Bayesian Optimization-guided EVOlutionary (BO-EVO) algorithm, to guide multiple rounds of robotic experiments to explore protein fitness landscapes of combinatorial mutagenesis libraries. We first examined various design specifications based on an empirical landscape of protein G domain B1. Then, BO-EVO was successfully generalized to another empirical landscape of an Escherichia coli kinase PhoQ, as well as simulated NK landscapes with up to moderate epistasis. This approach was then applied to guide robotic library creation and screening to engineer enzyme specificity of RhlA, a key biosynthetic enzyme for rhamnolipid biosurfactants. A 4.8-fold improvement in producing a target rhamnolipid congener was achieved after examining less than 1% of all possible mutants after 4 iterations. Overall, BO-EVO proves to be an efficient and general approach to guide combinatorial protein engineering without prior knowledge.

synthetic biology↗

bioRxiv2020

Large-scale Gradient Dysfunction of the Functional Connectome in Major Depression

BackgroundPatients with major depressive disorder (MDD) exhibit concurrent deficits in sensory processing and high-order cognitive functions such as self-awareness and rumination. Connectome mapping studies have suggested a principal primary-to-transmodal gradient in functional brain networks, supporting the spectrum from sensation to cognition. However, whether this principal connectome gradient is disrupted in patients with MDD and how this disruption is associated with gene expression profiles remain unclear. MethodsUsing a large cohort of resting-state functional magnetic resonance imaging data from 2,234 participants (1,150 patients with MDD and 1,084 healthy controls) recruited at 10 sites, we investigated MDD-related alterations in the principal connectome gradient. We further used Neurosynth and postmortem gene expression data to assess the cognitive functions and transcriptional profiles related to the gradient alterations in MDD, respectively. ResultsRelative to controls, patients with MDD exhibited abnormal global topography of the principal primary-to-transmodal gradient, as indicated by reduced explanation ratio, gradient range, and gradient variation (Cohens d = -0.16[~]-0.21). Focal alterations of gradient scores were mostly in the primary systems involved in sensory processing and in the transmodal systems implicated in high-order cognition. The transcriptional profiles explained 53.9% of the spatial variance in the altered gradient patterns, with the most correlated genes enriched in transsynaptic signaling and calcium ion binding. ConclusionsThese results highlight the dysfunction of the core connectome hierarchy in MDD and its linkage with gene expression profiles, providing insights into the neurobiological and molecular genetic underpinnings of sensory-cognitive deficits in this disorder.

neuroscience↗

bioRxiv2019

Reproducibility of functional brain alterations in major depressive disorder: evidence from a multisite resting-state functional MRI study with 1,434 individuals

Resting-state functional MRI (R-fMRI) studies have demonstrated widespread alterations in brain function in patients with major depressive disorder (MDD). However, a clear and consistent conclusion regarding a repeatable pattern of MDD-relevant alterations is still limited due to the scarcity of large-sample, multisite datasets. Here, we address this issue by including a large R-fMRI dataset with 1,434 participants (709 patients with MDD and 725 healthy controls) from five centers in China. Individual functional activity maps that represent very local to long-range connections are computed using the amplitude of low-frequency fluctuations, regional homogeneity and distance-related functional connectivity strength. The reproducibility analyses involve different statistical strategies, global signal regression, across-center consistency, clinical variables, and sample size. We observed significant hypoactivity in the orbitofrontal, sensorimotor, and visual cortices and hyperactivity in the frontoparietal cortices in MDD patients compared to the controls. These alterations are not affected by different statistical analysis strategies, global signal regression and medication status and are generally reproducible across centers. However, these between-group differences are partially influenced by the episode status and the age of disease onset in patients, and the brain-clinical variable relationship exhibits poor cross-center reproducibility. Bootstrap analyses reveal that at least 400 subjects in each group are required to replicate significant alterations (an extent threshold of P<.05 and a height threshold of P<.001) at 50% reproducibility. Together, these results highlight reproducible patterns of functional alterations in MDD and relevant influencing factors, which provides crucial guidance for future neuroimaging studies of this disorder.

neuroscience↗