Mathematical Biosciences and Engineering

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continuously evolved since its emergence in the human population in 2019. As of 1st August 2025, more than 1,700 Omicron subvariants have been designated by the Pango nomenclature system. The Pango nomenclature system designates a new lineage based on genetic and epidemiological information of SARS-CoV-2 strains. However, there is a possibility that strains that have similar genetic backgrounds and the same phenotype are given different Pango lineage names. In this paper, we propose a new algorithm, called FindPart-w, which can identify groups of viral lineages that share the same relative effective reproduction numbers. We introduced a new lineage replacement model, called the constrained RelRe model, which constrains groups of lineages to have the same relative effective reproduction numbers. The FindPart-w algorithm searches the equality constraints that minimise the Akaike Information Criterion of constrained RelRe models. Using hypothetical observation count data created by simulation, we found that the FindPart-w algorithm can identify groups of lineages having the same relative effective reproduction number in a practical computational time. Applying FindPart-w to actual real-world data of time-stamped lineage counts from the United States, we found that the Pango lineage nomenclature system may have given different lineage names to SARS-CoV-2 strains even if they have the same relative effective reproduction number and similar genetic backgrounds. In conclusion, this study showed that viruses that had the same relative effective reproduction number were identifiable from temporal count data of viral sequences. These findings will contribute to the future development of lineage designation systems that consider both genetic backgrounds and transmissibilities of lineages.

Within many bacterial colonies, persister cells exist as a subpopulation that is tolerant to antibiotics and other stressors, yet not genetically distinct from the rest of the colony. A recent study has proposed epigenetic inheritance as a mechanism that leads to the presence of persister cells. We analyze a nonlocal PDE--ODE model introduced in that study to describe the epigenetic inheritance process and establish its mathematical well-posedness, including existence, uniqueness, and nonnegativity of solutions. We identify a sharp parameter threshold delineating extinction from persistence of the colony: below this threshold the washout equilibrium is globally asymptotically stable, while above it a unique positive equilibrium exists and the population is weakly persistent. Notably, this threshold is independent of the internal community structure.

Understanding the dynamics of HIV epidemics is important to control them effectively. Classical methods that mainly rely on occurrence data are limited by the fact that an unknown part of the epidemic eludes sampling. Since the early 2000s, phylodynamic methods have enabled the estimation of key epidemiological parameters from virus genetic sequence data. These methods have the advantage of being less sensitive to partial sampling and to provide insights about epidemic history that even predates the first samples. In this study, we analysed 2,205 HIV sequences from the French ANRS PRIMO C06 cohort. We identified and were able to reconstruct the temporal dynamics of two large clades that represent the HIV-1 epidemics in the country. Using Bayesian phylodynamic inference models, we found that the first clade, from subtype B, originated in the end of 1970s, grew rapidly during the 80s before decreasing from 2000 to 2015 and stagnating since then. The second clade, from circulating recombinant form CRF02_AG, emerged and spread in the 80s, grew again in the early 2000s, before declining slightly. We also estimated key epidemiological parameters associated with each clade. Finally, using numerical simulations, we investigated prospective scenarios and assessed the possibility to meet the 2030 UNAIDS targets. This is one of the rare studies to analyse the HIV epidemic in France using molecular epidemiology methods. It highlights the value of routine HIV sequence data for studying past epidemic trends or designing public health policies.

The objective of this study was to investigate Arbuscular Mycorrhizal Fungi (AMF) associations in selected medicinal plants. In this study 15 commonly used medicinal plants viz., Abutilon indicum (L.) Sweet, Centella asiatica (L.) Urb, Piper longum(L.), Terminalia bellerica (Gaertner) Roxb, Tinospora cordifolia (Wild.) Miers, Withania somnifera, Azadirachta indica A. Juss., Asparagus racemosus Willd., Andrographis paniculata (Burm. Fil.) Nees, Ocimum sanctum L. Eclipta alba, Mentha arvensis, Elettaria cardamomum, Bacopa monnieri and Mimosa pudica were investigated for AMF colonization in the form of arbuscules, vesicles and hyphae from their roots and rhizosphere soil. The rhizosphere soil and root of the commonly used medicinal plants were procured from Banaras Hindu University (BHU). From the study it was clear that AMF spores are abundantly available in the rhizosphere of the plants chosen for this study with spores of Acaulosporaceae and Glomeraceae family being dominant and Funneliformis mossae having the highest relative abundance and isolation frequency among all the AMF species.

Despite the remarkable progress of artificial intelligence represented by large language models, how AI technologies can contribute to the construction of evidence in evidence-based medicine (EBM) remains an overlooked issue. Now, we need an AI that can be compatible with EBM. In the present paper, we aim to propose an example analysis that may contribute to this approach using variable Vision Transformer.

Background: Dengue remains a major public health challenge in Thailand despite decades of vector control implementation. While mathematical models have explored dengue transmission dynamics, systematic evaluation of current control strategies under realistic operational conditions remains limited. Methods: We developed a temperature-dependent, multi-serotype dengue transmission model that explicitly incorporates three primary vector control strategies: reduction in mosquito biting rates through personal protection measures, further reduction in mosquito birth rates beyond current larval control efforts, and further increase in adult mosquito mortality beyond current adulticide application levels. Using Approximate Bayesian Computation with Sequential Monte Carlo (ABC-SMC), we fitted the model to dengue hemorrhagic fever (DHF) surveillance data from nine province-year combinations representing high (Rayong), moderate (Ratchaburi), and low (Phrae) transmission settings across three years (2006, 2015, and 2017). The model accounts for four dengue serotypes, temperature-dependent mosquito dynamics, and temporary cross-protective immunity between serotypes. Results: The model closely reproduced observed monthly DHF case counts across all nine province-year combinations. Estimated reporting proportions ranged from 1.4% to 16.7%, with the highest values occurring in high-transmission provinces during the 2015 outbreak year. When each strategy was independently intensified by 50% relative to fitted baseline levels, reducing mosquito biting rates and increasing adult mosquito mortality consistently produced greater reductions in transmission than reducing mosquito birth rates. In the highest-transmission scenario (Rayong, 2015), a 50% reduction in biting rate from the baseline level yielded a 96.4% reduction in cumulative infections (95% CrI: 95.4-97.3%), compared with 94.3% (95% CrI: 91.8-95.6%) for a 50% increase in adult mosquito mortality and 77.0% (95% CrI: 58.6-84.6%) for a 50% reduction in mosquito birth rate. Analysis of the time-varying reproduction number (R_t) confirmed that interventions targeting adult mosquito-human contact achieved the greatest sustained epidemic suppression, although the relative ranking between bite prevention and adulticide application varied by epidemiological setting. Conclusions: Under the uniform 50% intensification scenario tested, interventions that directly disrupt adult mosquito-human contact, whether through personal protection or adulticide application, substantially outperformed larval control in reducing dengue transmission across diverse Thai settings. These findings support prioritizing personal protection and adulticide application, while the generalizability of this ranking to other intensification levels and settings warrants further investigation.

Non-pharmaceutical interventions (NPIs) have been important for controlling SARS-CoV-2 transmission, particularly before and during initial vaccine rollout. During the pandemic, the US Centers for Disease Control and Prevention issued isolation and masking guidance in case of COVID-19-like illness, a positive SARS-CoV-2 test, or known exposure to SARS-CoV-2. However, the impact of this guidance on mitigating transmission in office workplaces is unclear. We used a network-based mathematical model to estimate the impact of this guidance on SARS-CoV-2 transmission among office workers and their communities. The model represented social contacts in the home, office, and community. We used data from the CorporateMix study to parametrize social contacts among office workers and calibrated the model to represent the COVID-19 epidemic in Georgia, USA from January 2021 through August 2022. In the reference scenario (58% adherence to guidance among office workers and the broader population), workplace transmission accounted for a small fraction of total infections. Reducing adherence among office workers to 0% increased workplace transmissions by 27.1% and increasing adherence to 75% reduced workplace transmission by 7.0%. Increasing adherence to 75% among office workers had minimal impact on symptomatic cases and deaths; increasing it among the broader population was more effective in reducing office worker cases and deaths. In our model, moderate adherence to recommended NPIs in workplaces was effective in reducing transmission, but increasing adherence had limited benefit given workplaces that have low contact intensity and hybrid work arrangements. These results underscore the public health benefits of community-wide adoption of recommended NPIs.

There is an ongoing Oropouche Fever (OF) outbreak in Brazil since 2024. There are dengue and chikungunya prediction models available, but none to help discriminate dengue, chikungunya, and OF. Objective: This study aims to develop and validate clinical prediction models for dengue, chikungunya, OF. Methods: This study uses surveillance data from Espirito Santo state / Brazil, from 2023-2025. Epidemiological investigations and biological samples were used to conclude cases as either (a) clinical-epidemiologically confirmed, (b) laboratory confirmed, or (c) discarded. The predictors were all data related to signs, symptoms, and comorbidities available in the notification forms. The analysis was performed using random forest regression models, one for each outcome, in development and validation datasets. Results: A total of 465,280 observations were analyzed, 261,691 dengue cases (56.6%), 18,676 chikungunya cases (4.0%), 12,174 OF cases (2.6%), and 179,115 discarded cases (38.6%). All three models had good discrimination and moderate to good calibration after scaling prediction. The models retained from 26 to 16 predictors each. Leukopenia and vomiting were the most discriminatory predictors for dengue, arthritis, arthralgia, and rash were the most discriminatory for chikungunya, and epidemiological features were the most relevant for OF. The dengue, chikungunya, and OF models had ROC AUC of 0.726, 0.851, and 0.896 in the validation set, respectively. Conclusion: This research identified predictors most discriminative between dengue, chikungunya, and OF. We developed and validated predictive models, one for each condition, with moderate to very good performance available at https://pedrobrasil.shinyapps.io/INDWELL/. One may use them in diagnostic work-up and arbovirus surveillance.

We are pleased to submit our Original article entitled "Assessing medication-related burden and medication adherence among older patients from Central Nepal: A machine learning approach" for consideration in your esteemed journal. In this paper, we assessed medication burden using validated Living with medicines Questionnaire (LMQ-3) and medication adherence using Adherence to Medication refills (ARMS) Scale. In this paper we analysed our result through machine learning approach in spite of traditional statistical approach to identify the complex factors influencing both. Six ML architectures (Ordinary Least Square, LightGBM, Random Forest, XGBoost, SVM, and Penalized linear regression) were employed to predict ARMS and LMQ scores using various socio-demographic, clinical and medication-related predictive features. Model explainability was provided through SHAP (Shapley Additive exPlanations). Our study identified the moderate medication burden with moderate non-adherence among older adults. Requiring assistance for medication and polypharmacy were the strongest drivers for the medication burden and non-adherence. The high predictive accuracy by ML suggests the appropriate clinical intervention like deprescribing to cope with the high prevalent medication burden and non-adherence among older adults in Nepal.

Markhor (Capra falconeri) is a charismatic, threatened, large, high-altitude bovid found in parts of central and south Asia. The species faces threats such as habitat loss, hunting, poaching, livestock competition, hybridisation, and disease, yet research on wild populations is challenging. Various biological aspects, including surveys, diet, population dynamics, interactions with livestock, hybridisation, and disease, have been studied locally, along with behavior and reproductive biology, but details such as pregnancy, oestrus, and parturition timing remain unestablished. We conducted the first systematic, detailed, and fine-scale characterization of the reproductive steroid profiles of two males and five female markhors (Capra falconeri heptneri) in a captive population at the Padmaja Naidu Himalayan Zoological Park (PNHZP), West Bengal, India. We collected weekly fecal samples, standardized and validated measurements of progesterone (fP4M) and testosterone (fTM) metabolites, and conducted reproductive profiling to assess reproductive stages in both sexes. Analyses of annual fP4M and fTM data from male and female markhor individuals showed similar profiles and synchronicity, with individual variation, and peaks and baselines were evident for both hormones. In both sexes, significantly higher hormone titres were observed during the sexually active and inactive phases. Non-invasive measurement of reproductive hormones accurately reflected ovarian function in females, helping establish mating, gestation, and parturition timelines in female markhors and determine the breeding season in males. These approaches support husbandry and breeding management by identifying optimal pairing, diagnosing pregnancy, and predicting parturition in both captive and wild populations. When applied correctly, these tools could greatly aid population monitoring of other endangered species across high-altitude regions worldwide.

BackgroundIn computational biology, embedding known physical laws into deep learning models to construct "Physics-Informed Neural Networks" (PINNs) is a mainstream paradigm for enhancing model interpretability and extrapolation capability. However, in complex multi-physics coupling problems, there is a risk of competitive imbalance between the physical term and the flexible artificial intelligence (AI) residual term, causing the model to degenerate into a "black-box" fit and lose the original purpose of being physics-driven. MethodsIn this study, targeting the problem of predicting protein liquid-liquid phase separation (LLPS) behavior in response to environmental factors (temperature, salt concentration), we identified physical distortions, gradient vanishing, and numerical instability in the initial physics-AI hybrid model. Three core correction strategies were proposed: (1) Weight Allocation Logic Reconstruction: Force the physical trunk weight to 1.0 at the output layer, suppressing the AI residual term to the perturbation level of 0.05~0.1, ensuring physics dominance; (2) Robust Physics Formula Construction: Abandon the unstable power function and introduce a combination of Softplus and logarithmic functions to stably simulate the nonlinear effects of charge shielding; (3) Gain Compensation Alignment: Apply gain compensation to the weak signal branch (temperature) to ensure its effective participation in optimization. ResultsThe optimized model maintained a fitting accuracy of R2{approx}0.62 on the test set, while physical consistency was significantly enhanced. The model successfully restored the monotonic increase in solubility with temperature characteristic of UCST-type phase diagrams and correctly captured the nonlinear charge shielding features in the salt concentration response. The weights of key physical parameters (e.g., hydrophobic contribution w_h, net charge contribution w_ncpr) increased from <10-3 to the 10-2 magnitude, demonstrating the reactivation of the physical branch. ConclusionsThe weight control, formula stabilization, and signal gain alignment strategies proposed in this study effectively address the classic problem of "AI hijacking" physics in physics-AI hybrid models. This work provides a universal solution for constructing biophysical predictive models that combine high fitting accuracy with strong physical interpretability.

Mathematical modelling is essential for understanding how complex biological systems respond to genetic, physiological, and environmental changes. Existing approaches, however, often require trade-offs between mechanistic detail, model size, parameter uncertainty, and interpretability. Ordinary differential equation (ODE) models capture biochemical processes with quantitative precision but can demand extensive parameterisation. In contrast, large statistical and machine-learning models rely on substantial datasets and frequently lack mechanistic transparency. Qualitative approaches such as Boolean networks improve scalability but may oversimplify biological behaviour. To address some of these limitations, we present PSoup, an R package that automatically converts knowledge graphs into transparent, parameter-free, qualitative models. PSoup uses algebraic update rules designed around a fixed, biologically interpretable baseline, enabling predictions of relative change across diverse perturbations without requiring kinetic parameters. This design allows PSoup to integrate information across biological scales and from heterogeneous experimental sources. We evaluated PSoup using the well-studied shoot branching network of Bertheloot et al. (2019), which incorporates hormonal (auxin, strigolactone, cytokinin) and metabolic (sucrose) regulation. Across 78 experimental conditions, PSoup correctly predicted 88.5% of perturbation outcomes, including 89.5% accuracy for unique, biologically consistent comparisons. We further demonstrate how PSoup can distinguish among alternative plausible network topologies, revealing how structural differences influence emergent system behaviour. PSoup offers an intuitive, accessible, and mathematically transparent framework for exploring biological networks. Its capacity to integrate diverse knowledge and test alternative hypotheses positions it as a powerful tool for biological discovery and a valuable complement to existing modelling approaches.

Women living with HIV face about a six-fold higher risk of cervical cancer, yet screening uptake remains low in many sub-Saharan African settings. We explored factors influencing repeated decisions to decline cervical cancer screening during routine HIV care among women living with HIV at a large HIV clinic in Jos, Nigeria. Between September and December 2024, we conducted an exploratory qualitative study at the AIDS Prevention Initiative in Nigeria Clinic in Jos, Nigeria. We purposively recruited 27 women living with HIV aged 21 to 65 years who had never undergone cervical cancer screening and had repeatedly declined screening offers during routine HIV care, including at the current clinic visit. Semi-structured in-depth interviews were conducted in English or Hausa, audio-recorded, transcribed verbatim, and translated into English where needed. Data were analyzed thematically using theory-informed coding based on the Health Belief Model and Social Ecological Model. Among 27 women living with HIV who had repeatedly declined screening, perceived susceptibility was often low or uncertain despite recognition of cervical cancer severity. Perceived benefits were acknowledged but were frequently outweighed by overlapping barriers, including knowledge gaps and misinformation, indirect and downstream costs, emotional barriers, logistical constraints, clinic-flow and service-delivery barriers, and anticipated stigma. Education, reminders, and supportive clinic processes acted as cues to action, and most participants expressed willingness to screen in future. Among women living with HIV at this clinic who repeatedly declined screening when it was offered, perceived benefits were often outweighed by multilevel barriers. Screening programs may integrate fear-reduction and stigma-sensitive counseling with practical service delivery improvements, including shorter waiting times, reduced indirect costs, predictable and streamlined clinic flow, and consistent provider invitations and reminders, while addressing misinformation through community-embedded, culturally tailored messaging. These strategies may improve screening uptake and support more equitable cervical cancer prevention for women living with HIV in similar HIV-care settings.

High level of protein expression is usually welcomed in industry and research, and codon optimization is widely used to achieve high expression. Methods of implementing codon optimization can be divided into two branches, one is classical methods which develop cost functions based on empirical law, another is AI methods which learn the codon choice principles from endogenous genes with neural networks. Here we develop two codon optimization tools based on two branches respectively, namely OptimWiz 2.1 and OptimWiz 3.0. Results of fusion protein fluorescence detection indicate that both OptimWiz 2.1 and OptimWiz 3.0 are superior to all the other commercially available codon optimization tools. Principles of codon optimization are revealed in the process of machine learning on both tools.

Toxoplasma gondii is a widespread human pathogen that has multiple, clinically relevant stages in its complex life cycle, including fast-replicating tachyzoites and latent bradyzoites. Bradyzoite differentiation is triggered by stress responses that lead to changes in transcription, translation, and metabolism. Two aspects of this process are addressed in this report: first, whether proteins that play roles in bradyzoite differentiation are specific to T. gondii and other bradyzoite-forming parasites of the Sarcocystidae family, and second, whether new bradyzoite differentiation proteins can be identified in T. gondii. To answer these questions, a phylogenetic approach was used, comparing proteomes of select members of the Sarcocystidae family that form morphologically different bradyzoite cysts and members of the Eimeriidae family that do not form cysts. This approach resulted in 8 distinct clusters of T. gondii proteins that reflected different conservation patterns; for example, one cluster showed conservation among all organisms, while another showed conservation in bradyzoite cyst-forming organisms. Known T. gondii proteins involved in bradyzoite differentiation were found in all clusters, indicating that this process uses both highly conserved pathways as well as bradyzoite-specific pathways. Importantly, the cluster containing proteins that are conserved in bradyzoite-forming organisms contained several known regulators of bradyzoites, and will be a source for identifying novel T. gondii proteins that are involved in bradyzoite differentiation.

In this study, resource-constrained learning methods were developed as a model for the learning behavior of the fly brain, specifically the mushroom body. Recent research on the mushroom bodies of flies shows that unfamiliar odors activate certain output neurons (MBONs); however, these effects are rapidly suppressed upon repeated exposure to the same odor. Such MBON behaviors appear to reflect odor learning. We investigated how flies continue learning about odors throughout their lives despite their small brains. Researchers have suggested that learning about new odors can help flies forget existing memories. Therefore, we hypothesized that the main reason for continual learning is that it serves as a strategy for forgetting. To test the validity of this hypothesis, we designed three models using a kernel perceptron. This approach is suitable for estimating ongoing learning capacity within a budget. According to the results of computer simulations and theoretical analysis, the model demonstrated the importance of forgetting mechanisms for two reasons: first, to prepare for subsequent learning sessions, and second, to reduce the negative effects of deleting memories.

Background: The WHO End TB strategy targets 80% and 90% reductions in TB incidence and mortality, respectively, between 2015 and 2030. Objective: We assess which epidemiologic factors, including existing and new interventions, are most critical to reducing future TB in South Africa. Methods: We adapted an existing mathematical model of TB and HIV in South Africa. Prior distributions were specified to represent uncertainty ranges for 27 model parameters that are highly uncertain and potentially important in driving future TB dynamics. Latin Hypercube Sampling was used to sample 1000 parameter combinations from these distributions, and the model was projected to 2040 for each. Partial rank correlation coefficients (PRCCs) were calculated to assess correlation between each parameter and average adult TB incidence and mortality rates over 2025-2040. Results: Adult TB incidence and mortality rates in South Africa are projected to decline by 46% (95% CI: 17-69%) and 54% (95% CI: 21-84%) respectively by 2030, relative to 2015. The parameters most strongly associated with future TB incidence are the increase in microbiological testing in symptomatic individuals due to near-point-of-care/tongue swab (NPOC/TS) testing (PRCC=-0.67), reductions in social contact rates post-COVID (PRCC=-0.61), the probability of sputum testing in symptomatic individuals in the absence of NPOC/TS testing (PRCC=-0.39), and the efficacy of TB preventive therapy (PRCC=-0.35). TB mortality predictors are similar. Conclusions: Increasing testing among people with TB symptoms, including through new NPOC/TS technologies, is likely to have the largest impact on progress towards End TB goals in South Africa, though attainment by 2030 is unlikely.

Diabetes mellitus is characterized by chronic hyperglycemia and loss of pancreatic {beta}-cell function and mass. Current therapies focus on {beta}-cell protection and regeneration, led by GLP-1 receptor agonists. The G protein -subunit (Gs) acts as a key signaling node downstream of numerous GPCRs, integrating diverse signals that impact {beta}-cell mass and function. Elucidating the integrative role of pancreatic Gs signaling is thus crucial for understanding {beta}-cell biology. Our map of the pancreatic Gs-coupled GPCR landscape reveals sophisticated, cell-type-specific networks, positioning Gs as a central hub for intra-pancreatic communication. Previous studies in mice with {beta}-cell-specific or whole-pancreatic Gs deletion demonstrated reduced {beta}-cell mass, impaired insulin secretion, and glucose intolerance. The stronger phenotype in the whole-pancreas model--marked by -cell expansion and abnormal distribution--points to a crucial role for Gs in differential control of postnatal - and {beta}-cell proliferation. Here, we analyze the organ-wide consequences of Gs deletion using pancreas-specific Gs knockout mice (PGsKO). Consistent with prior findings, PGsKO mice exhibit reduced weight gain from four weeks and severe diabetes due to decreased {beta}-cell mass and concomitant -cell expansion. Furthermore, Gs loss induces profound architectural and functional defects in the exocrine pancreas, linked to YAP reactivation in acinar cells. Importantly, we observed attempted {beta}-cell regeneration in PGsKO mice. Although insufficient to reverse diabetes, our results delineate the full pancreatic phenotype that may facilitate these regenerative efforts and suggest that strategically biasing GPCR signaling network away from Gs could be a viable strategy to promote {beta}-cell regeneration from other pancreatic cell types. ARTICLE HIGHLIGHTSO_LIGs is a central signaling hub that integrates diverse GPCR inputs across pancreatic cell types, yet its organ-wide role remained poorly defined. C_LIO_LIWe addressed how pancreas-wide Gs deletion disrupts both endocrine and exocrine compartments, and whether regenerative programs are engaged. C_LIO_LIGs loss caused severe diabetes through {beta}-cell loss and -cell expansion, induced profound exocrine defects with YAP reactivation, and triggered attempted {beta}-cell regeneration from ducts and potentially other cell types. C_LIO_LIOur findings suggest that strategically biasing GPCR signaling away from Gs could promote regeneration from non-{beta}-cell sources, offering new therapeutic avenues for diabetes. C_LI

Intestinal epithelial cells are central players in mucosal barrier integrity and host-microbe interactions. Genetic studies have revealed that epithelial dysfunction is a key contributor to the pathogenesis of inflammatory bowel disease. Non-SMC condensin II complex subunit D3 (NCAPD3) is essential for chromatin organization and stability. NCAPD3 also promotes antimicrobial defense and autophagy responses in vitro. NCAPD3 expression is decreased in intestinal epithelial cells from patients with ulcerative colitis; however, it is not known whether loss of NCAPD3 expression drives intestinal barrier dysfunction or is a result of disease-associated inflammation. To investigate this relationship in vivo, a tissue-specific approach was required, as global constitutive knockout of NCAPD3 is embryonic lethal. Therefore, a transgenic mouse line with doxycycline-inducible expression of a short hairpin RNA targeting NCAPD3 restricted to villin-expressing cells was generated (NCAPD3KD mice) to enable the study of NCAPD3 function in the intestinal epithelium. Treatment of NCAPD3KD mice with 9-tert-butyl doxycycline resulted in [~]75% reduction of NCAPD3 protein in EpCAM intestinal cells. Short-term epithelial NCAPD3 knockdown did not induce spontaneous colitis but was associated with increased serum amyloid A and a trend towards increased intestinal permeability. Upon dextran sodium sulfate or Salmonella enterica serovar Typhimurium {Delta}AroA challenge, NCAPD3KD mice exhibited exacerbated weight loss, higher disease activity, increased histopathological damage, abnormal colonic cytokines and chemokines, and significantly increased intestinal permeability. These results indicate that NCAPD3 expression in the intestinal epithelium is required for optimal barrier maintenance and antimicrobial defense under chemical or microbial stress. These findings support prior in vitro observations and solidify NCAPD3 as a regulator of intestinal epithelial barrier function and mucosal host defense. Author SummaryNCAPD3 is a multifunctional protein with established roles in chromatin organization, genome stability, mitochondrial function, and antimicrobial defense. Dysregulated NCAPD3 is implicated in human diseases, such as inflammatory bowel disease (IBD) and microcephaly; however, due to its essential role in cellular division, determination of whether NCAPD3 loss drives these pathologies in vivo has been lacking. Using a new transgenic mouse model that selectively reduces NCAPD3 expression in intestinal epithelial cells, our study establishes NCAPD3 as an epithelial regulator of the mammalian intestine that enhances epithelial barrier resilience and antimicrobial defense during stress. Although dispensable for short-term basal homeostasis, NCAPD3 function becomes critical during epithelial injury and enteric infection. Reduced NCAPD3 expression may therefore lower the threshold for inflammatory disease by weakening barrier integrity, amplifying inflammatory cascades, and impairing antimicrobial defenses. These findings position NCAPD3 as a potential modulator of IBD susceptibility and highlight chromatin organization as an important, previously underappreciated layer of intestinal epithelial regulation.

We have curated and annotated the topologic determinants for all human membrane proteins made at the endoplasmic reticulum (ER). This census of 4,863 proteins allowed us to systematically analyze the physical properties of their 20,546 TMDs and flanking soluble regions. Single-pass proteins house the majority of large exoplasmic and cytosolic domains, whereas multipass proteins overwhelmingly contain short loops and tails. All classes of transmembrane domains (TMDs) have positively charged cytosolic flanks, but negatively charged exoplasmic flanks feature primarily on TMDs inserted by Oxa1-family insertases. The TMD-pair, a topologic unit of two TMDs with a short exoplasmic loop, is the dominant building block of multipass proteins. TMD-pairs accommodate high-hydrophilicity and charge-containing TMDs crucial for multipass protein functions. We interpret these context-dependent TMD features in light of current mechanistic models for membrane protein biogenesis and function. Our findings have implications for the evolution of membrane proteomes and for engineering new membrane proteins.