Significant sparse polygenic risk scores across 813 traits in UK Biobank.

We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,500 traits using genetic and phenotype data in the UK Biobank. We report 813 sparse PRS models with significant (p < 2.5 x 10-5) incremental predictive performance when compared against the covariate-only model that considers age, sex, types of genotyping arrays, and the principal component loadings of genotypes. We report a significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance (Spearman's ⍴ = 0.61, p = 2.2 x 10-59 for quantitative traits, ⍴ = 0.21, p = 9.6 x 10-4 for binary traits). The sparse PRS model trained on European individuals showed limited transferability when evaluated on non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).

Non-IRC Mechanism of Bimolecular Reactions with Submerged Barriers: A Case Study of Si+ + H2O Reaction.

Chemical reactions with submerged barriers may feature interesting dynamic behaviors that are distinct from those with substantial barriers or those entirely dominated by capture. The Si+ + H2O reaction is a prototypical example, involving even two submerged saddle points along the reaction path: one for the direct dissociation of H (H-dissociation SP) and another for H migration from the O-side to the Si-side (H-migration SP). We investigated the intricacies of this process by employing quasi-classical trajectory calculations on an accurate, full-dimensional ab initio potential energy surface. Through careful trajectory analysis, an interesting nonintrinsic reaction coordinate mechanism was found to play an important role in producing SiOH+ and H. This new pathway is featured as that the H atoms do not form HSiOH+ complexes along the minimum-energy path but directly dissociate into the products after passing through the H-migration SP. Furthermore, based on artificially scaled potential energy surfaces (PESs), the impact of barrier height on the reaction is also explored. This work provides new insights into the dynamics of the Si+ + H2O reaction and enriches our understanding of reactions with submerged barriers.

Mobile genetic elements affect the dissemination of antibiotic resistance genes (ARGs) of clinical importance in the environment.

The prevalence of antibiotic resistance genes (ARGs) in the environment is a quintessential One Health issue that threats both human and ecosystem health; however, the source and transmission of ARGs, especially clinically important ARGs (CLIARGs), in the environment have not yet been well studied. In the present study, shotgun metagenomic approaches were used to characterize the microbiome, resistome, and mobilome composition in human feces and six different environment sample types in South China. Overall, the resistome harbored 157 CLIARGs, with specific ARG hotspots (e.g., human feces, wastewater treatment plants, livestock manure and wastewater) excreting significantly higher abundance of CLIARGs compared with the natural environment. A redundancy analysis (RDA) was performed and revealed that the bacterial community compositions and mobile genetic elements (MGEs) explained 55.08% and 34.68% of the variations in ARG abundance, respectively, indicating that both bacterial community and MGEs are key contributors to the maintenance and dissemination of CLIARGs in the environment. The network analysis revealed non-random co-occurrence patterns between 200 bacterial genera and 147 CLIARGs, as well as between 135 MGEs and 123 CLIARGs. In addition to numerous co-shared CLIARGs among different sample types, the source tracking program based on the FEAST probabilistic model was used to estimate the relative contributions of the CLIARGs from potential sources to the natural environment. The source tracking analysis results delineated that mobilome, more than microbiome, contributed CLIARG transmission from those ARG hotspots into natural environment, and the MGEs in WWTPs seem to play the most significant role in the spread of CLIARGs to the natural environment (average contribution 32.9%-46.4%). Overall, this study demonstrated the distribution and dissemination of CLIARGs in the environment, and aimed to better inform strategies to control the spread of CLIARGs into the natural environment.

Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank.

We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the $L^1$-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in Qian and others (2019). Our algorithm is particularly suitable for large-scale and high-dimensional data that do not fit in the memory. The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow and others, 2015). We provide a publicly available implementation of the proposed approach for genetics data on top of the PLINK2 package and name it snpnet-Cox.

Comprehensive fundamental somatic variant calling and quality management strategies for human cancer genomes.

Next-generation sequencing (NGS) technology has revolutionised human cancer research, particularly via detection of genomic variants with its ultra-high-throughput sequencing and increasing affordability. However, the inundation of rich cancer genomics data has resulted in significant challenges in its exploration and translation into biological insights. One of the difficulties in cancer genome sequencing is software selection. Currently, multiple tools are widely used to process NGS data in four stages: raw sequence data pre-processing and quality control (QC), sequence alignment, variant calling and annotation and visualisation. However, the differences between these NGS tools, including their installation, merits, drawbacks and application, have not been fully appreciated. Therefore, a systematic review of the functionality and performance of NGS tools is required to provide cancer researchers with guidance on software and strategy selection. Another challenge is the multidimensional QC of sequencing data because QC can not only report varied sequence data characteristics but also reveal deviations in diverse features and is essential for a meaningful and successful study. However, monitoring of QC metrics in specific steps including alignment and variant calling is neglected in certain pipelines such as the 'Best Practices Workflows' in GATK. In this review, we investigated the most widely used software for the fundamental analysis and QC of cancer genome sequencing data and provided instructions for selecting the most appropriate software and pipelines to ensure precise and efficient conclusions. We further discussed the prospects and new research directions for cancer genomics.

MSIsensor-ct: microsatellite instability detection using cfDNA sequencing data.

MOTIVATION: Microsatellite instability (MSI) is a promising biomarker for cancer prognosis and chemosensitivity. Techniques are rapidly evolving for the detection of MSI from tumor-normal paired or tumor-only sequencing data. However, tumor tissues are often insufficient, unavailable, or otherwise difficult to procure. Increasing clinical evidence indicates the enormous potential of plasma circulating cell-free DNA (cfNDA) technology as a noninvasive MSI detection approach. RESULTS: We developed MSIsensor-ct, a bioinformatics tool based on a machine learning protocol, dedicated to detecting MSI status using cfDNA sequencing data with a potential stable MSIscore threshold of 20%. Evaluation of MSIsensor-ct on independent testing datasets with various levels of circulating tumor DNA (ctDNA) and sequencing depth showed 100% accuracy within the limit of detection (LOD) of 0.05% ctDNA content. MSIsensor-ct requires only BAM files as input, rendering it user-friendly and readily integrated into next generation sequencing (NGS) analysis pipelines. AVAILABILITY: MSIsensor-ct is freely available at https://github.com/niu-lab/MSIsensor-ct. SUPPLEMENTARY INFORMATION: Supplementary data are available at Briefings in Bioinformatics online.

Comprehensive review and evaluation of computational methods for identifying FLT3-internal tandem duplication in acute myeloid leukaemia.

Internal tandem duplication (ITD) of FMS-like tyrosine kinase 3 (FLT3-ITD) constitutes an independent indicator of poor prognosis in acute myeloid leukaemia (AML). AML with FLT3-ITD usually presents with poor treatment outcomes, high recurrence rate and short overall survival. Currently, polymerase chain reaction and capillary electrophoresis are widely adopted for the clinical detection of FLT3-ITD, whereas the length and mutation frequency of ITD are evaluated using fragment analysis. With the development of sequencing technology and the high incidence of FLT3-ITD mutations, a multitude of bioinformatics tools and pipelines have been developed to detect FLT3-ITD using next-generation sequencing data. However, systematic comparison and evaluation of the methods or software have not been performed. In this study, we provided a comprehensive review of the principles, functionality and limitations of the existing methods for detecting FLT3-ITD. We further compared the qualitative and quantitative detection capabilities of six representative tools using simulated and biological data. Our results will provide practical guidance for researchers and clinicians to select the appropriate FLT3-ITD detection tools and highlight the direction of future developments in this field. Availability: A Docker image with several programs pre-installed is available at https://github.com/niu-lab/docker-flt3-itd to facilitate the application of FLT3-ITD detection tools.

The application of HER2 and CD47 CAR-macrophage in ovarian cancer.

BACKGROUND: The chimeric antigen receptor (CAR)-T therapy has a limited therapeutic effect on solid tumors owing to the limited CAR-T cell infiltration into solid tumors and the inactivation of CAR-T cells by the immunosuppressive tumor microenvironment. Macrophage is an important component of the innate and adaptive immunity, and its unique phagocytic function has been explored to construct CAR macrophages (CAR-Ms) against solid tumors. This study aimed to investigate the therapeutic application of CAR-Ms in ovarian cancer. METHODS: In this study, we constructed novel CAR structures, which consisted of humanized anti-HER2 or CD47 scFv, CD8 hinge region and transmembrane domains, as well as the 4-1BB and CD3ζ intracellular domains. We examined the phagocytosis of HER2 CAR-M and CD47 CAR-M on ovarian cancer cells and the promotion of adaptive immunity. Two syngeneic tumor models were used to estimate the in vivo antitumor activity of HER2 CAR-M and CD47 CAR-M. RESULTS: We constructed CAR-Ms targeting HER2 and CD47 and verified their phagocytic ability to ovarian cancer cells in vivo and in vitro. The constructed CAR-Ms showed antigen-specific phagocytosis of ovarian cancer cells in vitro and could activate CD8+ cytotoxic T lymphocyte (CTL) to secrete various anti-tumor factors. For the in vivo model, mice with human-like immune systems were used. We found that CAR-Ms enhanced CD8+ T cell activation, affected tumor-associated macrophage (TAM) phenotype, and led to tumor regression. CONCLUSIONS: We demonstrated the inhibition effect of our constructed novel HER2 CAR-M and CD47 CAR-M on target antigen-positive ovarian cancer in vitro and in vivo, and preliminarily verified that this inhibitory effect is due to phagocytosis, promotion of adaptive immunity and effect on tumor microenvironment.

Subject matching for cross-subject EEG-based recognition of driver states related to situation awareness.

Situation awareness (SA) has received much attention in recent years because of its importance for operators of dynamic systems. Electroencephalography (EEG) can be used to measure mental states of operators related to SA. However, cross-subject EEG-based SA recognition is a critical challenge, as data distributions of different subjects vary significantly. Subject variability is considered as a domain shift problem. Several attempts have been made to find domain-invariant features among subjects, where subject-specific information is neglected. In this work, we propose a simple but efficient subject matching framework by finding a connection between a target (test) subject and source (training) subjects. Specifically, the framework includes two stages: (1) we train the model with multi-source domain alignment layers to collect source domain statistics. (2) During testing, a distance is computed to perform subject matching in the latent representation space. We use a reciprocal exponential function as a similarity measure to dynamically select similar source subjects. Experiment results show that our framework achieves a state-of-the-art accuracy 74.32% for the Taiwan driving dataset.

A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG.

Driver drowsiness is one of the main factors leading to road fatalities and hazards in the transportation industry. Electroencephalography (EEG) has been considered as one of the best physiological signals to detect drivers' drowsy states, since it directly measures neurophysiological activities in the brain. However, designing a calibration-free system for driver drowsiness detection with EEG is still a challenging task, as EEG suffers from serious mental and physical drifts across different subjects. In this paper, we propose a compact and interpretable Convolutional Neural Network (CNN) to discover shared EEG features across different subjects for driver drowsiness detection. We incorporate the Global Average Pooling (GAP) layer in the model structure, allowing the Class Activation Map (CAM) method to be used for localizing regions of the input signal that contribute most for classification. Results show that the proposed model can achieve an average accuracy of 73.22% on 11 subjects for 2-class cross-subject EEG signal classification, which is higher than conventional machine learning methods and other state-of-art deep learning methods. It is revealed by the visualization technique that the model has learned biologically explainable features, e.g., Alpha spindles and Theta burst, as evidence for the drowsy state. It is also interesting to see that the model uses artifacts that usually dominate the wakeful EEG, e.g., muscle artifacts and sensor drifts, to recognize the alert state. The proposed model illustrates a potential direction to use CNN models as a powerful tool to discover shared features related to different mental states across different subjects from EEG signals.

Classification Approach for Attention Assessment via Singular Spectrum Analysis Based on Single-Channel Electroencephalograms.

Attention refers to the human psychological ability to focus on doing an activity. The attention assessment plays an important role in diagnosing attention deficit hyperactivity disorder (ADHD). In this paper, the attention assessment is performed via a classification approach. First, the single-channel electroencephalograms (EEGs) are acquired from various participants when they perform various activities. Then, fast Fourier transform (FFT) is applied to the acquired EEGs, and the high-frequency components are discarded for performing denoising. Next, empirical mode decomposition (EMD) is applied to remove the underlying trend of the signals. In order to extract more features, singular spectrum analysis (SSA) is employed to increase the total number of the components. Finally, some typical models such as the random forest-based classifier, the support vector machine (SVM)-based classifier, and the back-propagation (BP) neural network-based classifier are used for performing the classifications. Here, the percentages of the classification accuracies are employed as the attention scores. The computer numerical simulation results show that our proposed method yields a higher classification performance compared to the traditional methods without performing the EMD and SSA.

A frequentist approach to dynamic borrowing.

There has been growing interest in leveraging external control data to augment a randomized control group data in clinical trials and enable more informative decision making. In recent years, the quality and availability of real-world data have improved steadily as external controls. However, information borrowing by directly pooling such external controls with randomized controls may lead to biased estimates of the treatment effect. Dynamic borrowing methods under the Bayesian framework have been proposed to better control the false positive error. However, the numerical computation and, especially, parameter tuning, of those Bayesian dynamic borrowing methods remain a challenge in practice. In this paper, we present a frequentist interpretation of a Bayesian commensurate prior borrowing approach and describe intrinsic challenges associated with this method from the perspective of optimization. Motivated by this observation, we propose a new dynamic borrowing approach using adaptive lasso. The treatment effect estimate derived from this method follows a known asymptotic distribution, which can be used to construct confidence intervals and conduct hypothesis tests. The finite sample performance of the method is evaluated through extensive Monte Carlo simulations under different settings. We observed highly competitive performance of adaptive lasso compared to Bayesian approaches. Methods for selecting tuning parameters are also thoroughly discussed based on results from numerical studies and an illustration example.

Placental Mesenchymal Stem Cells Alleviate Podocyte Injury in Diabetic Kidney Disease by Modulating Mitophagy via the SIRT1-PGC-1alpha-TFAM Pathway.

The use of mesenchymal stem cells (MSCs) has become a new strategy for treating diabetic kidney disease (DKD). However, the role of placenta derived mesenchymal stem cells (P-MSCs) in DKD remains unclear. This study aims to investigate the therapeutic application and molecular mechanism of P-MSCs on DKD from the perspective of podocyte injury and PINK1/Parkin-mediated mitophagy at the animal, cellular, and molecular levels. Western blotting, reverse transcription polymerase chain reaction, immunofluorescence, and immunohistochemistry were used to detect the expression of podocyte injury-related markers and mitophagy-related markers, SIRT1, PGC-1α, and TFAM. Knockdown, overexpression, and rescue experiments were performed to verify the underlying mechanism of P-MSCs in DKD. Mitochondrial function was detected by flow cytometry. The structure of autophagosomes and mitochondria were observed by electron microscopy. Furthermore, we constructed a streptozotocin-induced DKD rat model and injected P-MSCs into DKD rats. Results showed that as compared with the control group, exposing podocytes to high-glucose conditions aggravated podocyte injury, represented by a decreased expression of Podocin along with increased expression of Desmin, and inhibited PINK1/Parkin-mediated mitophagy, manifested as a decreased expression of Beclin1, the LC3II/LC3I ratio, Parkin, and PINK1 associated with an increased expression of P62. Importantly, these indicators were reversed by P-MSCs. In addition, P-MSCs protected the structure and function of autophagosomes and mitochondria. P-MSCs increased mitochondrial membrane potential and ATP content and decreased the accumulation of reactive oxygen species. Mechanistically, P-MSCs alleviated podocyte injury and mitophagy inhibition by enhancing the expression of the SIRT1-PGC-1α-TFAM pathway. Finally, we injected P-MSCs into streptozotocin-induced DKD rats. The results revealed that the application of P-MSCs largely reversed the markers related to podocyte injury and mitophagy and significantly increased the expression of SIRT1, PGC-1α, and TFAM compared with the DKD group. In conclusion, P-MSCs ameliorated podocyte injury and PINK1/Parkin-mediated mitophagy inhibition in DKD by activating the SIRT1-PGC-1α-TFAM pathway.

Preventive and synbiotic effects of the soluble dietary fiber obtained from Lentinula edodes byproducts and Lactobacillus plantarum LP90 against dextran sulfate sodium-induced colitis in mice.

BACKGROUND: Soluble dietary fiber (SDF) obtained from Lentinula edodes byproducts has beneficial effects on human intestinal health. This study aimed to examine the combined preventive and ameliorative effects of a kind of synbiotic (SDF with a molecular weight of 1.58 × 102 kDa and Lactobacillus plantarum LP90 (LP) at 1 × 109 CFU kg-1 ) on dextran sulfate sodium-induced colitis mice. RESULTS: The results demonstrated that synbiotic treatment could alleviate weight loss, decrease the disease activity index level and cause histological amelioration. Synbiotic treatment also promoted the production of goblet cells, increased the expression of tight junction proteins, and adjusted the production of myeloperoxidase, malondialdehyde and superoxide dismutase to repair intestinal epithelial injury. Clinical symptoms were alleviated by maintaining Th17/Treg balance, increasing interleukin 10 and immunoglobulin A levels, reducing interleukin 17a and tumor necrosis factor α production, and promoting mRNA to highly express of Foxp3 and vitamin D receptors. Moreover, synbiotic treatment could upregulate butyric acid production (4.71 ± 0.46 mol g-1 feces, P < 0.05) and diversity of intestinal microbial to maintain intestinal homeostasis. CONCLUSION: This study suggested that the combination of LP and SDF as a synbiotic has the potential for use as a nutritional supplement to alleviate colitis. © 2022 Society of Chemical Industry.

Cache-Based Privacy Protection Scheme for Continuous Location Query.

Users who initiate continuous location queries are prone to trajectory information leakage, and the obtained query information is not effectively utilized. To address these problems, we propose a continuous location query protection scheme based on caching and an adaptive variable-order Markov model. When a user initiates a query request, we first query the cache information to obtain the required data. When the local cache cannot satisfy the user's demand, we use a variable-order Markov model to predict the user's future query location and generate a k-anonymous set based on the predicted location and cache contribution. We perturb the location set using differential privacy, then send the perturbed location set to the location service provider to obtain the service. We cache the query results returned by the service provider to the local device and update the local cache results according to time. By comparing the experiment with other schemes, the proposed scheme in this paper reduces the number of interactions with location providers, improves the local cache hit rate, and effectively ensures the security of the users' location privacy.

Fast numerical optimization for genome sequencing data in population biobanks.

MOTIVATION: Large-scale and high-dimensional genome sequencing data poses computational challenges. General-purpose optimization tools are usually not optimal in terms of computational and memory performance for genetic data. RESULTS: We develop two efficient solvers for optimization problems arising from large-scale regularized regressions on millions of genetic variants sequenced from hundreds of thousands of individuals. These genetic variants are encoded by the values in the set {0,1,2,NA}. We take advantage of this fact and use two bits to represent each entry in a genetic matrix, which reduces memory requirement by a factor of 32 compared to a double precision floating point representation. Using this representation, we implemented an iteratively reweighted least square algorithm to solve Lasso regressions on genetic matrices, which we name snpnet-2.0. When the dataset contains many rare variants, the predictors can be encoded in a sparse matrix. We utilize the sparsity in the predictor matrix to further reduce memory requirement and computational speed. Our sparse genetic matrix implementation uses both the compact two-bit representation and a simplified version of compressed sparse block format so that matrix-vector multiplications can be effectively parallelized on multiple CPU cores. To demonstrate the effectiveness of this representation, we implement an accelerated proximal gradient method to solve group Lasso on these sparse genetic matrices. This solver is named sparse-snpnet, and will also be included as part of snpnet R package. Our implementation is able to solve Lasso and group Lasso, linear, logistic and Cox regression problems on sparse genetic matrices that contain 1 000 000 variants and almost 100 000 individuals within 10 min and using less than 32GB of memory. AVAILABILITY AND IMPLEMENTATION: https://github.com/rivas-lab/snpnet/tree/compact.

Survival analysis on rare events using group-regularized multi-response Cox regression.

MOTIVATION: The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data. RESULTS: We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. AVAILABILITYANDIMPLEMENTATION: https://github.com/rivas-lab/multisnpnet-Cox.

HotSpot3D web server: an integrated resource for mutation analysis in protein 3D structures.

MOTIVATION: HotSpot3D is a widely used software for identifying mutation hotspots on the 3D structures of proteins. To further assist users, we developed a new HotSpot3D web server to make this software more versatile, convenient and interactive. RESULTS: The HotSpot3D web server performs data pre-processing, clustering, visualization and log-viewing on one stop. Users can interactively explore each cluster and easily re-visualize the mutational clusters within browsers. We also provide a database that allows users to search and visualize proximal mutations from 33 cancers in the Cancer Genome Atlas. AVAILABILITY AND IMPLEMENTATION: http://niulab.scgrid.cn/HotSpot3D/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bombyx mori Apolipophorin-III inhibits Beauveria bassiana directly and through regulating expression of genes relevant to immune signaling pathways.

Insect Apolipophorin-III is a multifunctional protein and also plays an important role in insect innate immunity. Early transcriptome and proteome studies indicated that the gene expression level of Bombyx mori Apolipophorin-III (BmApoLp-III) in silkworm larvae infected with Beauveria bassiana was significantly up-regulated. In this study, BmApoLp-III gene was cloned, its expression patterns in different larval tissues investigated, the BmApoLp-III protein was successfully expressed with prokaryotic expression system and its antifungal effect was verified. The results showed that the BmApoLp-III gene was expressed in all the tested tissues of the 5th instar larvae infected by B. bassiana, with the highest expression in fat body. The fungistatic zone test showed that the recombinant BmApoLp-III has a significant antifungal effect on B. bassiana. Injecting purified BmApoLp-III to the larvae delayed the onset and death of the infected larvae. Conversely, silencing BmApoLp-III gene by RNAi resulted in early morbidity and death of the infected larvae. At the same time, injecting BmApoLp-III up-regulated the expression of genes including BmßGRP4 and BmMyd88 in the Toll signaling pathway, BmCTL5 and BmHOP in the Jak/STAT signaling pathway, serine proteinase inhibitor BmSerpin5, and antimicrobial peptide BmCecA, but down-regulated the expression of BmTak1 of Imd signaling pathway; while silencing BmApoLp-III gene down-regulated the expression of BmßGRP1 and BmSpaetzle, BmCTL5 and BmHOP, BmSerpin2 and BmSerpin5, BmBAEE and BmPPO2 of relevant pathways and BmCecA, but up-regulated the expression of BmPGRP-Lc and BmTak1 of Imd pathway. These results indicate that the BmApoLp-III could not only directly inhibit B. bassiana, but also participate in regulation of the expression of immune signaling pathway related genes, promote the expression of immune effectors, and indirectly inhibit the reproduction of B. bassiana in the silkworm.

Expressional analysis of the silkworm storage protein 1 and identification of its interacting proteins.

Storage proteins are haemolymph-specific proteins in insects, mainly synthesized in the fat body, released into the haemolymph, and then selectively reabsorbed by the fat body before pupation. These storage proteins play an important role in insect metamorphosis and egg development. Some of these storage proteins are responsive to pathogen infection and can even suppress pathogen multiplication. However, the mechanisms of the physiological, biochemical and immune-responsive functions of storage proteins remain unclear. In this study, the expression patterns of Bombyx mori storage protein 1 (BmSP1) during the larval stage were analysed. Then, BmSP1 protein fused with enhanced green fluorescent protein (EGFP) was successfully expressed in a B. mori baculovirus vector expression system. Quantitative real-time PCR showed that the expression level of BmSP1 increased with the advance of instars and reached the highest level in the fifth instar, especially in the fat body. Recombinant BmSP1 expressed in silkworm larvae inhibited haemolymph melanization. Then, proteins that interact with BmSP1 were identified with EGFP used as an antigenic determinant by co-immunoprecipitation. A 30 kDa low molecular weight lipoprotein PBMHP-6 precursor (BmLP6) was shown to interact with BmSP1. Yeast two-hybrid experiments confirmed the interaction between BmSP1 and BmLP6. The results obtained in this study will be helpful for further study of the functions of BmSP1 and BmLP6 in the regulatory network of silkworm development and innate immunity.