BGI Group (China)

Multidrug-resistant (MDR) pathogens, exemplified byKlebsiella pneumoniae, pose a critical and escalating threat in low- and middle-income countries (LMICs), where limited diagnostic and therapeutic options exacerbate mortality. Bacteriophage (phage) therapy has re-emerged as a promising alternative, offering high specificity, biofilm disruption, and potential for low-cost production. However, a significant translational gap persists; while in vitro studies and results personalized phage therapy demonstrate efficacy, the pathway to clinical integration, especially in LMICs, remains obstructed by systemic barriers including the lack of local phage banks and undefined regulatory pathways. This review moves beyond a summary of biological promise topropose a novel, structured framework for implementationtailored to resource-limited health systems. We synthesize current evidence to introduce the5-P roadmap, a cohesive strategy encompassing the establishment of integratedphage-host biobanks; development of context-appropriatepreparations; rigorouspreclinical validation emphasizing phage-antibiotic synergy; the parallel development ofpolicy and regulatory pathways; andpilot clinical trials designed for subsequent health system integration. By addressing these interconnected pillars simultaneously, this framework provides an actionable blueprint to advance phage therapy from a laboratory concept to a scalable, equitable, and sustainable adjunct within national antimicrobial resistance strategies in LMICs.

Abstract Oocyte growth and maturation depend on tightly coordinated programs within oocytes and their surrounding granulosa cells, yet defining the transcriptional continuum of growing oocytes has been challenging due to their large size and the limitations of current droplet-based and spatial transcriptomic platforms. Here, we optimized a high-density array–based platform called Stereo-cell for high-throughput dual-modality profiling of large mouse oocytes, allowing for both the preservation of morphology and transcript capture. By integrating unsupervised transcriptomic clustering with cell morphological features, we delineated successive temporal windows from growing oocytes to metaphase II and uncovered stage-linked shifts from early programs toward later programs. We also profiled the ovarian somatic fraction, reconstructed granulosa-cell subtype relationships, and placed ovarian cell states in tissue context using single-cell–resolution spatial data.

While homologous recombination deficiency (HRD) presents therapeutic opportunities in endometrial cancer (EC), its molecular determinants and clinical implications remain poorly characterized. Through genomic analysis of 688 cancer-related genes combined with genomic scar assessment, we investigated HRD molecular features and clinical relevance of HRD across three cohorts: an EC cohort from Sun Yat-sen University Cancer Center (SYSUCC, n = 114), the Cancer Genome Atlas EC cohort (n = 500), and a high-grade serous ovarian cancer (HGSOC) cohort (n = 118). HRD was identified in 23.7% of SYSUCC EC cases, and HRD tumors paradoxically had fewer short-nucleotide variations in HRR genes than proficient (HRP) tumors (18.52% vs. 48.28%, P = 0.007). Mechanistic analysis revealed large-scale transition (LST) losses as the potential predominant HRD driver in EC, occurring significantly more frequently in HRD versus HRP tumors (74.1% vs 5.7%; P < 0.001). Comparative genomics demonstrated enrichment of HRR gene LST losses was EC-specific, contrasting with HGSOC where LST distribution was HRD-independent. Clinically, elevated HRD scores predicted reduced progression-free survival (HR 1.74, 95% CI 1.03-2.94; P = 0.04) yet enhanced platinum sensitivity (HR 0.41, 95% CI 0.18-0.94; P = 0.034). Our findings indicate that the HRD phenotype in EC, driven primarily by LST losses rather than short-nucleotide variations, serves as both a prognostic and predictive biomarker.

Abstract The spatial organization of cell types and gene expression underlies tissue architecture and organismal physiology. However, resolving complete cellular and molecular landscapes within a three-dimensional context remains challenging, particularly in organisms with high cellular heterogeneity and complex geometry. Here, we developed a semi-supervised workflow to reconstruct high-resolution three-dimensional spatial transcriptomic models of the planarian Schmidtea mediterranea at single-cell resolution. Planarians are basal bilaterians capable of regenerating body structures after injury. Our reconstruction maps the distribution of cell types and spatially patterned gene expression across the intact organism and identifies 119 candidate positional control genes (PCGs). These genes are expressed across muscle, neural, and epidermal populations. Functional perturbation of selected candidates, including pitx3 , ptpn11 , pi4ka , upf3b , and cul1 , revealed roles in regeneration following injury. In addition, we identify intestinal cells as prominent components of the microenvironment surrounding adult tgs1 ⁺ pluripotent stem cells (neoblasts). Together, these results demonstrate the utility of three-dimensional spatial transcriptomic reconstruction for mapping cellular architecture and positional gene regulation in complex adult organisms.

Influenza's pandemic threat is driven by antigenic drift, which limits the efficacy of conventional vaccines. To address this challenge, we established a clinical serum-anchored computational design pipeline for a broad-spectrum multi-epitope mRNA vaccine (MEMV), bridging the gap between pure in silico design and clinical applicability. Using 36 longitudinal sera (d0/d28/d365) from 12 well-characterized human cohorts (6 vaccine recipients and 6 influenza patients) and high-density antibody-peptide microarrays, we empirically identified 12 immunodominant B-cell linear epitopes from the nucleoprotein (NP) of influenza A (H1N1/H3N2) and B viruses. These experimentally validated epitopes were combined with in silico-predicted conserved helper T-lymphocyte (HTL)/cytotoxic T-lymphocyte (CTL) epitopes (from NP/HA/NA) to construct MEMVs candidates, ensuring high antigenicity, non-toxicity, and 95.63% global HLA coverage. Molecular docking and 100 ns molecular dynamics (MD) simulations confirmed favorable conformational compatibility between MEMVs and Toll-like receptor 3 (TLR3) in silico immunization via C-ImmSim predicted robust B/T-cell responses and protective cytokine (IFN-γ/IL-10) production. Collectively, this pipeline shortens the preliminary design cycle for influenza vaccines, provides a standard epitope-combination strategy, and offers direct targets for follow-up in vitro/in vivo experiments.

<title>Abstract</title> Menopausal hormone therapy (MHT) alleviates climacteric symptoms, but still, microbiome-wide effects and efficacy determinants remain uncharacterized. In this six-month longitudinal study of 356 perimenopausal women receiving oral MHT, we integrated serial clinical assessments with multi-site metagenomic profiling. MHT elicited multi-system estrogenic responses, including neuroendocrine restoration, symptom alleviation, metabolic shifts, and uterine changes. MHT induced site-specific microbiome remodeling, most prominently in vaginal and urinary niches, with moderate oral and minimal gut changes. Generally, MHT reduced Gardnerella vaginalis and increased Lactobacillus crispatus levels in vagina. Baseline vaginal L. crispatus abundance strongly predicted subsequent urogenital restoration. Additionally, baseline urogenital community states stratified therapeutic trajectories: vaginal G. vaginalis-dominated individuals showed transitions toward eubiosis, whereas diversified communities improved less. MHT efficacy corresponded to serum estrogen elevation, linked to baseline gut community type. Individuals with a Phocaeicola vulgatus-driven community type exhibited greater hormonal responsiveness and superior outcomes than those dominated by Prevotella copri. These findings support a precision MHT framework integrating gut community and urogenital typing to guide stratification and targeted interventions.

This study investigates the genomic basis of immune adaptation in the transparent glass catfish (Kv: Kryptopterus vitreolus), focusing on the loss of the Toll-like receptor 21 (TLR21) gene. Comparative genomic analysis with closely related non-transparent North African catfish (Cg: Clarias gariepinus) revealed 11 TLR genes in the latter, while only 8 TLR genes (KvTLR1, 2, 3, 5, 7, 9, 13, and 20) were retained in the glass catfish, with TLR21 specifically absent. Collinearity analysis confirmed that the genomic region containing TLR21 is conserved across eight siluriform species, with loss exclusively in the glass catfish, supporting its lineage-specific absence. Structural expansion was notable in KvTLR5, KvTLR7, and KvTLR20. Molecular docking indicated that binding stability between CpG oligonucleotides and TLR21 varies significantly, with CpG-B 1681 showing the strongest interaction, which highlights sequence-dependent ligand recognition. Interestingly, absence of the TLR1 gene in another transparent teleost, the X-ray tetra (Pristella maxillaris), suggests that transparent fishes may share an evolutionary trend of lineage-specific TLR gene loss. Together, these findings reveal a distinctive evolutionary trajectory in the innate immune receptor family of transparent fishes and provide new molecular insights into their adaptive immune strategies. These insights will benefit the academic community by improving comparative frameworks for fish innate immunity, and they may inform disease prevention and health management strategies in aquaculture and the ornamental fish trade.

Single nucleotide variants (SNVs) are crucial in cancer occurrence and development. SNVs at the transcriptomic level generally come from genomic variants (g-tSNVs) and RNA editing (e-tSNVs). The types and quantities of e-tSNVs remain a subject of debate due to a relatively poor understanding of RNA editing processes. Herein, we developed TSCS (Transcript SNVs Classifier relying on complementary sequencings), a machine learning classifier that integrates short-read (MGI) and long-read (PacBio) RNA-seq data to accurately distinguish true transcript SNVs using stringent criteria. Applied to five colorectal cancer cell lines (HCT15, LoVo, SW480, SW620, and HCT116), TSCS demonstrated superior accuracy and sensitivity, outperforming established tools (GATK, BCFtools, Longshot, RED_ML). It increased the total detected transcript SNVs by 31.83% on average, with g-tSNVs and e-tSNVs exceeding conventional methods by >1-fold and >2-fold, respectively. TSCS achieved mean recall rates of 75.3% for g-tSNVs and 77.2% for e-tSNVs. Notably, for the first time, e-tSNVs were found in a relatively large proportion of total transcript SNVs in cancer cell lines, approximately 40%. Of the identified e-tSNVs, 80% were attributed to the known RNA editing, but the other e-tSNVs did not fall into any known category. Importantly, the e-tSNVs uniquely detected in this study showed distinct patterns in SNV types and genomic locations. Additionally, the transcript SNVs called by TSCS were partially confirmed using experimental approaches, such as Sanger sequencing, RNC-seq, and mass spectrometry. This study lays the foundation for surveying and appraising the cancer-related e-tSNVs.