Prolidase is a specialized metalloprotease that overcomes the resistance of proline-containing peptide bonds to proteolysis by exclusively hydrolyzing dipeptides with a C-terminal proline. Despite its potential significance in the life cycle of protozoan parasites, knowledge of prolidase in these organisms remains limited. In this study, the complete coding sequence of Leishmania donovani prolidase (LdProl) was cloned into an expression vector. The recombinant protein was purified using affinity chromatography, and its molecular weight was assessed by size-exclusion chromatography. The purified LdProl exhibited enzymatic activity with a strong substrate preference for Trp-Pro-pNA, and manganese ions were identified as essential cofactors for optimal activity. LdProl also stimulated innate immune mechanisms through elevating pro-inflammatory cytokines and nitric oxide levels, suggesting a role in macrophage-mediated immunomodulation. Fluorescence spectroscopy revealed pH-dependent conformational changes, particularly under strongly acidic conditions, whereas circular dichroism spectroscopy indicated a predominantly α-helical structure that was most stable at neutral pH. Computational modeling demonstrated a conserved three-dimensional structure, metal-binding site, and stable dimeric interface similar to prolidases from other organisms. Docking studies identified Trp-Pro-pNA as the substrate with the highest binding affinity, and molecular dynamics simulations confirmed the formation of stable enzyme-substrate complexes. Virtual screening yielded two lead compounds with higher binding affinities for LdProl than for its human counterpart, along with favorable ADME properties. Molecular dynamics simulations further validated these compounds by demonstrating the stability of enzyme-inhibitor complexes. Overall, this study presents the first comprehensive report on the enzyme kinetics, structural characteristics, and in silico inhibition of metal-dependent prolidase from trypanosomatid parasites.

Maize leaf sheath anthocyanin accumulation is governed by precise transcriptional regulation, yet its key regulator and molecular mechanisms for this tissue-specific pigmentation remain incompletely understood. Through EMS-based mutagenesis and CRISPR/Cas9 validation, we determined that the R2R3-MYB transcription factor Pl1, but not B1, is essential for anthocyanin biosynthesis in leaf sheaths. Two natural loss-of-function Pl1 alleles were screened, each harboring an 18-bp deletion in exon 3 of the mRNA that likely arises from GC-rich sequence-mediated mis-splicing. Genome-wide DNA affinity purification sequencing revealed 8053 high-confidence Pl1 target genes that were significantly enriched in arginine and proline metabolism. Nine flavonoid pathway structural genes were identified as Pl1 targets, including Zm00001d043174 encoding a putative cytochrome P450 superfamily protein, which is co-targeted by the bHLH factor R1. Comparative transcriptomics across four inbred lines revealed tissue-specific suppression of Pl1 in leaves relative to leaf sheaths. Overexpression of Pl1 intensified sheath pigmentation and induced pigmentation in green leaves. Cross-tissue transcriptome comparison identified 2784 concordantly differentially expressed genes (DEGs), including 218 transcriptional regulator genes. Pull-down coupled with LC-MS analysis of Pl1 promoter-binding proteins in both tissues identified 61 transcriptional effectors, five of which overlapped with the concordant DEGs. These encode AT-hook proteins, a DNA methyltransferase, a knotted homeobox protein, and a nascent polypeptide-associated complex subunit and are proposed as candidate upstream modulators for sheath-specific pigmentation. This study establishes Pl1 as the master regulator of sheath pigmentation, delineates its downstream target network and candidate upstream factors, thereby advancing the mechanistic understanding of spatially restricted anthocyanin accumulation in maize.

γ-Aminobutyric acid (GABA) plays a crucial role in human health. Although tomatoes with enhanced GABA levels have been engineered through single-gene editing, GABA-fortified maize has not previously been developed. In this study, CRISPR/Cas9 editing of the biosynthetic gene ZmGAD3, the degradation pathway gene ZmGABA-TP1-1, or both genes simultaneously, increased the GABA content of maize kernels 0.59-2.15 fold, 1.28-2.99 fold, or 2.63-4.87 fold, respectively. We thus developed an elite germplasm resource with significantly fortified GABA content and demonstrated that coordinated editing of ZmGAD3 and ZmGABA-TP1-1 could effectively enhance GABA accumulation. This study reveals important insight into the functional regulation of GABA content through maize metabolic engineering.

The identification of human remains is a central component of medico-legal practice, underpinning both judicial proceedings and the rights of families of missing persons. International protocols recognize three primary identification modalities - fingerprint comparison, DNA analysis, and dental comparison - yet the involvement of forensic odontologists remains discretionary in most jurisdictions. This paper examines the intersection between recent legislative reforms accelerating judicial declarations of presumed death and the reliability of forensic identification systems. Taking Italy's 2025 reduction of the general presumed-death threshold from ten to five years as a focal case, the paper argues that compressed legal timelines increase systemic reliance on forensic identification while simultaneously narrowing the margin for procedural insufficiency. The analysis situates presumed-death legislation within broader international human rights and humanitarian forensic frameworks, emphasizing that accelerated legal closure must be matched by adequate forensic capacity. Drawing on empirical evidence from mass fatality events and a recent systematic review demonstrating substantial pooled dental identification contribution in disaster contexts, the paper identifies specific scenarios in which single-modality identification approaches carry elevated failure risk, including advanced decomposition, carbonization, fragmentation, and DNA degradation. To address this gap, a risk-stratified model is proposed comprising seven trigger criteria under which forensic odontology consultation should be mandatory rather than discretionary. The model is operationally proportionate and adaptable across civil law and common law jurisdictions.

Conventional forensic DNA profiling predominantly utilizes established core sets of autosomal short tandem repeats (STRs), such as the FBI's Combined DNA Index System (CODIS) and the European Standard Set (ESS). While these panels are effective for standard forensic casework, they may be inadequate in more demanding scenarios, including severely degraded samples, complex multi-contributor mixtures, and kinship cases with deficiencies, where enhanced discriminatory capability is crucial. Additional non-core STR loci address these challenges while maintaining the non-coding, phenotypically uninformative nature that ensures the legal and ethical acceptability of forensic genetic evidence in court. This review assesses the forensic applications, population genetic parameters, validation requirements, and ethical considerations associated with non-core STR loci. A representative supplementary STR panel is presented as a case study to illustrate both the forensic value and the analytical requirements associated with the implementation of extended non-core STR systems. Challenges in implementation were identified in areas such as nomenclature standardization, backward compatibility with existing national databases, and geographic gaps in population reference data. The review concludes that a hybrid strategy, which retains core capillary electrophoresis (CE)-based profiling for routine casework and employs extended non-core panels for complex cases, represents the most practical path for the field.

Feature engineering (FE) is highly crucial in machine learning (ML), which directly governs a predictive model's performance and thereby serves as a foundational step for accurate and reliable biomolecular recognition. However, most existing ML-based sequencing approaches rely predominantly on conductance and current-based descriptors, and the absence of a well-defined molecular fingerprint highlights a pressing need for more informative FE. So, intending to expand the feature landscape, herein, we propose an ML-driven tool to address the challenges associated with the current ML-aided sequencing methods. Our approach enables the construction of a more robust feature space by integrating both conventional quantum transport descriptors and newly incorporated electronic density of states (DOS)-based descriptors. While the transmission signals encode the intrinsic transport properties of target molecules, the DOS provides key insights into the local electronic structure and the extent of electrode-molecule coupling, serving as a rich source of highly informative features. On implementing our idea, the proposed ML classification approach successfully distinguishes all the biomolecules (four DNA nucleotides as well as six structurally diverse blood antigens with regioisomeric glycosidic linkages), achieving an accuracy of up to 100%. This study, therefore, provides proof of concept that DOS-based descriptors are as reliable as transmission-based features for biomolecular recognition while offering a simpler experimental alternative.

Crimes against domestic and wild animals represent a growing global concern that introduces unique investigative challenges. Although Bloodstain Pattern Analysis (BPA) is a well-established discipline in human-centric forensics, its potential within veterinary medicine remains significantly underutilized. To bridge this critical gap, this review moves from foundational theory to the practical application of BPA in animal-related contexts. First, the article establishes the scientific validity of this application by demonstrating that fluid dynamic differences between human and animal blood are negligible at high shear rates, thereby supporting the use of standard forensic techniques. Subsequently, the focus shifts to the unique complexities of animal-involved crime scenes-such as the impact of complex topographies like fur and feathers on stain morphology-and the interpretation of multi-species scenes, including organized animal fighting and "The Link" to domestic violence. Furthermore, we discuss the essential integration of BPA with other forensic disciplines, particularly in guiding strategic sample selection for animal DNA analysis. Against a backdrop of recent legislative reforms introducing stricter penalties for animal cruelty, the systematic adoption of BPA into veterinary forensic protocols is essential to provide the robust scientific evidence required for modern investigations.

Argonaute (Ago) endonucleases are ubiquitous across all domains of life and, like Cas nucleases, can be programmed by guide oligonucleotides to target and cleave specific nucleic acid sequences. However, unlike Cas enzymes, Agos are not restricted by sequence requirements such as protospacer adjacent motifs (PAMs), enhancing their utility as molecular tools. This study demonstrates diverse applications of the DNA-guided RNA endonuclease Mucilaginibacter paludis Argonaute for in vitro mRNA processing, e.g., 3' end polishing, and for mRNA analysis in combination with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Applications of the latter include the comprehensive assessment of 5' capping efficiency, 3' poly(A) tail length and composition, one-pot multiplexed cleavage for RNA sequencing, modification detection, and fingerprinting.

Background constrains analytical sensitivity: surveying larger sensor areas samples more analyte molecules but also accumulates false positives, limiting gains in detection performance. Here we introduce FINDER-Fluorogenic INstantaneous Digital Enumeration and Recognition-a single-molecule platform that combines kinetic fingerprinting with fluorogenic transient probes for rapid molecular classification under near-zero-background conditions. By suppressing both solution and surface-associated background at micromolar probe concentrations, FINDER classifies individual molecules within seconds-scale observation windows per field of view. This regime allows sensitivity to scale with surveyed sensor area, enabling amplification-free quantification of the miRNA cancer biomarker hsa-miR-16 with an 11 aM detection limit. FINDER further generalizes to HPV16 DNA biomarker detection, two-color RNA/DNA co-profiling, and rapid discrimination of clinically relevant EGFR single-nucleotide variants using multidimensional kinetic filtering. Rapid per-field classification permits tens of fields to be surveyed within minutes. By converting kinetic specificity into area-scalable sensitivity, FINDER enables semi-automated attomolar biomarker counting without amplification in practical workflows.

Maize primary metabolism drives complex agronomic traits, yet its genetic regulation remains difficult to resolve. Here we integrated genomic, transcriptomic and metabolomic data from 1,404 maize progenies derived from 24 diverse founders to dissect the genetic architecture of primary metabolism. We constructed a high-confidence regulatory network that resolved causal genes underlying metabolic quantitative trait loci and successfully identified targets for improving maize nutritional quality. This systems-level framework further prioritized ZmAVT1A-1, encoding a putative amino acid transporter, as a key regulator of amino acid accumulation. Natural variation and transgenic analyses showed that ZmAVT1A-1 modulates nitrogen partitioning between vegetative tissues and kernels, revealing pleiotropic effects on agronomic traits. These findings illustrated the intricate trade-offs inherent in metabolic regulation. Together, our study provides a comprehensive multiomics resource for decoding metabolic networks and underscores the necessity of a systems approach to navigate the pleiotropic nature of crop improvement targets.