DNA Barcoding in Forensic Entomology: A Molecular Tool That Increases Reliability in Estimating the Postmortem Interval

Forensic entomology is a crucial discipline in forensic investigations, primarily used for postmortem interval (PMI) estimation, species identification, and crime scene reconstruction. Recent advancements in molecular techniques, computational models, and climate-adaptive forensic entomology have enhanced the field's forensic applications. However, challenges related to methodological standardization, environmental variability, and legal admissibility persist. This comprehensive review examines recent developments in forensic entomology, focusing on molecular methodologies, AI-assisted species identification, and environmental influences on forensic insect succession. Literature was sourced from Scopus, Web of Science, and PubMed, emphasizing peer-reviewed studies published in the last 2 decades. Key themes include PMI estimation advancements, forensic insect ecology, and legal challenges in forensic entomology. Findings highlight improved accuracy in PMI estimation through genetic and computational approaches, the expansion of forensic molecular entomology applications, and the impact of climate change on forensic insect activity. While AI and molecular forensics have revolutionized species identification, standardization and legal frameworks remain insufficient. The study underscores the need for globally harmonized forensic entomology protocols, forensic training programs, and ethical guidelines. Future research should integrate metagenomics, forensic AI, and climate-adaptive forensic models to enhance forensic accuracy and reliability.

Psychoda sp. is often collected from patchy habitats such as sewers, drains and decomposing organic matters. The discovery of Psychoda sp. in forensic studies indicated that it might have noteworthy value in assisting death investigations. This study reports on the first finding of Psychoda larvae collected from decomposing rabbit carcasses placed in Cameron Highlands, Pahang, Malaysia. The larvae were first observed on rabbit carcasses and were collected using tweezers and carefully preserved in 70% ethanol. They were subsequently mounted on microscopy slides using Hoyer's medium and identified as Psychoda sp. morphologically. The identification was also confirmed through a DNA barcoding analysis. Psychoda sp. larvae were collected on day-10 post-mortem where the rabbit carcasses were at the advanced decay stage of decomposition. The cytochrome c oxidase I (COI) gene sequences of the larvae had 90% similarity with the Psychoda spp. in the database. The finding of these larvae on carrion may provide additional valuable insights into forensic entomology and may assist in death investigations.

Accurate postmortem interval (PMI) estimation is critical for forensic investigations, aiding case classification and providing vital trial evidence. Early postmortem signs, such as body temperature and rigor mortis, are reliable for estimating PMI shortly after death. However, these indicators become less useful as decomposition progresses, making late-stage PMI estimation a significant challenge. Decomposition involves predictable microbial activity, which may serve as an objective criterion for PMI estimation. During decomposition, anaerobic microbes metabolize body tissues, producing gases and organic acids, leading to significant changes in skin and soil microbial communities. These shifts, especially the transition from anaerobic to aerobic microbiomes, can objectively segment decomposition into pre- and post-rupture stages according to rupture point. Microbial communities change markedly after death, with anaerobic bacteria dominating early stages and aerobic bacteria prevalent post-rupture. Different organs exhibit distinct microbial successions, providing valuable PMI insights. Alongside microbial changes, metabolic and volatile organic compound (VOC) profiles also shift, reflecting the body's biochemical environment. Due to insufficient information, unimodal models could not comprehensively reflect the PMI, so a muti-modal model should be used to estimate the PMI. Machine learning (ML) offers promising methods for integrating these multimodal data sources, enabling more accurate PMI predictions. Despite challenges such as data quality and ethical considerations, developing human-specific multimodal databases and exploring microbial-insect interactions can significantly enhance PMI estimation accuracy, advancing forensic science.

Circular RNAs (circRNAs) are conserved, abundant, stable, and specifically expressed in mammals. The postmortem interval (PMI) estimation is crucial in forensic medicine, particularly for case investigation and civil action. CircRNAs may serve as ideal PMI biomarkers. However, no research has explored PMI estimation in the brain using circRNAs. The total RNA, including circRNA, was sampled from mouse brain tissues at multiple temperatures (4℃, 25℃, and 35℃). The semi-quantitative reverse transcription (RT)-PCR and real-time quantitative PCR (RT-qPCR) were used to test the postmortem degradation levels at different PMIs. As a result, we found circFat3 is highly and specifically expressed in mouse brain tissue, with postmortem levels significantly correlated with PMI across multiple temperatures. In addition, mt-co1 and 28 S rRNA demonstrated stability under various temperature conditions, supporting their use as reliable reference genes for PMI models. Moreover, the error rates showed that the circFat3/28S rRNA model was more accurate at 4℃. The circFat3/mt-co1 and circFat3/28S rRNA models provided slightly better predictions for short-term and long-term PMI, respectively at 25℃, while the circFat3/mt-co1 model was more accurate at 35℃. The combined application of the two reference genes was beneficial primarily for long-term PMI estimation. Furthermore, the validation results confirmed that these models were more accurate for long-term PMI estimation. Thus, our mathematical models were constructed at multiple temperatures based on circFat3 and these two reference genes. Taken together, this is the first study to identify circRNA circFat3 as a novel biomarker that may serve as a complementary tool for PMI estimation.

Caddisflies (Trichoptera) are abundant and diverse aquatic insects. Their immature stages inhabit a wide range of aquatic environments, making them ideal candidates for water quality biomonitoring. However, the limited morphological characteristics available for species identification in the immature stages pose a significant challenge to their application in biomonitoring. In this study, we evaluated the effectiveness of DNA barcoding, based on mitochondrial cytochrome c oxidase I (COI) sequences, for species identification of caddisflies in Thailand. A total of 1,487 adult specimens were collected and morphologically identified into 13 species across 8 genera and 4 families. From these taxa, 88 COI sequences were generated from representative specimens. Maximum intraspecific genetic divergence ranged from 0% to 3.08%. Only three species were successfully matched to COI sequences in the BOLD database, while nine species are reported here for the first time, and one species remained ambiguous. Integrating COI barcoding sequences with morphological data revealed that one species, morphologically similar to Triplectides indicus (Walker 1852), represents a novel species, Triplectides buengkanensis sp. nov. We provide a detailed description, illustrations, diagnostic features, and DNA barcoding sequences for this new species.

Deep learning method based on image recognition for intra-puparial age and postmortem interval estimation in the forensically important Sarcophaga peregrina (Diptera: Sarcophagidae).

Application of metabolomic methods to forensic studies may expand the limits of the post-mortem interval (PMI) estimation, and improve the accuracy of the estimation. To this end, it is important to determine which tissue is the most suitable for analysis, and which compounds are the most promising candidates for PMI estimation. This work is aimed at the comparison of human serum, aqueous humor (AH), and vitreous humor (VH) as perspective tissues for metabolomic-based PMI estimation, at the determination of most promising PMI biomarkers, and at the development of method of PMI estimation based on the measurement of concentrations of PMI biomarkers. Quantitative metabolomic profiling of samples of the human serum, AH, and VH taken at different PMIs has been performed with the use of NMR spectroscopy. It is found that the metabolomic changes in anatomically isolated ocular fluids are slower and smoother than that in blood. A good positive time correlation (Pearson coefficient r > 0.5) was observed for several metabolites, including hypoxanthine, choline, creatine, betaine, glutamate, and glycine. A model for PMI estimation based on concentrations of several metabolites in AH and VH is proposed. The obtained results demonstrate that the metabolomic analysis of AH and VH is more suitable for the PMI estimation than that of serum. The compounds with good positive time correlation can be considered as potential PMI biomarkers.

Distinguishing Black Bullhead (Ameiurus melas) from Brown Bullhead (A. nebulosus) can be difficult, in part, due to conflicting evidence regarding reliable meristic and morphological characters with which to base identifications. The possibility of hybrids with intermediate traits further complicates diagnosis. Identification guides have typically focused on adults, leaving guidance for juveniles largely undescribed. We compared morphological identifications of Black Bullhead and Brown Bullhead with a cytochrome c oxidase I (COI) barcode and microsatellite assignments. Specifically, we examined: (1) gill-raker count, (2) anal-fin ray count, (3) prominence of pectoral-spine serrae, (4) DNA barcoding using the COI gene, and (5) cluster analysis of nine microsatellite loci. With the exception of anal-fin ray counts, we found a high degree of agreement among all methods. Microsatellite analysis identified two clearly distinct genetic groups, and these groups corresponded to individuals identified as Black Bullhead and Brown Bullhead based on morphology. Although Black Bullhead and Brown Bullhead did not share COI haplotypes (except for one individual), we found greater intraspecific genetic distances among individuals identified as Brown Bullhead than between Brown Bullhead and Black Bullhead, demonstrating that caution is warranted against using COI haplotypes as a barcode to solely distinguish the two species. Pectoral-spine serrae prominence was a useful character to distinguish the two species and worked especially well on individuals <125 mm total length. Gill-raker counts of euthanized fish in the laboratory were appropriate for the identification of all sizes of Black Bullhead and Brown Bullhead (using the lowest number of gill rakers on the first gill arch if asymmetry was observed). Finally, microsatellites did not show evidence of recent hybridization at our sampling sites. We recommend using pectoral-spine serrae prominence to distinguish between Black Bullhead and Brown Bullhead in the field and using gill-raker counts for laboratory based identification.

Classical methods for postmortem interval (PMI) estimation have been applied for nearly a century. Contrary to the notion of being simple or easily accessible, these approaches require highly specialized training, including a medical degree, postgraduate specialization in forensic pathology, and extensive practical experience. Classical PMI estimation relies on observable physical and chemical changes in the human cadaver, such as rigor mortis, livor mortis, algor mortis, and transformative processes during decomposition. These methods are fundamental in medicolegal practice but remain largely influenced by environmental and individual variability. Recent advances in forensic research, particularly in microbiology and biochemistry, have introduced innovative approaches that complement traditional methods, offering greater accuracy and reliability, though resource-intensive. Emerging approaches leverage the predictable postmortem succession of microbial communities (thanatomicrobiome) and biochemical alterations in cadaver fluids and tissues. Techniques such as metagenomics, metatranscriptomics, and metabolomics enable detailed analysis of these changes, while computational models and machine learning further refine PMI estimates. Despite advancements, challenges persist, including variability due to environmental factors and limited access to human decomposition data. Integrating multi-omics approaches and artificial intelligence offers a path forward, addressing these limitations and enhancing the accuracy of PMI estimation. This review provides a comprehensive overview of PMI estimation, critically examining classical approaches and highlighting cutting-edge methodologies rooted in thanatomicrobiology and thanatochemistry. We emphasize the transformative potential of multi-omics integration and artificial intelligence in improving PMI accuracy. Importantly, we propose a paradigm shift: redefining PMI estimation through evidence-based, interdisciplinary research that bridges scientific rigor and judicial application. Transdisciplinary collaboration and standardized methodologies will be essential to translate emerging knowledge into robust forensic tools that serve both science and justice.

Entomology

Cytochrome c oxidase I (COI) gene-based DNA barcode was generated for sugarcane scale insect, Melanaspis glomerata (Green). The generated DNA barcode would certainly serve as an ideal diagnostic kit for M. glomerata. The barcode developed by us (GenBank Accession Number KR153875) would probably be the first for this species. The amino acid sequence of the M. glomerataCOI fragment did not have any stop codon, and the uninterrupted open reading frame confirmed the quality of the DNA barcode. The extent of identity between the COI sequences of sugarcane scale and other three species of Diaspidids (Aonidiella aurantii, A. taxus and Melanaspis bromeliae) varied between 87.37 and 88.75 %. Therefore, the barcode generated in this study could easily delineate M. glomerata from other armoured scales of economic importance.

Specific scopeThis Standard describes a diagnostic protocol for adults ofEpitrix cucumeris,Epitrix papa, Epitrix subcrinitaandEpitrix tuberis.Specific approval and amendmentFirst approved in 2011‐09.Revision approved in 2016‐01.

We sequenced 665bp of the Cytochrome C Oxidase I (COI) barcoding marker for 257 specimens and 482bp of Elongation Factor 1-α (EF1-α) for 237 specimens belonging to the leafmining subgenus Ectoedemia (Ectoedemia) in the basal Lepidopteran family Nepticulidae. The dataset includes 45 out of 48 West Palearctic Ectoedemia s. str. species and several species from Africa, North America and Asia. Both COI and EF1-α proved reliable as an alternative to conventional species identification for the majority of species and the combination of both markers can aid in species validation. A clear barcode gap is not present, and in some species large K2P intraspecific pairwise differences are found, up to 6.85% in COI and 2.9% in EF1-α. In the Ectoedemia rubivora species complex, the species E. rubivora, E. arcuatella and E. atricollis share COI barcodes and could only be distinguished by EF1-α. Diagnostic base positions, usually third codon positions, are in this and other cases a useful addition to species delimitation, in addition to distance methods. Ectoedemia albifasciella COI barcodes fall into two distinct clusters not related to other characters, whereas these clusters are absent in EF1-α, possibly caused by mtDNA anomalies or hybridisation. In the Ectoedemia subbimaculella complex, both sequences fail to unequivocally distinguish the species E. heringi, E. liechtensteini, E. phyllotomella and one population of E. subbimaculella. DNA barcodes confirm that North American Ectoedemia argyropeza are derived from a European introduction. We strongly advocate the use of a nuclear marker in addition to the universal COI barcode marker for better identifying species, including cryptic ones.

Forensic entomology when the evidence is “no insect.” Best carrion fly species for predicting maximum postmortem interval in the United Arab Emirates

DNA barcoding is a useful technique for diversity analysis whereby a standardized region of DNA is used for the identification of a species or a taxonomic group of organisms. These standard regions used for identification are called the DNA barcode. These are small sequences of the entire genome. In plants, DNA barcoding has application in phylogenetic analysis, authentication, inter- and intraspecific diversity, classification into wild and cultivated genotypes, the study of phylogeographical patterns, and in the detection of adulteration. The barcode loci, i.e., the DNA regions used for the identification are able to discriminate the closely related species and identify new cryptic species as well. Depending on the taxon and complexity of the species, different barcode loci are used for the purpose. In animals, the universal DNA barcode, i.e., mitochondrial cytochrome c oxidase I (COI) gene is used for species discrimination. However, this gene cannot be used for plants due to its limited divergence. Thus, its use is limited only to some algae. Efforts are going on to find suitable universal barcode loci for plants. Since the last decades, matK, rbcL, trnH-psbA, ITS, trnL-F, 5S-rRNA, and 18S-rRNA candidate regions are being used as DNA barcodes in plants. The article provides an overview of the use of these candidate regions through different approaches which have gained importance due to the challenges in DNA barcoding of plants. The development of multilocus and tiered approaches along with the new frontier areas for application of this technique has been analyzed in detail.