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Article

Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae) and Intra-Puparial Age Estimation Utilizing Multiple Methods at Constant and Fluctuating Temperatures

by
Yanjie Shang
1,†,
Fengqin Yang
1,†,
Fernand Jocelin Ngando
1,
Xiangyan Zhang
1,
Yakai Feng
2,
Lipin Ren
1,* and
Yadong Guo
1,*
1
Department of Forensic Science, School of Basic Medical Sciences, Central South University, Changsha 410013, China
2
Department of Forensic Medicine, School of Basic Medical Sciences, Xinjiang Medical University, Urumqi 830017, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this study.
Animals 2023, 13(10), 1607; https://doi.org/10.3390/ani13101607
Submission received: 13 March 2023 / Revised: 29 April 2023 / Accepted: 2 May 2023 / Published: 11 May 2023
(This article belongs to the Section Animal Physiology)
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Abstract

:

Simple Summary

Constant and fluctuating temperatures can affect the growth and development of insects, which will affect the accuracy of minimum postmortem interval estimation. Our study found that the development time of S. peregrina was prolonged under fluctuating temperatures. The estimation of the pupal stage has a significant effect on the estimation of PMI. We explored the feasibility of different methods for intra-puparial age estimation and found that the DEG expression profile, ATR-FTIR, and CHCs detection, combined with chemometrics, had the potential to estimate the intra-puparial age of S. peregrina under constant and fluctuating temperatures.

Abstract

Sarcophaga peregrina (Robineau-Desvoidy, 1830) has the potential to estimate the minimum postmortem interval (PMImin). Development data and intra-puparial age estimation are significant for PMImin estimation. Previous research has focused on constant temperatures, although fluctuating temperatures are a more real scenario at a crime scene. The current study examined the growth patterns of S. peregrina under constant (25.75 °C) and fluctuating temperatures (18–36 °C; 22–30 °C). Furthermore, differentially expressed genes, attenuated total reflectance Fourier-transform infrared spectroscopy, and cuticular hydrocarbons of S. peregrina during the intra-puparial period were used to estimate age. The results indicated that S. peregrina at fluctuating temperatures took longer to develop and had a lower pupariation rate, eclosion rate, and pupal weight than the group at constant temperatures did. Moreover, we found that six DEG expression profiles and ATR-FTIR technology, CHCs detection methods, and chemometrics can potentially estimate the intra-puparial age of S. peregrina at both constant and fluctuating temperatures. The findings of the study support the use of S. peregrina for PMImin estimation and encourage the use of entomological evidence in forensic practice.

1. Introduction

Entomology evidence could be the only evidence that can be used to estimate the minimum postmortem interval (PMImin) in decaying corpses. After insects are collected from the corpse and identified, the PMImin is estimated using development data of necrophagous flies [1,2]. Therefore, development data collection and accurate age estimation of necrophagous flies are meaningful for PMImin estimation.
Insects are poikilothermic animals. The temperature has a significant impact on the development and growth of insects [3,4]. Previous studies that used necrophagous insects to estimate PMImin focused on constant temperatures. These studies focused on several necrophagous Dipterans (e.g., Chrysomya megacephala (Fabricius, 1794) [5], Sarcophaga dux (Thomson, 1869) [6], Lucilia cuprina (Wiedemann, 1830) [7], Megaselia scalaris (Loew, 1866) [8], and Musca domestica (Linnaeus, 1758) [9]), Coleoptera (e.g., Necrobia rufipes (De Geer, 1775) [10]), and Hymenoptera (e.g., Nasonia vitripennis (Walker, 1836) [11]). However, fluctuating temperatures are more realistic than constant temperatures are [1,12,13,14]. Exploring the development and growth of necrophagous flies at fluctuating temperatures is more important for PMImin estimation. Wu et al. [15] concluded that the impact of constant and fluctuating temperatures on the development and growth of flies varied from study to study, with some studies accelerating development and others retarding development. For example, temperature fluctuations decelerated the development of Scathophaga stercoraria (Linnaeus, 1785) [16], Aldrichina grahami (Aldrich, 1930) [12], Calliphora vicina (Robineau-Desvoidy, 1830), and Calliphora vomitoria (Linnaeus, 1758) [13]. However, the authors of studies about Sarcophaga argyrostoma (Robineau-Desvoidy, 1830), Lucilia illustris (Meigen, 1826) [13] and Protophormia terraenovae (Robineau-Desvoidy, 1830) [14] found that the development was accelerated. The explanation for this inconsistent result is that when the rearing temperature falls within the moderate temperature range of the insect, the development time of the insect appears to be the same at fluctuating and constant temperatures. The development times differed when rearing temperatures were below or near the lower developmental threshold temperature or above or near the higher fatal developmental threshold temperature [17,18]. For more accurate data to determine PMImin, it is required to gather development data from necrophagous flies under fluctuating temperatures.
The intra-puparial period, which occupies half of the immature stage [19], is crucial for estimating PMImin. Many methods for estimating pupal age have been reported, including puparium color variation [20,21], intra-puparial morphological characteristics [22], x-irradiation [23], micro-computed tomography imaging [24], and optical coherence tomography [25]. However, morphological methods require morphological knowledge, and the application of development data in the intra-puparial period is limited. Micro-CT techniques, for example, are uncommon at forensic workstations and inconvenient to use at crime scenes. Therefore, it is crucial to find a fast, accurate, and convenient method for estimating the intra-puparial period age.
Studying differentially expressed genes (DEGs) as a method for age estimation has been used for blow flies and flesh flies [26,27]. Fourier-transform infrared (FTIR) spectroscopy can determine the metabolic properties of insects based on absorption characteristics, such as lipids, proteins, and cellular processes [28]. Attenuated total reflection Fourier-transform infrared (ATR-FTIR) has been used to evaluate the effects of the cause of death on PMI estimation [29], to discriminate between normal and pathological populations [30,31,32] and to detect SARS-CoV2 infections on the spot [33]. However, there are a limited number of reports of FTIR applications in forensic entomology. Cuticular hydrocarbons (CHCs), the major components of the waxy layer of the insect cuticle [34,35], which has been proved to be a reliable method of predicting the age of the fly [36,37,38,39]. As a result, further research into the value of DEGs, ATR-FTIR, and CHCs in intra-puparial age estimation is required.
Sarcophaga peregrina (Robineau-Desvoidy, 1830) (Diptera: Sarcophagidae) is a valuable flesh fly species of medical, veterinary, ecological, and forensic entomological importance [40,41,42]. It plays a crucial role in decomposing cadavers of forensic significance, especially in PMImin estimation. Studies associated with S. peregrina are limited in number [43], and they largely focus on larval morphology [44,45], molecular research [46,47], cuticular hydrocarbon profile investigation [48,49,50], development data collecting [19], chromosome-level research [51,52], and effects of drugs and heavy metals [53,54,55]. Until this study, we have not searched the literature on the effect of fluctuating temperature on the development of S. peregrina through commonly used literature search platforms (PubMed and Web of Science).
In the current study, developmental data of S. peregrina were collected at constant and fluctuating temperatures for PMImin estimation. In addition, six DEG expression profiles, the ATR-FTIR spectroscopy characters, and the CHC profiles of S. peregrina were investigated and analyzed during the intra-puparial period. This study provides a basis for rapid and accurate PMImin estimation.

2. Materials and Methods

2.1. Insect Rearing and Experimental Temperature Settings

Sarcophaga peregrina was captured using pig carcasses in Changsha (28°12′ N, 112°58′ E), Hunan Province, China, in July 2017 and reared in Guo’s Lab (Changsha China). Traditional morphological characteristics [56] the long cytochrome oxidase subunit I (CO I) sequences were used to identify the species. Adults were fed in an artificial climate chamber (LRH-250-GSI, Taihong Co., Ltd., Shaoguan, China) with 25.0 ± 1.0 °C, 75% RH, a 12 h light and 12 h dark cycle, and the feeding conditions were fresh water and mixed milk powder and sugar.
The experimental constant and fluctuating temperatures (Figure 1) were designed based on the temperature variations in Changsha, China, from June to October 2011 to 2021 (Figure S1). The temperature groups were recorded as fluctuating model A (group A: 18–36 °C), fluctuating model B (group B: 22–30 °C), and constant model C (group C: 25.75 °C). The temperature tolerance of the artificial climate chamber was 1 °C with 75% RH and a 12 h: 12 h (L: D) cycle, as shown in Figure 1 and Table S1, respectively.

2.2. Collection of Samples for Developmental Analysis and Multi-Method Analysis

A pig lung was used to induce the larvae to hatch. Larvae (ca. 1500) produced within 1 h were divided into three groups: A, B, and C. Ten larval samples were randomly taken every eight hours. Larval instar was identified using a Zeiss 2000-C stereomicroscope (Carl Zeiss, Jena, Germany). Larval length was measured using a digital caliper (Mineette, Shanghai, China). The developmental time, pupation rate, eclosion rate, and pupal weight were recorded during the experiment. Data were analyzed using Origin Pro 8.6 software (OriginLab, Northampton, MA, USA, SCR: 015636). Three replications were performed.
Larvae produced within 1 h were cultured at constant temperature (group C) and fluctuating temperature (group A). Thirty pupae were harvested at 24 h intervals until the emergence of adults. The samples collected each time were divided into three groups on average for DEGs, ATR-FTIR, and CHCs analysis, respectively. Three replications were performed. A total of 1620 pupae were collected: 810 in group A and 810 in group C.

2.3. DEGs Study

Total RNA from each intra-puparial tissue obtained by removing the puparium was extracted using the SteadyPure Quick RNA Extraction Kit (Code No. AG21017) (Accurate Biotechnology (Hunan) Co., Ltd. Changsha, China) and stored at −80 °C following the manufacturer’s instructions. Total RNA quantification was performed using NanoDrop 2000 (Thermo Fisher Scientific, Wilmington, DE, USA). The ratios of optical density (OD) of 260/280 and 260/230 were selected as the evaluation indicators. cDNA was synthesized with the Evo M-MLV RT Mix kit (Code No. AG11728) (Accurate Biotechnology (Hunan) Co., Ltd. Changsha, China), and then qPCR was performed.
The candidate DEGs were identified according to the genome [51] (NCBI no. JABZEU000000000) and transcriptome data [57] (NCBI nos. PRJNA795032 and PRJNA810308) of S. peregrina. Quantitative real-time PCR operation was performed according to the instructions of the SYBR® Green Premix Pro Taq HS qPCR Kit (ROX Plus) (Code No. AG11718) (Accurate Biotechnology (Changsha, China) Co., Ltd.) on an ABI 7500 Real-Time PCR system (Applied Biosystem, Carlsbad, CA, USA). Primers for the DEGs and reference genes were designed using Primer Premier 5.0 (Biosoft Premier, Palo Alto, CA, USA) and are listed in Table S2. The 2−△△Ct method was used to calculate the relative quantification (RQ) of the DEGs. All statistical analysis was conducted using the software Origin Pro 8.6.

2.4. ATR-FTIR Study

The intra-puparial tissue was ground using liquid nitrogen, and later, transferred into a 1.5 mL EP tube and shaken using a vortex mixer (Guangzhou GenXion Biotechnology Co. LTD, Guangzhou, China). An 850 Fourier-transform infrared spectrometer equipped with an ATR attachment was used (Tianjin Gangdong Technology Development Co. LTD, Tianjin, China) to collect spectra. The ATR attachment was cleaned with absolute ethanol before each measurement, and background spectra were acquired before each operation. Spectra were recorded at a resolution of 4 cm−1 in the frequency range from 4000 to 900 cm−1, with 32 scans per sample. OMNIC, version 9.2 (Thermo Fisher Scientific, Wilmington, MA, USA), was used to process the spectral data. Unnecessary background information was reduced by spectral preprocessing. The standard normal variate (SNV) was used to decrease the impact of light scattering and spectral size due to sample thickness [58]. Furthermore, all spectral data were smoothed and denoised (Savitzky–Golay convolution algorithm; number of smoothing points: 15). Finally, the spectral range of 1800–900 cm−1 was used for further analysis.
In this study, Principal Component Analysis (PCA), Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), and Partial Least Squares Discriminant Analysis (PLS) were performed using SIMCA 14.1 software to observe the spectral characters of the intra-puparial tissue at different times of sampling. PCA can reduce the dimensions of the data and retain original data features. OPLS-DA is a supervised statistical method of discriminant analysis [59,60]. For OPLS-DA, values of R2 (cum) and Q2 (cum) close to 1.0 indicate an excellent model in theory, >0.5 indicates a better model, and >0.4 indicates an acceptable model. The permutation test was used to examine if the model was overfitted. PLS regression analysis can decompose X variable and extract latent variable under the guidance of Y variable [61,62]. R2 and the root mean square error (RMSE) were used to evaluate the PLS model; values close to 1 for R2 and 0 for RMSE indicate a better model. The variable importance in projection (VIP) was used to assess the contribution of each variable to the OPLA-DA and PLS models, and a VIP value of >1.0 was considered to be significant.

2.5. CHCs Study

Referring to Zhang et al.’s [49,57] operation, the intra-puparial tissue was immersed in a 2 mL glass vial containing 1 mL of redistilled hexane for 30 min at room temperature. An alkane mixture of heptane and tetracontane (C7–C40, 1 μg/mL, O2SI) dissolved in 1 mL of hexane was selected as an external standard. Using a syringe with a nylon membrane and filter, the soaking solution was transferred into a new 2 mL glass vial. Following that, the samples were vacuum concentrated and dried before being dissolved in 200 μL redistilled hexane for GC-MS analysis. GC–MS (Agilent Technologies, 7890B–5977A GC/MSD) fitted with a DB-5MS capillary column was used to analyze the CHC. Operations of sample loading were performed and instrument condition settings were used according to references [49] that have already been reported. Due to the length limitation, specific setting contents are not described here.
MSD ChemStation Data Analysis F.01.03 was used to integrated the peak areas, and the qualitative identification of CHC was carried out by comparing the chromatographic retention time, characteristic fragment ions, and abundance ratio with those of standard values. The Kováts retention index, NIST 11 library, and literature [49,63,64,65,66,67,68,69,70] were selected to assist identification, which we called the CHCs database. PCA, OPLS-DA, and PLS models were used to visualize the filtered data.

3. Results

3.1. Development Analysis

The correlation between larval body length and development time under different temperatures is shown in Figure 2. The model of fourth-order polynomials was used to establish nonlinear regression. Table 1 shows the equations and R2 values of the model. The results show that the larval body length of group C was longer than those of both group A and group B before 80–90 h, and the longest larvae can be observed in group C.
S. peregrina could complete their life history under constant and fluctuating temperatures, and the development duration is listed in Table 2. Group C spent the minimum amount of time reaching full development. The development rate of group A was similar to that of group B and slower than that of group C. As shown in Figure 3, the pupariation rate, eclosion rate, and pupal weight were lower and statistically significant in groups A and B than they were in group C. These results indicate that fluctuating temperatures can affect the development of S. peregrina.

3.2. DEGs Analysis

The expression profiles of six DEGs (circRNA_2143, circRNA_3489, circRNA_2847, fln, UQCRFS1, and COX5A) changed with the increase in intra-puparial age (Figure 4). Polynomial regression analysis described the relationship between the level of DEG expression of S. peregrina and developmental age (days) (Table 3). The increasing expression tendencies of the six DEGs, which are similar under constant and fluctuating temperatures, indicate that these six DEGs can be gene marker candidates to estimate the intra-puparial age of S. peregrina at both constant and fluctuating temperatures.

3.3. ATR-FTIR Analysis

Figure 5 shows the average FTIR spectra of different intra-puparial development ages (days) in the range of 1800–900 cm−1. Based on previous studies [71,72,73], the main infrared absorption band identification is shown in Table 4. The OPLS-DA classification model (Figure 6) showed that the same day’s spectra were well clustered and distinguished from those of the other days for both groups, except for a few data overlaps between day 5 and day 9 for group C. Common wavenumber bands with VIP > 1 (Table S3) were 989.31–898.672 cm−1 and 1716.35–1675.85 cm−1. The model evaluation parameters of the PLS are listed in Table 5: R2 > 0.9 and RMSE of 0.4271 day and 0.7683 day for groups A and C, respectively. Figure 6 shows the regression model to predict the intra-puparial age. Wavenumbers with VIP > 1 are shown in Table S3. The common wavenumber bands were 1760.7–1754.92 cm−1, 1729.85–1681.64 cm−1, 1660.42–1616.07 cm−1, 1544.71–1506.14 cm−1, 1033.67–1031.74 cm−1, 1022.09–1016.31 cm−1, and 960.384–898.672 cm−1. These results suggest that the spectral differences observed in this study were caused by changes in proteins, lipids, and other intra-puparial tissue components. The error of intra-puparial age inference is smaller in a fluctuating temperature environment.

3.4. CHC Analysis

A total of 41 hydrocarbons were identified, including 21 n-alkanes, 17 branched alkanes, and 3 alkenes with a carbon chain length between C9 and C32 (Table 6). According to the mini bar chart, branched alkanes were the most abundant compound from day 1 to day 8 at constant or fluctuating temperatures; on day 9, it was n-alkanes. The CHCs profiles (Table S4) show that the most abundant compounds in group A were 4-Methyl C17 on days 1–3 and DiMethyl C29 on days 6–9. Additionally, a transitional stage could be observed on days 4–5. Except for a slightly different transitional stage on days 4–5, group C showed nearly the same change as group A did. In addition, dominant CHCs in the intra-puparial tissues showed a shift from a low to a high molecular weight with the age of the pupae. The OPLS-DA plot (Figure 7) shows that the clustering effect of group A was better than that of group C. Common variables (Table S6) of groups A and C with VIP > 1 (Table S5) were selected. 4-Methyl C10, Methyl C29, C19, C21, and 7-Methyl C27 were identified. The parameters of PLS analysis are listed in Table 5: R2 > 0.9, and RMSEs of 0.4837 day and 0.7653 day for groups A and C, respectively. C27, C25, C23, and DiMethyl C29 were identified. The CHCs identified above suggest a possible link between changes in the most abundant CHCs and increasing intra-puparial age, particularly DiMethyl C29, which could be useful in estimating S. peregrina intra-puparial age at constant and fluctuating temperatures. Additionally, the error of intra-puparial age inference is smaller in a fluctuating temperature environment.

4. Discussion

Fluctuating temperatures can influence the development of flies. In studies, temperature fluctuations have been shown to either accelerate or slow the development of insects [13,14] or have no significant difference in development [74]. This study found that fluctuating temperatures influenced the feeding stage of S. peregrina. It led to smaller lengths and weights, longer developmental times, and a lower survival rate for larvae and pupae. The thermal mean and its proximity to the developmental threshold [17,18] could be the main reasons for this phenomenon. The developmental rate difference between fluctuating and constant temperatures grows as the temperature fluctuation range increases [4]. When the range is within the suitable range for insect growth, the effect of constant and fluctuating temperatures will not be significant. However, when the range exceeds the suitable range, such as a lower or higher temperature, the effects of fluctuating temperature are relatively large [75]. Changes in the development time at fluctuating temperatures can lead to incorrect age estimations, which in turn leads to incorrect PMImin estimations.
More development data under fluctuating temperatures are required to accurately determine the intra-puparial age of necrophagous flies. However, this can greatly increase forensic entomologists’ workload. Therefore, it is crucial to develop an approach that is faster, simpler, more accurate, and universally applicable.
The value of DEGs for inferring the age of flies has been confirmed [27,57,76]. Studies on Sarcophaga dux [6], Aldrichina graham [77], Lucilia illustris [78], Glossina morsitans [79], and Calliphora vicina [1,80,81] showed that DEGs can be used to determine the age of necrophagous flies. However, the expression trends of the DEGs in the above research were not singular, and most of them were performed under constant temperatures. DEGs that can stably express at constant and fluctuating temperatures and show a single temporal expression trend are expected to be genetic markers for age estimation. For example, a study on the expression of bcd, sll, and cs shows a significant linear trend during the development of blowfly eggs [27]. Candidate DEGs for S. peregrina were chosen from previously published results in our previous study [26]. Their expression trends were not singular, and they were demonstrated under constant temperatures. The candidate DEGs in this study were chosen from S. peregrina’s reference genome and transcriptome data [49,51]. We found that the six DEGs showed a single up-regulated temporal expression, with the intra-puparial period increasing under constant and fluctuating temperatures. This result suggested that the six DEGs could become insect gene markers for estimating the intra-puparial age of S. peregrina. Notably, three circular RNAs (circRNAs), which are more stable than linear RNAs are [82,83], were introduced as candidate DEGs for the first time in this paper. Although DEGs can be used to estimate the PMImin, the number of studies on the profiles of necrophagous flies is still limited. More research should be conducted.
The amount of research on the application of vibrational spectroscopy in biochemistry and forensic science is increasing [32,84,85]. Based on vibrational bands, the biochemical information associated with the fingerprint region (1800–900 cm−1) depicts the structure and function of cell samples [86,87,88,89]. With the enormous quantity of data produced by IR spectroscopy, chemometrics can help with data pre-processing, sample classification, and reducing the dimensionality of chemical information [84]. Perez-Mendoza [90] and Pickering et al. [91] successfully identified the age of different species by using infrared spectroscopy. Our study found that the proteins, lipids, and other components of the intra-puparial tissues, represented by the common bands, were affected by the development of flies. These substances are important for the model’s establishment and age estimation. The model parameters suggested that better results were obtained at fluctuating temperatures, demonstrating that temperature settings that match the real environment are crucial for accurate age estimation. Moreover, the automation and convenience of the test instruments make immediate analysis at crime scenes possible [86]. The use of spectroscopic methods in forensic science could improve the use of entomological evidence.
Cuticular hydrocarbons are the main component of the waxy layer of insect cuticles [35] and play a key role in insect survival. They can act as a barrier for insects to prevent water loss, avoid mechanical damage, isolate microorganisms and chemicals, and they also act as pheromones for insects [35,92,93,94]. Sarcophaga bullata CHC profiles during development have been published [95]. Studies on Chrysomya albiceps [38], Chrysomya rufifacies [37], and Sarcophaga peregrina [49] also confirmed that the dynamics of CHC profiles during the development of flies can be used to determine the age of necrophagous flies [96]. The carbon chain length of the CHCs identified in this study was between C9 and C32, which was within the range of C8–C36 discovered by Zhang et al. [49]. However, the most abundant CHC differed from that in Zhang et al.’s study [49]. This is because the tissue used in the study, intra-puparial tissue, differs from the pupal shell used in their study. Meanwhile, as the intra-puparial age increased, the dominant CHCs shifted from low- to high-molecular-weight hydrocarbons. This is similar to the study of Chrysomya rufifacies [37]. The next stage of S. peregrina after completing metamorphic development is the adult stage. The heavier, long-chain hydrocarbons, which are the most important compounds in the insect cuticle [48,97], are essential for waterproofing [98,99,100] and help adults to survive in complex external environments. Additionally, the model parameters showed that better results could be obtained from group A, which was placed under fluctuating temperatures. These results indicate that more accurate data can be obtained for precise age estimation in a more realistic simulated situation. Additionally, CHC’s profiles can be used to determine the intra-puparial age of S. peregrina, as well as the PMImin.

5. Conclusions

This study investigated methods for estimating the intra-puparial age of Sarcophaga peregrina under constant and fluctuating temperatures, such as DEGs, ATR-FTIR, and CHC profiles. We found that the development of S. peregrina was different under constant and fluctuating temperatures and demonstrated that the three methods are meaningful for pupal age estimation.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ani13101607/s1, Figure S1: The temperature variation from June to October 2011 to 2021 in Changsha city, China; Figure S2: The visualization results of PCA analysis and permutation test of the data that the ATR-FTIR study generated. a,b: PCA analysis of group A and group C, respectively. c, d: Permutation test result of group A and group C, respectively; Figure S3: The visualization results of PCA analysis and permutation test of the data that the CHCs study generated. a,b: PCA analysis of group A and group C, respectively. c,d: Permutation test result of group A and group C, respectively; Table S1: The temperature setting of development of Sarcophaga peregrina at constant and fluctuating temperatures; Table S2: Primers used in RT-qPCR analysis; Table S3: The ATR-FTIR spectral with VIP>1 of PLS and OPLS-DA model; Table S4: CHCs profile of S. peregrina intra-puparial tissue at constant and fluctuating temperatures; Table S5: The CHCs data with VIP>1 of PLS and OPLS-DA model; Table S6: Common VIP recognition.

Author Contributions

All authors contributed to the study’s conception and design. Material preparation and data analysis were performed by Y.S.; The first draft of the manuscript was written by F.Y.; F.J.N., X.Z. and Y.F. commented on previous versions of the manuscript; Supervision: L.R. and Y.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China (No. 82072114), the Natural Science Foundation of Changsha (No. kq2202129), Natural Science Foundation of Hunan Province (2022JJ40671) and the China Scholarship Council (CSC) (No. 202206370160).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All raw data involved in this study are kept in Guo’s laboratory. To request the data, please contact the corresponding author of the article.

Acknowledgments

We thank Lushi Chen (Guizhou Police Officer Vocational College) for the identification of insects.

Conflicts of Interest

The authors declare no competing interests.

References

  1. Hartmann, K.; Herrmann, E.; Amendt, J.; Verhoff, M.A.; Zehner, R.E. Age-dependent gene expression of Calliphora vicina pupae (Diptera: Calliphoridae) at constant and fluctuating temperatures. Int. J. Leg. Med. 2021, 135, 2625–2635. [Google Scholar] [CrossRef] [PubMed]
  2. Martín-Vega, D.; Hall, M.J. Estimating the age of Calliphora vicina eggs (Diptera: Calliphoridae): Determination of embryonic morphological landmarks and preservation of egg samples. Int. J. Leg. Med. 2016, 130, 845–854. [Google Scholar] [CrossRef] [PubMed]
  3. Byrd, J.; Castner, J.L. Forensic Entomology: The Utility of Arthropods in Legal Investigations; CRC Press: Boca Raton, FL, USA, 2000. [Google Scholar]
  4. Wang, Y.; Gong, Q.; Liu, Z.-J.; Wang, M.; Xu, W.; Wang, Y.-H.; Wang, J.-F. Research Progress on Developmental Biology of Sarcosaprophagous Insect s. Fa Yi Xue Za Zhi 2021, 37, 673–682. [Google Scholar] [CrossRef] [PubMed]
  5. Ma Yukun, H.C.; Min, J. Effects of temperature on the growth and development of four common necrophagous flies and their significance in forensic medicine. Chin. J. Forensic Med. 1998, 81–84, 81–84. [Google Scholar]
  6. Zhang, X.Y.; Li, Y.; Shang, Y.J.; Ren, L.P.; Chen, W.; Wang, S.W.; Guo, Y.D. Development of Sarcophaga dux (diptera: Sarcophagidae) at constant temperatures and differential gene expression for age estimation of the pupae. J. Therm. Biol. 2020, 93, 102735. [Google Scholar] [CrossRef] [PubMed]
  7. Ze-min, L. Pupal morphogenesis of Lucilia cuprina in different constant temperatures and its significance in Forensic Medicine. J. Forensic Med. 2008, 23, 324–325+326. [Google Scholar]
  8. Feng, D.-X.; Liu, G.-C. Pupal age estimation of forensically important Megaselia scalaris (Loe w) (Diptera: Phoridae). Forensic Sci. Int. 2014, 236, 133–137. [Google Scholar] [CrossRef]
  9. Wang, Y.; Yang, L.; Zhang, Y.; Tao, L.; Wang, J. Development of Musca domestica at constant temperatures and the first case report of its application for estimating the minimum postmortem interval. Forensic Sci. Int. 2018, 285, 172–180. [Google Scholar] [CrossRef]
  10. Hu, G.; Wang, M.; Wang, Y.; Tang, H.; Chen, R.; Zhang, Y.; Zhao, Y.; Jin, J.; Wang, Y.; Wu, M.; et al. Development of Necrobia rufipes (De Geer, 1775) (Coleoptera: Cleridae) under constant temperatures and its implication in forensic entomology. Forensic Sci. Int. 2020, 311, 110275. [Google Scholar] [CrossRef]
  11. Zhang, Y.; Wang, Y.; Liu, C.; Wang, J.; Hu, G.; Wang, M.; Yang, L.; Chu, J. Development of Nasonia vitripennis (Hymenoptera: Pteromalidae) at Constant Temperatures in China. J. Med. Entomol. 2019, 56, 368–377. [Google Scholar] [CrossRef]
  12. Chen, W.; Yang, L.; Ren, L.; Shang, Y.; Wang, S.; Guo, Y. Aldrichina grahami Impact of Constant Versus Fluctuating Temperatures on the Development and Life History Parameters of (Diptera: Calliphoridae). Insects 2019, 10, 184. [Google Scholar] [CrossRef] [PubMed]
  13. Niederegger, S.; Pastuschek, J.; Mall, G. Preliminary studies of the influence of fluctuating temperatures on the development of various forensically relevant flies. Forensic Sci. Int. 2010, 199, 72–78. [Google Scholar] [CrossRef] [PubMed]
  14. Warren, J.-A.; Anderson, G.S. Effect of Fluctuating Temperatures on the Development of a Forensically Important Blow Fly, Protophormia terraenovae (Diptera: Calliphoridae). Environ. Entomol. 2013, 42, 167–172. [Google Scholar] [CrossRef] [PubMed]
  15. Wu, T.-H.; Shiao, S.F.; Okuyama, T. Development of insects under fluctuating temperature: A review and case study. J. Appl. Entomol. 2015, 139, 592–599. [Google Scholar] [CrossRef]
  16. Kjærsgaard, A.; Pertoldi, C.; Loeschcke, V.; Blanckenhorn, W.U. The effect of fluctuating temperatures during development on fitness-r elated traits of Scatophaga stercoraria (Diptera: Scathophagidae). Environ. Entomol. 2013, 42, 1069–1078. [Google Scholar] [CrossRef]
  17. Colinet, H.; Sinclair, B.J.; Vernon, P.; Renault, D. Insects in fluctuating thermal environments. Annu. Rev. Entomol. 2015, 60, 123–140. [Google Scholar] [CrossRef]
  18. Kingsolver, J.G.; Ragland, G.J.; Diamond, S.E. Evolution in a constant environment: Thermal fluctuations and thermal sensitivity of laboratory and field populations of Manduca sexta. Evolution 2009, 63, 537–541. [Google Scholar] [CrossRef]
  19. Wang, Y.; Wang, J.F.; Zhang, Y.N.; Tao, L.-Y.; Wang, M. Forensically Important Boettcherisca peregrina (Diptera: Sarcophagidae) in China: Development Pattern and Significance for Estimating Postmortem Interval. J. Med. Entomol. 2017, 54, 1491–1497. [Google Scholar] [CrossRef]
  20. Robertson, C.W. The metamorphosis of Drosophila melanogaster, including an accurately timed account of the principal morphological changes. J. Morphol. 1936, 59, 351–399. [Google Scholar] [CrossRef]
  21. Bainbridge, S.P.; Bownes, M. Staging the metamorphosis of Drosophila melanogaster. J. Embryol. Exp. Morphol. 1981, 66, 57–80. [Google Scholar] [CrossRef]
  22. Davies, K.; Harvey, M.L. Internal Morphological Analysis for Age Estimation of Blow Fly Pupae (Diptera: Calliphoridae) in Postmortem Interval Estimation. J. Forensic Sci. 2013, 58, 79–84. [Google Scholar] [CrossRef] [PubMed]
  23. Sato, C. Effects of x-irradiation on the development of the respiratory system and survival time in pupae of Sarcophaga peregrina. Tohoku J. Exp. Med. 1966, 90, 1–8. [Google Scholar] [CrossRef] [PubMed]
  24. Nur Aliah, N.A.; Heo, C.C.; Noor Shafini, M.; Mohd Hafizi, M. Age estimation of forensically important blowfly, Chrysomya megacephala (Diptera: Calliphoridae) pupae using micro-computed tomography imaging. Trop. Biomed. 2019, 36, 640–653. [Google Scholar] [PubMed]
  25. Brown, K.; Harvey, M. Optical coherence tomography: Age estimation of Calliphora vicina pupae in vivo? Forensic Sci. Int. 2014, 242, 157–161. [Google Scholar] [CrossRef] [PubMed]
  26. Shang, Y.J.; Ren, L.P.; Yang, L.; Wang, S.W.; Chen, W.; Dong, J.N.; Ma, H.M.; Qi, X.; Guo, Y.D. Differential Gene Expression for Age Estimation of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae) Intrapuparial. J. Med. Entomol. 2020, 57, 65–77. [Google Scholar] [CrossRef] [PubMed]
  27. Tarone, A.M.; Jennings, K.C.; Foran, D.R. Aging Blow Fly Eggs Using Gene Expression: A Feasibility Study. J. Forensic Sci. 2007, 52, 1350–1354. [Google Scholar] [CrossRef]
  28. de Lima, L.A.S.; Baia, T.C.; Gama, R.A.; da Silva Gasparotto, L.H.; Lima, K.M.G. Near Infrared Spectroscopy as an Emerging Tool for Forensic Entomotoxicology. NIR News 2014, 25, 5–7. [Google Scholar] [CrossRef]
  29. Zhang, K.; Wang, Q.; Liu, R.; Wei, X.; Li, Z.; Fan, S.; Wang, Z. Evaluating the effects of causes of death on postmortem interval estimation by ATR-FTIR spectroscopy. Int. J. Leg. Med. 2019, 134, 565–574. [Google Scholar] [CrossRef]
  30. Movasaghi, Z.; Rehman, S.; ur Rehman, D.I. Fourier Transform Infrared (FTIR) Spectroscopy of Biological Tissues. Appl. Spectrosc. Rev. 2008, 43, 134–179. [Google Scholar] [CrossRef]
  31. Baker, M.J.; Trevisan, J.; Bassan, P.; Bhargava, R.; Butler, H.J.; Dorling, K.M.; Fielden, P.R.; Fogarty, S.W.; Fullwood, N.J.; Heys, K.A.; et al. Using Fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 2014, 9, 1771–1791. [Google Scholar] [CrossRef]
  32. Mitchell, A.L.; Gajjar, K.; Theophilou, G.; Martin, F.L.; Martin-Hirsch, P.P.L. Vibrational spectroscopy of biofluids for disease screening or diagnosis: Translation from the laboratory to a clinical setting. J. Biophotonics 2014, 7, 153–165. [Google Scholar] [CrossRef] [PubMed]
  33. Barauna, V.G.; Singh, M.N.; Barbosa, L.L.; Marcarini, W.D.; Vassallo, P.F.; Mill, J.G.; Ribeiro-Rodrigues, R.; Campos, L.C.G.; Warnke, P.H.-H.; Martin, F.L. Ultrarapid On-Site Detection of SARS-CoV-2 Infection Using Simple ATR-FTIR Spectroscopy and an Analysis Algorithm: High Sensitivity and Specificity. Anal. Chem. 2021, 93, 2950–2958. [Google Scholar] [CrossRef] [PubMed]
  34. Chung, H.; Carroll, S.B. Wax, sex and the origin of species: Dual roles of insect cuticular hydrocarbons in adaptation and mating. Bioessays 2015, 37, 822–830. [Google Scholar] [CrossRef] [PubMed]
  35. Botella-Cruz, M.; Velasco, J.; Millán, A.; Hetz, S.K.; Pallarés, S. Cuticle Hydrocarbons Show Plastic Variation under Desiccation in Saline Aquatic Beetles. Insects 2021, 12, 285. [Google Scholar] [CrossRef] [PubMed]
  36. Zhu, G.-H.; Yè, G.; Hu, C.; Xu, X.H.; Li, K. Development changes of cuticular hydrocarbons in Chrysomya rufifacies larvae: Potential for determining larval age. Med. Vet. Entomol. 2006, 20, 438–444. [Google Scholar] [CrossRef] [PubMed]
  37. Sharma, A.; Drijfhout, F.P.; Tomberlin, J.K.; Bala, M. Cuticular hydrocarbons as a tool for determining the age of Chrysomya rufifacies (Diptera: Calliphoridae) larvae. J. Forensic Sci. 2020, 66, 236–244. [Google Scholar] [CrossRef]
  38. Alotaibi, F.; Alkuriji, M.A.; AlReshaidan, S.; Alajmi, R.; Metwally, D.M.; Almutairi, B.F.; Alorf, M.S.; Haddadi, R.; Ahmed, A. Body Size and Cuticular Hydrocarbons as Larval Age Indicators in the Forensic Blow Fly, Chrysomya albiceps (Diptera: Calliphoridae). J. Med. Entomol. 2020, 58, 1048–1055. [Google Scholar] [CrossRef]
  39. Zhu, G.-H.; Xu, X.H.; Yu, X.J.; Zhang, Y.T.; Wang, J.-f. Puparial case hydrocarbons of Chrysomya megacephala as an indicator of the postmortem interval. Forensic Sci. Int. 2007, 169, 1–5. [Google Scholar] [CrossRef]
  40. Rodriguez, W.C.; Bass, W.M. Insect Activity and Its Relationship to Decay Rates of Human Cadavers in East Tennessee. J. Forensic Sci. 1983, 28, 423–432. [Google Scholar] [CrossRef]
  41. Byrd, J.; Butler, J.F. Effects of temperature on Sarcophaga haemorrhoidalis (Diptera: Sarcophagidae) development. J. Med. Entomol. 1998, 35, 694–698. [Google Scholar] [CrossRef]
  42. Szpila, K.; Mądra, A.; Jarmusz, M.; Matuszewski, S. Flesh flies (Diptera: Sarcophagidae) colonising large carcasses in Central Europe. Parasitol. Res. 2015, 114, 2341–2348. [Google Scholar] [CrossRef] [PubMed]
  43. Frère, B.; Suchaud, F.; Bernier, G.; Cottin, F.; Vincent, B.; Dourel, L.; Lelong, A.L.M.; Arpino, P. GC-MS analysis of cuticular lipids in recent and older scavenger insect puparia. An approach to estimate the postmortem interval (PMI). Anal. Bioanal. Chem. 2013, 406, 1081–1088. [Google Scholar] [CrossRef] [PubMed]
  44. Sukontason, K.; Bunchu, N.; Chaiwong, T.; Moophayak, K.; Sukontason, K.L. Forensically important flesh fly species in Thailand: Morphology and developmental rate. Parasitol. Res. 2010, 106, 1055–1064. [Google Scholar] [CrossRef] [PubMed]
  45. Samerjai, C.; Sukontason, K.L.; Sukontason, K.; Limsopatham, K.; Chareonviriyaphap, T.; Somboon, P.; Tomberlin, J.K.; Sanit, S. Ultrastructure of male terminalia of Boettcherisca peregrina and Boettcherisca nathani (Diptera: Sarcophagidae), flesh fly species of forensic importance. Acta Trop. 2021, 224, 106148. [Google Scholar] [CrossRef]
  46. Wells, J.D.; Stevens, J.R. Application of DNA-based methods in forensic entomology. Annu. Rev. Entomol. 2008, 53, 103–120. [Google Scholar] [CrossRef]
  47. Guo, Y.D.; Cai, J.F.; Li, X.; Xiong, F.; Su, R.-n.; Chen, F.L.; Liu, Q.L.; Wang, X.H.; Chang, Y.F.; Zhong, M.; et al. Identification of the forensically important sarcophagid flies Boerttcherisca peregrina, Parasarcophaga albiceps and Parasarcophaga dux (Diptera: Sarcophagidae) based on COII gene in China. Trop. Biomed. 2010, 27, 451–460. [Google Scholar]
  48. Yè, G.; Li, K.; Zhu, J.; Zhu, G.; Hu, C. Cuticular Hydrocarbon Composition in Pupal Exuviae for Taxonomic Differentiation of Six Necrophagous Flies. J. Med. Entomol. 2007, 44, 450–456. [Google Scholar] [CrossRef]
  49. Zhang, X.; Shang, Y.; Ren, L.; Qu, H.; Zhu, G.; Guo, Y. A Study of Cuticular Hydrocarbons of All Life Stages in Sarcophaga peregrina (Diptera: Sarcophagidae). J. Med. Entomol. 2021, 59, 108–119. [Google Scholar] [CrossRef]
  50. Zhang, X.; Bai, Y.; Ngando, F.J.; Qu, H.; Shang, Y.; Ren, L.; Guo, Y. Predicting the Weathering Time by the Empty Puparium of Sarcophaga peregrina (Diptera: Sarcophagidae) with the ANN Models. Insects 2022, 13, 808. [Google Scholar] [CrossRef]
  51. Ren, L.P.; Shang, Y.J.; Yang, L.; Wang, S.W.; Wang, X.; Chen, S.; Bao, Z.G.; An, D.; Meng, F.M.; Cai, J.F.; et al. Chromosome-level de novo genome assembly of Sarcophaga peregrina provides insights into the evolutionary adaptation of flesh flies. Mol. Ecol. Resour. 2021, 21, 251–262. [Google Scholar] [CrossRef]
  52. Kim, J.Y.; Lim, H.Y.; Shin, S.E.; Cha, H.K.; Seo, J.-H.; Kim, S.-K.; Park, S.H.; Son, G.H. Comprehensive transcriptome analysis of Sarcophaga peregrina, a forensically important fly species. Sci. Data 2018, 5, 180220. [Google Scholar] [CrossRef] [PubMed]
  53. Goff, M.L.; Brown, W.A.; Hewadikaram, K.A.; Omori, A. Effect of heroin in decomposing tissues on the development rate of Boettcherisca peregrina (Diptera, Sarcophagidae) and implications of this effect on estimation of postmortem intervals using arthropod development patterns. J. Forensic Sci. 1991, 36, 537–542. [Google Scholar] [CrossRef] [PubMed]
  54. Goff, M.L.; Omori, A.; Goodbrod, J.R. Effect of cocaine in tissues on the development rate of Boettcherisca peregrina (Diptera: Sarcophagidae). J. Med. Entomol. 1989, 26, 91–93. [Google Scholar] [CrossRef] [PubMed]
  55. Guoxing, W.; Jiaying, Z.; Kai, L.; Xi, G.; Cui, H.; Jiaan, C.; Gong-yin, Y. A proteomic analysis of larval midguts of Boettcherisca peregrina in response to cadmium exposure. Bull. Insectology 2013, 66, 225–229. [Google Scholar]
  56. Chen, L.S. Necrophagous Flies in China; Guizhou Science and Technology Press: Guiyang, China, 2013. [Google Scholar]
  57. Ren, L.; Shang, Y.; Zhang, X.; Chen, S.; Zheng, Y.; Zou, Y.; Qu, Y.; Cai, J.; Zhang, C.; Guo, Y. Temporal Expression Profiles Reveal Potential Targets during Postembryonic Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae). Insects 2022, 13, 453. [Google Scholar] [CrossRef] [PubMed]
  58. Barnes, R.J.; Dhanoa, M.S.; Lister, S.J. Standard Normal Variate Transformation and De-Trending of Near-Infrared Diffuse Reflectance Spectra. Appl. Spectrosc. 1989, 43, 772–777. [Google Scholar] [CrossRef]
  59. Trygg, J.; Wold, S. Orthogonal projections to latent structures (O-PLS). J. Chemom. 2002, 16, 119–128. [Google Scholar] [CrossRef]
  60. Bylesjö, M.; Rantalainen, M.; Cloarec, O.; Nicholson, J.K.; Holmes, E.; Trygg, J. OPLS discriminant analysis: Combining the strengths of PLS-DA and SIMCA classification. J. Chemom. 2006, 20, 341–351. [Google Scholar] [CrossRef]
  61. Wold, S.; Sjöström, M.; Eriksson, L. PLS-regression: A basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2001, 58, 109–130. [Google Scholar] [CrossRef]
  62. Andersen, C.M.; Bro, R. Variable selection in regression—A tutorial. J. Chemom. 2010, 24, 728–737. [Google Scholar] [CrossRef]
  63. Ren, L.P.; Zhang, X.Y.; Li, Y.; Shang, Y.J.; Chen, S.; Wang, S.W.; Qu, Y.H.; Cai, J.F.; Guo, Y.D. Comparative analysis of mitochondrial genomes among the subfamily Sarcophaginae (Diptera: Sarcophagidae) and phylogenetic implications. Int. J. Biol. Macromol. 2020, 161, 214–222. [Google Scholar] [CrossRef] [PubMed]
  64. Kováts, E.; Weisz, P. Über den Retentionsindex und seine Verwendung zur Aufstellung einer Polaritätsskala für Lösungsmittel. Ber. Bunsenges. Phys. Chem. 1965, 69, 812–820. [Google Scholar] [CrossRef]
  65. Espelie, K.E.; Bernays, E.A. Diet-related differences in the cuticular lipids of Manduca sexta larvae. J. Chem. Ecol. 1989, 15, 2003–2017. [Google Scholar] [CrossRef]
  66. Bernier, U.R.; Carlson, D.A.; Geden, C.J. Gas chromatography/mass spectrometry analysis of the cuticular hydrocarbons from parasitic wasps of the genus Muscidifurax. J. Am. Soc. Mass. Spectrom. 1998, 9, 320–332. [Google Scholar] [CrossRef] [PubMed]
  67. Carlson, D.A.; Bernier, U.R.; Sutton, B.D. Elution Patterns from Capillary GC for Methyl-Branched Alkanes. J. Chem. Ecol. 2004, 24, 1845–1865. [Google Scholar] [CrossRef]
  68. Pang, T.; Zhu, S.; Lu, X.; Xu, G. Identification of unknown compounds on the basis of retention index data in comprehensive two-dimensional gas chromatography. J. Sep. Sci. 2007, 30, 868–874. [Google Scholar] [CrossRef] [PubMed]
  69. Krkosová, Z.; Kubinec, R.; Soják, L.; Amann, A. Temperature-programmed gas chromatography linear retention indices of all C4-C30 monomethylalkanes on methylsilicone OV-1 stationary phase. Contribution towards a better understanding of volatile organic compounds in exhaled breath. J. Chromatogr. A 2008, 1179, 59–68. [Google Scholar] [CrossRef]
  70. Park, S.J.; Pandey, G.; Castro-Vargas, C.; Oakeshott, J.G.; Taylor, P.W.; Mendez, V. Cuticular Chemistry of the Queensland Fruit Fly Bactrocera tryoni (Froggatt). Molecules 2020, 25, 4185. [Google Scholar] [CrossRef]
  71. de Paula, M.C.; Michelutti, K.B.; Eulalio, A.D.M.d.M.; Mendonça, A.; Cardoso, C.A.L.; Andrade, L.H.C.; Lima, S.M.; Antonialli-Júnior, W.F. New approach to application of mid-infrared photoacoustic spectroscopy in forensic analysis: Study with the necrophagous blow fly Chrysomya megacephala (Diptera: Calliphoridae). J. Photochem. Photobiol. B Biol. 2020, 209, 111934. [Google Scholar] [CrossRef]
  72. Yu, K.; Wang, G.; Cai, W.; Wu, D.; Wei, X.; Zhang, K.; Liu, R.; Sun, Q.; Wang, Z. Identification of antemortem, perimortem and postmortem fractures by FTIR spectroscopy based on a rabbit tibial fracture model. Spectrochim. Acta. A Mol. Biomol. Spectrosc. 2020, 239, 118535. [Google Scholar] [CrossRef]
  73. Ojeda, J.J.; Romero-González, M.E.; Banwart, S.A. Analysis of bacteria on steel surfaces using reflectance micro-Fourier transform infrared spectroscopy. Anal. Chem. 2009, 81, 6467–6473. [Google Scholar] [CrossRef] [PubMed]
  74. Wu, X.; Liu, S. The effect of temperature change on the development rate of two insects. Insect Knowl. 1994, 31, 237–240. [Google Scholar]
  75. Shi, P.; Ge, F. A comparison of different thermal performance functions describing temperature-dependent development rates. J. Therm. Biol. 2010, 35, 225–231. [Google Scholar] [CrossRef]
  76. Boehme, P.; Spahn, P.N.; Amendt, J.; Zehner, R.E. The analysis of temporal gene expression to estimate the age of forensically important blow fly pupae: Results from three blind studies. Int. J. Leg. Med. 2013, 128, 565–573. [Google Scholar] [CrossRef] [PubMed]
  77. Liu, Z.Y.; Han, H.; Chen, W.; Wang, S.W.; Meng, F.M.; Cai, J.F.; Guo, Y.D. Evaluation of Reference Genes and Age Estimation of Forensically Useful Aldrichina grahami (Diptera: Calliphoridae) During Intrapuparial Period. J. Med. Entomol. 2021, 58, 47–55. [Google Scholar] [CrossRef]
  78. Wang, Y.; Gu, Z.-Y.; Xia, S.-X.; Wang, J.-G.; Zhang, Y.; Tao, L.-Y. Estimating the age of Lucilia illustris during the intrapuparial period using two approaches: Morphological changes and differential gene expression. Forensic Sci. Int. 2018, 287, 1–11. [Google Scholar] [CrossRef]
  79. Lucas, E.R.; Darby, A.C.; Torr, S.J.; Donnelly, M.J. A gene expression panel for estimating age in males and females of the sleeping sickness vector Glossina morsitans. PLoS Negl. Trop. Dis. 2021, 15, e0009797. [Google Scholar] [CrossRef]
  80. Baqué, M.; Amendt, J.; Verhoff, M.A.; Zehner, R.E. Descriptive analyses of differentially expressed genes during larval development of Calliphora vicina (Diptera: Calliphoridae). Int. J. Leg. Med. 2015, 129, 891–902. [Google Scholar] [CrossRef]
  81. Boehme, P.; Spahn, P.N.; Amendt, J.; Zehner, R.E. Differential gene expression during metamorphosis: A promising approach for age estimation of forensically important Calliphora vicina pupae (Diptera: Calliphoridae). Int. J. Leg. Med. 2012, 127, 243–249. [Google Scholar] [CrossRef]
  82. Salzman, J.; Gawad, C.; Wang, P.L.; Lacayo, N.; Brown, P.O. Circular RNAs are the predominant transcript isoform from hundreds of human genes in diverse cell types. PLoS ONE 2012, 7, e30733. [Google Scholar] [CrossRef]
  83. Jeck, W.R.; Sorrentino, J.A.; Wang, K.; Slevin, M.K.; Burd, C.E.; Liu, J.; Marzluff, W.F.; Sharpless, N.E. Circular RNAs are abundant, conserved, and associated with ALU repeats. RNA 2013, 19, 141–157. [Google Scholar] [CrossRef] [PubMed]
  84. Jacques, S.L. Optical properties of biological tissues: A review. Phys. Med. Biol. 2013, 58, R37–R61. [Google Scholar] [CrossRef] [PubMed]
  85. Kendall, C.; Isabelle, M.; Bazant-Hegemark, F.; Hutchings, J.; Orr, L.; Babrah, J.; Baker, R.; Stone, N. Vibrational spectroscopy: A clinical tool for cancer diagnostics. Analyst 2009, 134, 1029–1045. [Google Scholar] [CrossRef] [PubMed]
  86. Jales, J.T.; Barbosa, T.M.; Medeiros, J.R.; Lima, L.A.S.; Lima, K.M.G.; Gama, R.A. Infrared spectroscopy and forensic entomology: Can this union work? A literature review. J. Forensic Sci. 2021, 66, 2080–2091. [Google Scholar] [CrossRef] [PubMed]
  87. Kelly, J.G.; Trevisan, J.; Scott, A.D.; Carmichael, P.L.; Pollock, H.M.; Martin-Hirsch, P.L.; Martin, F.L. Biospectroscopy to metabolically profile biomolecular structure: A mul tistage approach linking computational analysis with biomarkers. J. Proteome Res. 2011, 10, 1437–1448. [Google Scholar] [CrossRef] [PubMed]
  88. Chiriboga, L.A.; Yee, H.T.; Diem, M. Infrared Spectroscopy of Human Cells and Tissue. Part VI: A Comparative Study of Histopathology and Infrared Microspectroscopy of Normal, Cirrhotic, and Cancerous Liver Tissue. Appl. Spectrosc. 2000, 54, 1–8. [Google Scholar] [CrossRef]
  89. Obinaju, B.E.; Martin, F.L. Novel biospectroscopy sensor technologies towards environmental health monitoring in urban environments. Environ. Pollut. 2013, 183, 46–53. [Google Scholar] [CrossRef]
  90. Perez-Mendoza, J.; Dowell, F.E.; Broce, A.B.; Throne, J.E.; Wirtz, R.A.; Xie, F.; Fabrick, J.A.; Baker, J.E. Chronological age-grading of house flies by using near-infrared spectr oscopy. J. Med. Entomol. 2002, 39, 499–508. [Google Scholar] [CrossRef]
  91. Pickering, C.L.; Hands, J.R.; Fullwood, L.M.; Smith, J.A.; Baker, M.J. Rapid discrimination of maggots utilising ATR-FTIR spectroscopy. Forensic Sci. Int. 2015, 249, 189–196. [Google Scholar] [CrossRef]
  92. Ginzel, M.D.; Blomquist, G.J. Insect Hydrocarbons: Biochemistry and Chemical Ecology. In Extracellular Composite Matrices in Arthropods; Springer: Cham, Switzerland, 2016. [Google Scholar]
  93. Gołębiowski, M.; Boguś, M.I.; Paszkiewicz, M.; Stepnowski, P. Cuticular lipids of insects as potential biofungicides: Methods of lip id composition analysis. Anal. Bioanal. Chem. 2011, 399, 3177–3191. [Google Scholar] [CrossRef]
  94. Blomquist, G.J.; Ginzel, M.D. Chemical Ecology, Biochemistry, and Molecular Biology of Insect Hydroc arbons. Annu. Rev. Entomol. 2021, 66, 45–60. [Google Scholar] [CrossRef] [PubMed]
  95. Armold, M.T.; Regnier, F.E. A developmental study of the cuticular hydrocarbons of Sarcophaga bullata. J. Insect Physiol. 1975, 21, 1827–1833. [Google Scholar] [CrossRef] [PubMed]
  96. Moore, H.E.; Pechal, J.L.; Benbow, M.E.; Drijfhout, F.P. The potential use of cuticular hydrocarbons and multivariate analysis to age empty puparial cases of Calliphora vicina and Lucilia sericata. Sci. Rep. 2017, 7, 1933. [Google Scholar] [CrossRef] [PubMed]
  97. Roux, O.; Gers, C.; Legal, L. Ontogenetic study of three Calliphoridae of forensic importance through cuticular hydrocarbon analysis. Med. Vet. Entomol. 2008, 22, 309–317. [Google Scholar] [CrossRef] [PubMed]
  98. Toolson, E.C.; Kuper-Simbrón, R. Laboratory Evolution of Epicuticular Hydrocarbon Composition and Cutic Ular Permeability in Drosophila Pseudoobscura: Effects on Sexual Dimor Phism and Thermal-Acclimation Ability. Evolution 1989, 43, 468–473. [Google Scholar] [CrossRef] [PubMed]
  99. Ferveur, J.-F. Cuticular hydrocarbons: Their evolution and roles in Drosophila pherom onal communication. Behav. Genet. 2005, 35, 279–295. [Google Scholar] [CrossRef]
  100. Tregenza, T.; Buckley, S.H.; Pritchard, V.L.; Butlin, R.K. Inter- and Intrapopulation Effects of Sex and Age on Epicuticular Composition of Meadow Grasshopper, Chorthippus Parallelus. J. Chem. Ecol. 2000, 26, 257–278. [Google Scholar] [CrossRef]
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Figure 1. The specific temperature settings of the artificial climate chamber.
Figure 1. The specific temperature settings of the artificial climate chamber.
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Figure 2. Nonlinear regression of larval body length changes under different temperature conditions. (A represents the fluctuating model A, B represents the fluctuating model B, and C represents the constant model C).
Figure 2. Nonlinear regression of larval body length changes under different temperature conditions. (A represents the fluctuating model A, B represents the fluctuating model B, and C represents the constant model C).
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Figure 3. The pupariation rate, eclosion rate, and pupal weight were lower and statistically significant in groups A and B than they were in group C. (a) Pupariation rate. (b) Eclosion rate. (c) Pupal weight (* represents p < 0.05, ** represents p < 0.01).
Figure 3. The pupariation rate, eclosion rate, and pupal weight were lower and statistically significant in groups A and B than they were in group C. (a) Pupariation rate. (b) Eclosion rate. (c) Pupal weight (* represents p < 0.05, ** represents p < 0.01).
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Figure 4. The expression levels of the six DEGs at constant (group C at 25.75 °C) and fluctuating temperatures (group A at 18–36 °C) in correlation to the intra-puparial age (days) of S. peregrin.
Figure 4. The expression levels of the six DEGs at constant (group C at 25.75 °C) and fluctuating temperatures (group A at 18–36 °C) in correlation to the intra-puparial age (days) of S. peregrin.
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Figure 5. The average FTIR spectra of different intra−puparial development ages (days) in the range of 1800–900 cm−1. (a) Group A, fluctuating temperature. (b) Group C, constant temperature.
Figure 5. The average FTIR spectra of different intra−puparial development ages (days) in the range of 1800–900 cm−1. (a) Group A, fluctuating temperature. (b) Group C, constant temperature.
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Figure 6. Chemometrics was used to analyze the data that the ATR−FTIR study generated. (a,b) OPLS-DA analysis of group A and group C, respectively. (c,d) PLS analysis of group A and group C, respectively.
Figure 6. Chemometrics was used to analyze the data that the ATR−FTIR study generated. (a,b) OPLS-DA analysis of group A and group C, respectively. (c,d) PLS analysis of group A and group C, respectively.
Animals 13 01607 g006
Animals 13 01607 g007 550
Figure 7. Chemometrics was used to analyze the data that the CHCs study generated. (a,b) OPLS−DA analysis of group A and group C, respectively. (c,d) PLS analysis of group A and group C, respectively.
Figure 7. Chemometrics was used to analyze the data that the CHCs study generated. (a,b) OPLS−DA analysis of group A and group C, respectively. (c,d) PLS analysis of group A and group C, respectively.
Animals 13 01607 g007
Table
Table 1. The relationship between the body length (y) (mm) and the time after larviposition (x) (h) of S. peregrina larvae at constant and fluctuating temperatures was described using equations. p values, degrees of freedom (df), and coefficients of determination (R2) are listed.
Table 1. The relationship between the body length (y) (mm) and the time after larviposition (x) (h) of S. peregrina larvae at constant and fluctuating temperatures was described using equations. p values, degrees of freedom (df), and coefficients of determination (R2) are listed.
Temperature (°C)EquationdfpR2
Fluctuating (A)y = 4.21804 − 0.24142 × x+ 0.01422 × x2 − 1.55605 × 10-4 × x3 + 4.88045 × 10-7 × x415<0.0010.99118
Fluctuating (B)y = 3.92763 − 0.21602 × x + 0.01444 × x2 − 1.67742 × 10-4 × x3 + 5.61143 × 10-7 × x413<0.0010.99808
Constant(C)y = 3.70744 − 0.20709 × x + 0.0174 × x2 − 2.25212 × 10-4 × x3 + 8.25105 × 10-7 × x411<0.0010.99991
Table
Table 2. Mean (±SD) development duration (h) of S. peregrina at constant and fluctuating temperatures.
Table 2. Mean (±SD) development duration (h) of S. peregrina at constant and fluctuating temperatures.
Developmental StagesFirstInstarSecondInstarThirdInstarWanderingPupariationTotal Duration
Fluctuating (A)23.7 ± 2.5127.7 ± 3.5170.6 ± 5.137.6 ± 5.85223 ± 11.35382.6 ± 9.07
Fluctuating (B)22.6 ± 2.5725.6 ± 2.0867.6 ± 7.235.6 ± 4.5218.6 ± 5.5370.3 ± 10.7
Constant (C)22.3 ± 2.0823.3 ± 2.0865.3 ± 4.130.3 ± 2.08217.3 ± 6.1358.6 ± 15.04
Table
Table 3. The relationship between the relative gene expression (y) (fold change) and the intra-puparial age (x) (d) of S. peregrine at different temperatures (group A and group C) are described using simulation equations. F values, p values, and the coefficient of determination (R2) are listed.
Table 3. The relationship between the relative gene expression (y) (fold change) and the intra-puparial age (x) (d) of S. peregrine at different temperatures (group A and group C) are described using simulation equations. F values, p values, and the coefficient of determination (R2) are listed.
GeneTemperature (°C)Simulation EquationFpR2
circRNA_2143Ay = 1.3774 + 1.04846 × x+ 0.81578 × x2 − 0.30251 × x3 + 0.02911 × x44.52766<0.0010.93603
Cy = 7.63272 − 12.85128 × x + 6.8054 × x2 − 1.21003 × x3 + 0.07401 × x45.33739<0.0010.92318
circRNA_3489Ay = 0.46214 + 2.31708 × x − 1.10734 × x2 + 0.17956 × x3 − 0.00686 × x40.37142<0.0010.98587
Cy = 2.22275 − 3.19588 × x + 1.62495 × x2 − 0.31026 × x3 + 0.02049 × x44.67951<0.0010.9675
−circRNA_2847Ay = − 4.19518 + 8.98938 × x − 3.86827 × x2 + 0.60917 × x3 − 0.03011 × x40.41914<0.0010.98452
Cy = − 3.59782 + 5.57439 × x − 2.12695 × x2 + 0.31238 × x3 − 0.01444 × x41.89332<0.0010.97677
flnAy = 12.56685 − 29.36167 × x + 23.64982 × x2 − 7.276 × x3 + 0.77481 × x43.52198<0.0010.96925
Cy = − 20.85524 + 38.63413 × x − 21.49614 × x2 + 3.80815 × x3 − 0.05313 × x48.86323<0.0010.91811
UQCRFS1Ay = 2.57914 − 2.57384 × x + 1.15168 × x2 − 0.2 × x3 + 0.01229 × x41.78359<0.0010.94842
Cy = 6.03845 − 4.55755 × x + 1.42931 × x2 − 0.20105 × x3 + 0.0112 × x41.34105<0.0010.98228
COX5AAy = 2.39587 − 2.59247 × x + 1.44986 × x2 − 0.29885 × x3 + 0.02108 × x42.5781<0.0010.93142
Cy = 10.4182 − 11.11295 × x + 4.40356 × x2 − 0.72446 × x3 + 0.04283 × x44.32924<0.0010.89024
Table
Table 4. FTIR spectroscopy identification of major characteristic peaks.
Table 4. FTIR spectroscopy identification of major characteristic peaks.
Baseline Points (cm−1)Wavenumber (cm−1)Infrared Band
1760~17301740Lipid (C = O stretching vibration)
1680~16101640Amide I (C = O stretching)
1580~15101544Amide II (N-H bending coupled to C-N stretching)
1480~14201458C–H bending from CH2 and CH3
1420~13501405C=O vibrations of COO− from free fatty acids, free amino acids and polypeptides
1330–12771309Amide III
1245–12301241CH3–CO Symmetric stretching
1161–10951121C–O, C–OH and P–O vibration
1083–10781080PO2-symmetric stretching
1100~10001041C–O(H) stretching vibration
945~906927C–O or C–OH vibrations from carbohydrates
Table
Table 5. Model evaluation parameters and regression equations.
Table 5. Model evaluation parameters and regression equations.
Model Evaluation Parameters and Regression Equations of ATR-FTIR Study
PLSOPLS-DA
EquationR2RMSE(DAY)R2X (cum)R2Y (cum)Q2 (cum)
Ay = x + 3.356 × 10−70.97260.427110.8010.793
Cy = 1x − 1.2916 × 10−60.91150.768310.7440.707
Model evaluation parameters and regression equations of CHCs study
PLSOPLS-DA
EquationR2RMSE(DAY)R2X (cum)R2Y (cum)Q2 (cum)
Ay = x + 2.489e × 10−70.96540.48370.9730.9270.551
Cy = x + 1.306 × 10−70.91220.76530.9490.8240.44
Table
Table 6. The distribution of three compound classes of S. peregrina intra-puparial tissue at constant and fluctuating temperatures.
Table 6. The distribution of three compound classes of S. peregrina intra-puparial tissue at constant and fluctuating temperatures.
Fluctuating Temperature (Group A)
Compounds1 Day2 Day3 Day4 Day5 Day6 Day7 Day8 Day9 Day
n-alkanes(21) *40.92%28.11%31.42%39.46%39.66%41.51%43.87%45.69%51.19%
branched alkanes(17) *55.61%67.85%63.46%55.47%56.88%54.83%50.41%46.16%39.54%
alkenes(3) *3.47%4.04%5.12%5.07%3.46%3.66%5.72%8.14%9.27%
Constant temperature (group C)
Compounds1 day2 day3 day4 day5 day6 day7 day8 day9 day
n-alkanes(21) *41.54%31.99%31.05%38.17%27.79%37.42%38.40%39.28%47.20%
branched alkanes(17) *52.93%63.94%64.87%58.40%68.29%58.51%53.48%49.31%40.68%
alkenes(3) *5.52%4.07%4.08%3.43%3.92%4.07%8.12%11.41%12.11%
* The number of each compound.
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Shang, Y.; Yang, F.; Ngando, F.J.; Zhang, X.; Feng, Y.; Ren, L.; Guo, Y. Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae) and Intra-Puparial Age Estimation Utilizing Multiple Methods at Constant and Fluctuating Temperatures. Animals 2023, 13, 1607. https://doi.org/10.3390/ani13101607

AMA Style

Shang Y, Yang F, Ngando FJ, Zhang X, Feng Y, Ren L, Guo Y. Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae) and Intra-Puparial Age Estimation Utilizing Multiple Methods at Constant and Fluctuating Temperatures. Animals. 2023; 13(10):1607. https://doi.org/10.3390/ani13101607

Chicago/Turabian Style

Shang, Yanjie, Fengqin Yang, Fernand Jocelin Ngando, Xiangyan Zhang, Yakai Feng, Lipin Ren, and Yadong Guo. 2023. "Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae) and Intra-Puparial Age Estimation Utilizing Multiple Methods at Constant and Fluctuating Temperatures" Animals 13, no. 10: 1607. https://doi.org/10.3390/ani13101607

APA Style

Shang, Y., Yang, F., Ngando, F. J., Zhang, X., Feng, Y., Ren, L., & Guo, Y. (2023). Development of Forensically Important Sarcophaga peregrina (Diptera: Sarcophagidae) and Intra-Puparial Age Estimation Utilizing Multiple Methods at Constant and Fluctuating Temperatures. Animals, 13(10), 1607. https://doi.org/10.3390/ani13101607

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