Transcriptomic signature of cancer cachexia by integration of machine learning, literature mining and meta-analysis

BACKGROUND

Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies.

METHODS

Cachectic mouse muscle transcriptomic datasets of ten different studies were combined and mined by seven attribute weighting models, which analysed both categorical variables and numerical variables. The transcriptomic signature of cancer cachexia was identified by attribute weighting algorithms and was used to evaluate the performance of eleven pattern discovery models. The signature was employed to find the best combination of drugs (drug repurposing) for developing cancer cachexia treatment strategies, as well as to evaluate currently used cachexia drugs by literature mining.

RESULTS

Attribute weighting algorithms ranked 26 genes as the transcriptomic signature of muscle from mice with cancer cachexia. Deep Learning and Random Forest models performed better in differentiating cancer cachexia cases based on muscle transcriptomic data. Literature mining revealed that a combination of melatonin and infliximab has negative interactions with 2 key genes (Rorc and Fbxo32) upregulated in the transcriptomic signature of cancer cachexia in muscle.

CONCLUSIONS

The integration of machine learning, meta-analysis and literature mining was found to be an efficient approach to identifying a robust transcriptomic signature for cancer cachexia, with implications for improving clinical diagnosis and management of this condition.

Splice-disrupt genomic variants in prostate cancer

Background

Splice-disrupt genomic variants are one of the causes of cancer-causing errors in gene expression. Little is known about splice-disrupt genomic variants.

Methods and results

Here, pattern of splice-disrupt variants was investigated using 21,842,764 genomic variants in different types of prostate cancer. A particular attention was paid to genomic locations of splice-disrupt variants on target genes. HLA-A in prostate cancer, MSR1 in familial prostate cancer, and EGFR in both castration-resistant prostate cancer and metastatic castration-resistant had the highest allele frequencies of splice-disrupt variations. Some splice-disrupt variants, located on coding sequences of NCOR2, PTPRC, and CRP, were solely present in the advanced metastatic castration-resistant prostate cancer. High-risk splice-disrupt variants were identified based on computationally calculated Polymorphism Phenotyping (PolyPhen), Sorting Intolerant From Tolerant (SIFT), and Genomic Evolutionary Rate Profiling (GERP) + + scores as well as the recorded clinical significance in dbSNP database of NCBI. Functional annotation of damaging splice-disrupt variants highlighted important cancer-associated functions, including endocrine resistance, lipid metabolic process, steroid metabolic process, regulation of mitotic cell cycle, and regulation of metabolic process. This is the first study that profiles the splice-disrupt genomic variants and their target genes in prostate cancer. Literature mining based variant analysis highlighted the importance of rs1800716 variant, located on the CYP2D6 gene, involved in a range of important functions, such as RNA spicing, drug interaction, death, and urotoxicity.

Conclusions

This is the first study that profiles the splice-disrupt genomic variants and their target genes in different types of prostate cancer. Unravelling alternative splicing opens a new avenue towards the establishment of new diagnostic and prognostic markers for prostate cancer progression and metastasis.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11033-022-07257-9.

Alternative RNA splicing in stem cells and cancer stem cells: Importance of transcript-based expression analysis

Alternative ribonucleic acid (RNA) splicing can lead to the assembly of different protein isoforms with distinctive functions. The outcome of alternative splicing (AS) can result in a complete loss of function or the acquisition of new functions. There is a gap in knowledge of abnormal RNA splice variants promoting cancer stem cells (CSCs), and their prospective contribution in cancer progression. AS directly regulates the self-renewal features of stem cells (SCs) and stem-like cancer cells. Notably, octamer-binding transcription factor 4A spliced variant of octamer-binding transcription factor 4 contributes to maintaining stemness properties in both SCs and CSCs. The epithelial to mesenchymal transition pathway regulates the AS events in CSCs to maintain stemness. The alternative spliced variants of CSCs markers, including cluster of differentiation 44, aldehyde dehydrogenase, and doublecortin-like kinase, α6β1 integrin, have pivotal roles in increasing self-renewal properties and maintaining the pluripotency of CSCs. Various splicing analysis tools are considered in this study. LeafCutter software can be considered as the best tool for differential splicing analysis and identification of the type of splicing events. Additionally, LeafCutter can be used for efficient mapping splicing quantitative trait loci. Altogether, the accumulating evidence re-enforces the fact that gene and protein expression need to be investigated in parallel with alternative splice variants.

Advances in understanding the specificity function of transporters by machine learning

Understanding the underlying molecular mechanism of transporter activity is one of the major discussions in structural biology. A transporter can exclusively transport one ion (specific transporter) or multiple ions (general transporter). This study compared categorical and numerical features of general and specific calcium transporters using machine learning and attribute weighting models. To this end, 444 protein features, such as the frequency of dipeptides, organism, and subcellular location, were extracted for general (n = 103) and specific calcium transporters (n = 238). Aliphatic index, subcellular location, organism, Ile-Leu frequency, Glycine frequency, hydrophobic frequency, and specific dipeptides such as Ile-Leu, Phe-Val, and Tyr-Gln were the key features in differentiating general from specific calcium transporters. Calcium transporters in the cell outer membranes were specific, while the inner ones were general; additionally, when the hydrophobic frequency or Aliphatic index is increased, the calcium transporter act as a general transporter. Random Forest with accuracy criterion showed the highest accuracy (88.88% ±5.75%) and high AUC (0.964 ± 0.020), based on 5-fold cross-validation. Decision Tree with accuracy criterion was able to predict the specificity of calcium transporter irrespective of the organism and subcellular location. This study demonstrates the precise classification of transporter function based on sequence-derived physicochemical features.

A Transcription Regulatory Sequence in the 5' Untranslated Region of SARS-CoV-2 Is Vital for Virus Replication with an Altered Evolutionary Pattern against Human Inhibitory MicroRNAs

Our knowledge of the evolution and the role of untranslated region (UTR) in SARS-CoV-2 pathogenicity is very limited. Leader sequence, originated from UTR, is found at the 5' ends of all encoded SARS-CoV-2 transcripts, highlighting its importance. Here, evolution of leader sequence was compared between human pathogenic and non-pathogenic coronaviruses. Then, profiling of microRNAs that can inactivate the key UTR regions of coronaviruses was carried out. A distinguished pattern of evolution in leader sequence of SARS-CoV-2 was found. Mining all available microRNA families against leader sequences of coronaviruses resulted in discovery of 39 microRNAs with a stable thermodynamic binding energy. Notably, SARS-CoV-2 had a lower binding stability against microRNAs. hsa-MIR-5004-3p was the only human microRNA able to target the leader sequence of SARS and to a lesser extent, also SARS-CoV-2. However, its binding stability decreased remarkably in SARS-COV-2. We found some plant microRNAs with low and stable binding energy against SARS-COV-2. Meta-analysis documented a significant (p < 0.01) decline in the expression of MIR-5004-3p after SARS-COV-2 infection in trachea, lung biopsy, and bronchial organoids as well as lung-derived Calu-3 and A549 cells. The paucity of the innate human inhibitory microRNAs to bind to leader sequence of SARS-CoV-2 can contribute to its high replication in infected human cells.

Fast school closures correspond with a lower rate of COVID-19 incidence and deaths in most countries

School closures have been used as one of the main nonpharmaceutical interventions to overcome the spread of SARS-CoV-2. Different countries use this intervention with a wide range of time intervals from the date of the first confirmed case or death. This study aimed to investigate whether fast or late school closures affect the cumulative number of COVID-19 cases or deaths. A worldwide population-based observational study has been conducted and a range of attributes were weighted using 10 attribute weighting models against the normalized number of infected cases or death in the form of numeric, binominal and polynomial labels. Statistical analysis was performed for the most weighted and the most common attributes of all types of labels. By the end of March 2021, the school closure data of 198 countries with at least one COVID-19 case were available. The days before the first school closure were one of the most weighted factors in relation to the normalized number of infected cases and deaths in numeric, binomial, and quartile forms. The average of days before the first school closure in the lowest quartile to highest quartile of infected cases (Q1, Q2, Q3 and Q4) was -6.10 [95% CI, -26.5 to 14.2], 9.35 [95% CI, 2.16 to 16.53], 17.55 [95% CI, 5.95 to 29.15], and 16.00 [95% CI, 11.69 to 20.31], respectively. In addition, 188 countries reported at least one death from COVID-19. The average of the days before the first school closure in the lowest quartile of death to highest quartile (Q1, Q2, Q3 and Q4) was -49.4 [95% CI, -76.5 to -22.3], -10.34 [95% CI, -30.12 to 9.44], -18.74 [95% CI, -32.72 to -4.77], and -12.89 [95% CI, -27.84 to 2.06], respectively. Countries that closed schools faster, especially before the detection of any confirmed case or death, had fewer COVID-19 cases or deaths per million of the population on total days of involvement. It can be concluded that rapid prevention policies are the main determinants of the countries' success.

Genomic analysis of fluoroquinolone-susceptible phylogenetic group B2 extraintestinal pathogenic Escherichia coli causing infections in cats

Extraintestinal pathogenic Escherichia coli (ExPEC) can cause urinary tract and other types of infection in cats, but the relationship of cat ExPEC to human ExPEC remains equivocal. This study investigated the prevalence of ExPEC-associated sequence types (STs) from phylogenetic group B2 among fluoroquinolone-susceptible cat clinical isolates. For this, 323 fluoroquinolone-susceptible cat clinical E. coli isolates from Australia underwent PCR-based phylotyping and random amplified polymorphic DNA analysis to determine clonal relatedness. Of the 274 group B2 isolates, 53 underwent whole genome sequencing (WGS), whereas 221 underwent PCR-based screening for (group B2) sequence type complexes (STc) STc12, STc73, ST131, and STc372. Group B2 was the dominant phylogenetic group (274/323, 85 %), whereas within group B2 ST73 dominated, according to both WGS (43 % of 53; followed by ST127, ST12, and ST372 [4/53, 8 % each]) and ST-specific PCR (20 % of 221). In WGS-based comparisons of cat and reference human ST73 isolates, cat isolates had a relatively conserved virulence gene profile but were phylogenetically diverse. Although in the phylogram most cat and human ST73 isolates occupied host species-specific clusters within serotype-specific clades (O2:H1, O6:H1, O25:H1, O50/O2:H1), cat and human isolates were intermingled within two serotype-specific clades: O120:H31 (3 cat and 2 human isolates) and O22:H1 (3 cat and 5 human isolates). These findings confirm the importance of human-associated group B2 lineages as a cause of urinary tract infections in cats. The close genetic relationship of some cat and human ST73 strains suggests bi-directional transmission may be possible.

Periodontal disease in free-ranging koalas (Phascolarctos cinereus) from the Mount Lofty Ranges, South Australia, and its association with koala retrovirus infection

BACKGROUND

In northern Australian koala populations (Queensland and New South Wales), periodontal disease (gingivitis and periodontitis) is common while koala retrovirus subtype A is endogenous, with other subtypes transmitted exogenously. Koala retrovirus has been hypothesised to cause immune suppression and may predispose koalas to diseases caused by concurrent infections. In southern Australia populations (Victoria and South Australia) periodontal disease has not been investigated, and koala retrovirus is presumably exogenously transmitted. This study described oral health in South Australian koalas and investigated if an association between periodontal disease and koala retrovirus exists.

METHODS

Oral health was examined for wild-caught koalas from the Mount Lofty Ranges (n = 75). Koala retrovirus provirus was detected in whole blood using nested PCR and proviral load determined with qPCR. Periodontal disease severity was recorded and used to calculate the Final Oral Health Index (0-normal, 24-severe).Results Periodontal disease was observed in 84% (63/75) of koalas; 77% had gingivitis (58/75) and 65% (49/75) had periodontitis. The average Final Oral Health Index was 5.47 (s.d 3.13). Most cases of periodontal disease were associated with the incisors. Koala retrovirus-infected koalas were more likely to present with periodontitis (p = 0.042) and the Final Oral Health Index was negatively correlated with proviral load (ρ = -0.353, p = 0.017).

CONCLUSION

South Australian koalas had a high prevalence of gingivitis and periodontitis. Periodontal disease was more prevalent in the incisors. Exogenous koala retrovirus infection may also facilitate the development of periodontitis by modulation of the immune response to concurrent oral bacterial infections.

MicroRNA-based regulatory circuit involved in sperm infertility

miRNAs (MicroRNAs), known as noncoding and important endogenous factors regulating the expression protein-coding genes, are vital regulators in each biological process. Thus, this study aims to explore the key role of four microRNAs in regulating the spermatogenesis. To conduct this experiment, 55 infertile and fertile men provided the study with the sperm and testicular tissue samples. To study the spermatozoa in terms of the morphology, Diff-Quick was applied. Then, quantitative real-time polymerase chain reaction (RT-PCR) was conducted on samples. Our data indicated that in contrast to the miR-15b, significant increasing of miR-383 and miR-122 occurred in both severe oligoasthenoteratozoospermia (SOAT) and moderate oligoasthenoteratozoospermia (MOAT) compared to normal sperm group (N). In addition, it was observed that miR-15b and miR-122 increased in patients with nonobstructive azoospermia (NOA) compared with obstructive azoospermia (OA) group. Expression levels of target genes including P53, CASPASE-9 and CYCLIN D1 underwent principle changes according to miRNAs expression level. Our finding indicated that miRNAs had essential role in the regulation of spermatogenesis, and their expression altering was associated with sperm abnormalities. Thus, microRNAs can be introduced as useful biomarkers to determine male infertility reasons to choose the effective treatment.

Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models

Sub-clinical bovine mastitis decreases milk quality and production. Moreover, sub-clinical mastitis leads to the use of antibiotics with consequent increased risk of the emergence of antibiotic-resistant bacteria. Therefore, early detection of infected cows is of great importance. The Somatic Cell Count (SCC) day-test used for mastitis surveillance, gives data that fluctuate widely between days, creating questions about its reliability and early prediction power. The recent identification of risk parameters of sub-clinical mastitis based on milking parameters by machine learning models is emerging as a promising new tool to enhance early prediction of mastitis occurrence. To develop the optimal approach for early sub-clinical mastitis prediction, we implemented 2 steps: (1) Finding the best statistical models to accurately link patterns of risk factors to sub-clinical mastitis, and (2) Extending this application from the farms tested to new farms (method generalization). Herein, we applied various machine learning-based prediction systems on a big milking dataset to uncover the best predictive models of sub-clinical mastitis. Data from 364,249 milking instances were collected by an electronic automated in-line monitoring system where milk volume, lactose concentration, electrical conductivity (EC), protein concentration, peak flow and milking time for each sample were measured. To provide a platform for the application of the models developed to other farms, the Z transformation approach was employed. Following this, various prediction systems [Deep Learning (DL), Naïve Bayes, Generalized Liner Model, Logistic Regression, Decision Tree, Gradient-Boosted Tree (GBT) and Random Forest] were applied to the non-transformed milking dataset and to a Z-standardized dataset. ROC (Receiver Operating Characteristics Curve), AUC (Area Under The Curve), and high accuracy demonstrated the high sensitivity of GBT and DL in detecting sub-clinical mastitis. GBT was the most accurate model (accuracy of 84.9%) in prediction of sub-clinical bovine mastitis. These data demonstrate how these models could be applied for prediction of sub-clinical mastitis in multiple bovine herds regardless of the size and sampling techniques.

Oxalate-degrading bacteria, including Oxalobacter formigenes, colonise the gastrointestinal tract of healthy koalas (Phascolarctos cinereus) and those with oxalate nephrosis

BACKGROUND

Koalas in the Mount Lofty Ranges, South Australia, have a high prevalence of oxalate nephrosis, or calcium oxalate kidney crystals. Gastrointestinal tract oxalate-degrading bacteria, particularly Oxalobacter formigenes, have been identified in other animal species and humans, and their absence or low abundance is postulated to increase the risk of renal oxalate diseases. This study aimed to identify oxalate-degrading bacteria in the gastrointestinal tract of koalas and determine their association with oxalate nephrosis.

METHODS

Caecal and faecal samples were collected at necropsy from 22 Mount Lofty Ranges koalas that had been euthanased on welfare grounds, with 8 koalas found to have oxalate nephrosis by renal histopathology. Samples were analysed by PCR for the oxc gene, which encodes oxalyl-CoA decarboxylase, and also by Illumina sequencing of the V3-V4 region of the bacterial 16S rRNA gene.

RESULTS

The oxc gene was detected in 100% of koala samples, regardless of oxalate nephrosis status. Oxalobacter formigenes was detected in all but one faecal sample, with no difference in abundance between koalas affected and unaffected by oxalate nephrosis. Other species of known oxalate-degrading bacteria were infrequently detected.

CONCLUSION

This is the first study to identify Oxalobacter and other oxalate-degrading bacterial species in koalas, but an association with oxalate nephrosis and absence or low abundance of Oxalobacter was not found. This suggests other mechanisms underlie the risk of oxalate nephrosis in koalas.

Non-coding and coding genomic variants distinguish prostate cancer, castration-resistant prostate cancer, familial prostate cancer, and metastatic castration-resistant prostate cancer from each other

A considerable number of deposited variants has provided new possibilities for knowledge discovery in different types of prostate cancer. Here, we analyzed variants located on 3'UTR, 5'UTR, CDs, Intergenic, and Intronic regions in castration-resistant prostate cancer (8496 variants), familial prostate cancer (3241 variants), metastatic castration-resistant prostate cancer (3693 variants), and prostate cancer (16599 variants). Chromosome regions 10p15-p14 and 2p13 were highly enriched (P < 0.00001) for variants located in 3'UTR, 5'UTR, CDs, intergenic, and intronic regions in castration-resistant prostate cancer. In contrast, 10p15-p14, 10q23.3, 12q13.11, 13q12.3, 1q25, and 8p22 regions were enriched (P < 0.001) in familial prostate cancer. In metastatic castration-resistant prostate cancer, 10p15-p14, 10q23.3, 11q22-q23, 14q21.1, and 14q32.13 were highly variant regions (P < 0.001). Chromosome 2 and chromosome 1 hosted many enriched variant regions. AKR1C3, BRCA1, BRCA2, CHGA, CYP19A1, HOXB13, KLK3, and PTEN contained the highest number of 3'UTR, 5'UTR, CDs, Intergenic, and Intronic variants. Network analysis showed that these genes are upstream of important functions including prostate gland development, tumor recurrence, prostate cancer-specific survival, tumor progression, cancer mortality, long-term survival, cancer recurrence, angiogenesis, and AR. Interestingly, all of EGFR, JAK2, NR3C1, PDZD2, and SEMA3C genes had single nucleotide polymorphisms (SNP) in castration-resistant prostate cancer, consistent with high selection pressure on these genes during drug treatment and consequent resistance. High occurrence of variants in 3'UTRs suggests the importance of regulatory variants in different types of prostate cancer; an area that has been neglected compared with coding variants. This study provides a comprehensive overview of genomic regions contributing to different types of prostate cancer.

Tissue-specific transcriptional biomarkers in medicinal plants: Application of large-scale meta-analysis and computational systems biology

Biosynthesis of secondary metabolites in plant is a complex process, regulated by many genes and influenced by several factors. In recent years, the next-generation sequencing (NGS) technology and advanced statistical analysis such as meta-analysis and computational systems biology have provided novel opportunities to overcome biological complexity. Here, we performed a meta-analysis on publicly available transcriptome datasets of twelve economically significant medicinal plants to identify differentially expressed genes (DEGs) between shoot and root tissues and to find the key molecular features which may be effective in the biosynthesis of secondary metabolites. Meta-analysis identified a total of 880 genes with differential expression between two tissues. Functional enrichment and KEGG pathway analysis indicated that the functions of those DEGs are highly associated with the developmental process, starch metabolic process, response to stimulus, porphyrin and chlorophyll metabolism, biosynthesis of secondary metabolites and phenylalanine metabolism. In addition, systems biology analysis of the DEGs was applied to find protein-protein interaction network and discovery of significant modules. The detected modules were associated with hormone signal transduction, transcription repressor activity, response to light stimulus and epigenetic processes. Finally, analysis was extended to search for putative miRNAs that are associated with DEGs. A total of 31 miRNAs were detected which belonged to 16 conserved families. The present study provides a comprehensive view to better understand the tissue-specific expression of genes and mechanisms involved in secondary metabolites synthesis and may provide candidate genes for future researches to improve yield of secondary metabolites.

Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis

Plant root symbiosis with Arbuscular mycorrhizal (AM) fungi improves uptake of water and mineral nutrients, improving plant development under stressful conditions. Unraveling the unified transcriptomic signature of a successful colonization provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonization and its regulating transcription factors in roots of Medicago truncatula. Expression profiles of roots in response to AM species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalization steps: Robust Multi-array Average algorithm, Z-standardization, and quartiling normalization. Then, expression profile of 33685 genes in 18 root samples of Medicago as numerical features, as well as study ID and Arbuscular mycorrhiza type as categorical features, were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, Chi Squared, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated in response to AM (Z-value difference > 0.5). Feature weighting models also documented that this signature is independent from study (batch) effect. The AM inoculation signature obtained was able to differentiate efficiently between AM inoculated and non-inoculated samples. The AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes identified in the signature. We found high correspondence between the AM colonization signature obtained in this study and independent RNA-seq experiments on AM colonization, validating the repeatability of the colonization signature. Promoter analysis of upregulated genes in the transcriptomic signature led to the key regulators of AM colonization, including the essential transcription factors for endosymbiosis establishment and development such as NF-YA factors. The approach developed in this study offers three distinct novel features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalization steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for the transcriptomic signature which contributes to robustness of the signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings.

Novel approach for identification of influenza virus host range and zoonotic transmissible sequences by determination of host-related associative positions in viral genome segments

Background

Recent (2013 and 2009) zoonotic transmission of avian or porcine influenza to humans highlights an increase in host range by evading species barriers. Gene reassortment or antigenic shift between viruses from two or more hosts can generate a new life-threatening virus when the new shuffled virus is no longer recognized by antibodies existing within human populations. There is no large scale study to help understand the underlying mechanisms of host transmission. Furthermore, there is no clear understanding of how different segments of the influenza genome contribute in the final determination of host range.

Methods

To obtain insight into the rules underpinning host range determination, various supervised machine learning algorithms were employed to mine reassortment changes in different viral segments in a range of hosts. Our multi-host dataset contained whole segments of 674 influenza strains organized into three host categories: avian, human, and swine. Some of the sequences were assigned to multiple hosts. In point of fact, the datasets are a form of multi-labeled dataset and we utilized a multi-label learning method to identify discriminative sequence sites. Then algorithms such as CBA, Ripper, and decision tree were applied to extract informative and descriptive association rules for each viral protein segment.

Result

We found informative rules in all segments that are common within the same host class but varied between different hosts. For example, for infection of an avian host, HA14V and NS1230S were the most important discriminative and combinatorial positions.

Conclusion

Host range identification is facilitated by high support combined rules in this study. Our major goal was to detect discriminative genomic positions that were able to identify multi host viruses, because such viruses are likely to cause pandemic or disastrous epidemics.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3250-9) contains supplementary material, which is available to authorized users.

Indirect somatic embryogenesis from petal explant of endangered wild population of Fritillaria imperialis

Fritillaria imperialis is an endangered bulbous plant and therefore in vitro micropropagation of this plant will have a great importance for germplasm conservation and commercial production. Petal explants, for the first time, were cultured on media containing various concentrations of plant growth regulators. In addition, the effects of cold pretreatment and light on induction and regeneration of somatic embryogenesis trough callus were studied in detail. Cold pretreatment had inhibitory effects on somatic embryogenesis pathway. Among the different combinations of 6-Bnzylaminopurine (BAP), alpha-naphthaleneacetic acid (NAA) and indole-3-aceticacid (IAA) tested, B5 medium supplemented with 0.1 mg L(-1) BAP + 0.6 mg L(-1) NAA + 0.4 mg L(-1) IAA was the best treatment for bulblet production (6 bulblets per somatic embryogenesis callus). This research presents petal as a reliable material for micropropagation and germplasm conservation of Fritillaria imperialis.