The incidence and mortality rate of cancer has been quickly increasing in the past decades. At present, cancer has become the leading cause of death worldwide. Most of the cancers cannot be effectively diagnosed at the early stage. Although there are multiple therapeutic treatments, including surgery, radiotherapy, chemotherapy, and targeted drugs, their effectiveness is still limited. The overall survival rate of malignant cancers is still low. It is necessary to further study the mechanisms for malignant cancers, and explore new biomarkers and targets that are more sensitive and effective for early diagnosis, treatment, and prognosis of cancers than traditional biomarkers and methods. Long non-coding RNAs (lncRNAs) are a class of RNA transcripts with a length greater than 200 nucleotides. Generally, lncRNAs are not capable of encoding proteins or peptides. LncRNAs exert diverse biological functions by regulating gene expressions and functions at transcriptional, translational, and post-translational levels. In the past decade, it has been demonstrated that the dysregulated lncRNA profile is widely involved in the pathogenesis of many diseases, including cancer, metabolic disorders, and cardiovascular diseases. In particular, lncRNAs have been revealed to play an important role in tumor growth and metastasis. Many lncRNAs have been shown to be potential biomarkers and targets for the diagnosis and treatment of cancers. This review aims to briefly discuss the latest findings regarding the roles and mechanisms of some important lncRNAs in the pathogenesis of certain malignant cancers, including lung, breast, liver, and colorectal cancers, as well as hematological malignancies and neuroblastoma. It was estimated that there were about 18.1 million newly diagnosed cancer cases and about 9.6 million cancer-related deaths worldwide in 2018 . Becausehttp://cpc2.cbi.pku.edu.cn [http://www.bioinfo.org/software/cnci), CPAT (http://lilab.research.bcm.edu/cpat/index.php), ESTScan (http://estscan.sourceforge.net/), PLEK (https://sourceforge.net/projects/plek/files/), PORTRAIT (http://bioinformatics.cenargen.embrapa.br/portrait), FEELnc (https://github.com/tderrien/FEELnc), TransDecoder (http://trinityrnaseq.sf.net), and GeneMarkS-T (http://topaz.gatech.edu/GeneMark/license_download.cgi); CPAT and ESTScan can also provide a web server [http://www.cuilab.cn/gic/) [Long non-coding RNAs (lncRNAs) are a class of RNA transcripts with a length larger than 200 nucleotides. Generally, lncRNAs do not encode proteins or peptides. In addition to the size of other classes of non-coding RNAs , lncRNAs also have secondary and three-dimentional structures which enable them to have both RNA- and protein-like functions . LncRNAsu.edu.cn , and canb server . Moreovecn/gic/) .cis-regulation or trans-regulation [It has been shown that the majority of lncRNAs are localized in the nucleus , but somgulation . It has gulation . So far,gulation ,15, and gulation ,18,19,20LncRNAs are a class of RNA transcripts with a length greater than 200 bp, characterized by more spatial and temporal specificity and lower interspecific conservation when compared with mRNAs . LncRNAsFirstly, lncRNAs play a crucial role in regulating gene expression though chromatin modification and remodeling, histone modification, and nucleosome localization changes . Among tFurthermore, microRNAs (miRNAs) inhibit the translation efficiency and/or induce mRNA degradation of the target gene by complementary pairing with the target RNA, regulating gene expression at the post-transcriptional level. There is a type of competitive endogenous RNA (ceRNA) that can indirectly regulate the expression of target genes via competitively binding with miRNAs; this competitive binding of miRNAs is also called microRNA sponges ,38. ManyLC is the leading cause of cancer-related deaths, accounting for 18.4% of total cancer-related deaths and taking the first place with an incidence rate of 11.6% among males and females . LC is pMALAT1, also known as nuclear-enriched abundant transcript 2 (NEAT2), is an 8.7 kb intergenic lncRNA located on chromosome 11q13 , and is Several studies have suggested that MALAT1 could serve as a potential prognostic biomarker and therapeutic target for patients with early-stage LC due to its specificity and stability ,49,50,51Functionally, MALAT1 is considered as an oncogenic lncRNA because of its role in promoting tumor differentiation, proliferation, migration, invasion, EMT, and chemoresistance ,41. SileH19 is a 2.3 kb intergenic and maternally-expressed lncRNA located on chromosome 11p15.5, which is also one of the first identified imprinting lncRNA ,41,46,63Studies have revealed that the expression of H19 is significantly increased in LC tissues when compared with that in adjacent tissues ,66. H19 H19 plays an important role in promoting cell proliferation and differentiation, cell migration and invasion, and EMT . Zhou etTUG1 is a 7.1 kb intergenic lncRNA located on chromosome 22q12 ,79,80. TTUG1 is upregulated in the majority of the above cancer tissues but downregulated in LSCC and LAD tissues ,82,83,84TUG1 expression is induced by the wild-type p53 in three NSCLC cell lines , and TUG1 knockdown increases cell proliferation in vitro and in vivo ,84. AddiMEG3 is 6.9 kb in length and acts as a tumor suppressor gene located on chromosome 14q32.2 within the DLK1-MEG3 locus ,88,89. MMEG3 is significantly downregulated in LC cell lines A549 and HCC823 . UpregulThe drug resistance to chemotherapy drug vincristine is negatively associated with the expression of MEG3 in vitro . UpregulAFAP1-AS1 is a 6.8 kb antisense lncRNA, which is located on chromosome 4p16.1. AFAP1-AS1 is transcribed from AFAP1 in the antisense direction and can also affect the expression of AFAP1 . AFAP1-AAFAP1-AS1 expression is positively correlated with tumor pathological grade, TNM staging, smoking history, infiltration degree, distant metastasis, and clinical outcomes in NSCLC patients ,100,101.AFAP1-AS1 is upregulated in NSCLC tissues and cell lines ,106,107.According to the Global Cancer Statistics 2018, the incidence of BC in both sexes is 11.6%, which is almost as much as that of LC . HerceptHOTAIR is located on human chromosome 12q13.13, and is a 2158 bp intergenic lncRNA situated inside the Homeobox C (HOXC) gene. HOTAIR is among the first lncRNAs reported to be involved in the development of cancer . HOTAIR The expression of HOTAIR is important for BC cell growth, and loss of HOTAIR induces apoptosis in MCF-7 cells . The proHOTAIR plays a critical role in regulating BC cell apoptosis, proliferation, migration, and invasion. Knockdown of HOTAIR suppresses cell proliferation and promotes apoptosis in several BC cell lines ,130. MorIn the MDA-MB-231 BC cell line, overexpression of HOTAIR promotes the expression of stemness-related genes such as SOX1, SOX10, aldehyde dehydrogenase 2 family member (ALDH2), and CD44 . When coGAS5 is localized on chromosome 1q25.1, and was originally isolated from the NIH 3T3 cell line . AlthougMiR-21 is an important oncogene in various malignant tumors ,152,153.GAS5 is associated with drug and chemotherapy resistance in BC. Trastuzumab administration is a therapy method in clinic to treat HER2-positive, early-stage, and metastatic BC, but the effectiveness is limited . The levThe expression of H19 is remarkably increased in BC tissues ,160 and Abolished H19 expression suppresses cell survival and blocks estrogen-induced cell growth in BC cells . KnockdoH19 has been also reported to be involved in chemoresistance in BC. In paclitaxel-resistant TNBC cells, the expression of level H19 is significantly upregulated, and knockdown of lncRNA H19 enhances paclitaxel sensitivity and promotes apoptosis by suppressing the Akt signaling pathway in TNBC cells . The expUCA1 is a 2314 bp lncRNA composed of three exons and two introns located in human chromosome 19p13.12 ,179. UCAP27 (Kip1) is a tumor suppresser, and its level is decreased in BC tissues . UCA1 coTamoxifen is usually used to treat ER-positive BC patients, but the resistance of tamoxifen is still a question to be resolved . When coLincRNA-ROR is a 2.6 kb lncRNA, which is located at 18q21.31 ,198. The+/CD44+/CD24− MCF10DCIS stem cell population to regulate self-renewal of CSCs in BC [In TNBC tissues ,203 and Cs in BC .Silencing of lincRNA-ROR inhibits BC cell proliferation, increases sensitivity to tamoxifen, and represses the activation of the PI3K/Akt/mTOR signaling pathway in MDA-MB-231 cell . InhibitLiver cancer is the sixth most common malignancy worldwide, and the mortality rate is strikingly high. In 2018, there were approximately 782,000 deaths worldwide, which accounted for 8.2% of all cancer deaths . AlthougHOTAIR has been considered to be a risk factor of HCC ,208. In Inhibition of HOTAIR represses the proliferation, migration, and invasion of HCC cells ,211,213.HULC is located on chromosome 6p24.3, which is firstly characterized as a novel mRNA-like lncRNA, and HULC is shown to be significantly upregulated in human HCC . A high Knockdown of HULC in HCC cell lines represses cell proliferation, invasion, and migration, and promotes cell apoptosis , whereasHepatitis B Virus X protein (HBx) plays vital roles in the development of HCC. The expression levels of HULC are positively related with HBx in HCC patients, and HBx can induce the expression of HULC . AnotherNEAT1 is located on chromosome 11q13.1. NEAT1 has two transcription variants, which are termed as NEAT1_1 and NEAT1_2, respectively. Both of them are located in nuclear paraspeckles and constitute an important structure of paraspeckles . When coIn one study, it was reported that although cell growth is not affected after knockdown of NEAT1 in HepG2, cell invasion is significantly decreased . Ling et+T cell apoptosis and the cytolysis activity in HCC patients [The balance between NEAT1_1 and NEAT1_2 may take part in the process of HCC cell malignant growth and metastasis . Silencipatients . CollectSNHG1 is located in the 11q12.3 region of the chromosome. Both public microarray data and a cohort study discovered that SNHG1 levels are significantly increased in HCC tissues, and the high expression of SNHG1 is positively associated with tumor size ,254. A mIn HepG2 cells, the expression level of SNHG1 is remarkably increased, and the high level of SNHG1 downregulates miR-195 to promote HCC cell proliferation, invasion, and migration . AnotherP21 is a canonical transcriptional target of p53 and plays a critical role in cell cycle and tumorigenensis . lincRNAIn different tumor cell lines, lincRNA-p21 is induced by p53. lincRNA-p21 can interact with hnRNP-K to be involved in the apoptosis regulated by p53 . UpregulCRC is one of the most common gastrointestinal tumors, with the second highest incidence among females (9.5%) and the third among males 10.9%) [0.9% 1,2. About 1CCAT1 is a newly discovered oncogenic lncRNA, 2628 bp in length, which is located on chromosome 8q24.21 ,273. CCAMechanistically, CCAT1 expression is considered to be associated with cell cycle, proliferation, apoptosis, migration, invasion, and EMT process in multiple cells . OverexpCCAT2 is a 1752 bp lncRNA located on chromosomal region 8q24.21 ,289,290.Overexpression of CCAT2 in CRC cells could promote tumor growth, migration, and invasion in vitro and vivo and also induce chromosomal instability in vitro . There iCRNDE is located on chromosome 16, and contains six exons: E1–E6 . CRNDE iCRNDE is significantly upregulated in both CRC tissues and cell lines compared with that in adjacent tissues and normal colon cell line . CRNDE kA research clarified that the level of UCA1 in CRC tissues is positively correlated with CRC tumor size and advanced tumor stages . A meta-UCA1 is demonstrated to be involved in the process of CRC tumorigenesis and progression, including cell proliferation, cell cycle, autophagy, apoptosis, and drug resistance ,310. UCAHematological malignancies are one of the most common types of malignant tumors that are genetically divided into acute leukemia (AL), myelodysplastic syndromes (MDS), myeloproliferative neoplasms (MPNs), chronic lymphocytic leukemia (CLL), and lymphoma . AccordiPVT1 is an oncogenic lncRNA located at chromosome 8q24.2 downstream of c-Myc, and this region is thought to be cancer-related because of its function with c-Myc and rearrangement ,317,318.Zeng et al. reported that PVT1 level in peripheral blood samples from patients with acute promyelocytic leukemia (APL) is significantly upregulated when compared with healthy donors, which indicates that PVT1 may act as a novel biomarker for APL diagnosis . IzadifaLncRNA DLEU1 and DLEU2 are located in the region of chromosome 13q14.3 ,333, andLee et al. found that knockdown of DLEU1 in the human Burkitt lymphoma (BL) cell line results in a significant increase in cell proliferation and a reduction in apoptosis when compared to WT cells. Conversely, overexpression of DLEU1 in Raji cells causes a significant decrease in cell proliferation and an increase in apoptosis compared to control cells. The authors further showed that DLEU1 knockdown leads to a significant upregulation of anti-apoptotic genes and a downregulation of pro-apoptotic genes . AdditioResearches have shown that DLEU1, DLEU2, and embedded miRNA cluster miR-15a/16-1 are frequently deleted in CLL, MM, mantle cell lymphoma (MCL), and some types of solid tumors ,343,344.MEG3 is significantly downregulated in AML patients’ tissue samples and cell lines when compared to normal controls, particularly in the Wilms’ tumor 1 (WT1) or ten–eleven translocation 2 (TET2) mutant AML patients ,345,346.Huang et al. reported that the expression of MALAT1 in patients with AML-M5 is significantly increased when compared with healthy controls. The OS of AML-M5 patients with high MALAT1 expression is significantly decreased compared with that of AML-M5 patients with low MALAT1 expression . UpregulThe expression of MALAT-1 in patients with MM and MM cell lines is significantly higher than that in normal controls ,353,354.The expression of MALAT-1 is significantly higher in diffuse large B cell lymphoma (DLBCL) cell lines than that in normal controls . MALAT1 The expression of HOTAIR is significantly upregulated in DLBCL tissues and cell lines when compared with normal controls. DLBCL patients with higher expression of HOTAIR have poorer OS . KnockdoHOTAIR expression level is tested to be significantly upregulated in BM/peripheral white blood cell (PB) samples from AML patients when compared with AML-complete remission (CR) patients and healthy controls. Its level is markedly downregulated in AML patients after treatment ,360,361.HOTAIR can interact with miR-143, regulating CML cell proliferation, knockdown of HOTAIR, or overexpression of miR-143, inhibiting the phosphorylation of the PI3K/AKT/mTOR signaling pathway . MoreoveNB is the most common pediatric extracranial solid tumor, which derives from primitive sympathetic neural precursors, and accounts for more than 7–10% of all pediatric cancers and 15% of all pediatric cancer deaths ,367,368.Under hypoxic conditions, the expression of MALAT1 in NB cells promotes endothelial cell migration, invasion, and vasculature formation . MALAT1 The expression of SNHG7 is markedly higher in NB tissues than that in non-tumor tissues . Kaplan–Cancer susceptibility 15 (CASC15) and neuroblastoma associated transcript 1 (NBAT1) are tumor suppressors that are located at the NB risk-associated 6p22.3 locus ,380. CASLncRNA Paupar is transcribed from 8.5 kb upstream of the paired box 6 (Pax6). Reports have shown that Paupar knockdown can regulate the expression of multiple genes related to cell cycle, DNA replication, and mitosis, induce elevation of cell count in S and G2 phases, and increase neurite outgrowth in N2A cells . Paupar LINC01105 and hyperpolarization activated cyclic nucleotide gated potassium channel 3 (HCN3) are significantly upregulated, whereas MEG3 is downregulated in NB tissues when compared to control tissues . It has Kaplan–Meier curves analyses have shown that NB patients with high expression of Ets-1 promoter–associated noncoding RNA (pancEts-1) and hnRNPK are associated with poor OS and EFS. Significant higher pancEts-1 and hnRNPK transcription levels can be observed in primary NB tumors than that in normal dorsal ganglia tissues, and upregulation of pancEts-1 and hnRNPK is also correlated with NB cases with poor differentiation and advanced International Neuroblastoma Staging System (INSS) stage. Mechanistically, pancEts-1 can promote NB cell growth, invasion, and metastasis in vitro and in vivo through β-catenin transactivation and other mechanisms .Zhang et al. found that X inactive specific transcript (XIST) is markedly upregulated in NB tissues when compared with that in control tissues. XIST can stimulate NB cell proliferation, migration, and invasion by epigenetically downregulating dickkopf WNT signaling pathway inhibitor 1 (DKK1) expression through induction of H3 histone methylation via EZH2 .Knockdown of lncRNA myocardial infarction associated transcript (MIAT) reduces cell growth and migration, and enhances apoptosis in NB cell line when compared with that in control . AtmadibOverall, multiple lncRNAs have been shown to be involved in the pathogenesis of NB. Further study is needed to explore the distinct roles and mechanisms of lncRNAs in the development of NB. Moreover, whether lncRNAs could serve as plasma biomarkers for predicting the risk of NB also awaits exploration. LncRNAs play an important role in the pathogenesis of various malignant cancers by promoting cell proliferation, migration, and invasion at transcriptional, translational, and post-translational levels. In particular, several lncRNAs play important roles in the pathogenesis of various cancers, suggesting that they represent a new class of targets for treating different cancers , Table 2MALAT1 plays an important role in the development of various cancers by modulating cell proliferation, migration and apoptosis. The symbol “↑” means activation, and symbol “⊥” represents repression.HOTAIR plays an important role in the pathogenesis of various cancers by multiple signaling pathways. Targeting HOTAIR represents a novel strategy for treating various cancers. The symbol “↑” means activation, and symbol “⊥” represents repression.


Nail abnormalities are an indicator of systemic disease but are always neglected during clinic visit. Here, we report a rare case of Muehrcke’s lines accompany with normal range serum albumin after chemotherapy, unlike the hypoalbuminemia in most cases, which suggest malnutrition is not the only cause of Muehrcke’s lines. The patient was treated with cyclophosphamide, doxorubicin, and 5‐fluorouracil chemotherapy in our hospital. After the fifth cycle of chemotherapy, Muehrcke’s lines were presented on the fingernails  with invasion to adipose tissues and axillary lymph nodes cancer 10/31), 0/31, pT2Muehrcke’s lines are the two symmetrical, narrow, transverse, horizontal, nonpalpable white lines on the fingernails that span the entire breadth of the nail and run parallel to the edge of the lunula. Compression of the digits results in temporary fading of the lines. Because the lines are in the nail bed and do not move with the growth of the fingernails. Normal pink nail bed tissue can be seen between the two white lines and thumb involvement is rare. These features are different from Mees’ lines.XC: diagnosed the case, took photo and wrote the manuscript. PZ and JZ: had advisory roles in the management of the patient.None declared.


Xanthohumol (XN) and four minor hops prenylflavonoids: α,β-dihydroxanthohumol (2HXN), isoxanthohumol (IXN), 8-prenylnaringenin (8PN), and 6-prenylnaringenin (6PN), were tested for antiproliferative activity towards human cancer and normal cell lines. Nonprenylated naringenin (NG) was used as a model compound. Xanthohumol, α,β-dihydroxanthohumol and 6-prenylnaringenin were the most active compounds. Xanthohumol exhibited higher antiproliferative activity than cisplatin (CP) against five cancer cell lines: ovarian resistant to cisplatin A2780cis, breast MDA-MB-231 and T-47D, prostate PC-3, and colon HT-29. Isoxanthohumol was more potent than cisplatin against breast cancer cell lines MDA-MB-231 and T-47D whereas 6-prenylnaringenin was stronger than cisplatin against breast cancer cell line T-47D. It was found that tested chalcones possessed highly selective antiproliferative activity towards all tested breast cancer lines compared to the normal breast MCF 10A cell line . Low antiproliferative activity of naringenin indicates the importance of the prenyl group with respect to antiproliferative activity. Hop cones (hops) are a rich source of prenylated flavonoids. The major flavonoid found in hops with 0.1–1% of dry weight is xanthohumol (XN), the chalcone with a very broad spectrum of biological activities, such as antioxidant, anti-inflammatory and anticancer ones, suggesting a potential chemo-preventive effect ,3.Other prenylated flavonoids occurred at 10–100 fold lower concentrations than xanthohumol and, therefore, they were studied less intensely with the exception of 8-prenylnaringenin (8PN)—the most potent phytoestrogen so far known .Although xanthohumol is the major flavonoid found in hops, during wort boiling it undergoes thermal isomerisation to isoxanthohumol (IXN), which becomes the major hop flavonoid in beer (500 µg/L–3.44 mg/L)  or intesSo as to evaluate the effects of xanthohumol, isoxanthohumol and 8-prenylnaringenin on breast cancer tissue, Bolca and co-workers assessed their concentration, nature of metabolites, and biodistribution and compared them to 17β-estradiol . Phase IThe most common treatment for breast cancer patients involves using doxorubicin and docetaxel. However, therapeutic effects of these anticancer drugs are frequently accompanied by harmful side effects and also chemoresistance of cancer cells is often observed. Hence, there is a constant search for new effective and safe drugs . A part Delmulle and co-workers studied the antitumor activity of prenylated flavonoids against two prostate cancer cell lines: PC-3 and DU 145. In the case of PC-3 cells treated with isoxanthohumol and 6-prenylnaringenin and DU 145 cells treated with IXN, 8PN and 6PN, the formation of vacuoles was observed. The results showed that IXN, 6PN and 8PN, but not XN, induced a caspase-independent form of cell death and seem to be promising drugs for cancer therapy  for A-2780 to 187.10 ± 72.41 [µM] for MCF7), but its selectivity towards cancer cell lines compared to two normal cell lines (with the exception of PC-3 and MDA-MB-231) was within the range of selectivity index SI = 0.88–1.87. Naringenin  isoxanthohumol was more active than 8-prenylonaringenin. For the rest of the cell lines the cytotoxic activity of both compounds was similar. Comparing the sensitivity of all investigated cell lines to tested flavonoids one can observe that except for the hydrogenation of the α,β-double bond in xanthohumol, most of the structural modifications, such as isomerization of XN to IXN or demethylation of IXN to 8PN led to reduction of the cytostatic activity. Among the tested compounds, cisplatin exhibited higher antiproliferative activity only against three of the examined cancer cell lines: MCF7, A2780 and DU 145, and against one of the lines (HT-29), the activity of cisplatin was comparable to xanthohumol. However, this reference compound showed very high antiproliferative against normal human lung microvascular endothelial cells (HLMEC) and normal breast epithelial cell line (MCF 10A), which was 143 and 126-fold higher, respectively, than for tested flavonoids.50 for a normal cell line  to IC50 value for the respective cancerous cell line using the following equation:Commonly used anticancer drugs, such as cisplatin or doxorubicin have a big disadvantage of being toxic to both cancer and normal cells. Hence, to solve this problem, there is a search for new effective and safe pharmaceuticals. The selective action of a drug can be expressed by the selectivity index (SI), which is determined by comparing the cytotoxic activity of each compound to each cancerous cell line with that to a normal cell line. SI was calculated as a ratio of ICThe SI values higher than 1.0 indicate the compounds of considerable anticancer specificity, and SI much greater than 1.0—highly selective ones.A and SIB calculated for normal cell lines HLMEC and MCF 10A, respectively, are presented in SIBB = 5.16–9.91) and the highest one to the ovarian cancer cells (A2780) (SIB 1.94–2.85 times higher than cisplatin). The main prenylflavonoid present in human diet, isoxanthohumol, also showed very high selectivity to the A2780 line, and, what is more important, to cisplatin-resistant ovarian cancer cell line (A2780cis).All investigated compounds were selective towards the MCF 10A cell line  was noted for the chalcones XN and 2HXN against the A2780 cell line, which is almost 5-8 times higher than the reference. What is the most interesting is xanthohumol and α,β-dihydroxanthohumol were found to be selective agents towards all examined cancer cell lines, with the exception of MCF7 and PC-3 respectively (SIA).The selectivity of the tested compounds towards cancer cell lines in relation to the normal human lung microvascular endothelial cell line is not as high though. The highest SIThe results of our research indicate that all tested flavonoids, with particular emphasis on chalcones, are compounds with selective action, and as dietary supplements, they can effectively prevent carcinogenesis and work in conjunction with a known anticancer drug.Especially interesting are the results related to the breast cancer cells, because in developed countries they are the most often diagnosed tumors in women along with other estrogen-dependent cancers.Among the selected in this study of breast cancer and the mammary gland tumor cell lines, the most sensitive to the antiproliferative activity of flavonoids was the T-47D cell line, and the least sensitive were the cells of the MDA-MB-231 line. Our results need further verification in estrogen-sensitive tissue using a proper in vivo model, so as to find whether this activity depends on the estrogen receptor level in the cells.The examined compounds demonstrated high antiproliferative activity and very high selectivity with respect to cancer cells. They could be used as a dietary supplement, which can effectively prevent carcinogenesis in combination with a known anticancer drug.The isolation of xanthohumol from spent hops, the isomerization of XN into isoxantohumol, and chemical synthesis of 8-prenylnaringenin were described by Bartmańska et al. ,34. α,β-d6, CD3OD or acetone-d6. The structures of compounds  were con1H NMR (DMSO-d6) δH: 14.65 , 10.61 , 10.11 , 7.76 , 7,65 , 7.56 , 6.85 , 6.09 , 5.14 , 3.86 , 3.14 , 1.69 , 1.60 . 13C NMR δC: 191.7 (C=O), 164.7 (C-2′), 162.4 (C-4′), 160.6 (C-6′), 159.9 (C-4), 142.6 (C-β), 130.5 (C-3”), 129.9 , 126.1 (C-1), 123.8 (C-α), 123.0 (C-2”), 116.0 , 107.4 (C-3′), 104.6 (C-1′), 90.1, (C-5′), 55.8 (C-6′-OCH3), 25.5 (C-5”), 21.1 (C-1”), 17.7 (C-4”).Xanthohumol -4,2′,4′-trihydroxy-6′-methoxychalcone], 1H NMR (CD3OD): δH = 7.03 , 6.70 , 5.98 , 5.18 , 3.82 , 3.20 , 2.83 , 1.75 , 1.64 . 13C NMR δC: 206.1 (C=O), 165.7 (C-2′), 163.7 (C-4′), 162.6 (C-6′), 156.5 (C-4), 133.9 (C-1), 131.3 (C-3”), 130.3 , 124.2 (C-2”), 116.2 , 109.2 (C-3′), 105.8 (C-1′), 91.2 (C-5′), 55.9 (C-6′-OCH3), 47.5 (C-α), 31.6 (C-β), 26.0 (C-5”), 22.2 (C-1”), 17.9 (C-4”).α,β-Dihydroxanthohumol -4,2′,4′-trihydroxy-6′-methoxy-α,β-dihydrochalcone), 1H NMR (acetone-d6): δH = 10.43 , 9.53 , 7.28 , 6.77 , 6.14 , 5.32 , 5.09 , 3.70 , 3.11 , 2.92 , 2.57 , 1.58 , 1.53 ; 13C NMR δC: 188.8 (C=O), 162.0 (C-5), 161.9 (C-9), 160.1 (C-7), 157.8 (C-4′), 130.5 (C-1′), 130.1 (C-3”), 128.3 , 123.3 (C-2”), 115.5 , 107.9 (C-8), 105.0 (C-10), 93.1 (C-6), 78.3 (C-2), 55.8 (C-5-OCH3), 45.1 (C-3), 26.0 (C-5”), 22.0 (C-1”), 18.0 (C-4”).Isoxanthohumol -4,7-dihydroxy-5-methoxyflavanone), 1H NMR (DSMO-d6): δH = 12.10 , 10.80 , 9.60 , 7.31 , 6.78 , 5.96 , 5.41 , 5.08 , 3.19 , 3.07 , 2.72 , 1.58 , and 1.53 . 13C NMR: δC = 196.8 (C=O), 164.4 (C-7), 161.2 (C-5), 159.7 (C-9), 157.6 (C-4′), 130.2 (C-3″), 129.3 (C-1′), 128.1 , 122.70 (C-2″), 115.2 , 107.0 (C-8), 101.8 (C-10), 95.3 (C-6), 78.3 (C-2), 41.9 (C-3), 25.6 (C-5″), 21.3 (C-1″), and 17.6 (C-4″).8-Prenylnaringenin -5,7,4′-trihydroxyflavanone) 1H NMR (acetone-d6): δH = 12.46 , 9.74 , 8.64 , 7.40 , 6.89 , 6.03 , 5.41 , 5.23 , 3.23 , 3.15 , 2.72 , 1.75 , and 1.63 . 13C NMR δC = 196.40 (C=O), 163.7 (C-7), 161.3 (C-5), 161.0 (C-9), 157.7 (C-4′), 130.3 (C-3”), 129.9 (C-1′), 128.2 , 122.6 (C-2”), 115.2 , 108.0 (C-6), 102.1 (C-10), 94.3 (C-8), 78.9 (C-2), 42.6 (C-3), 24.90 (C-5”), 20.6 (C-1”), and 16.8 (C-4”). 6-Prenylnaringenin -5,7,4′-trihydroxyflavanone), 1H NMR and 13C NMR spectra of tested compounds can be found in The following established in vitro cell lines were applied: MCF7, T-47D, MDA-MB-231 (human breast cancer cell line), A2780 (human ovarian cancer cell line), A2780cis (human cisplatin-resistant ovarian cancer cell line), PC-3 and DU 145 (human prostate cancer cell lines), HT-29 (human colon cancer cell line), HLMEC , and MCF 10A .L-glutamine, 5% fetal bovine serum and 8 µg/mL of insulin (T-47D) or 1 mM sodium pyruvate (HT-29). MDA-MB-231, PC-3 and HLMEC cell lines were cultured in RPMI 1640 medium supplemented with 2 mM L-glutamine and 10% fetal bovine serum. Medium for MCF 10A cell line consisted of F-12 nutrient mixture supplemented with cholera toxin (0.05 µg/mL), insulin (10 µg/mL), hydrocortisone (0.5 µg/mL), 5% horse serum, and EGFH (20 ng/mL). All the culture media contained antibiotics: 100 units/mL penicillin , and 100 µg/mL streptomycin . All cell lines were grown at 37 °C with 5% CO2 humidified atmosphere.The cell lines: MDA-MB-231, HT-29, PC-3, DU 145, and MCF 10A were obtained from the American Type Culture Collection , MCF7, T-47D, A2780, A2780cis from the European Collection Cell Cultures , while the HLMEC cell line was obtained by M. Paprocka from the institute . All celTwenty-four hours before the incubation with the tested compounds, the cells were seeded in 96-plates  at a density of 1 × 104 cells per well (0.75 × 104 per well for MCF7 and 1 × 103 per well for HLMEC) in 100 µL of a culture medium.Each agent at each concentration (from 0.1 to 100 µg/mL) was tested in a single experiment, which was repeated four to five times. DMSO, which was used as a solvent (in a dilution corresponding to its highest concentration applied to tested compounds), did not exert any inhibitory effect on cell proliferation. Cisplatin was used as a reference drug.50 (inhibitory concentration 50%)—the concentration of the tested compound which inhibits 50% of the cells population. IC50 values were calculated for each experiment separately and mean values ± SD are presented in the The results were calculated as the ICAn assay was performed after 72 h of exposure to varying concentrations of the tested compounds (from 0.1 to 100 µg/mL); the time of incubation was chosen according to studies of Yong et al.  who analTested flavonoids have antiproliferative activity against human cancer cell lines and they are very selective.50 [µM]) and their selectivity indexes (SI) were much greater than one, i.e., T-47D, MDA-MB-231, and A2780cis.The anticancer activity is related to the structures of flavonoids. Comparing the antiproliferative activity and cancer selectivity of tested compounds, we came to the conclusion that there are chalcones that are promising potential chemotherapeutics, especially to these cancer lines, to which they showed higher antiproliferative activity than cisplatin (IC50 lower than for cisplatin) and more selective.The most promising compound concerning high antiproliferative activity against tested human cancer cell lines is xanthohumol, which is inexpensively accessible from waste (spent hops). However, in comparison to the activity on normal cell lines (selective action), it seems that chalcone 2HXN is even more interesting in the context of using it as a potent and selective anticancer drug. As far as the antiproliferative activity is concerned, the presence of a prenyl group is necessary, whereas O-methylation of the hydroxyl group at C-5 increased the cytotoxicity of the compounds against all tested cancer cell lines. The prenyl group at C-8 increased the antiproliferative activity, with the exception of the breast cancer cell lines. For these lines, especially T-47D, it was 6-prenylnaringenin that proved to be more active (ICThe results of our research provide valuable information, which after the complementary in vivo study may be useful for designing new medicines that are safe and devoid of harmful side effects.


Polyketides belong to the most valuable natural products, including diverse bioactive compounds, such as antibiotics, anticancer drugs, antifungal agents, immunosuppressants and others. Their structures are assembled by polyketide synthases (PKSs). Modular PKSs are composed of modules, which involve sets of domains catalysing the stepwise polyketide biosynthesis. The acyltransferase (AT) domains and their “partners”, the acyl carrier proteins (ACPs), thereby play an essential role. The AT loads the building blocks onto the “substrate acceptor”, the ACP. Thus, the AT dictates which building blocks are incorporated into the polyketide structure. The precursor- and occasionally the ACP-specificity of the ATs differ across the polyketide pathways and therefore, the ATs contribute to the structural diversity within this group of complex natural products. Those features make the AT enzymes one of the most promising tools for manipulation of polyketide assembly lines and generation of new polyketide compounds. However, the AT-based PKS engineering is still not straightforward and thus, rational design of functional PKSs requires detailed understanding of the complex machineries. This review summarizes the attempts of PKS engineering by exploiting the AT attributes for the modification of polyketide structures. The article includes 253 references and covers the most relevant literature published until May 2018. Polyketides are a large class of structurally complex compounds with interesting and valuable activities. Those agents are widely used as antibiotics, antifungals, and drugs for other clinical applications ,2,3. Erycis-AT (AT-containing) type I PKSs and trans-AT (AT-less) type I PKSs), iterative type I PKSs, type II PKSs (iterative), and type III PKSs ) [The polyketide chain synthesizing enzymes, the polyketide synthases (PKSs), often differ in their composition and organization, and the iterative or non-iterative fashion. Based on those features, the PKSs were classified into “different” groups: modular type I PKSs ) . Howevere PKSs)) ,25,26,27e PKSs))  and SceQe PKSs)) , respectcis-AT PKS) [trans-AT PKS) [Modular type I PKSs  from erythromycin) are multifunctional megaenzymes, equipped with domains catalysing the successive linkage of simple building blocks to a polyketide chain . The PKS-AT PKS)  or encod-AT PKS) ,32. In c-AT PKS)  or long--AT PKS) ,35, whicAfter the ACP-loading step, the KS domain of the PKS catalyses the decarboxylative Claisen-like condensation of the newly loaded unit and the already existing polyketide chain. Additional optional domains, such as the ketoreductase (KR), dehydratase (DH), enoylreductase (ER) and methyltransferase (MT) process the generated β-ketoacyl thioester intermediate. Finally, the polyketide chain is released from the last module of the assembly line, which is usually catalysed by thioesterase (TE). Tailoring enzymes (if present) further modify the intermediate into the final product . The structural diversity of polyketides can be assigned to several factors. Those include the length of the polyketide chain, oxidation state and stereochemistry of the β-keto groups, mechanism of termination and chain release, as well as post-PKS tailoring steps . Most ofThe goal of this review is to present an overview on the AT-targeted PKS engineering attempts and the recently gained knowledge, which renews the hope for programmable PKS modification and the production of new polyketide derivatives. The synopsis includes an introduction into the PKS assembly lines and its AT-domains, a brief summary of the biosynthesis of polyketide precursors, mechanistic and structural insights into PKSs and finally a more detailed description of the AT-based engineering strategies applied to modular type I PKSs and their future perspectives. The PKSs are the core enzymes of the polyketide biosynthetic machineries. In modular PKSs, the PKS domains are combined into modules. Each module contains one set of domains required for one elongation and potential modification cycle .cis-AT type I PKSs) and the corresponding products was defined as the “collinearity rule” [→ AT → DH → ER → KR → ACP → TE; from the KS to the ACP) was reconsidered very recently. In 2017, Zhang et al. reported that processing enzymes co-migrate during assembly line evolution with the KS domain downstream (not upstream) of the ACP [cis-AT PKSs had to be redefined and the polyketide assembly lines updated [trans-AT PKS assembly lines were analysed using bioinformatic tools [a–y) were defined based on the chemistry they perform and classes of enzymes present in the module. A cladogram of ACPs that belong to defined module types was generated and ACP families, which contain related module types, were specified [trans-AT assembly lines [In this review, we discuss the AT-based PKS engineering efforts applied to modular type I PKSs. The structure of a polyketide scaffold, synthesized by a modular PKS assembly line reflects the order of the catalytic domains within the PKS-modules, which usually corresponds to the chromosomal order of the underlying genes. This assembly line-like structure of the textbook modular PKSs  to the PKS. More specifically, the precursor- and occasionally the ACP-specificity of the ATs are features that essentially contribute to structural diversity of polyketides and encourage the AT-based PKS engineering. Albeit there are still gaps in understanding of the complex polyketide assembly lines ,47, variapo-ACP form), which results in the activated ACP (holo-ACP form). The ATs of PKSs load the building block onto the Ppant group of the holo-ACP. The units for loading of the holo-ACP originate from thioester-activated precursors . The loading of the ACP in PKSs is accomplished in a two-step reaction [O-AT intermediate . Subsequently, the acyl-O-AT interacts with the activated ACP (holo-ACP) and transfers the unit onto the protein, which leads to the formation of an acyl-S-ACP .ATs are indispensable for polyketide assembly as they select the precursors and load the building blocks (units) for the polyketide biosynthesis onto the ACPs of PKSs. However, the ACPs have to be “prepared” for accepting the unit prior to the loading reaction. This is accomplished in a post-translational modification by the 4-phosphopantetheinyl (Ppant) transferase (PPTase) ,69,70. Treaction . In the cis-AT PKSs usually load only one type of extender units  onto its ACP, which strongly indicates that the ATs are substrate-specific at the native polyketide production conditions. However, there are examples of assembly lines where an AT domain transfers different substrates simultaneously to the same ACP, resulting in the production of polyketide mixtures. This is the case, for example, for the AT domain in module five of the monensin PKS (AT5mon). The monensin AT5 accepts both, ethylmalonyl- and methylmalonyl-CoA as substrates, leading to the production of monensins A and B [N-acetylcysteamine , propargylmalonyl-SNAC, propylmalonyl-SNAC, butylmalonyl-SNAC, and hexyl-SNAC) onto the cognate ACP in in vivo feeding experiments [The ATs of  A and B ,73 at steriments . Except In the following, we briefly summarized the biosynthetic routes for those precursors, which are frequently used by the ATs for the assembly of polyketide structures. Streptomyces coelicolor [Rhizobium [S. coelicolor MatB and have shown that this adenylate-forming enzyme is capable of producing most CoA-linked polyketide extender units, as well as pantetheine- and N-acetylcysteamine-linked analogs, useful for in vitro PKS studies [N-acetylcysteamine were utilized and incorporated into a triketide pyrone by the terminal module of the 6-deoxyerythronolide B synthase (Mod6TE) in vitro [Malonyl-CoA and methylmalonyl-CoA are most commonly used and metabolically available precursors for the biosynthesis of polyketides. In general, there are two pathways for the biosynthesis of malonyl- or methylmalonyl-CoA. One route is the carboxylation of the acetyl- and propionyl-CoA, respectively ,76. The elicolor  and Rhizhizobium ,81,82. H studies . It was in vitro .Rhizobium trifolii has shown a tolerance for a variety of C2 substituted malonic acids [The MatB enzyme of ic acids  however,ic acids ,83,84,85The incorporation of some of the generated unnatural building blocks into polyketide scaffolds was confirmed in vivo ,88,89,90In addition to the typically used substrates malonyl-CoA and methylmalonyl-CoA, some ATs of PKSs utilize unusual alkylmalonyl-CoA precursors for polyketide assembly. Such exotic precursors are generated by crotonyl-CoA carboxylase/reductase (CCR) and homologues (CCRs were broadly defined as enoyl-CoA carboxylase/reductases (ECRs)) ,92,93,94Recent structure-based engineering of the active-site binding pocket of CCRs enabled significant alteration of their catalytic activity towards non-native substrates ,97. For An alternative strategy to the construction of modified biocatalysts for the provision of unusual polyketide precursors is the screening and identification of independent pathways or novel enzymes, directly delivering the exotic substrates. In 2016 Ray et al. described a CCRC (crotonyl-CoA reductase/carboxylase)-independent mechanism for assembly of unusual PKS precursors, where an acyl-CoA carboxylase  directly carboxylates medium chain acyl-CoA thioesters to alkylmalonyl-CoA . 2S)-isomer of methylmalonyl-CoA for each of the extension step [S)-enantiomers might be the better route to obtain 2-substituted malonyl-CoAs, compared to malonyl-CoA synthetases providing (R)-enantiomers. Nevertheless, both enantiomeric forms are useful for testing and characterization of the AT-stereochemistry, which is an important determinant for the efficient incorporation of unnatural moieties into polyketide structures. The provision of diverse precursors is the prerequisite for the exploitation of the AT promiscuity and the production of polyketide derivatives. Malonyl-CoA synthetases  and CCR enzymes enable the generation of a variety of substrates, which can be utilized by the ATs. However, for their use in directed production of AT substrates, the enantiomeric selectivity of the ATs needs to be considered . For exaion step . In suchThe challenges of polyketide engineering, resulting from the complex modularity and functionality of PKSs became more obvious after some of the initial PKS engineering experiments, including skipping and swapping of domains/modules failed ,102,103.trans-AT Dis/Dsz AT from the disorazole pathway exhibited different kinetics, compared to the classical “ping-pong” mechanism. Wong et al. demonstrated that the transacylation in case of the trans-AT Dis/Dsz AT depends on the interaction with its ACP before CoASH is released from the building block [Typically, the ATs accomplish the loading of the ACP by binding of the precursor and the release of the CoASH group prior to the interaction with the respective ACP domain ,104. Durng block . The significance of the AT-ACP interactions for the transacylation was also confirmed for other assembly lines . In the erythromycin PKS DEBS, protein-protein interactions between AT domains, ACP domains and the linkers that flank AT domains were systematically probed ,107,108.The examples of AT-substrate interactions described above involve ATs which select free precursors and load the building blocks onto ACPs being integrated into the PKS. In addition, AT-substrate recognition and protein-protein “communication” were investigated for the less abundant ACP-linked units . In zwittermicin A biosynthesis, the acyltransferase domain of the PKS ZmaA (ZmaA-AT) recognizes the hydroxymalonyl-ACP as substrate and transfers the hydroxymalonyl unit to a downstream ACP via a transacylated AT domain intermediate . The X-rIn the biosynthesis of vicenistatin, the AT VinK transfers a very early vicenistatin-intermediate between two ACPs, the standalone ACP VinL and VinP1LdACP (loading module) ,112. ThuThe condensation of ACP-bound extender units with the growing polyketide chain is essential for completing the biosynthesis and for the formation of the final product. Those steps (translocation and elongation) are dependent on protein-protein recognition between the AT, KS and the ACP domains ,117,118.More recently, the synchronized processing of intermediates on a PKS assembly line, was reported as “turnstile mechanism”  . In thiscis-AT PKS structure and function [cis-AT PKSs. A typical “docking domain”, which enables the intersubunit communication, was identified in the erythromycin assembly line [α-helix bundles, which together mediate not only specific docking interactions, but also promote dimerization of each homodimer [The recent mechanistic and structural data on the PKS assembly lines and their modules provided additional details about these complex systems and uncovered some of the problems, which led to the failure of the initial PKS engineering efforts. From the obtained data, it was possible to generate a more nuanced map of modular function ,123, incfunction . The N- bly line   [Docking domains were postulated and experimentally confirmed for the less well understood -AT PKSs ,126,127.pathway) ,129.cis-AT PKSs, in which the DDs for intersubunit communication occur between the PKS modules , trans-AT PKSs are disconnected within and between modules. Specifically, the junctions between subunits in trans-AT PKSs occur within and between PKS-modules [trans-AT PKS pathway of virginiamycin [CDD) or the downstream catalytic domain, significantly affects the N-terminal DD (VirFG NDD). The N-terminal DD (VirFG NDD) exhibited multiple characteristics of an intrinsically disordered protein [NDD) was recovered after fusion of the docking domains. Similar results were provided for the trans-AT PKS system of macrolactin [Escherichia coli [Unlike the -modules ,126. Sucniamycin . In this protein . The starolactin . Two-heltrans-AT PKS gladiolin (gbn) [Very recently, a fundamentally different mechanism of intersubunit communication at the KS/DH was reported for the in (gbn) . In contin (gbn) ,126, thein (gbn) . The datSuccessful engineering of PKS pathways, especially in case of non-canonical assembly lines, requires the identification of crucial interaction regions for the PKS subunits and the elucidation of the mechanism involved in the communication between their domains. The new insights are valuable for the maintenance of proper protein-protein interactions, dynamics, and finally, for the chemical outcome of the modified PKS assembly lines.The most significant and commonly applied strategies of AT-based engineering of PKS pathways are: the AT domain-swapping, site-specific mutagenesis of the ATs and the cross-complementation of an AT-inactivated PKS by another AT, either from the cognate assembly line or using heterologous ATs .In general, there are two strategies to achieve AT domain swapping in a PKS system. The AT domain can be exchanged by another AT or indirectly replaced by an entire module swapping. For both principles, endogenous ATs/modules from the native assembly line or ATs/modules of a foreign pathway (heterologous parts) can be utilized. In cases where the original AT substrate specificity was altered and functional engineered PKSs were generated, new polyketide derivatives are produced .One of the most prominent AT-based engineering approaches for PKSs and polyketide structures is the substitution of the AT domain with an AT of a different substrate specificity. The best known example of an assembly line to which this strategy was applied, is erythromycin. In the following, examples of AT-swapping in the erythromycin PKS (DEBS) and in a modified variant of the DEBS (DEBS1-TE), and cases of AT-substitution combined with the optimization of precursor supply are presented. Moreover, the replacement of an AT in a non-erythromycin PKS, is described. Streptomyces hygroscopicus ATCC 29253 (Hyg-AT2 from module 2 of a type I PKS and RAPS-AT14 from module 14 of the rapamycin PKS) and from Streptomyces venezuelae ATCC 15439, (Ven-AT from a PKS-like pathway) were inserted into the DEBS of the wild type strain Saccharopolyspora erythraea ER720 [Sacch. erythraea ER720, harbouring the engineered DEBS1 produced novel, bioactive erythromycins  [The erythromycin PKS DEBS was successfully engineered by replacing the methylmalonyl-CoA specific ATs from modules 1 and 2. For example, malonyl-CoA specific ATs from ea ER720 ,131. Themycin A) . S. coelicolor CH999 [Streptomyces lividans K4–114 (in both strains the actinorhodin gene cluster was deleted) [S. coelicolor CH999 strain, with the AT originating from module 6 of a rapamycin PKS, leads to the production of the antibacterial erythromycin analog 2-nor-6-deoxyerythronolide B [S. coelicolor CH999 and S. lividans K4–114 and applied AT-substitution, combined AT and KR swapping, and other engineering strategies to generate a library of >50 macrolides, including examples of analogs with one, two, and three altered carbon centers of the polyketide products [The engineering of the DEBS-PKS was also successful in heterologous hosts, such as or CH999  and Stredeleted) ,134, expnolide B . McDanieproducts .n-hexanoic acid δ-lacton and 2-methyl-3,5-dihydroxy-n-heptanoic acid δ-lacton) were produced by S. coelicolor containing the modified DEBS1-TE [S. coelicolor strain, containing the engineered DEBS1+TE, was 50% relative to the production levels of the parent triketide lactone -2,4-dimethyl-3,5-dihydroxy-n-heptanoic acid δ-lactone), 20 mg/L [To minimize the DEBS and achieve a premature release of the polyketide chain from the erythromycin PKS, the assembly line’s TE domain was relocated to the C-terminus of various ACPs  ,137. TheDEBS1-TE . AlthougDEBS1-TE ,51. In cDEBS1-TE . The pro 20 mg/L . Sacch. erythraea JC2 strain [Sacch. erythraea JC2 is a genome reduced derivative of Sacch. erythraea NRRL2338 lacking almost all erythromycin PKS genes). In another study the strain Sacch. erythraea JC2 was exploited for the replacement of the AT1  in the model PKS DEBS1-TE with the epothilone EpoAT2  and EpoAT3  [Further engineering attempts of the DEBS1-TE fusion include the replacement of module 2 of DEBS1-TE with the module 12 from rapamycin or module 3 from the erythromycin assembly line ,60. In b2 strain  (Sacch. ditions) . Functioditions) . Streptomyces collinus [Sacch. erythraea EAT4, yielding Sacch. erythraea EAT4-crr. The crr-expressing strain synthesized the derivative 6-ethylErA [In some cases the alteration of the AT with an AT incorporating a different extender unit is not sufficient as the substrate is not available in the producer and it requires the external supplementation with the precursor or further genetic modification of the strain to overcome this limitation. Such an observation was made for a substitution experiment in which the AT domain of module 4 in erythromycin  was exchanged with the AT domain from module 5  of the niddamycin PKS . The strcollinus  was intrethylErA .S. lividans K4–114, harbouring a plasmid with genes encoding the erythromycin DEBS was modified: namely, the AT6 domain was replaced by the presumably hydroxymalonate-specifying fkbA-AT8 domain [fkbA-AT8  of the modified DEBS in S. lividans K4–114 was exchanged by the heterologous AT8 domain  from ascomycin . In addition, a subcluster of five genes (asm13-17) from the ansamitocin biosynthetic gene cluster of Actinosynnema pretiosum was coexpressed in the modified S. lividans K4–114 strain to provide the methoxymalonyl extender unit for the engineered erythromycin PKS. Two novel analogs of erythronolide, 2-desmethyl-2-methoxy-DEB and 2-desmethyl-6-DEB, were produced by this strain [Another example of a combined AT-substitution- and precursor supply optimization approach was provided by Kato and co-authors . In thei8 domain ,58. This8 domain . The fkbs strain .S. hygroscopicus [Besides the AT of the erythromycin PKS, other assembly lines were modified by AT-swapping to produce polyketide derivatives. The AT domains in the synthesizing PKS-complex of geldanamycin, a potential anticancer drug, were substituted in six AT swaps  of the seven GdmPKS modules . The AT-scopicus .The AT substitution resulting from the exchange of entire modules of a PKS assembly line is an indirect strategy and often a “side-effect” of an engineering experiment, primary aiming at alteration of a module and not necessarily at AT-swapping. The attempts of module swapping were recently discussed elsewhere ,146,147.Sacch. erythraea [The exchange of a whole module was successful for loading modules as well as for a chain extension module of a PKS ,149,150.Streptomyces avermitilis or the erythromcyin PKS of Sacch. erythraea was also successful for the spinosyn PKS in Saccharopolyspora spinosa BIOT-1066 [Sacch. spinosa BIOT-1066, expressing the hybrid PKS pathways produced the anticipated spinosyn analogs [S. avermitilis mutants, blocked in the biosynthesis of native starter units [S. avermitilis strain was replaced with a cyclohexanecarboxylic (CHC) unique loading module from phoslactomycin [Replacing of an entire loading module by a loading module of the avermectin PKS from IOT-1066 . The res analogs . In a prer units . Structuer units  were isoer units . In thisctomycin ,155. FurS. coelicolor CH999  was swapped with cognate modules of the erythromycin assembly line (DEBS module 3 and 6) and heterologous module 5 from rifamycin . The engsee also . This deA different approach to the whole domain swapping, aiming at the substitution of AT-substrate specificity, is the site-directed mutagenesis . Recent Analysis of the amino acid sequences of ATs resulted in the identification of approximately 100 residues at the C-terminal end of the active-site serine in the analysed ATs, which was assigned to different AT-substrate specificities. Indeed, in most cases, the substrate specificity of the ATs could be predicted based on this motif. This observation encouraged the modification of the AT substrate specificity by exchanging the specific sequence signature. Such strategy was applied, for example, to the model PKS-complex DEBS and its ATs. The methylmalonyl-CoA specific YASH-motif of AT1, AT4 and AT6 of DEBS was altered to the malonyl-CoA specific HAFH (and HASH) ,61,62, wHarvey et al. exploited the features of the loading AT by mutating one of the ATs of DEBS and obtaining a new class of alkynyl- and alkenyl-substituted macrolides  with activities comparable to that of the natural product . In other studies, the prototypical PKS (DEBS) was subjected to mutagenesis and the generated active site-mutant library was screened for substrate selectivity ,63,64,65Bravo-Rodriguez et al. used a combined approach, which involved molecular modelling and mutagenesis of the AT6 of DEBS and showed that the V295A mutation of this AT leads to a wider active site and improves the promiscuity for non-native substrates ,63,64.Based on AT amino acid sequences, loading ATs (ATs incorporated into the loading module) usually have relaxed substrate specificity and load the loading module with diverse starter units. For example, the loading AT from the modular PKS of avermectin accepts more than 40 carboxylic acids as substrate for the loading module leading to the biosynthesis of a series of congeners . Using mcis-AT PKS, accepting long aliphatic chains, up to C7-malonyl-CoA and aromatic benzylmalonyl-CoAs were solved [Very recently, high-resolution X-ray crystal structures of a broadly selective AT SpnD-AT of the splenocin e solved . To the e solved . Furthere solved . These re solved .cis-AT PKS, the discrete AT from the trans AT-PKS pathways [cis-AT PKSs occasionally harbour ATs providing non-malonate building blocks to the assembly line, the ATs of the trans-AT PKSs are mostly malonyl-CoA-specific at natural, non-manipulated production conditions. The KirCII-AT [trans-acylation activity were identified. Further in vitro investigation of the substrate specificity of KirCII in ACP loading assays revealed that the AT is able to load KirACP5 with allyl- and propargyl-malonate and to a lesser extent with azidoethyl- and phenyl-malonate [In addition to the AT of pathways ,32,172 wirCII-AT  from theirCII-AT  is an exirCII-AT . Additiomalonate . Those pmalonate . Similarmalonate . cis-ATs, such as the ATs of the erythromycin assembly line are relatively well studied and were manipulated for several times, there is not much known about the substrate flexibility and the exact mechanisms of partner (precursor or ACP) recognition for ATs from other PKS pathways . The knowledge obtained from manipulations conducted in the past years will contribute to understanding of the less-explored systems and help to overcome the still existing limitations for AT-based engineering of PKS pathways.While the cis-AT PKS can be complemented in two ways. One option is the insertion of an AT domain  in place of the original domain into the PKS module (cis-to-cis AT-complementation). Another approach is the complementation of the PKS by introduction of a discrete AT, which provides the building blocks in trans (cis-to-trans AT-complementation)  . cis-AT PKS assembly line erythromycin from Sacch. erythraea and related polyketide pathways. Their modification by deletion of the native AT and integration of a foreign AT, often regarded as domain substitution or swapping, was described in Most of the past AT complementation studies were carried out using the PKSs of the model trans-AT PKS pathways were overlooked for a long time and are less well characterized than the cis-AT PKSs, the number of this type of biosynthetic assembly lines is constantly increasing [trans-ATs) exhibit malonyl-CoA specificity in the native producer strain -aminomalonyl-ACP-specific ZmaF [in trans to complement the cognate PKSs, their development to a tool for PKS engineering might be easier, compared to the in the ATs embedded in the PKS (cis-AT PKSs). In in vitro studies, it was demonstrated that a trans-AT from kirromycin (KirCII) and disorazole (Dis/Dsz AT) can complement the AT-null DEBS of the cis-AT type [S. coelicolor . BryP was also able to catalyse the acyl-transfer onto ACPs of pikromycin and other bryostatin PKS modules [Although the creasing ,183. In  Dis/Dsz , bryosta Dis/Dsz , bacilla Dis/Dsz , rhizopo Dis/Dsz , and kir Dis/Dsz ) and thec KirCII  from kirc KirCII  from oxafic ZmaF  requiredfic ZmaF ,173,176.-AT type . In the elicolor , which l modules . trans-AT PKSs was successful for the ∆mmpC-AT1 mutant from mupirocin [Pseudomonas fluorescens). P. fluorescens produces a mixture of several pseudomonic acids, named mupirocin [mmpC-AT1 mutant [mmpC-AT1 mutant expressing the BryP-AT1 restored the biosynthesis of the main compound of mupirocin, the pseudomonic acid A (PA-A), to approximately 83% of WT production levels. In addition, an in vivo cross-species complementation, using ATs from two upirocin  -methylmalonyl-CoA confirmed the role of the proposed active-site residues and revealed residues important for the substrate binding [The protein structures of PKS domains ,204,205, binding . The obt binding .apo module 5 from the VirA subunit) and to identify the positions of domains flanking the ACP and KS in the in the virginamycin PKS [trans-AT PKS system [In another study, the small-angle X-ray scattering (SAXS) was used to characterize the structure of a module  gained from the planned and currently conducted research on PKSs. This will lead to the development of new computational tools (or tool features) and improve the predictability of PKS-variation as well.trans-AT from the disorazole PKS [In addition, the structural biology technique itself is constantly improving. A new method for the direct delivery of the sample into an X-ray free-electron laser was used for the zole PKS . The novzole PKS . The cryDirected evolution ,217,218 In the future, those AT-based PKS engineering strategies and methodologies, which rely on DNA steps, will be supported by advanced cloning methods such as Red/ET system ,224,225,E. coli [Bacillus [Saccharomyces [Aspergillus strains [Finally, the efficient production of natural and engineered polyketides often requires the tuning of the native producer strain or the development of suitable heterologous expression hosts ,239,240.E. coli ,247,248,Bacillus , Sacchararomyces ,251 and/ strains ,253 wereIn the past, many attempts of combinatorial biosynthesis, aiming at engineered PKSs resulted in inactive or inefficient enzymes and/or assembly lines. The newly obtained knowledge about the ATs and their PKS pathways in combination with methodology advancements provide exciting new perspectives for the AT-based PKS engineering. The design and generation of functional and efficient PKS pathways might enable the production of new bioactive compounds of which structures would be extremely difficult to access using traditional synthetic chemistry approaches.


An admission criterion is one of the key indicators of academic success. The purpose of admission process is to select best candidates for the particular program who will complete the program successfully. Thus rigorous admission criteria can predict academic performance, decrease failure rate and successful completion of degree. The objective of this study was to determine the relationship between various variables in the existing admission criteria and academic performance.A cross sectional study design was chosen. Data (2009-2017) was gathered from the records of Institute of Nursing Science, Khyber Medical University. Variables in the admission criteria included age, gender, previous academic performance, entry test score and experience. The dependent variable was academic performance measured in CGPA. The data was analyzed using correlation and regression analysis through SPSS and STATA.The results reported a significant relationship between admission criteria and the academic performance of nursing students. Various variables in the admission criteria i.e. SSC marks , previous academic score at diploma level  and entry test scores  have significant relationship between academic performance. However, previous academic scores at diploma level were better predictors of the academic performance.The study concludes to use integrated admission criteria for the selection of students and bring changes in the traditional admission process. Admission criteria is one of the key indicators to predict academic performance.2Literature reviews suggests that admission criteria constitutes of cognitive and non-cognitive factors.5Available evidence suggests that a better formulated admission criteria including mix of academic and non-academic factors can predict better academic performance.A correlational study was conducted. A specifically designed Checklist was used to record the data from the available records on admission criteria and academic performance. The checklist includes the information about Marks in SSC, Previous academic Marks in General nursing and midwifery/speciality, Entry Test Marks, Academic performance in GPA. It was validated from the expert in the field of medical education and nursing education. The checklist was also field tested for errors before data collection. The records of all 197 Post RN nursing students enrolled and passed out between (2008-2017) in Khyber Medical University, Peshawar was reviewed using the checklist. Those who failed to complete the course due to any reason were not included in the study. Data was collected after ASRB and ethical approval from ethical review board of Khyber Medical University. Data regarding enrolment characteristics was collected from the students’ personal files and admission records while data about academic performance in Post-RN Program were obtained from examination section of Khyber Medical University, Peshawar. The study variables included previous academic marks measured in percentage obtained in Matric (higher secondary school certificate), Nursing diploma, entry test marks, gender and age of the student at the time of admission. Outcome variable was academic performance that was measured in CGPA. Descriptive analysis was done on all 197 participants. Regression analysis, t-test and Pearson’s correlation analysis were also done. Data analysis was carried out using SPSS version 22.0 and STATA.Results showed that majority of the participants 86.3% (n=170) were females. The mean ages of the participants were 34.33 +- SD5.66. This study shows a significant (p<0.001) difference (0.29) in the mean academic performance of males and females. This difference is statistically significant (p>0.001) after using t test. Apart from this, a moderate relationship  was observed between SSC marks and academic performance. A moderate relationship  was also observed between the previous academic score and the academic performance. The results show a significant weak relationship between academic performance and entry test score  and also between age at the time of admission and the academic performance , besides weak non-significant negative relationship between years of experience and the academic performance of the nursing students . As shown in The results of this study has shown a significant weak relationship between SSC marks and the academic performance  and also between the previous academic score at diploma nursing and the academic performance . These results are consistent with previous research studies which support the notion that previous academic performance could predict subsequent academic performance of a student.9A significant but weak relationship  was observed between academic performance and entry test score. However, previous academic score at diploma nursing level , SSC marks , years of experience  and entry test score  were better predictor of a good academic performance of the post RN nursing students academic performance. The findings of this research study confirmed the relationship between admission entrance test and the academic performance as in the literature.14This study also shows a significant weak negative relationship r= -0.15, p=0.030) between age and the academic performance, whereas age is a negative predictor  of the academic performance. The result of this study is partly in contrast and partly in lined with other studies that old age is negatively co-related to academic performance., p=0.03017This study shows a significant (p<0.001) difference (0.29) in the mean academic performance of males and females. This difference is statistically significant. A research study by Ali et al. reported that female gender is negatively co-related with academic performance. However, the co-relation is insignificant.20The results of this study have shown significant relationship of various variables in the existing admission criteria with the academic performance that can be used to select nursing students who can be academically successful in future. Previous academic score at diploma nursing level, SSC marks, years of experience and entry test score were better predictor of the academic performance in nursing students. Moreover, the study concludes a significant relationship of academic performance with SSC marks, previous academic score at diploma nursing and entry test score. This study recommends using an integrated approach of selecting students for the post RN nursing program. However, different variables in the integration should be given weightage in accordance with the strength of relationship between the variables and the academic performances. The study may be replicated to other provinces and disciplines of health professions education programs so that a debate can be generated for rigorous admission criteria and changes in the current admission criteria.IIY: Conceived, Designed, Data collection and did statistical analysis & manuscript writing.BJ: Did review, Editing of manuscript and final approval of manuscript.


Malus crabapples, cv. “Radiant”, and their chemical structures were elucidated by UV, IR, ESI-MS, 1H-NMR and 13C-NMR analyses. These compounds, which include trilobatin (A1), phloretin (A2), 3-hydroxyphloretin (A3), phloretin rutinoside (A4), phlorizin (A5), 6′′-O-coumaroyl-4′-O-glucopyranosylphloretin (A6), and 3′′′-methoxy-6′′-O-feruloy-4′-O-glucopyranosyl-phloretin (A7), all belong to the phloretin class and its derivatives. Compounds A6 and A7 are two new rare dihydrochalcone compounds. The results of a MTT cancer cell growth inhibition assay demonstrated that phloretin and these derivatives showed significant positive anticancer activities against several human cancer cell lines, including the A549 human lung cancer cell line, Bel 7402 liver cancer cell line, HepG2 human ileocecal cancer cell line, and HT-29 human colon cancer cell line. A7 had significant effects on all cancer cell lines, suggesting potential applications for phloretin and its derivatives. Adding a methoxyl group to phloretin dramatically increases phloretin’s anticancer activity.Seven dihydrochalcone compounds were isolated from the leaves of  Malus plants  cell viability assay was used. Succinate dehydrogenase in the mitochondria of live cells can convert the insoluble violet crystalline formazan produced from MTT and deposit it in the cells, but dead cells cannot not. Dimethyl sulfoxide (DMSO) could dissolve the formazan in cells, and its light absorption value was measured by ELISA at 490 nm. The number of living cells can be determined from the measured absorbance value .2. An aliquot of 10μL of the test compounds was added to cells and cultured at 37 °C for 3 days under 5% CO2. The cells were measured by a modified MTT assay [Human lung adenocarcinoma cell line A549, human hepatoma cell line Bel7402, human cancer colorectal adenomas cell line HepG2 and colon cancer cell HT29  were obtained from Dr. Wen Xu . These four tumor cell lines were picked in their logarithmic growth phase with trypsin digestion and cultured supplemented with 10% fetal bovine serum  in RPMI1640 Dulbecco's modified Eagle medium (DMEM) and reached a 150,00/mL cell suspension. Then, these cells were seeded into 96-well plates at 190 μL/plate at 37 °C for 24 h under 5% COTT assay ,29. The p < 0.05). Duncan’s multiple analysis and Student’s t-test were used to identify significant differences among groups . Graphs were prepared in Origin Pro 8.0 SR4  and Adobe Photoshop CS6.Data are presented as the mean ± standard deviation (SD). Analysis of variance  of all values was used to assess differences in the means among different samples  and A7 (3′′′-methoxy-6′′-O-feruloyl-4′-O-gluco-pyranosylphloretin) could be developed into promising anticancer agents.Seven dihydrochalcone compounds separated and purified from


Multiparametric high-content imaging assays have become established to classify cell phenotypes from functional genomic and small-molecule library screening assays. Several groups have implemented machine learning classifiers to predict the mechanism of action of phenotypic hit compounds by comparing the similarity of their high-content phenotypic profiles with a reference library of well-annotated compounds. However, the majority of such examples are restricted to a single cell type often selected because of its suitability for simple image analysis and intuitive segmentation of morphological features. The aim of the current study was to evaluate and compare the performance of a classic ensemble-based tree classifier trained on extracted morphological features and a deep learning classifier using convolutional neural networks (CNNs) trained directly on images from the same dataset to predict compound mechanism of action across a morphologically and genetically distinct cell panel. Our results demonstrate that application of a CNN classifier delivers equivalent accuracy compared with an ensemble-based tree classifier at compound mechanism of action prediction within cell lines. However, our CNN analysis performs worse than an ensemble-based tree classifier when trained on multiple cell lines at predicting compound mechanism of action on an unseen cell line. This allows the use of more physiologically relevant cell-based assay conditions and also provides a phenotypic profile to help elucidate the MoA for hits discovered by target-agnostic phenotypic screening.6Cellular morphology is influenced by multiple intrinsic and extrinsic factors acting on cell physiology. Striking changes in morphology are observed when cells are exposed to biologically active small molecules. Compound-induced alteration in morphology is a manifestation of various perturbed cellular processes. We can hypothesize that compounds with a similar mechanism of action (MoA), which act upon the same signaling pathways, will produce comparable phenotypes, and that cell morphology can predict compound MoA. Multiparametric high-content imaging assays have become established across a number of screening groups to classify cell phenotypes from functional genomic and small-molecule library screening assays.2 The majority of early high-content phenotypic profiling studies, utilizing morphological profiling, applied unsupervised hierarchical clustering in order to group treatments into bins that produce similar cellular phenotypes.7 More recently, several groups have evolved phenotypic profiling through the application of machine learning classifiers to predict the MoA of phenotypic hits, by comparing the similarity of the high-content phenotypic profiles with a reference library of well-annotated compounds.8 This can be performed by arranging unannotated compounds in feature space and using proximity to nearby labeled data to infer MoA.10 A slightly different approach is to train a classifier with labeled data and then attach labels to unknown compounds.12 However, the majority of such examples of compound MoA prediction are restricted to a single cell type, often selected because of its suitability for simple image analysis and intuitive segmentation of morphological features. The restriction of multiparametric high-content image analysis to single “easy-to-image” cell line models limits the application of phenotypic profiling and MoA classification studies across more morphologically complex and disease-relevant cell-based assay systems. Furthermore, the expansion of multiparametric high-content studies across broader panels of morphologically and genetically distinct cell lines, which more accurately represents the heterogeneity of human disease, has several benefits. This allows correlation of phenotypic response data with basal genomic, transcriptomic, or proteomic data to support further understanding of compound MoA at the molecular level and identification of biomarkers of phenotypic response. Such application of multiparametric high-content phenotypic screens across larger cell line panels, equivalent to the Cancer Cell Line Encyclopedia (CCLE) or Genomics of Drug Sensitivity in Cancer (GDSC) and new emerging induced pluripotent stem cell (iPSC)-derived model resources, can further support drug repurposing and pharmacogenomic studies across more complex cell-based phenotypes.A landmark paper in the field of high-content phenotypic profiling was published in 2004, when Perlman et al. first demonstrated that multiparametric phenotypic fingerprints could be clustered according to compound MoA using a custom similarity metric and hierarchical clustering.14 (Suppl. Table S1) applied to eight genetically and morphologically distinct human breast cancer cell lines, representing four clinical subtypes Convolutional neural networks (CNNs) trained on five-channel imagesMore specifically, we evaluate and quantify the performance of an ensemble-based tree classifier and a CNN classifier, with regards to predicting compound MOA, when trained and predicted on each individual cell line and on “previously unseen” additional cell types.l-glutamine, incubated at 37 °C humidified and 5% CO2.The breast cancer cell line panel Table 1 Cells were plated in 96-well optical bottom plates  at a density of 2500 cells per well in 100 µL of media in the inner 60 wells and incubated for 24 h before compound treatment.Compounds Table 2 Suppl. Table S1). Thirty microliters of cell painting solution was added to plates and left to incubate for 30 min at room temperature in the dark. Plates were then washed with 100 µL of PBS three times; after the final aspiration and addition of PBS, plates were sealed with a transparent plate seal .To label cells in 96-well plates, the cells were fixed by adding an equal volume of 8% paraformaldehyde  to the existing media, resulting in a final paraformaldehyde concentration of 4%, which was left to incubate for 30 min at room temperature. The plates were then washed with 100 µL of phosphate-buffered saline (PBS) and permeabilized with 50 µL of 0.1% Triton X-100 solution for 20 min at room temperature. A solution of cell painting reagents was made up in 1% bovine serum albumin (BSA) solution (see Suppl. Table S1) at 20× magnification; exposure times were kept constant between plates and batches as to not influence intensity values. Images were captured across four different sites per well, with each site containing approximately 50–200 cells in negative control wells depending on the cell line, with the MDA-MB-157 cell line containing fewer cells per image compared with the other tested cell lines due to the large cell size characteristics of that particular cell line.Imaging was carried out on an ImageXpress micro XL , a multiwavelength wide-field fluorescent microscope equipped with a robotic plate loader . In the cell painting assay used for this study, images were acquired in five fluorescent channels . Using these masks marking the boundary of cellular objects, we measured morphological features for multiple image channels returning per object measurements. Out-of-focus and low-quality images were detected through saturation and focus measurements and removed from the dataset. Image averages of single-object (cell) measurements were aggregated by taking the median of each measured feature per image. Features were normalized on a plate-by-plate basis by dividing each feature by the median DMSO response for that feature; a z score was then calculated for each feature over the entire pooled dataset to standardize each feature to a mean of zero and unit variance. Feature selection was performed by calculating pairwise correlations of features and removing one of a pair of features that have a correlation greater than 0.9, and removing features with very low or zero variance of cellular objects.Images were analyzed using CellProfiler v2.1.1The ensemble tree-based classifier was implemented using scikit-learn’s (version 0.19) “GradientBoostingClassifier” with default parameters except for the number of estimators, which was increased from 100 to 600. Data used for the tree-based classifier were median profiles representing an image average of the morphological features, and test accuracy was measured using the Jaccard similarity score.16 to accept input in the form of five-channel arrays rather than the typical RGB three-channel images. The models were trained with batches of 32 images per graphics processing unit (GPU) with random 90° rotations for 20 epochs with an initial learning rate of 0.01 with an ADAM optimizer17 using categorical cross-entropy as the loss function; test accuracy was measured with the Jaccard similarity score.The CNN classifier was implemented in PyTorch (version 0.3.0) by modifying the ResNet18 architectureSuppl. Fig. S1).The number of epochs was chosen based as the point at which training and validation accuracies plateaued, as well as losses stopped decreasing, when training and predicting MoAs on a single cell line , for each individual breast cancer cell type, are presented in Suppl. Fig. S2). Training the CNN classifier on a dataset in which the training examples contained equal numbers of each class greatly improved accuracy when predicting an unseen cell line. Accuracies were slightly below those of the tree-based classifier , likely due to the reduced number of training examples available.The predictive performance of the classifiers applied to unseen cell lines was compared by training on seven out of the eight cell lines and testing on the withheld eighth cell line. The ensemble-based tree classifier saw a reasonable reduction in classification accuracy when tested on unseen cell lines Fig. 3a.Suppl. Fig. S4), with accuracies similar to those of ResNet18 models trained and tested on the same cell line  are becoming increasingly common in a wide range of machine learning classification tasks, and the convolution aspect of CNNs plays an important role when working with image data. CNNs have recently been applied to the classification of cell phenotypes from high-content imaging data, where they have been shown to perform well when using highly optimized publicly available high-content benchmarking datasets.11 Many groups have reported increased accuracy of CNN classifiers on high-content imaging data over approaches using extracted morphological features, which is in contrast to our own findings , a single dataset is split into training and test partitions. The method used in this study randomly sampled proportions, which may lead to overfitting and overoptimistic prediction accuracies due to the test data containing replicates from the same wells as found in the training set, as well as the same compound at different concentrations. Alternative approaches, which can be used in such instances, include a “hold one compound out” cross-validation strategy9 . As would be expected, using the LOCO and LOCACLO cross-validation strategies decreases prediction accuracies when compared with training and testing on random partitions of image data for both CNN and gradient-boosted tree classifiers. However, the cross-validation strategies mirror the results of the random partition of training and test method in that there was a significant loss of prediction accuracy when the CNN model was transferred to an unseen cell line .Predicting compound MoA from high-content image-based screening is a classification task, which can be approached in two ways. The first method is to develop an image analysis algorithm to extract morphological information from the images of cells and generate a multivariate dataset describing cellular phenotypes, which are subsequently used to train a classifier.findings Fig. 2.918 The ability to predict compound MoA with a high degree of accuracy, despite the machine learning model never having seen this cell line before, will be very useful, for example, in cases where an investigator has trained a model on a large annotated compound set and would like to predict MoAs from further compound screens in new cell lines, without having to rescreen and retrain.Specific experimental questions addressed by this current study include direct comparison of the predictive performance between classical machine learning (ensemble-based tree classifier) and deep learning (CNN) models, when applied to predicting compound MoA across a disease-relevant panel of morphologically distinct human breast cancer cell lines. We further address how well the performance of each machine learning model can generalize to new cell line data. We trained our machine learning models on seven breast cancer cell lines and tested prediction accuracy on a withheld eighth cell line. Both types of classifier suffered a reduction in classification accuracy when applied to unseen cell lines, although the CNN performed noticeably worse on certain cell lines. This difference between classifier performance is likely explained by the data preprocessing steps in which the data for the ensemble tree-based classifier are subjected to plate-by-plate normalization to the negative control values, which will remove many of the cell line-specific morphologies, while this normalization step is not replicated in the CNN data preprocessing. As this is essentially an overfitting problem, there may be additional measures, such as further image augmentation, that may improve the generalizability of CNN models to distinct cell lines.We also assessed if the addition of more data from morphologically distinct cell lines during model training increases prediction accuracy when applied to the classification of a single cell line. It was found that the CNN classifier did not benefit from the additional data, and that the possible benefit of more training examples did not overcome the increased heterogeneity and morphological differences between the cell lines. The ensemble-based tree classifier did generally benefit from additional training examples, although model performance was highly variable and sensitive to the combination of additional cell lines used during training. This leads us to the conclusion that more data are not necessarily better when training classifiers for MoA prediction, and those cell line-specific morphologies can dramatically impact model performance.There are a number of reasons why transferring an MoA classifier from one cell line to another may fail. One possible explanation is that the intrinsic morphological differences between different cell lines are greater than the morphological changes induced by a compound, and that classifiers with no knowledge of cell line labels may confuse compound-induced morphologies with cell line morphologies. For example, if a cell line inherently has a large cell area morphology, it may often cause classification errors for compounds, which cause more spindle-like cell lines to spread. Alternatively, there may be a biological explanation, such as differential expression, mutation, or alternative splicing of pharmacological targets between the cell lines or altered downstream pathways, which may alter the phenotypic response of a cell line to a particular small molecule.Our results indicate that while a CNN classifier may represent an efficient approach to study compound MoAs across larger cell line panels, the trained models are difficult to generalize between morphologically distinct cell lines. The more classical machine learning models trained on extracted morphological features offer opportunities to normalize the data in order to remove cell line-specific effects and improve model generalizability and transferability between cell lines. Broad cell panel screening studies that include cell line models presenting with more complex heterogeneous cell morphologies, including “clumpy” cell cultures, present significant challenges to efficient cell segmentation. The use of CNNs and the omission of custom image analysis algorithms for each cell line remove considerable resource burden and experimental or human bias when performing high-content imaging studies across morphologically distinct cell panels. Therefore, investigators need to weigh the pros and cons of each approach and its suitability to the dataset under investigation. Furthermore, the implementation of CNNs is likely to support the application of high-content phenotypic profiling and MoA classification across a broader variety of mechanistic classes and more complex assays, including iPSC differentiation, co-culture, and 3D models, which are often unsuitable for image-based segmentation. Alternatively, classical machine learning techniques, like ensemble-based tree classifiers, are much easier to interpret than CNNs, and inform which morphological features are important for distinguishing between mechanistic compound classes.www.github.com/CarragherLab/2018-08_transfer_ML_between_cell_lines). We anticipate that these data will support the high-content image analysis and machine learning communities, to further evolve new approaches to high-content phenotypic profiling and MoA prediction across morphologically distinct cell panels and more complex cell-based assay formats.We anticipate that the methods and results presented in this article will support an increased prediction accuracy of compound MoAs across a broader variety of cell-based assay systems, including genetically distinct cell panels. Such approaches are well placed to advance in vitro pharmacogenomic studies beyond simple univariate assay endpoints toward more complex assays and phenotypes. Finally, we provide all source code and MoA datasets used in this study through a dedicated open-access link (Click here for additional data file.Supplemental material, Supplemental_Material_for_Evaluation_of_machine_learning_classifiers_by_Warchal_et_al-final-version3 for Evaluation of Machine Learning Classifiers to Predict Compound Mechanism of Action When Transferred across Distinct Cell Lines by Scott J. Warchal, John C. Dawson and Neil O. Carragher in SLAS Discovery