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"Toxicogenomics : principles and applications fills the need for a single, thorough text on the key breakthrough technologies in genomics, proteomics, metabolomics, and bioinformatics, and their applications to toxicology research. The first section following a general introduction is on genomics and toxicogenomics, and qPCR. The next sections are toxicoproteomics and metabolomics. The final section covers bioinformatics aspects, from databases to data integration strategies. A practical resource for specialists and non-specialists alike, this book includes numerous illustrations that support the textual explanations."

"Toxicogenomics is the integration of genomics to toxicology. This technology is a powerful tool for collecting information from a large number of biological samples simultaneously and thus it is very useful for large-scale screening of potential toxicants.Toxicogenomics : a powerful tool for toxicity assessment provides up-to-date state-of-the-art information presented by the recognized experts, and is therefore an authoritative source of current knowledge in this field of research. The potential link between toxicology, genetics and human diseases makes this book very useful to investigators in many and varied disciplines of science and toxicology. "

"Gen DefH9-iaaM merupakan gen pengkode senyawa prekursorpembentukan auksin. Kandungan auksin yang tinggi menginduksipembentukan buah partenokarpi, tanpa melalui polinasi dan fertilisasi. Tigagalur tanaman tomat transgenik yang membawa insersi danmengekspresikan gen DefH9-iaaM, yaitu OvR1#14-4, OvM2#10-1, danOvM2#6-2, telah dihasilkan melalui transformasi genetik denganAgrobacterium oleh kelompok peneliti di BB-BIOGEN. Penelitian bertujuanmenguji stabilitas insersi dan mengetahui ekspresi gen DefH9-iaaM padatanaman T3 dari ketiga galur tersebut. Uji stabilitas gen dilakukan denganmetode PCR menggunakan primer spesifik IAAM 5 dan IAAM 3. Keduaprimer tersebut menghasilkan fragmen gen iaaM sebesar ± 148 pb. FragmenDNA produk PCR divisualisasikan menggunakan gel elektroforesis. Hasil ujimolekuler dianalisis menggunakan uji chi-square dengan level of significant0,05. Galur OvR1#14-4 memiliki insersi gen yang telah stabil denganperbandingan filial transgenik dan non transgenik yang memenuhiperbandingan penyilangan monohibrid Mendel yaitu 3:1. Tanaman denganhasil uji molekuler positif kemudian ditanam di lapang untuk uji ekspresifenotipik dan evaluasi daya hasil. Hasil uji fenotipik dianalisis dengan ujiANOVA level of significant 0,05. Ekspresi gen partenokarpi DefH9-iaaM padatanaman tomat transgenik meningkatkan jumlah tandan sebesar 191--227%,jumlah bunga sebesar 191--310%, dan jumlah buah sebesar 331--426% dibandingkan dengan kontrol Opal, serta menyebabkan terbentuknya buah tomat berbiji sedikit dan tanpa biji. Galur OvR1#14-4 menghasilkan jumlah tandan, jumlah bunga, dan jumlah buah paling tinggi dibandingkan dua galur lain."

"PendahuluanPeriodontitis adalah penyakit pada jaringan penyangga gigi yang dikategorikan sebagai inflamasi tidak menular dan berkaitan dengan keadaan disbiosis biofilm. Penyakit tersebut memengaruhi seluruh jaringan periodontal dan dapat menyebabkan destruksi progresif pada tulang alveolar. Periodontitis dapat dipicu oleh bakteri seperti Aggregatibacter actinomycetemcomitans yang selanjutnya mempengaruhi osteoklastogenesis dan terjadinya kerusakan jaringan. Internalisasi dan proliferasi bakteri A. actinomycetemcomitans di dalam sel osteoklas dapat meningkatkan faktor virulensi seperti lipoposakarida (LPS) yang berperan dalam diferensiasi osteoklas yang ditandai dengan gen penanda, salah satunya Cathepsin K (CTSK). Penelitian ini bertujuan untuk menganalisis diferensiasi osteoklas melalui analisis ekspresi gen penanda diferensiasi osteoklas yaitu CTSK.MetodeOsteoklas (OC) diperoleh dari kultur primer bone marrow macrophage (BMM), yang dipaparkan dengan nuclear factor kappa-B ligand (RANKL) selama 3 hari. Kemudian diinfeksikan dengan bakteri A. actinomycetemcomitans (ATCC 29522) dengan perbandingan MOI 1:1 dan 1:5. Selanjutnya dievaluasi 1,5 jam dan 18 jam pasca infeksi (hpi) menggunakan RT-qPCR dengan teknik Livak (2-∆∆Ct).HasilEkspresi relatif gen CTSK pada BMM dengan perlakuan MOI 1:1 pada 1,5 hpi (2-∆∆Ct = 1,00) dan 18 hpi (2-∆∆Ct = 0,99), serta perlakuan MOI 1:5 pada 1,5 hpi (2-∆∆Ct = 1,00) dan 18 hpi (2-∆∆Ct = 1,04) cenderung tidak menunjukan adanya perubahan. Sedangkan pada OC dengan MOI 1:1 pada 1,5 hpi (2-∆∆Ct = 1,00) dan 18 hpi (2-∆∆Ct = 1,64), serta perlakuan MOI 1:5 pada 1,5 hpi (2-∆∆Ct = 1,00) dan 18 hpi (2-∆∆Ct = 3,27) cenderung menunjukan adanya peningkatan pada 18 hpi. Perbandingan kelompok OC 18 hpi pada perlakuan MOI 1:5 (2-∆∆Ct = 4,26) menunjukkan peningkatan ekspresi gen CTSK sekitar 4 kali dibanding perlakuan MOI 1:1 (2-∆∆Ct = 1,00).KesimpulanPeningkatan ekspresi gen CTSK pada osteoklas berkorelasi positif dengan jumlah bakteri yang menginfeksi dan waktu paparan bakteri dengan sel osteoklas. Peningkatan ekspresi CTSK tersebut diduga berhubungan dengan terjadinya internalisasi bakteri kedalam sel osteoklas.

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Introduction

Periodontitis is a disease affecting the supportive tissues of the teeth, categorized as a non-communicable inflammation associated with dysbiosis of the biofilm. This disease impacts the entire periodontal tissue and can cause progressive destruction of alveolar bone. Periodontitis can be triggered by bacteria such as Aggregatibacter actinomycetemcomitans, subsequently affecting osteoclastogenesis and tissue damage. The internalization and proliferation of A. actinomycetemcomitans bacteria inside osteoclast cells can directly increase virulence factors such as lipopolysaccharide (LPS), playing a role in osteoclast differentiation marked by genes, including Cathepsin K (CTSK). This study aims to analyze osteoclast differentiation through the analysis of the marker gene expression for osteoclast differentiation, namely CTSK.

Methods

Osteoclasts (OC) were obtained from primary cultures of bone marrow macrophages (BMM), exposed to nuclear factor kappa-B ligand (RANKL) for 3 days. They were then infected with A. actinomycetemcomitans bacteria (ATCC 29522) with a ratio of MOI 1:1 and 1:5. Subsequently, they were evaluated at 1.5 hours and 18 hours post-infection (hpi) using RT-qPCR with the Livak method (2-∆∆Ct).

Results

The relative expression of CTSK gene in BMM cells with MOI 1:1 treatment at 1.5 hpi (2-∆∆Ct = 1.00) and 18 hpi (2-∆∆Ct = 0.99), as well as MOI 1:5 treatment at 1.5 hpi (2-∆∆Ct = 1.00) and 18 hpi (2-∆∆Ct = 1.04), tended to show no significant changes. In OC with MOI 1:1 treatment at 1.5 hpi (2-∆∆Ct = 1.00) and 18 hpi (2-∆∆Ct = 1.64), as well as MOI 1:5 treatment at 1.5 hpi (2-∆∆Ct = 1.00) and 18 hpi (2-∆∆Ct = 3.27), there was a tendency to increase at 18 hpi. The comparison of OC groups at 18 hpi with MOI 1:5 (2-∆∆Ct = 4.26) showed a fourfold increase in CTSK gene expression compared to MOI 1:1 treatment (2-∆∆Ct = 1.00).

Conclusion

The increased expression of the CTSK gene in osteoclasts positively correlates with the number of infecting bacteria and the duration of bacterial exposure to osteoclast cells. This heightened CTSK expression is presumed to be associated with the internalization of bacteria into osteoclast cells."

"In this broad-based introduction Jonathan Pevsner presents the background theory to bioinformatics. Each chapter includes a problem set, pitfalls, boxes explaining key techniques and mathematics/​statistics principles, summary, recommended reading, and a list of freely available software."

"Bioinformatika merupakan ilmu yang ditujukan untuk menganalisis informasi biologis. Dalam perkembangan penelitian bioinformatika, data diperoleh salah satunya dengan menggunakan teknologi microarray. Teknologi microarray digunakan oleh lingkup biologi molekuler dalam melihat perbedaan tingkat ekspresi gen dengan cara mengonversi gambar monokromik yang berisi ratusan bahkan ribuan gen dari sampel sel dan menghasilkan data ekspresi gen. Teknologi microarray sering kali menghasilkan data ekspresi gen yang hilang atau tidak terdeteksi akibat adanya kesalahan teknis. Oleh karena itu, diperlukannya suatu metode imputasi pada data untuk mengatasi missing values. Pada penelitian ini, akan dikembangkan suatu metode imputasi yang disebut Biclustering Terurut berbasis k-Nearest Neighbor, Mean Squared Residual, dan Jarak Euclidean. Metode ini merupakan metode imputasi berbasis biclustering dimana bicluster dibentuk berdasarkan suatu kriteria yang melibatkan skor Mean Squared Residue dan Jarak Euclidean. Penggunakan k-Nearest Neighbor sebagai metode pra-imputasi didasarkan pada data ekspresi gen yang sering kali memiliki pola kompleks dan sulit terdeteksi, sehingga perlu pendekatan yang dapat memetakan struktur korelasi pada data. k-Nearest Neighbor mempertimbangkan korelasi pada data microarray dengan menyeleksi kumpulan gen yang memiliki profil ekspresi mirip dengan gen yang ingin diimputasi (gen target). Pada penelitian ini, metode SBi-kNN-MSREimpute diterapkan pada data ekspresi gen pasien penderita COVID-19 yang dilakukan tes rapid harian. Evaluasi kinerja metode SBi-kNN-MSREimpute dilakukan dengan menggunakan NRMSE, dimana hasilnya dibandingkan dengan metode SBi-MSREimpute. Berdasarkan penelitian yang dilakukan, metode SBi-kNN-MSREimpute dinilai lebih baik dibandingkan dengan SBi-MSREimpute untuk setiap missing rate pada tingkatan c berbeda. Nilai c optimal untuk imputasi missing values pada data COVID-19 adalah c = 10% untuk missing rate 25%, 30%, 40% dan c = 15% untuk missing rate 5%, 10%, 15%, 20%, dan 50%. Hasil akhir juga menunjukkan bahwa nilai NRMSE untuk SBi-kNN-MSREimpute relatif stabil bahkan untuk data dengan missing rate tinggi hingga 50%.

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Bioinformatics is a study designed to analyze biological information. In the development of bioinformatics research, data was obtained using microarray technology. Microarray technology is used by the scope of molecular biology in transposing hundreds and even thousands of genes from cellular samples simultaneously and producing a gene expression data. Microarray technology often produces data that is lost or undetected as a result of technical error. Therefore, an imputation method is needed to address the missing values. In this study, a new imputation method called Sequential Biclustering based k-Nearest Neighbor, Mean Squared Residual, and Euclidean Distance (SBi-kNN-MSRE) will be developed. This method is a biclustering-based imputation method where the bicluster is formed based on a criterion involving Mean Squared Residue and Euclidean Distance. The use of k-Nearest Neighbor as a pre-imputation method is based on data on gene expression that often has a complex and difficult pattern of detection, so it requires an approach that can map correlation structures on data. K-nearest neighbor considers a correlation on a microarray data by selecting groups of genes that have an expression profile similar to a gene that wants to be imputed (the target gene). In this study, the SBi-kNN-MSRE method was applied to the data on the genes of patients with covid-19 that daily rapid tests were performed. The performance evaluation of the SBi-kNN-MSRE method is done using NRMSE, where the results are compared to the SBi-MSRE method. According to the result, the SBi-kNN-MSRE method performed better than SBi-kNN-MSRE for each missing rate on different c levels. The optimal c value on the covid-19 data is c = 10% for missing rate 25%, 30%, 40% and c = 15% for missing rate 5%, 10%, 15%, 20% and 50%. The results also showed that NRMSE scores"

"Diagnostic, prognostic and therapeutic value of gene signatures provides readers a useful and comprehensive resource about the range of applications of microarray technology in oncological diseases. Topics covered include gene signatures and soft tissue sarcomas, prognostic relevance of breast cancer signatures, gene expression profiling of colorectal cancer and liver metastasis, gene signatures in GISTs, CNVs and gene expression profiles in pancreatic cancer, and gene signatures in head/neck, lung and gastric tumors."

"Endometriosis adalah kelainan ginekologis yang dimanifestasikan dengan adanya kelenjar dan sel endometrium yang berkembang di luar uterus. Endometriosis merupakan penyakit multifaktorial dimana faktor genetik dan lingkungan berinteraksi menyebabkan timbulnya penyakit ini. Beberapa penelitian telah melaporkan bahwa endometriosis merupakan penyakit yang terkait dengan hormon estrogen. Mekanisme kerja estrogen ditentukan oleh kuantitas dan aktivitas reseptor estrogen. Namun demikian analisa variasi genetik, ekspresi dan aktivitas estrogen reseptor sampai saat ini belum banyak diketahui. Tujuan Penelitian ini adalah untuk menganalisa variasi genetik, ekspresi mRNA dan aktivasi ER pada jaringan endometriosis. Alel gen reseptor estrogen REα dan REβ dari 83 sampel penderita endometriosis dibandingkan dengan 76 kontrol menggunakan metoda PCR RFLP. Pengukuran ekspresi mRNA dari 18 jaringan penderita endometriosis dan 18 kontrol dilakukan dengan menggunakan metoda kuantitatif Real Time PCR (qRT-PCR). Pengukuran kadar estrogen serum (E2) dilakukan dengan metoda ELISA. Deteksi aktivasi ER dilakukan dengan uji fosforilasi reseptor estrogen β (serin 105) dengan metoda Western Blot. Hasil uji Chi-square ditemukan bahwa frekuensi alel A (normal) dan alel G (mutan) pada gen REα SNP rs9340799 dalam populasi berbeda bermakna (p=0,012) dan OR 1,772 dan kedua frekuensi alel dari hasil uji keseimbangan menurut Hardy-Weinberg berbeda bermakna (p = 0,003). Frekuensi alel (normal dan mutan) dalam populasi REα SNP rs2234693 tidak menunjukkan perbedaan bermakna dan seimbang dalam populasi. Frekuensi genotip pada SNP REβ rs4986938 pada endometriosis dibandingkan kontrol berbeda bermakna (p=0,015) dan OR 0,311 dengan populasi seimbang. Menurut keseimbangan Hardy-Weinberg dan frekuensi alel normal G dan alel mutan A juga berbeda bermakna (p=0,034) dan OR =0,438. Hasil pengukuran ekspresi mRNA menunjukkan terjadi peningkatan ekspresi ERβ 49,52 kali disbanding kontrol sedangkan REα tidak menunjukkan perbedaan dibandingkan kontrol. Kadar estradiol serum (E2) fase proliferasi tidak menunjukkan perbedaan bermakna dibanding kontrol. Hasil uji Spearman menunjukkan tidak ada korelasi kadar estradiol dengan ekspresi REα dan REβ (p>0,05). Fosforilasi ERβ pada Serin 105 menunjukkan penurunan pada kelompok endometriosis dibandingkan jaringan normal dengan perbandingan nilai intensitas pita yakni 0,1 pada endometriosis dan 4,2 pada kontrol. Sebagai kesimpulan, frekuensi alel A dan G gen REα berbeda bermakna pada SNP rs9340799 dan frekuensi alel di dalam populasi tidak seimbang. Distribusi genotip normal GG dan mutan AA serta frekuensi alel G dan alel A gen REβ berbeda bermakna pada SNP rs4986938. Ekspresi (mRNA) REβ lebih tinggi secara signifikans pada kelompok endometriosis dibandingkan kontrol. Ekspresi protein Fosforilasi ERβ pada Serin 105 menunjukkan penurunan pada endometriosis dibandingkan jaringan normal.

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Endometriosis is a gynecological disorder that is manifested by the presence of endometrium glands and cells that grow and develop outside the uterus. Endometriosis is a multifactorial with genetic and environmental factors interacting to cause this disease. Several studies have reported that endometriosis is a disease associated with the hormone estrogen. The mechanism of action of estrogen depends on the quantity and activity of estrogen receptors. However, genetic variation, expression and estrogen receptor activation in endometriois patients have not been fully characterized.

The aim of this study was to analyze genetic variation, ER expression and determine ER activation in endometriosis patients. This study determined the alleles of the estrogen receptor gene REα and REβ from 83 blood samples from endometriosis patients compared with 76 controls using the RFLP PCR method. Quatitative Real time PCR was used to analyze mRNA expression of REβ genes from 18 tissues with endometriosis and 18 controls. Measurement of serum estrogen (E2) levels was carried out using the ELISA method. Furthermore, the phosphorylation test of estrogen receptor (serin 105) was carried out using the Western Immunobloting method.

The results of the Chi-square test found that the frequencies of the A (normal) allele and G (mutant) allele in the REα SNP gene rs9340799 in the population were significantly different (p=0.012) and OR 1.772 and the two allele frequencies from the results of the balance test according to Hardy-Weinberg were significantly different. (p = 0.003). Allele frequencies (normal and mutant) in the REα SNP population of rs2234693 did not show significant and balanced differences in the population. The genotype frequency of SNP REβ rs4986938 in endometriosis compared to control was significantly different (p=0.015) and OR 0.311 with a balanced population. According to the Hardy-Weinberg balance, the frequencies of the normal G allele and the mutant A allele were also significantly different (p=0.034) and OR=0.438. Erβ expression showed 49,52 folds increase compared to control (p=0.00), whereas ERα did not show a significat different compared to control. There was no different in serum estradiol (E2) levels compared to controls.

The results of the Spearman test showed that there was no correlation between serum estradiol levels and the expression of REβ and REβ (p>0.05). Phosphorylation Ser105 of ERβ showed a decrease in the endometriosis group compared to control with a comparison of values ​​of 0.1 and 4 2. As a conclusion, The A and G allele frequencies of the RE gen gene were significantly different in SNP rs9340799 and the allele frequencies in the population were not balanced. The distribution of normal genotypes of GG and AA mutants and the frequency of G allele and A allele of REβ gene were significantly different at SNP rs4986938. REβ (mRNA) expression was significantly higher in the endometriosis group than the control group. Phosphorylated ERβ protein expression in Serin 105 showed a decrease in endometriosis compared to normal tissue."