Biology subjects

Fujita, K.

Publications and source records attributed to Fujita, K..

3 recordsLinked to original sources

bioRxiv2018

Two Novel α-L-Arabinofuranosidases from Bifidobacterium longum subsp. longum belonging to Glycoside Hydrolase Family 43 Cooperatively Degrade Arabinan

Arabinose-containing poly-or oligosaccharides are suitable carbohydrate sources for Bifidobacterium longum subsp. longum, though their degradation pathways are poorly understood. In this study, we found that the gene expression levels of bllj 1852 and bllj 1853 from B. longum subsp. longum JCM 1217 were enhanced in the presence of arabinan. Both genes encode previously uncharacterized glycoside hydrolase (GH) family 43 enzymes. Subsequently, we cloned those genes and characterized the recombinant enzymes expressed in Escherichia coli. Both enzymes exhibited -L-arabinofuranosidase activity toward synthetic p-nitrophenyl glycoside, but the specificities for L-arabinofuranosyl linkages were different. BLLJ_1852 catalyzed the hydrolysis of 1,2- and 1,3-L-arabinofuranosyl linkages found in the side chains of arabinan and arabinoxylan. BLLJ_1852 released L-arabinose 100 times faster from arabinan than from arabinoxylan but did not act on arabinogalactan. BLLJ_1853 catalyzed the hydrolysis of 1,5-L-arabinofuranosyl linkages found on the arabinan backbone. BLLJ_1853 released L-arabinose from arabinan but not from arabinoxylan or arabinogalactan. Both enzyme activities were largely suppressed with EDTA treatment, suggesting that they require divalent metal ions. BLLJ_1852 was moderately activated in the presence of all divalent cations tested, whereas BLLJ_1853 activity was inhibited by Cu2+. The GH43 domains of BLLJ_1852 and BLLJ_1853 are classified into GH43 subfamilies 27 and 22, respectively, but hardly share similarity with other biochemically characterized members in the corresponding subfamilies.\n\nIMPORTANCEWe identified two novel -L-arabinofuranosidases from B. longum subsp. longum JCM 1217 that act on different linkages in arabinan. These enzymes may be required for efficient degradation and assimilation of arabinan in the probiotic bifidobacteria. The genes encoding these enzymes are located side-by-side in a gene cluster involved in metabolic pathways for plant-derived polysaccharides, which may confer adaptability in adult intestines.

biochemistry↗

bioRxiv2017

Non-invasive detection of bladder cancer through the analysis of driver gene mutations and aneuploidy

Current non-invasive approaches for bladder cancer (BC) detection are suboptimal. We report the development of non-invasive molecular test for BC using DNA recovered from cells shed into urine. This \"UroSEEK\" test incorporates assays for mutations in 11 genes and copy number changes on 39 chromosome arms. We first evaluated 570 urine samples from patients at risk for BC (microscopic hematuria or dysuria). UroSEEK was positive in 83% of patients that developed BC, but in only 7% of patients who did not develop BC. Combined with cytology, 95% of patients that developed BC were positive. We then evaluated 322 urine samples from patients soon after their BCs had been surgically resected. UroSEEK detected abnormalities in 66% of the urine samples from these patients, sometimes up to 4 years prior to clinical evidence of residual neoplasia, while cytology was positive in only 25% of such urine samples. The advantages of UroSEEK over cytology were particularly evident in low-grade tumors, wherein cytology detected none while UroSEEK detected 67% of 49 cases. These results establish the foundation for a new, non-invasive approach to the detection of BC in patients at risk for initial or recurrent disease.

cancer biology↗

bioRxiv2017

Experience-dependent modulation of behavioral features in sensory navigation of nematodes and bats revealed by machine learning

Animal behavior is the final and integrated output of the brain activity. Thus, recording and analyzing behavior is critical to understand the underlying brain function. While recording animal behavior has become easier than ever with the development of compact and inexpensive devices, detailed behavioral data analysis requires sufficient previous knowledge and/or high content data such as video images of animal postures, which makes it difficult for most of the animal behavioral data to be efficiently analyzed to understand brain function. Here, we report a versatile method using a hybrid supervised/unsupervised machine learning approach to efficiently estimate behavioral states and to extract important behavioral features only from low-content animal trajectory data. As proof of principle experiments, we analyzed trajectory data of worms, fruit flies, rats, and bats in the laboratories, and penguins and flying seabirds in the wild, which were recorded with various methods and span a wide range of spatiotemporal scales--from mm to 1000 km in space and from sub-seconds to days in time. We estimated several states during behavior and comprehensively extracted characteristic features from a behavioral state and/or a specific experimental condition. Physiological and genetic experiments in worms revealed that the extracted behavioral features reflected specific neural or gene activities. Thus, our method provides a versatile and unbiased way to extract behavioral features from simple trajectory data to understand brain function.

neuroscience↗