Archaeal β diversity patterns under the seafloor along geochemical gradients

Recently, deep drilling into the seafloor has revealed that there are vast sedimentary ecosystems of diverse microorganisms, particularly archaea, in subsurface areas. We investigated the β diversity patterns of archaeal communities in sediment layers under the seafloor and their determinants. This study was accomplished by analyzing large environmental samples of 16S ribosomal RNA gene sequences and various geochemical data collected from a sediment core of 365.3 m, obtained by drilling into the seafloor off the east coast of the Shimokita Peninsula. To extract the maximum amount of information from these environmental samples, we first developed a method for measuring β diversity using sequence data by applying probability theory on a set of strings developed by two of the authors in a previous publication. We introduced an index of β diversity between sequence populations from which the sequence data were sampled. We then constructed an estimator of the β diversity index based on the sequence data and demonstrated that it converges to the β diversity index between sequence populations with probability of 1 as the number of sampled sequences increases. Next, we applied this new method to quantify β diversities between archaeal sequence populations under the seafloor and constructed a quantitative model of the estimated β diversity patterns. Nearly 90% of the variation in the archaeal β diversity was explained by a model that included as variables the differences in the abundances of chlorine, iodine, and carbon between the sediment layers

Purification of HGcel.

<p>HGcel was purified from crushed <i>H</i>. <i>gigas</i> by anion-exchange column chromatography as described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042727#s4" target="_blank">Methods</a> section. HGcel migrated as a single 59-kDa band on an SDS-PAGE gel (5–20% gradient) (A). The effects of pH (B) and temperature on the enzyme's activity and stability are expressed relative to their maximum respective values (C). HGcel converted cellulose to glucose (Glu) and cellobiose (C2). In these reactions, 200 mU of HGcel was added to 500 µl of 5% cellulose solutions (pH 5.6) (D). One of the 5% (w/v) cellulose suspensions was added to HGcel (+E). Another was not added and was used as a reference (R). The kinetics of the glucose production from cellulose are described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042727#s4" target="_blank">Methods</a> section of this paper (E). The products of the HGcel reaction with cellobiose (C2), cellotriose (C3), cellotetraose (C4), and cellopentaose (C5) were analyzed using TLC. Addition of HGcel (12 mU) is indicated by a ‘+’. HGcel was more efficient at hydrolyzing cello-oligosaccharides larger than cellotriose (F). HGcel (12 mU) was allowed to react with <i>p</i>-nitro phenyl cello-oligosaccharides at 35°C for 1 h. <i>p</i>-nitro phenyl binds to the reducing ends of cello-oligosaccharides. The release of glucose or <i>p</i>-nitro phenol in the reactions is shown (G). The effect of hydrostatic pressure (100 MPa) on the enzymatic activity is expressed as the percentage of its activity at atmospheric pressure (0.1 MPa) (H). The enzymatic reaction was performed using 10 mU of HGcel with 1% CMC solution in airtight plastic tubes at 2°C. The enzymatic activities were measured after 8 h and 16 h of incubation. The kinetics of sawdust digestion by HGcel were measured by determining the production of glucose in a reaction containing HGcel (380 mU) and either sawdust or CMC at 35°C (I).</p

<i>H</i>. <i>gigas</i> possesses polysaccharide hydrolase activities.

<p>We captured deep-sea animals using baited traps containing a slice of mackerel. One baited trap contained approximately 50 individuals (A). Digestive enzyme activities were assessed by halo formation in agar plates containing starch azure (amylase), CMC and trypan blue (cellulase), glucomannan (mannanase), and xylan (xylanase). The halos produced by the amylase and cellulase activities were visualized directly, while the halos resulting from mannanase and xylanase were detected after staining with 0.5% Congo red followed by washing with DDW (B). The kinetics of the reactions were determined by TLC as described in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0042727#s4" target="_blank">Methods</a> section of this paper. Reactions with crushed <i>H</i>. <i>gigas</i> with 0.5% (w/v) starch (C), 0.2% (w/v) glucomannan (E) or 1% (w/v) CMC (D) were conducted in 100 mM sodium acetate buffer (pH 5.6) at 30°C. The pH dependencies of the amylase, mannanase, and cellulase activities were measured with protein extracts (F). The enzyme reactions were conducted in 100 mM sodium acetate buffer (pH 4.4–5.6) or 100 mM sodium phosphate buffer (pH 6.2–6.8). The relative activities are shown.</p

Identification of oligosaccharides in <i>H</i>. <i>gigas</i> whole-body extracts.

<p>Oligosaccharides were extracted with DDW from 3 crushed <i>H</i>. <i>gigas</i> and were separated by TLC and stained with H₂SO₄ for oligosaccharides (A, left) or the Glucose CII Kit for glucose (A, right). Glucose (G), maltose (M), and maltotriose (M3) were used as standard. The maltose and cellobiose contents were measured by the increase in the glucose content after α- or β-glucosidase treatment (B).</p

Acute myeloid leukemia and myelodysplastic syndrome associated with a combination of immune checkpoint inhibitor and platinum‐based chemotherapy

Abstract Therapy related‐acute myeloid leukemia (t‐AML) and myelodysplastic syndrome (t‐MDS) are complications of chemotherapy and/or radiation therapy for malignant diseases. In this report, we describe a patient with advanced lung adenocarcinoma who developed autoimmune hemolytic anemia and MDS associated with a combination of atezolizumab and platinum‐based chemotherapy. The patient showed progression from t‐MDS to t‐AML 20 months after the treatment was initiated. A combination of immune checkpoint inhibitor (ICI) and chemotherapy may increase the risk of developing therapy‐related myeloid neoplasms. As the prognosis of t‐AML and t‐MDS is poorer than that of de novo AML and MDS, proper surveillance, follow‐up, and treatment are needed throughout the course of immunotherapy