Straightforward method for calibration of mechanistic cation exchange chromatography models for industrial applications

Mechanistic modeling of chromatography processes is one of the most promising techniques for the digitalization of biopharmaceutical process development. Possible applications of chromatography models range from in silico process optimization in early phase development to in silico root cause investigation during manufacturing. Nonetheless, the cumbersome and complex model calibration still decelerates the implementation of mechanistic modeling in industry. Therefore, the industry demands model calibration strategies that ensure adequate model certainty in a limited amount of time. This study introduces a directed and straightforward approach for the calibration of pH-dependent, multicomponent steric mass action (SMA) isotherm models for industrial applications. In the case investigated, the method was applied to a monoclonal antibody (mAb) polishing step including four protein species. The developed strategy combined well-established theories of preparative chromatography (e.g. Yamamoto method) and allowed a systematic reduction of unknown model parameters to 7 from initially 32. Model uncertainty was reduced by designing two representative calibration experiments for the inverse estimation of remaining model parameters. Dedicated experiments with aggregate-enriched load material led to a significant reduction of model uncertainty for the estimates of this low-concentrated product-related impurity. The model was validated beyond the operating ranges of the final unit operation, enabling its application to late-stage downstream process development. With the proposed model calibration strategy, a systematic experimental design is provided, calibration effort is strongly reduced, and local minima are avoided.     

Down-regulation of miR-let-7e attenuates LPS-induced acute lung injury in mice via inhibiting pulmonary inflammation by targeting SCOS1/NF-κB pathway

Excessive pulmonary inflammatory response is critical in the development of acute lung injury (ALI). Previously, microRNAs (miRNAs) have been recognized as an important regulator of inflammation in various diseases. However, the effects and mechanisms of miRNAs on inflammatory response in ALI remain unclear. Herein, we tried to screen miRNAs in the processes of ALI and elucidate the potential mechanism. Using a microarray assay, microRNA let-7e (let-7e) was chose as our target for its reported suppressive roles in several inflammatory diseases. Down-regulation of let-7e by antagomiR-let-7e injection attenuated LPS-induced acute lung injury. We also found that antagomiR-let-7e could obviously improve the survival rate in ALI mice. Moreover, antagomiR-let-7e treatment reduced the production of proinflammatory cytokines (i.e., TNF-α, IL-1β and IL-6) in bronchoalveolar lavage fluid (BALF) of LPS-induced ALI mice. Luciferase reporter assays confirmed that suppressor of cytokine signaling 1 (SOCS1), a powerful attenuator of nuclear factor kappa B (NF-κB) signaling pathway, was directly targeted and suppressed by let-7e in RAW264.7 cells. In addition, it was further observed that SOCS1 was down-regulated, and inversely correlated with let-7e expression levels in lung tissues of ALI mice. Finally, down-regulation of let-7e suppressed the activation of NF-κB pathway, as evidenced by the reduction of p-IκBα, and nuclear p-p65 expressions in ALI mice. Collectively, our findings indicate that let-7e antagomir protects mice against LPS-induced lung injury via repressing the pulmonary inflammation though regulation of SOCS1/NF-κB pathway, and let-7e may act as a potential therapeutic target for ALI.     

Independent component analysis based gene co-expression network inference (ICAnet) to decipher functional modules for better single-cell clustering and batch integration

With the tremendous increase of publicly available single-cell RNA-sequencing (scRNA-seq) datasets, bioinformatics methods based on gene co-expression network are becoming efficient tools for analyzing scRNA-seq data, improving cell type prediction accuracy and in turn facilitating biological discovery. However, the current methods are mainly based on overall co-expression correlation and overlook co-expression that exists in only a subset of cells, thus fail to discover certain rare cell types and sensitive to batch effect. Here, we developed independent component analysis-based gene co-expression network inference (ICAnet) that decomposed scRNA-seq data into a series of independent gene expression components and inferred co-expression modules, which improved cell clustering and rare cell-type discovery. ICAnet showed efficient performance for cell clustering and batch integration using scRNA-seq datasets spanning multiple cells/tissues/donors/library types. It works stably on datasets produced by different library construction strategies and with different sequencing depths and cell numbers. We demonstrated the capability of ICAnet to discover rare cell types in multiple independent scRNA-seq datasets from different sources. Importantly, the identified modules activated in acute myeloid leukemia scRNA-seq datasets have the potential to serve as new diagnostic markers. Thus, ICAnet is a competitive tool for cell clustering and biological interpretations of single-cell RNA-seq data analysis.     

耕作方式对潮土土壤团聚体微生物群落结构的影响

为探究不同耕作方式对潮土土壤团聚体微生物群落结构和多样性的影响,采用磷脂脂肪酸(PLFA)法测定了土壤团聚体中微生物群落。试验设置4个耕作处理,分别为旋耕+秸秆还田(RT)、深耕+秸秆还田(DP)、深松+秸秆还田(SS)和免耕+秸秆还田(NT)。结果表明:与RT相比,DP处理显著提高了原状土壤和>5 mm粒级土壤团聚体中真菌PLFAs量和真菌/细菌,为真菌的繁殖提供了有利条件,有助于土壤有机质的贮存,提高了土壤生态系统的缓冲能力;提高了5～2 mm粒级土壤团聚体中细菌PLFAs量,降低了土壤革兰氏阳性菌/革兰氏阴性菌,改善了土壤营养状况;提高了<0.25 mm粒级土壤团聚体中微生物丰富度指数。总的来说,深耕+秸秆还田(DP)对土壤团聚体细菌和真菌生物量有一定的提高作用,并且在一定程度上改善了土壤团聚体微生物群落结构,有利于增加土壤固碳能力和保持土壤微生物多样性。冗余分析结果表明,土壤团聚体总PLFAs量、细菌、革兰氏阴性菌和放线菌PLFAs量与土壤有机碳相关性较强,革兰氏阳性菌PLFAs量与总氮相关性较强。各处理较大粒级土壤团聚体微生物群落主要受碳氮比、含水量、pH值和团聚体质量分数的影响,较小粒级土壤团聚体微生物群落则主要受土壤有机碳和总氮的影响。     

闽东南地区马尾松古树对气候变化和虫灾的生态弹性

马尾松是我国南方地区广泛分布的先锋造林树种。在全球变暖、气候干旱化和虫灾频发的趋势下,研究马尾松对环境干扰的生态弹性对森林管理有重要意义。本文对福建省仙游县百松村的马尾松古树进行树木年轮样品采集,建立区域首个马尾松树轮宽度标准年表(1865—2014年)。结果表明: 当年7—9月低相对湿度和5—9月极端高温是树木生长的主要限制因素。根据树轮极端窄年确定1869、1889、1986、1991和1993是极端事件年。时序叠加分析发现,极端事件发生前两年的持续低值加剧了极端事件的影响。干旱年份更容易引发虫灾。1889年是受虫灾影响最严重的年份,1986和1991年受到虫灾和干旱气候的双重影响,其余极端年主要受干旱气候的影响。树木对虫灾的抵抗力弱于对干旱事件的抵抗力;除1991年外,树木对虫灾的相对弹性力高于对干旱事件的相对弹性力。1889年的相对弹性力最高,1991年受到连续极端事件的影响,相对弹性力最低。2000年以来研究区干旱化趋势加强,马尾松古树遭受干旱和虫灾的干扰加强,部分树木死亡。     

α1-肾上腺素受体对胶原诱导性关节炎小鼠Treg细胞功能的影响*

目的: 利用胶原诱导性关节炎 (CIA) 模型小鼠,探讨去甲肾上腺素 (NE) 及其α1-肾上腺素受体 (AR ) 对CIA小鼠Treg细胞的作用。方法: 雄性DBA/1小鼠 32 只,随机分为对照组 (n=8) 和CIA模型组 (n=24)。II型胶原 (CII) 乳剂100 μl 尾根部注射DBA/1小鼠制备CIA小鼠模型,在初次免疫后第 41 日,用免疫荧光法检测小鼠脾脏中CD4⁺T与α1-AR的共定位情况;用Western blot法检测小鼠踝关节和脾脏中α1-AR的蛋白表达。分离纯化CIA小鼠脾脏中CD4⁺ T细胞,用抗CD3和抗CD28的单克隆抗体刺激CD4⁺T细胞,进行细胞培养,分为未加药组和加药组,加药组用NE或α1-AR激动剂苯肾上腺素 (phenylephrine) 处理细胞,用流式细胞术检测CIA小鼠CD4⁺T细胞中Treg的细胞数;用Western blot法检测CIA小鼠CD4⁺T中转化生长因子-β (TGF-β) 和IL-10的蛋白表达。结果: CD4⁺ T细胞能够表达α1-AR;与对照组相比,CIA小鼠踝关节和脾脏中α1-AR的蛋白表达显著降低(P＜0.01);与未加药的CIA小鼠的CD4⁺ T细胞相比,NE加入后的CIA小鼠CD4⁺ T细胞中Treg细胞的功能显著增强(P＜0.01);α1-AR激动剂phenylephrine加入后的CIA小鼠CD4⁺ T细胞中Treg细胞的功能显著增强(P＜ 0.01)。结论: 激活CIA小鼠CD4⁺ T细胞上的α1-AR可增强Treg细胞的功能,促进CD4⁺ T细胞向Treg细胞方向分化,发挥抗炎作用。     

安徽北部地区恶性肿瘤人群卡波氏肉瘤相关疱疹病毒血清学分析

卡波氏肉瘤相关疱疹病毒是一种新发现的γ疱疹病毒(Kaposi'ssarcoma-associated herpesvirus,KSHV).在中国新疆,KSHV感染率比较高,KSHV在免疫缺陷患者和静脉吸毒者中感染率也比在普通人群中高.为了进一步研究KSHV在安徽北部地区恶性肿瘤人群中的感染率和高风险因素,初步探讨KSHV与肿瘤的发生之间相关性,研究KSHV的高发人群特点,本研究选用KSHV病毒重组蛋白ORF65、ORF 73和K8.1为抗原,利用酶联免疫吸附实验(ELISA)对500份恶性肿瘤患者血清样本及200份健康体检人群血清样本进行KSHV抗体检测,分析KSHV的感染率及危险因素.结果显示500例肿瘤患者KSHV阳性总数170例,总阳性率为34％,200份健康人群KSHV阳性总数22例,总阳性率为11％,肿瘤人群KSHV阳性率明显高于健康人群(P＜0.001).其中肺癌、肝癌、结肠癌、宫颈癌、乳腺癌样本KSHV阳性率分别为36.4％,34.1％,41.7％,28.6％,30.3％,此5组肿瘤组KSHV阳性率也分别高于健康人群,都具有统计学意义(P＜0.001).但5组肿瘤样本之间KS-HV阳性率无明显差异(P＞0.05),且各组肿瘤样本分别与性别、乙肝五项、丙肝之间无统计学意义(P＞0.05).本研究结果提示安徽北部地区恶性肿瘤患者KSHV抗体阳性率显著高于中国普通人群总体KSHV抗体阳性率,证明KSHV在恶性肿瘤人群中呈现较高比例的分布.     

柽柳灌丛关键物候参数多种植被指数遥感提取的适用性——基于CO2通量观测和Sentinel-2数据

本研究以额济纳绿洲四道桥超级站为研究区,结合2018—2019年涡度通量、气象数据和2017—2020年Sentinel-2遥感影像,分析通量塔总初级生产力(GPP)与环境因子的关系,评估12种遥感植被指数对柽柳灌丛长势模拟和关键物候参数提取的适用性。采用7参数双逻辑斯蒂函数(DL-7)+全局模型函数(GMF)拟合GPP和各植被指数生长曲线,并逐年提取生长季始期(SOS)、生长季峰期(POS)和生长季末期(EOS)3种关键物候参数。结果表明: 有效积温(GDD)和土壤含水量是影响柽柳灌丛物候动态的主要环境因子。与2018年相比,2019年由于气温较低,SOS前的积温累积速率较慢,柽柳灌丛需要更长时间的热量积累来进入生长季,从而导致2019年SOS比2018年晚。在SOS与POS之间,2018和2019年水热条件相似,但2019年POS比2018年晚8 d,可能是2019年SOS较晚所致。POS以后,2019年较高的GDD和较低的土壤含水量使柽柳灌丛遭受水分胁迫,导致其生长季后期时间缩短。标准化的Sentinel-2植被指数与10:00—14:00 GPP均值的线性回归结果表明,宽波段植被指数中的增强型植被指数和窄波段植被指数中的叶绿素红边指数、倒红边叶绿素指数、红边归一化植被指数(NDVI705)能够较好地反映与柽柳灌丛GPP具有较高的一致性。柽柳灌丛SOS和EOS的遥感提取结果表明,Sentinel-2窄波段植被指数比宽波段植被指数的准确性更高,尤其是修正叶绿素吸收反射率指数提取SOS最准确,MERIS陆地叶绿素指数提取EOS最准确;Sentinel-2宽波段植被指数提取POS的准确性更高,尤其是两波段增强型植被指数和植被近红外反射率指数最准确。综合所有物候参数来看,NDVI705综合表现最佳。