Silencing of TT8 and HB12 Affects Nutritional ... - ACS Publications

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Biotechnology and Biological Transformations

Silencing of TT8 and HB12 Affects Nutritional Profiles and in vitro gas production Relating to Molecular Structures of Alfalfa (Medicago sativa) Plants Yaogeng Lei, Abdelali Hannoufa, Luciana Louzada Prates, Haitao Shi, Yuxi Wang, Bill Biligetu, David Christensen, and Peiqiang Yu J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.8b01573 • Publication Date (Web): 11 May 2018 Downloaded from http://pubs.acs.org on May 12, 2018

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Journal of Agricultural and Food Chemistry

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Silencing of TT8 and HB12 Affects Nutritional Profiles and

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in vitro gas production Relating to Molecular Structures of

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Alfalfa (Medicago sativa) Plants

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Yaogeng Lei1, Abdelali Hannoufa2, Luciana Louzada Prates1, Haitao Shi1, Yuxi

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Wang3, Bill Biligetu3, David Christensen1, Peiqiang Yu1*

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1

Department of Animal and Poultry Science, University of Saskatchewan, 51 Campus

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Drive, Saskatoon, SK S7N5A8, Canada

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2

London Research and Development Centre, Agriculture and Argi-Food Canada,

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1391 Sandford Street, London, ON N5V 4T3, Canada

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3

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Lethbridge Research and Development Centre, Agriculture and Argi-Food Canada, 5403 1st Avenue South, Lethbridge, AB T1J 4B1, Canada

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4

Department of Plant Sciences, University of Saskatchewan, 51 Campus Drive,

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Saskatoon, SKS7N5A8, Canada

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Running Head: Gene Transformations of TT8 and HB12 RNAi Affected Nutrient

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Profiles and gas production correlated with molecular structure of alfalfa

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Corresponding author:

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Peiqiang Yu

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College of Agriculture and Bioresources, University of Saskatchewan

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51 Campus Drive, Saskatoon, SK, S7N 5A8 Canada.

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Tel:

1-306-966-4128

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Fax:

1-306-966-4151

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Email: [email protected]

1

ACS Paragon Plus Environment


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Abstract

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The objective of this study was to investigate the effects of silencing TT8 and HB12

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genes on the nutritive profiles and in vitro gas production of alfalfa relating to spectral

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molecular structures of alfalfa. TT8-silenced (TT8i, n=5) and HB12-silenced (HB12i,

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n=11) alfalfa were generated by RNA interference (RNAi), and were grown with non-

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transgenic control (NT, n=4) in a greenhouse. Alfalfa plants were harvested at early-

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to-mid vegetative stage. Samples were analyzed for chemical compositions, CNCPS

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fractions and in vitro gas production. Correlations and regressions of chemical and

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CNCPS profiles with molecular spectral structures were also determined. Results

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showed that transformed alfalfa had higher digestible fiber and lower crude protein

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with higher proportion of PC. HB12 RNAi had lower gas production compared to

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others. Some chemical, CNCPS and gas production profiles were closely correlated

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with spectral structures and could be well predicted from spectral parameters. In

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conclusion, RNAi silencing of TT8 and HB12 in alfalfa altered the chemical profiles

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and CNCPS fractions of alfalfa, and such alterations were closely correlated with

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inherent spectral structures of alfalfa.

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Keywords: Genetic Transformation, Alfalfa (Medicago sativa), CNCPS Fractions,

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Spectral Structures, Correlation and Regression, ATR-FTIR, HB12, TT8

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Introduction

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Significant advances have been achieved in genetic engineering-based breeding of

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agricultural crops in recent years1. Compared to conventional breeding, genetic

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engineering is more efficient, more accurate, and less time-consuming. Taking

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advantage of the advanced molecular techniques and up-to-date bioinformatics tools,

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genetic engineering is extremely powerful in manipulating important agronomic traits

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of plants to meet human needs2–6. Apart from developing new genetic technologies,

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research efforts have also focused on identification and characterization of genes that

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have the potential to improve forage quality. Transparent Testa 8 (TT8) and Homo

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box 12 (HB12) are two transcription factors in plants. TT8 encodes a bHLH (basic

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helix-loop-helix) protein which interacts with myeloblastosis oncogene (MYB) and

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WD40-repeat proteins to form a ternary complex7,8. This complex controls

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transcription of late biosynthesis genes in the phenylpropanoid pathway, thereby

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regulating biosynthesis of anthocyanins and proanthocyanidins9,10. The HB12, on the

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other hand, belongs to the homeodomain-leucine zipper class I (HD-Zip I) family and

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is more related to drought resistance in plants. The expression of HB12 increased

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when plants were treated with salt or abscisic acid11,12. Observations from Brassica

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napus showed that expression levels of TT8 and HB12 are positively associated with

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lignin content13, indicating their potential use as targets towards lignin reduction in

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forage crops.

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Although alfalfa (Medicago sativa L.) contains favorable nutrient content and has

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good palatability, the relatively high lignin content and rapid degradation of protein

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are two drawbacks that limit the utilization of alfalfa forage1. The rapid degradation of

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protein in the rumen can lead to rumen bloat in grazing animals, causing huge

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economic losses to producers14. In contrast, high lignin content of alfalfa hinders the 3



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degradation of carbohydrate and other nutrients1. Altogether, these two drawbacks

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make alfalfa more imbalanced in terms of ruminal nutrient synchronization.

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Therefore, we transformed alfalfa with TT8 and HB12 RNAi constructs to explore the

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effect of silencing these two genes on chemical profiles, CNCPS fractions and in vitro

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gas productions of alfalfa. Meanwhile, the Attenuated Total Reflectance Fourier

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Transform Infrared (ATR-FTIR) spectroscopy was used to explore relationships

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between nutritional and fermentation profiles with structural spectral structures. We

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hypothesized that genetic transformations will alter the chemical and nutritional

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profiles of alfalfa, and that such alterations will affect the in vitro fermentation of

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alfalfa and will be closely related to spectral structures.

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Materials and Methods

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Alfalfa Samples

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Both transformed and non-transformed (NT) alfalfa samples were obtained from

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Agriculture Agri-Food Canada (AAFC) Saskatoon Research Center. Details about

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designing and making RNAi constructs of TT8 and HB12, transformation of alfalfa,

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growth conditions of alfalfa plants, and harvests method were described in our

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previous publications13,15. Briefly, total RNA was extracted from alfalfa for cDNA

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synthesis, and making RNAi constructs for TT8 and HB12 by using Gateway systems

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and pHellsgate12 vector. Afterwards, RNAi constructs were used to transform alfalfa

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explants via Agrobacterium tumefaciens according to Aung et al.16. All plants were

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grown in a greenhouse under normal conditions and harvested at early-to-mid

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vegetative stage. Alfalfa plants were then freeze-dried and stored in individual bags

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with each bag containing samples from one pot in the greenhouse. There were 11

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HB12 RNAi (HB12i), 5 TT8 RNAi (TT8i) and 4 NT plants. Samples were ground 4


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through a 1 mm sieve (Retsch SM-3000, Brinkmann Instruments, ON, Canada) for

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chemical analysis, and through a 0.02 mm sieve for spectra collection, respectively.

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Chemical Analysis

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Contents of dry matter (DM), ash, crude protein (CP) and ether extract (EE) in

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alfalfa samples were analyzed according to the AOAC methods17. Neutral detergent

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fiber (NDF), acid detergent fiber (ADF) and acid detergent lignin (ADL) were

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analyzed using ANKOM A200 Filter Bag Technique following the procedure

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provided by ANKOM Technology18. Neutral detergent insoluble crude protein

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(NDICP), acid detergent insoluble crude protein (ADICP) and non-protein nitrogen

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(NPN) were determined according to the procedure described by Licitra et al.19.

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Soluble crude protein (SCP) was analyzed according to the method of Roe et al.20.

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Total starch was determined using the Megazyme method21, and sugar content was

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analyzed according to Dubois et al22. Moreover, total carbohydrate content (CHO)

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and non-fiber carbohydrate (NFC) were calculated according to Higgs et al.23.

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CNCPS Fractions

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The updated version (v6.5) of Cornell Net Carbohydrate and Protein System

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(CNCPS) was used to calculate CNCPS carbohydrate and protein fractions23,24. The

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CNCPS divides carbohydrate into eight fractions, CA1, CA2, CA3, CA4, CB1, CB2,

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CB3, and CC; divides protein into five fractions, PA1, PA2, PB1, PB2, and PC. As

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alfalfa hay rarely contains organic acids13 and ammonia, we calculated CA4 (sugar),

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CB1 (starch), CB2 (soluble fiber), CB3 (digestible fiber), CC (indigestible fiber) in

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carbohydrate fractions and PA2 (soluble true protein), PB1 (insoluble true protein),

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PB2 (fiber-bound protein), PC (indigestible protein) in protein fractions in the current

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study. The CNCPS carbohydrate and protein fractions were calculated on total

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carbohydrate (CHO) and CP basis, respectively. 5



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ATR-FTIR spectrum and univariate analysis

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Alfalfa samples that were finely-ground (0.02 mm) were used for FTIR spectra

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collection with JASCO FT/IR-4200 with ATR (JASCO Corp., Tokyo, Japan) at

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University of Saskatchewan, Canada. Five spectra of each sample were collected at

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the mid-IR region (ca. 4000-700 cm-1) with 128 scans at a resolution of 4 cm-1.

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Afterwards, FTIR spectra were normalized and saved as “csv” file along with their

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second derivatives by using OMNIC 7.3 software (Spectra Tech, Madison, WI, USA).

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Spectral parameters of peak heights and peak areas were calculated with Excel®

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macro in carbohydrate region (ca. 1484-941 cm-1), amide (ca. 1710-1484 cm-1) and

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lipid-related region (ca. 3000-2761 cm-1). In carbohydrate region, there were four

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peaks (TC1, ca. 1025 cm-1; TC2, ca. 1074 cm-1; TC3, ca. 1104 cm-1; TC4, ca. 1149

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cm-1) in total carbohydrate region (TC ca. 1178-941 cm-1); four peaks (STC1, ca.

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1317 cm-1; STC2, ca. 1370 cm-1; STC3 ca. 1397 cm-1; STC4, ca. 1453 cm-1) in

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structural carbohydrate region (STC, ca. 1484-1178 cm-1); and one peak (CEC, ca.

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1237 cm-1) in cellulosic compounds region (CEC, ca. 1283-1178 cm-1). In amide

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region, peaks of amide I (ca. 1649 cm-1) and amide II (ca. 1540 cm-1), and protein

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secondary structure of α-helix (ca. 1653 cm-1) and β-sheet (ca. 1629 cm-1) were

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measured. In lipid-related region, asymmetric CH3 (AsCH3, ca. 2955 cm-1),

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asymmetric CH2 (AsCH2, ca. 2920 cm-1), symmetric CH3 (SyCH3, ca. 2872 cm-1)

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and symmetric CH2 (SyCH2, ca. 2850 cm-1) were measured. Peak areas of total

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carbohydrate (TCA), structural carbohydrate (STCA), cellulosic compounds (CECA),

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total amide (AA), amide I (AIA), amide II (AIIA) and (a)symmetric CH2/CH3

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(ASCCA) were also determined.

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In vitro gas production fermentation

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In vitro gas production fermentation was conducted at Lethbridge Research and 6


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Development Center of AAFC (Alberta, Canada), according to the description of

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Wang et al25 with minor modifications. About 0.3 g of ground samples was placed in

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ANKOM F57 bags and then incubated with rumen fluid and buffer solution in a 125

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mL serum vial. Rumen fluid was collected from three angus heifers at the Research

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Center farm after morning feeding. Samples were incubated for 48h in the incubator

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at 39 Celsius degrees with two experimental runs. Two replicates of each alfalfa

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sample and two blanks were withdrawn from the incubator after 2, 4, 8, 12, 24 and 48

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h of incubation. Gas production was measured at 2, 4, 8, 12, 24, 48 h for all samples

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available by using a water replacement equipment, according to Wang et al.25 .

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Statistical Analyses

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Gas production kinetics model

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Gas production kinetics were calculated with the non-linear model of ܲ = ܽ(1 −

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݁ ି௖(௧ି௟௔௚ሻ ሻ according to the description of Jonker

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production at the time of t, a is asymptotic gas production, c is the fractional rate

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(%/h), t is the incubation hours, and lag is the initial delay of gas production onset.

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Average production (AP) was calculated as ‫( ÷ ܿ × ܽ = ܲܣ‬2 × (݈݊2 + ܿ × ݈ܽ݃ሻሻ ,

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according to Jonker 26.

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Correlations between nutritional and gas profiles with spectral parameters

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26

. In this model, P is gas

Correlations and regressions between nutritional profiles and spectral parameters

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were performed with R software

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were obtained with the rcorr() function in HMISC package. Prior to correlations

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analysis, normality of each variable was tested with the shapiro-test() function in

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STATS package. Correlations involved non-normally distributed variables were

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performed with “Spearman method”, while others were performed with “Pearson”

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method.

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. Correlation coefficients and their significances

7



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Multilinear regression of predicting nutritional and gas production profiles from

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spectral parameters

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Multiple linear regressions of predicting chemical and CNCPS profiles from

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spectral parameters were conducted with the lm() function in STATS package. Then,

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the alias() function in STATS package was used to identify predictors that were

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completely linearly dependent on other predictors. The complete aliased predictors

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were then deleted, and the vif() function from CAR package was used to check the

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variance inflation factor (VIF) of each predictor. The predictor that had the highest

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VIF, which was greater than 10, was removed from the model. The linear modeling

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was re-conducted until the VIF values of all predictors were lower than 10.

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Afterwards, the step() function with “direction = both” was used to select the linear

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model that had the lowest AIC value. Then, predictors that were insignificant at

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α=0.05 were removed from the model.

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Statistical analysis

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PROC MIXED procedure in SAS 9.4 (SAS Institute, Inc., Cary, NC, USA) was

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used for analyzing chemical composition and gas production and kinetics data in this

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study. The model for chemical data was ܻ௜௝ = μ + trt ௜ + ߝ௜௝ , where Yij is the

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dependent variable, µ is the population mean, trti is the treatment effect, and εij is the

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random error. The model for gas production and kinetics was ܻ௜௝ = μ + trt ௜ +

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ܾ݈‫݇ܿ݋‬௝ + ߝ௜௝ , where blockj is the fermentation run and other parameters are the same

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as the previous model. Prior to variance analysis, observations with Studentized

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residual greater than 2.5 were considered as outliers and removed from the dataset.

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Differences between NT and transformed alfalfa were determined with contract

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statement. The Tukey-Kramer method was used in multi-comparison after variance

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analysis and a SAS macro called “pdmix800”28 was used to denote the letter for each 8


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treatment mean at the significance level of 0.05. Normality of residual of each

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variable was tested by using PROC UNIVARIATE in SAS 9.4 with normal and plot

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options.

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Results and Discussion

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Effect of TT8 and HB12 silencing on Chemical Profiles

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Chemical compositions of transformed and NT alfalfa are shown in Table 1.

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Compared with NT control, transformed alfalfa had lower DM, ash, CP, EE, and

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starch, but higher OM, CHO, NDF, ADF, ADL, NDICP, ADICP, and sugar (P 0.59, P < 0.01). Moreover, TC2, TC3 and TCA were negatively correlated with

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CB1 (r < -0.45, P < 0.05). The CEC height was found to be negatively correlated with

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PC fraction (r = -0.61, P < 0.01). In STC profiles, all STC peaks and STCA were

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negatively correlated with CB1 and PB1 (r < -0.56, P < 0.01), while positively

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correlated PC (r > 0.47, P < 0.05). Except for STC2, all other STC profiles were

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positively correlated with CC fraction (r > 0.58, P < 0.01). In amide region, β-sheet,

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AA and AIA were negatively correlated with CB1 and PB1 (r < -0.57, P < 0.01), but

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positively correlated with CC and PC fraction (r > 0.57, P < 0.01). In lipid-related

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ASCC region, SyCH2 and AsCH2 were negatively correlated with CB1 and PB1 (r
0.63, P < 0.01).

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In our pilot study15, STC3 was positively correlated with CA4 and CB1 (r = 0.83),

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while STC2 was negatively correlated with CB3 (r = -0.81). Moreover, TC1, TC3,

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TC4 and TCA tended to be positively correlated with CA4 and CB1 in our pilot

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study15 (r > 0.75, P < 0.1). Except for TC1 and TC4, which also tended to have

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weakly positive correlations with CB1 in the present study, all other correlations

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found in the pilot study were inconsistent with the present study. In CNCPS fractions,

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CB1, CC, PB1, PA2 and PC are starch, indigestible fiber, insoluble true protein, fiber15



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bound protein and indigestible protein, respectively23. In the present study, structural

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carbohydrate spectral profiles had negative correlations with CB1, and positive

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correlations with CC and PC, which were consistent with the CNCPS descriptions.

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This implies that samples with higher STC spectral heights will be more resistant to

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rumen degradation. As for amide profiles, β-sheet was found to be negatively related

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to CB1 and PB1, which are easily degraded in the rumen. On the other hand, β-sheet

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had positively relationship with PC, the indigestible protein fraction. It was reported

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that a higher intensity of β-sheet in samples would lead to lower protein digestibility

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because of the resistance of

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digestion44, which is in line with our results.

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Correlations between gas production profiles and spectral parameters

β-sheet to microbial attachment and enzymatic

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Correlations between gas production profiles and spectral parameters were mostly

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negative, except that CEC was positively correlated with gas production after 4h and

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12h of fermentation, as well as asymptotic gas production (Table 6). The TC2 and

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TC3 in TC region, all STC profiles (except for STC2), β-sheet, AA, and AIA in amide

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region, SyCH2, AsCH2 and ASCCA in lipid-related region, were all negatively

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correlated with gas production during fermentation, as well as asymptotic and average

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gas production (P

Silencing of TT8 and HB12 Affects Nutritional ... - ACS Publications

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